Caving 2014, Chile

3RD INTERNATIONAL SYMPOSIUM ON BLOCK AND SUBLEVEL CAVING

MASS MINING PROJECTS AND KNOWLEDGE FOR THE FUTURE EDITOR: RAÚL CASTRO

5-6 JUNE 2014 SANTIAGO - CHILE


Proceedings of the Third International Symposium on Block and Sublevel Caving 5-6 June 2014, Santiago, Chile

EDITOR Raúl Castro Universidad de Chile

© Copyright 2014. Universidad de Chile. All rights reserved. No part of this publication may be reproduced, stored or transmitted in any form without the prior permission of The Universidad de Chile.

Disclaimer The information contained in this publication is for general education and informative purposes only. Except to the extent required by law, the Universidad de Chile makes no representations or warranties express or implied as to the accuracy, reliability or completeness of the information stored therein. To the extent permitted by law, the Universidad de Chile excludes all liability for loss or damage of any kind at all (including indirect or consequential loss or damage) arising from the information in this publication or use of such information. You acknowledge that the information provided in this publication is to assist you with undertaking your own enquires and analyses and that you should seek independent professional advice before acting in reliance on the information contained therein. While all care has been taken in presenting this information herein, no liability is accepted for errors or omissions. The views expressed in this publication are those of the authors and may not necessarily reflect those of the Universidad de Chile. The papers contained in this publication are for general information only, and readers are cautioned to take advice on cave mine projects. Photographs courtesy of Codelco.

ISBN 978-956-19-0857-4

Av. Libertador Bernardo O’Higgins 1058, Santiago de Chile | Teléfono: (56 2) 29782000

Universidad de Chile

The Universidad de Chile was founded in 1842 being the oldest higher education institution in Chile. Generating, developing, integrating and communicating knowledge in all the areas of knowledge and culture are the mission and basis of the activities of the University. The Universidad de Chile (UCH) has also a 160-year tradition of educating mining engineers. The first mining engineering program was created under the leadership of Andrés Bello in 1853, during the presidency of Manuel Montt. Since then, several hundred mining engineers have been trained at the UCH contributing greatly to the development of the Chilean mining industry. Mining engineers from the UCH have lead important technological changes, institution and to open new horizons in the mining and metallurgy industry. Examples of the involvement of UCH graduates are numerous including technological developments in block caving, the generation of El Teniente´s convertor and the development of heap leaching technologies. Today the mining training activities at the UCH are multiple and largest than the first Bello´s dream. The Mining Engineering Department is in charge of delivering undergraduates, postgraduates (master and doctorate) and continuous mining education programs. Fundamental and applied research in mining is achieved through the Advanced Mining Technology Center (AMTC) a multidisciplinary center aimed to develop technology-based applied solutions for the industry. In terms of underground mining, block caving research is conducted at the Block Caving Laboratory, where the next generation of underground mining specialists is being trained. The communication of knowledge is one of the missions of the University of Chile. Therefore, seminars and publications in mining are the platforms through which we present and discuss the latest advancements in mining related technology and knowledge. The Universidad de Chile is honored to be the organizer of Caving 2014 and to host it here in Santiago.

TECHNICAL REVIEWERS The editor thanks the following people who contributed their time and expertise as reviewers of manuscripts for the Third International Symposium on Block and Sublevel Caving held in Santiago, Chile. Dr. Eleonora Widzyk-Capehart, Universidad de Chile, Chile Prof. Juan Pablo Vargas, Universidad de Santiago de Chile, Chile Prof. Javier Vallejos, Universidad de Chile, Chile Prof. Nelson Morales, Universidad de Chile, Chile Prof. Xavier Emery, Universidad de Chile, Chile Prof. Yves Potvin, Australian Centre for Geomechanics, Australia Prof. Hans Göpfert, Universidad de Chile, Chile Dr. Matthew Pierce, Itasca, United States Prof. Italo Onederra, University of Queensland, Australia Prof. Leandro Alejano, Universidad de Vigo, Spain Dr. Enrique Rubio, REDCO, Chile Local Organizing Committee – Universidad de Chile Carolina Bahamondez Sebastián Valerio María Elena Valencia Verónica Moller Paula Alfaro Marcela Muñoz Bernardita Ponce Javier Gutiérrez International Organizing Committee Andrzej Zablocki

Atlas Copco

Dr. Matthew Pierce

Itasca Consulting Group

Prof. Gideon Chitombo University of Queensland Gustavo Reyes

Hatch

Danie Burger

Sandvik

Jarek Jakubec

SRK

Victor Encina

JRI

Alfonso Ovalle

AMEC

Mauricio Larraín

Codelco

PREFACE To be profitable, the extraction of large amounts of valuable minerals from the ground requires the use of efficient mine technologies. Equally important is the sustainability of the operations and high safety standards. Underground mining methods produce less impact on the environment than open pit practices. Caving methods are also the natural replacement for open pit operations as the ore reserves near the surface become depleted. Mine caving offers the lowest cost and highest production, provided that this method is correctly selected and implemented for the orebody’s geotechnical and geological conditions. Australia, Canada, Chile, Indonesia, Mongolia, China, Sweden, South Africa and the USA all have cave mines. Currently, worldwide mine caving research is being pursued within mining companies, universities and research centers. Current research is analyzing some of the technical challenges that the block caving industry faces, including:

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Large amount of development required in a short period of time.

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Scarcity of highly qualified people.

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Need for high productivity material handling systems.

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Understanding and tracking of the cave and the material flow.

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Mud rush and rockburst prediction and control, especially when the mud has a high grade content.

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Mine costs and dilution control.

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High stress conditions.

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Ventilation and high temperature conditions.

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Stability of the mine infrastructure.

Many operations are considering, or have decided, to use block caving as their preferred mining method. Currently, about 400,000 ton per day are extracted by caving methods. It is estimated that this figure will increase to a rate of 1 M ton per day by 2018. Production rates will also increase. This will present new and exciting challenges and opportunities for the mining industry and for the R&D community. I would like to present to you the proceedings of the Third International Symposium on Block and Sublevel Caving, which will be held in Santiago on the 5th and 6th of June 2014 in Chile. In this Proceedings, you would find two key notes and sixty eight technical articles written by people from all over the globe. Technical topics include innovation, mine planning, mine geomechanics gravity flow, seismicity, production and development planning, ventilation, blasting and case studies. I would also like to acknowledge the people that believed in the dream of making Chile not only a center of copper production but also a center of knowledge production: Fidel Baez, Sergio Fuentes, Ernesto Arancibia, Gideon Chitombo, Octavio Araneda, Mauricio Larraín, Marko Didyk and to the many other professionals and friends that have contributed to the dream. We hope that this book, the presentations and the workshops would contribute to define the state of the art of caving and to help us think about our future, the future of mining.

Prof. Raúl Castro Co-chairman Caving 2014 Universidad de Chile

SPONSORS

The Universidad de Chile proudly thanks and acknowledges the Principal and Major Sponsors of the Third International Symposium on Block and Sublevel Caving

PRINCIPAL SPONSOR

ORGANIZING INSTITUTIONS

Caving 2014, Santiago, Chile

TABLE OF CONTENTS

KEYNOTE SPEAKERS Future Challenges and Why Cave Mining Must Change

German Flores, Newcrest Mining Limited, Australia

It’s Not Mine Safety But Mind Safety - A Henderson Approach

GK Carlson Climax Molybdenum Company, USA

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53

CASE STUDIES Fracturing in the footwall at the Kiirunavaara mine, Sweden

M Nilsson Luleå University of technology, Sweden D Saiang SRK Consulting (Sweden) AB, Sweden E Nordlund Luleå University of technology, Sweden

63

Draw control strategy at the New Gold New Afton Mine

A Chaudhary New Gold, Canada K Keskimaki New Gold, Canada S Masse New Gold, Canada

72

Caving experiences in Esmeralda Sector, El Teniente Mine

M Orellana Codelco, Chile C Cifuentes Codelco, Chile J Díaz Codelco, Chile

78

Undercut advance direction management at the North 3rd Panel, Rio Blanco Mine, División Andina Codelco Chile

L Quiñones Codelco, Chile C Lagos Codelco, Chile F Ortiz Codelco, Chile E Farías Codelco, Chile L Toro Codelco, Chile D Villegas Codelco, Chile

91

New growth strategy in Esmeralda Mine

N Jamett Codelco, Chile RQ Alegría Codelco, Chile

98

CAVING MECHANICS Assessment of broken ore density variations in a block cave draw column as a function of fragment size distributions and fines migration

L Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman University of British Columbia, Canada A Aguayo Codelco, Chile

109

Assessing the state of the rock mass in operating block caving mines: A review

D Cumming-Potvin, University of Western Australia, Australia J Wesseloo, University of Western Australia, Australia

118

13

Caving 2014, Santiago, Chile Influence of secondary fragmentation and column height on block size distribution and fines migration reaching drawpoints

L Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman B. University of British Columbia, Canada A Aguayo Codelco, Chile

128

Analysis of hangup frequency in Bloque 1-2, Esmeralda Sur Mine

E Viera Codelco, Chile E Diez Codelco, Chile

138

A 3DEC-FLAC3D Model to predict primary fragmentation distribution in Cave Mines

T V Garza-Cruz Itasca Consulting Group, Inc., USA M Fuenzalida Itasca Consulting Group, Inc., USA M Pierce Itasca Consulting Group, Inc., USA P Andrieux Itasca Consulting Group, Inc., USA

146

ALCODER, challeges of paradigms in caving methods

Gl Krstulovic Geomecánica Ltda., Chile GA Bagioli Tetra Tech Metálica, Chile

159

Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente Mine, Chile. Part I

A Brzovic Codelco, Chile P Schachter Codelco, Chile C de los Santos Codelco, Chile JA Vallejos, University of Chile, Chile D Mas Ivars Itasca Consultans AB, Sweden

171

Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente mine, Chile. Part II

JA Vallejos University of Chile, Chile K Suzuki University of Chile, Chile A Brzovic Codelco Chile, Chile D Mas Ivars Itasca Consultans AB, Sweden

179

FRAGMENTATION Fragmentation estimates using BCF software – Experiences and pitfalls

J Jakubec, SRK Consulting Ltd., Canada

191

An alternative approach to verifying predicted fragmentation in weak rock

RN Greenwood SRK Consulting Inc., Canada BN Viljoen SRK Consulting (Canada) Inc., Canada

201

FUTURE PROJECTS Block Caving using Macro Blocks

S Fuentes Codelco, Chile F Villegas Codelco, Chile

211

La Encantada: An inclined cave design

J Valencia NCL Ingeniería y Construcción, Chile P Paredes NCL Ingeniería y Construcción, Chile F Macías First Majestic Silver Corporation, Mexico

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217

Caving 2014, Santiago, Chile GEOMECHANIC DESIGN Considerations for designing a geomechanics monitoring plan for each engineering stage

AE Espinosa Codelco, Chile P Jorquiera, Codelco, Chile J Glötzl, Glötzl GmbH, Germany

227

Integrated support quality system at El Teniente Mine

MS Celis, Codelco, Chile RA Parraguez, Codelco, Chile E Rojas, Codelco Chile, Chile

234

Management indicators for the cave geometry control, El Teniente mine

J Cornejo Codelco, Chile C Pardo Codelco, Chile

243

Geomechanical issues and concepts associated with scoping study and prefeasibility stage of a Block/Panel Caving Project

J Díaz DERK Ltda., Chile P Lledó DERK Ltda., Chile F Villegas Codelco, Chile

250

GEOMECHANICAL CHARACTERIZATION Ciresata geotechnical evaluation and caving study, Romania

N Burgio Stratavision Pty Ltd, Australia

263

Identification of different geomechanics zones in panel caving- application to Reservas Norte El Teniente

P Landeros Codelco, Chile J Cornejo Codelco, Chile J Alegría Codelco, Chile E Rojas Codelco, Chile

271

Geostatistical evaluation of fracture frequency and crushing

SA Séguret MINES ParisTech, France C Guajardo Codelco, Chile R Freire Rivera Codelco, Chile

280

Geomechanical ground control in block/panel caving

J Díaz DERK Ltda., Chile Y Sepúlveda DERK Ltda., Chile P Lledó DERK Ltda., Chile

289

GRAVITY FLOW Use of experiments to quantify the flow-ability of caved rock for block caving

RE Gómez, University of Chile, Chile R Castro, University of Chile D Olivares, University of Chile, Chile

299

An analysis of the lateral dilution entry mechanisms in Panel Caving

PS Paredes University of Chile, Chile MF Pineda University of Chile, Chile

307

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Caving 2014, Santiago, Chile Application of a methodology for secondary fragmentation prediction in cave mines

MA Fuenzalida Itasca Consulting Group, Inc., USA T Garza-Cruz Itasca Consulting Group, Inc., USA M Pierce Itasca Consulting Group, Inc., USA P Andrieux Itasca Consulting Group, Inc., USA

318

Case Study: Improving SLC recovery by measuring ore flow with electronic markers

S Steffen Elexon Mining, Australia P Kuiper Elexon Mining, Australia

Stochastic models for gravity flow: numerical considerations

WH Gibson SRK Consulting (Australasia) Pty Ltd, Australia

328

337

First steps in monitoring gravity flow at El Teniente Mine: installagion stage in Block-2, Esmeralda Mine

E Viera Codelco, Chile M Montecino Codelco, Chile M Meléndez Codelco, Chile

348

Experimental study of fines migration for caving mines

F Armijo BCTEC Engineering and Technology, Chile S Irribarra Universidad de Chile, Chile R Castro Universidad de Chile, Chile

356

Towards an understanding of mud rush behaviour in block-panel caving mines

ME Valencia University of Chile, Chile K Basaure University of Chile, Chile R Castro University of Chile, Chile J Vallejos University of Chile, Chile

363

Statistical analyses of mud entry at Diablo Regimiento sector-El Teniente’s Mine

IM Navia Universidad de Chile, Chile RL Castro Universidad de Chile, Chile MA Valencia, Universidad de Chile, Chile

372

INNOVATION

Hybrid composite, a way to enhance the mechanical properties of breakable ground support

V Barrera Mining and Metallurgy Innovation Institute IM2 – Codelco, Chile P Lara Mining and Metallurgy Innovation Institute IM2 – Codelco, Chile G Pinilla Codelco, Chile F Báez Codelco, Chile

381

Pilot tests as a tool for the design of autonomous mining systems

J Riquelme University of Chile, Chile R Castro University of Chile, Chile S Valerio University of Chile, Chile J Baraqui Codelco Chile, Chile

386

Implementation of LiDAR technology to evaluate deformation field induced by panel caving exploitation, Codelco Chile El Teniente Division

AE Espinosa Codelco, Chile P Landeros Codelco, Chile

16

394

Caving 2014, Santiago, Chile Semi-autonomous Mining Model

M Fishwick Codelco, Chile M Telias IM2-Codelco, Chile

403

Future automated mine operation: Synergistic collaboration between humans and automated systems

J Ruiz-del-Solar University of Chile, Electrical Engineering Dept-AMTC, Chile E Widzyk-Capehart University of Chile-AMTC, Chile P Vallejos University of Chile-AMTC, Chile R Asenjo University of Chile-AMTC, Chile

415

MINE PLANNING Mine sequence optimization for Block Caving using concept of ‘best and worst case’

D Villa, DASSAULT SYSTEMS GEOVIA, Canada

Fast-track Detailed Engineering for Panel Caving

JC Vienne, AMEC Internacional, Chile

426

437

Optimizing Hill of Value for Block Caving

A Ovalle, AMEC International, Chile M Vera, AMEC International, Chile

442

Footprint and economic envelope calculation for Block/Panel Caving Mines under geological uncertainty

E Vargas University of Chile, Chile N Morales University of Chile, Chile X Emery University of Chile, Chile

449

Determination of the best height of draw in block cave sequence optimization

F Khodayari University of Alberta, Canada Y Pourrahimian University of Alberta, Canada

Block Caving strategic mine planning using Risk-Return Portfolio Optimization

E Rubio REDCO Mining Consultants, Chile

457

466

NUMERICAL MODELLING Numerical modelling of Pilar Norte Mine development using Abaqus

R Cabezas MVA Geoconsulta, Chile F García MVA Geoconsulta, Chile M Van Sint Jan MVA Geoconsulta, Chile R Zepeda CODELCO, Chile

479

Geomechanical evaluation of large excavations at the New Level Mine - El Teniente

E Hormazabal SRK Consulting, Chile J Pereira Codelco,Chile G Barindelli, Codelco, Chile R Alvarez SRK Consulting, Chile

486

Design of 3-D models in mining

E Córdova Codelco, Chile P González, Codelco, Chile C Pardo Codelco, Chile

501

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Caving 2014, Santiago, Chile PRECONDITIONING Study of the impact of rock mass preconditioning on a Block Caving Mine Operation

C Castro IM2-Codelco, Chile F Báez Codelco, Chile E Arancibia Codelco, Chile V Barrera, Im2-Codelco, Chile

515

Pre-conditioning with hydraulic fracturing — when and how much?

C Valderrama Pontificia Universidad Católica de Chile-IM2 Codelco, Chile F Báez Codelco, Chile E Arancibia Codelco, Chile V Barrera IM2-Codelco, Chile

525

Caving propagation and dilution control through the preconditioning technology V Barrera Codelco, Chile C Valderrama Codelco, Chile P Lara IM2 Codelco, Chile E Arancibia Codelco, Chile F Báez Codelco, Chile E Molina Codelco, Chile

532

Numerical analysis of preconditioning using blasting and its relationship with the geomechanical properties of the rock mass and its interaction with Hydraulic fracturing F Báez Codelco, Chile E Arancibia Codelco, Chile I Piñeyro IM2 S.A., Chile J León IM2 S.A., Chile

538

Intensity rock mass preconditioning and fragmentation performance at the El Teniente Mine, Chile

A Brzovic Codelco, Chile JP Hurtado Universidad de Santiago de Chile, Chile N Marín Codelco, Chile

547

SEISMICITY Improved microseismic event hypocentre location in Block Caving Mines using local earthquake tomography

J Philippe Mercier, Golder Associates, Canada W de Beer, Golder Associates, Canada J Pascal Mercier, Advanced GeoScience Imaging Solutions, Canada

559

Seismic risk management for underground miningprojects - Codelco Chile División El Teniente

AE Espinosa CODELCO Chile División El Teniente, Chile RA Fuentes CODELCO Chile División El Teniente, Chile EG Moscoso ERDBEBEN Ltda, Chile

567

Seismic hazard analysis at the El Teniente Mine ising a clustering approach

J Cornejo Codelco, Chile J Vallejos University of Chile, Chile X Emery University of Chile, Chile E Rojas Codelco, Chile

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575

Caving 2014, Santiago, Chile Modeling induced seismicity in 4D

E Cordova Codelco, Chile M Nelson University of Utah, USA

586

SUBSIDENCE Application of InSAR technologies to measure the subsidence at El Teniente´s Mine

AE Espinosa Codelco, Chile O Mora Altamira Information, España F Sánchez Altamira Information, España

603

Chuquicamata Underground Project subsidence analysis

A Aguayo Codelco, Chile D Villegas Codelco, Chile

611

UNIT MINING OPERATIONS Methodology for up-hole drilling accuracy measurements at Kiruna SLC mine

M Wimmer LKAB, Sweden AA Nordqvist LKAB, Sweden D Billger Inertial Sensing One AB, Sweden

625

Analysis of geometric design in ventilation raises for Block Cave production level drifts

JP Hurtado, Universidad de Santiago de Chile, Chile YH San Martín, Universidad de Santiago de Chile, Chile

638

Simulating the logistic of an underground mine

M Moretti Paragon Decision Science, Brazil L Franzese Paragon Decision Science, Brazil M Capistran Paragon Decision Science, Brazil J Cordeiro Alkmim/AngloGold Ashanti, Brazil B Penna Alkmim/AngloGold Ashanti, Brazil G Mendes Alkmim/AngloGold Ashanti, Brazil

647

Engineering approach for the design and analysis of drawbell blasting in block and panel caving

Á Altamirano BCTEC Ingeniería y Tecnología SpA, Chile R Castro Universidad de Chile, Chile I Onederra University of Queensland, Australia

656

Analysis of induced damage due to undercut blasting

D Morales Hatch, Chile R Olivares Codelco, Chile

665

How high a draw column in Block Caving?

C Cerrutti AMEC International, Chile A Ovalle AMEC International, Chile Y Vergara Universidad de Chile, Chile

674

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Keynote Speakers

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Keynote Speakers

Future Challenges and Why Cave Mining Must Change German Flores, Newcrest Mining Limited, Australia

Abstract The evolution of the cave mining industry has been driven by the requirement to adapt to change. In the late 70’s, the driver for change was when lower grade and hard ore rock was encountered, after easier caving in near surface weaker rocks. Arguably, this major change was first introduced at Codelco’s El Teniente mine in order to mine the hard rock efficiently, safely and economically. This step change was the introduction of mechanised panel caving based on load, haul and dump (LHDs) machines subsequently resulting in the development of different cave mining layouts. Given the hard rock and the size of the drives needed to accommodate the LHDs at that time, jumbos and new rock support systems were also introduced. Since then, incremental changes have been introduced into the cave mining industry primarily to increase safety, mining efficiencies and reduce mining costs. These have included increasing LHD capacity to handle up to 2 m3 rocks and increasing productivity, electric LHDs to improve underground environment, and rapid development technology in order to increase development rates and access orebodies quicker. During this same period, semi- autonomous technology has been introduced for the purposes of increasing productivity, safety and further reducing mining costs. Preconditioning techniques were introduced with the view to change the characteristics of the rockmass in order to enhance the caving process, especially the cavability and fragmentation. The cave mining industry is now moving rapidly into a new and less certain environment where arguably, another revolutionary change is required in order to continue sustaining the industry. The potential challenges include technical, economical, licence to operate and human capital issues. As it was the case in the late 70’s when hard ore rock was first encountered, the industry must now change in order to sustain itself technically and economically. This paper, which supplements a keynote address by the author, argues that in some geotechnical environments, future cave mining may not be effectively applied with today’s practice and technology that has evolved in the last 30 years. It is also argued that the development of future cave mining systems can be accelerated covering a much wider range of mining conditions, requirements and even mining philosophies. Revolutionary changes are required in order for the industry to sustain its future. This means that the cave mining industry must change.

1

Introduction

Cave mining methods have become viable and preferred mass mining options where the objectives are low cost and high production rates. However, the cave mining industry is now entering a potentially less certain period where current cave mining methods may not be suitable to achieve the low cost and high productivity objectives. This environment includes greater depths, lower average grade deposits, demand for increased productivity, escalating mining cost (capital and operating), harder and heterogeneous rock masses, higher stress and, in some cases, higher temperature environments. In addition, there is increasing shortage of technical skills, becoming more difficult to access capital and communities are after higher environmental standards. Radical changes to current practices are thus needed. In the 70s, Codelco was successfully applying block caving methods designed for weak rock mass in low stress environment and with relatively high grades (Ovalle 1981; Baeza et al. 1987; Kvapil et al.

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Caving 2014, Santiago, Chile 1989). During this period, Codelco then encountered hard ore rock with much lower average grades. The consequences of these two issues were: very slow cave propagation, coarse fragmentation and low productivity leading to higher mining costs. In order to continue achieving similar levels of productivity and profitability, step changes were required to the block caving practices at that time. Chacón et al. (2004) discussed these step changes, which included the introduction of the mechanised panel caving method using LHDs, suitable layouts, new support systems, alternative undercutting sequence and underground primary crushers. Historically, block caving had been the preferred method because of the weak rock masses being caved at the time. The area required to achieve continuous caving in such rock masses was small (e.g. 90 m x 60 m = 5,400 m2) making block caving suitable for a wide range of conditions (Figure 1). Because hard ore rock required much bigger footprint to achieve caving (e.g. 15,000 m2), the concept of panel caving was introduced (Chacón et al. 2004). Larger 3.5 t capacity LHDs were introduced for the first time in underground cave mining as a means to handle the coarse fragmentation (Haley 1982). In order to increase the productivity from LHDs, a different horizontal extraction level layout was developed. Following a number of trials, the “El Teniente layout” was created (Figure 2). The introduction of larger LHDs required the development of bigger drives of up to 3.6 m x 3.6 m. The development of these size drives resulted in the introduction of development jumbos and alternative support systems including grouted rebars, mesh and shotcrete as shown in (Figure 3). In addition to the use of LHDs, part of the strategy to manage the big rocks was the introduction of an underground gyratory crusher to improve the efficiencies of subsequent material handling. The above changes formed the basis of current mechanised caving. Since then, there have been a number of incremental changes that have been introduced to further increase mining efficiencies, safety and reduce mining costs. Such changes, which include rapid development, undercutting strategies and geometries and material handling systems, are discussed in this paper. However, in themselves, they are not expected to effectively deal with the future challenges listed earlier. Additionally, the relatively near surface orebodies where current mechanised caving techniques were developed are now being exhausted and new orebodies are increasingly been found at much greater depth than current. Such orebodies are bringing with them new challenges. This paper discusses the future challenges and why cave mining, in particular, must change in order to exploit the future orebodies efficiently and economically. The need for the industry to change is reinforced in recent published work by Ernst & Young (2014) and Deloitte (2013). They discuss business risk facing mining and metals during 2013-2014 and the top ten issues that the mining companies will face in the coming year, respectively. The risks discussed by these authors are consistent with those presented in this paper.

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Keynote Speakers

Figure 1 Block caving method used in weak ore rock at El Teniente mine (Sisselman 1978)

Figure 2 Panel caving method used in hard ore rock at El Teniente mine (Hartman 1992)

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Figure 3 Ground support for hard ore rock (Ovalle & Albornoz 1981)

2

Incremental changes in the last 30 years

Following engineering studies, the key processes in cave mining include design, cave establishment and production. Numerous and significant incremental changes in the last 30 years have mainly been focussed in these areas. In this context, incremental changes refer to improvements but still within the current practice umbrella or changes that only affect a component of the entire cave mining process. In spite of their significance, they do not necessarily result in a complete transformation of the caving practices such as when cave mining moved from weak ore rock to hard ore rock. 2.1

Design

Once the cavability and fragmentation have been assessed, the key design features in cave mining practices are mining strategy, block height, extraction level layout and undercutting (strategy and geometry). The effective design of the caving operation is pivotal to the success of any mining business. It is crucial that proper orebody knowledge (geological, geotechnical, hydrogeological, metallurgical and environmental) including associated uncertainties is collected early in the design process. This should be followed by a proper and rigorous analysis in order to establish most appropriate design parameters to suit a given orebody. This is instead of simply adopting parameters from existing operations which unfortunately remains common practice. Of the above design activities the key incremental changes have been in the following areas:

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Keynote Speakers 2.1.1

Mining strategy

The move from weak to hard ore rock masses resulted in the change from block to panel caving strategy as shown in Figure 4. This was due to the size of the area required to initiate the caving in harder ore rock. As indicated earlier, the area required to achieve continuous caving in weak rock masses was small and general less than 10,000 m2 (Flores & Karzulovic 2004). For harder rock masses, the area required to initiate continuous caving has been reported to be as high as 25,000 m2 (Catalán et al. 2010). However, more recently and given a number of technical and operational problems associated with large panels such as discussed by Araneda & Sougarret (2008), there are now designs and operations utilising block caving strategies but at a much larger scale. These are referred to as macroblocks (Aguayo et al. 2012; Madrid & Constanzo 2013; Villegas & Fuentes 2014). The advantages of this move back to block strategy includes better management of cave establishment, production and panel cave front stability and, in some cases, better management of potential operational hazards (e.g. collapses). An added advantage of the macro block concept, in cases where the orebody footprint is very large, is the ability to develop a mining strategy or sequence to reduce the payback period of the project thereby maximising the return of the entire deposit. Some refer to this strategy as “value engineering”. Figures 5 and 6 are illustrations of this concept using the Cadia East deposit (Manca & Flores 2013).

Block caving for weak ore rock

Panel caving for hard ore rock

Figure 4 Cave mining step change from block caving to panel caving method (Chacón et al. 2004)

2.1.2

Block heights

Block height is defined, in this context, as the height of the block to be caved from the extraction level to the surface, the base of a pre-existing open pit, a level or a mined-out area above (after Brown 2003 & 2007). Block heights have today ranged from 150 m to approximately 500 m (Flores et al. 2004). However, more recently block heights of up to 1,000 m or slightly greater have been designed such as shown in Figure 7 (Manca & Flores 2013). The main driver behind this increase in block height has been the requirement to exploit low grade orebodies profitably (i.e. economic consideration).

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Figure 5 Value engineering – vertical section (Manca & Flores 2013)

Figure 6 Value engineering – plan view (after Manca & Flores 2013)

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Keynote Speakers

Figure 7 Block height (Manca & Flores 2013)

2.1.3

Extraction layout

In a paper by Chacón et al. (2004) on “Thirty Years Evolution of block caving in Chile”, the different extraction layouts established since caving of hard rock was encountered are discussed. These were primarily designed to improve the efficiency of LHDs in hard rock mining with coarse fragmentation. The layout included the herringbone design specifically adopted for Salvador mine (Figure 8) and later modified for Andina mine (Figure 9). At the same time, Henderson operations in the USA introduced the herringbone layout (Figure 10). Based on detailed analysis of these geometries, the “El Teniente layout” was introduced for the first time in the El Teniente-4 South production sector. The advantage of this new layout was an increase in the 5 t LHD productivity from approximately 100 to 150 tph (Chacón et al. 2004). In the last 30 years, the most commonly used layouts are the herringbone and the El Teniente as shown in Figures 11 and 12 (Leach et al. 2000; Botha et al. 2008). An advantage of the herringbone layout is the LHD manoeuvrability when electric tethered machines are used. In the case of the El Teniente layout, the advantages are the easiness of construction, the effectiveness of LHD digging (attacking the muckpile head-on) resulting in a better ore flow into the drawpoint and the stability of the extraction level pillars. The extraction level geometry (grid) has changed from the original 24 m x 12.5 m grid (Chacón et al. 2004) to 34 m x 20 m (Castro et al. 2012), with the most common being 30 m x 15 m (Chitombo 2010). Laubscher (1994), however stresses that the grid size should be a function of fragmentation and the requirement to achieve flow interaction between adjacent drawpoints.

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Figure 8 Herringbone layout adapted at Salvador mine (Chacón et al. 2004)

Figure 9 Herringbone layout adapted at Andina mine (Chacón et al. 2004)

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Keynote Speakers

Figure 10 Herringbone layout adapted at Henderson mine (Chacón et al. 2004)

Figure 11 Typical Herringbone layout (after Brown 2007)

Figure 12 Typical El Teniente layout (after Brown 2007)

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Caving 2014, Santiago, Chile 2.1.4

Undercutting strategy

One of the most critical processes in cave mine design is undercutting. This is in terms of effectively initiating the caving of a block or panel, ensuring earlier start of production and depending on the undercutting sequence used, managing of stresses. Brown (2003 & 2007) emphasises that the undercutting strategy adopted can have a significant influence on cave propagation and on the stresses induced in, and the performance of, the extraction level installations. The three mostly used undercutting strategies are post, pre and advanced undercutting as shown in Figure 13 (Rojas et al. 2000; Barraza & Crorkan 2000; Barber et al. 2000). Historically, the post-undercut was used and later the most commonly used became the advanced. Currently, there is an increasing interest in applying post-undercutting strategy (Manca & Flores 2013). The reason for this shift is mainly to reduce the interaction between the activities associated with cave preparation (i.e. undercut and extraction level development) and those associated with production. The main benefit of this is the reduction of the overall cave establishment time.

Figure 13 Undercutting strategies – post, pre and advanced undercutting (after Brown 2007)

2.1.5

Undercutting Geometry

With respect to geometry, the high undercut (sublevel caving ring geometry), narrow and flat and, narrow and inclined options have been used as shown in Figures 14, 15 and 16 (Jofré et al. 2000; Barraza & Crorkan 2000; Flores et al. 2004; Silveira 2004). The advantages and disadvantages of these options have been debated widely in the industry and have included the following:

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Keynote Speakers 1. High undercuts (up to 20 m) have historically been the most common geometry used for undercutting as shown in Figure 14 (Ovalle & Albornoz 1981; Manca & Flores 2013). They are relatively flexible in that they can be drilled with a range of drilling equipment and hole diameters. In addition, high undercut can be used to produce tonnage with finer fragmentation for the mill at the beginning of the caving process. However, a common problem associated with high undercuts is blast hole deviation and hole loss resulting in poor ring breakage necessitating redrilling of the undercut rings. 2. As the term implies, a flat undercut is formed by using flat lying drill holes rather than fans or steeply inclined holes (Figure 15). As a result, the undercut is narrow with a height equal or slightly greater than that of the drill drives (e.g. 4 m). The advantages of narrow flat undercut as reported by Butcher (2000a) include that they produce higher advance rates because less drilling and charging is required and reduce the magnitudes of the induced stresses which may otherwise cause problems. However, the major disadvantage of narrow flat undercut is the potential of the formation of pillars (remnants) due to either blast hole loss and deviation or confined blasting conditions arising from inadequate cleaning of the previously blasted undercut rings. In addition, coarse cave fragmentation is generally encountered earlier in the natural caving process (Leiva & Duran 2003). 3. In order to offset one of the disadvantages of the narrow flat geometry (i.e. assuring complete breakage), the narrow inclined undercut was introduced (Figure 16). This allowed easier flow of the blasted material in the inclined section of this geometry. In spite of this, the problem of encountering coarse cave fragmentation during earlier caving still remains. (Calder et al. 2000). Because of the disadvantages associated with flat undercut discussed earlier and in particular poor undercut breakage and formation of remnant pillars with the potential of causing collapses in the extraction level below, some operations are now implementing and/or reconsidering high undercuts but utilising better drilling and blasting practices and technologies (Manca & Flores 2013; Manca & Dunstan 2013).

Figure 14 High undercut geometry (after Manca & Flores 2013)

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Figure 15 Narrow and flat undercut geometry (after Brown 2007)

Figure 16 Narrow and inclined undercut geometry (after Brown 2007)

2.2

Cave establishment

Cave establishment includes activities associated with mine set-up with those associated with cave set-up as described in Manca & Flores (2013) and shown in Figures 17 and 18. More specifically, the associated key mining activities in cave establishment include access development as well as associated material handling systems; mining services (ventilation, power, water); extraction, undercut and haulage level development; civil works (permanent ground support and concrete roadways); drawbell opening and undercutting rate. Where preconditioning is applied, this activity becomes integral part of the cave establishment.

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Keynote Speakers

Figure 17 Mine set-up strategies (after Manca & Flores 2013)

Figure 18 Cave establishment (after Manca & Flores 2013)

Of the above cave establishment activities the key incremental changes have been in the following areas:

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Access Development

In the last 10 years, the focus on access development has been on single heading rapid development but still using conventional drilling and blasting methods. The advance rates using such methods have been of the order of 160 m/month (Willcox 2008). As part of this focus, rapid development technology has been introduced leading to advance rate of up to 265 m/month with a record of 311 m/month in a single heading (access decline) for a 5.5 m x 6.0 m decline access mined at a gradient of 1:7 (Willcox 2008; Zablocki & Nord 2012) as shown in Figures 19 and 20. The use of mechanical excavators (TBM, road header) for rapid access development continues to be an active area of R&D.

Figure 19 Rapid development (Flores & Logan 2008)

Figure 20 Rapid development (Willcox 2008)

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Keynote Speakers 2.2.2

Undercut, extraction and haulage level development

The rapid development technology has been extended to footprint development, which includes multiple heading undercut, extraction and haulage level development. An average development rate of 540 m/month for a single long round jumbo, has been achieved for such headings (Manca & Flores 2013). 2.2.3

Civil Works

In cave mining, civil works refers mainly to permanent ground support and concreting of roadways. With respect to permanent ground support associated with drawpoint support, the changes have mainly been associated with the number and type of steel sets used in conjunction with cable support and concreting. The number of steel sets have been reduced from as many as 7 down to two for a single drawpoints, however there have been cases where no steel sets have been used as shown in Figure 21 (Bartlett 1992; Rojas et al. 1992; Golden & Fronapfel 2008; Dunstan & Popa 2012). Recently, Andina and El Teniente operations have trialled the use of pre-fabricated support systems for drawpoint to reduce the installation time by 50% as shown in Figure 22 (Fuenzalida & Baraqui 2012).

Figure 21 Drawpoint support with and without steel sets

Figure 22 Drawpoint using prefabricated support (Fuenzalida & Baraqui 2012)

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Caving 2014, Santiago, Chile Shotcrete was introduced as part of the support system in the late 70s (Wilson 2000). In most cases it was used in conjunction with mesh (Dolipas 2000). The incremental change associated with shotcreting included the thickness 50 mm to 100 mm and the introduction of steel fibre reinforcement. At the same time, there were significant improvement in the delivery and spraying systems. In the 80s, the practice of concreting extraction level roadways (extraction drives and drawpoint drives) was introduced. This was purely to achieve high speed tramming and therefore high LHD productivity as well as operator comfort (Butcher 2000b; Duffield 2000). The incremental change in concreting of roadways was the number of layers and the strength of the concrete used. Current practice includes a bottom layer of 25 to 30 MPa and an upper layer of 70 to 85 MPa as shown in Figure 23.

Figure 23 Roadways design (Duffield 2000)

2.2.4

Drawbell opening

The opening of the drawbells was traditionally a very lengthy process requiring up to 3 to 4 blasting stages (Music & San Martin 2012). However, there have been cases where the process has been significantly longer. With the introduction of accurate small diameter drill rigs, the onset of electronic detonators, emulsion products and large diameter blind hole drilling, drawbell blasting has now been reduced to a single step blasting as shown in Figure 24 (Silveira et al. 2005; Dunstan & Popa 2012). As a result of these changes, drawbell heights have been increased from around 10 m to up to 18 m and opening rates from around 3 to up to 12 drawbells/month are being achieved (Silveira 2004; Casten et al. 2008; Manca & Flores 2013). 2.2.5

Undercutting Rate

The incremental change associated with undercutting has been the rate of undercutting expressed in m2/ month due to the improvements in drilling and blasting technologies. The most recent industry benchmark for block and panel caves indicated that the undercutting rate is in the range of 2,000 to 4,000 m2/month for low undercuts (Chitombo 2010). Some of the current operations have achieved undercutting rates in the range of 4,000 to 6,000 m2/month. This has enabled rapid cave establishment and reduction of the ramp-up time (Silveira 2004; Manca & Flores 2013).

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Keynote Speakers

Figure 24 Drawbell fired in a single blasting

2.2.6

Preconditioning

Preconditioning was an incremental change introduced to better manage the caving process. The intent was to alter the characteristics of hard rockmass such that it behaves similar to a weak rockmass (van As & Jeffrey 2000; Chacón et al. 2004; van As et al. 2000). In cave mining, the processes of interest are cavability and fragmentation (Sougarret et al. 2004, Catalán et al. 2010; 2012). In addition to the enhancement of the caving processes, preconditioning is also being used to manage seismicity (Araneda & Sougarret 2008). The techniques currently used are hydraulic fracturing, confined blasting and combination of the two as shown in Figure 25. Catalán et al. 2012 refers to the latter as intensive preconditioning.

Figure 25 Intensive preconditioning (after Manca & Flores 2013)

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Caving 2014, Santiago, Chile Preconditioning for caving applications remains a very active area of research. From technical and operational perspectives, the objective is to engineer the volume of hard rock to be caved in order to achieve faster cave propagation rates and therefore higher draw rates, finer fragmentation resulting in less hangups and secondary breakage activities, reduce area required to initiate caving and reduce the magnitude of the seismicity due to caving. From a business perspective, these benefits will translate into shorter ramp up times, more continuous production process, smaller underground primary crushers and therefore lower mining costs. From a safety point of view, preconditioning should enable better management of the stresses resulting in safer working conditions. 2.3. Production With respect to production, the most significant incremental changes have been associated with the following: 2.3.1

LHD capacity and type

The LHD capacity has progressively been increased from 3.5 t in the 80s to current 21 t as shown in Figures 26. This was for the purposes of handling coarse fragmentation and achieving higher productivity (Stevens & Acuña 1982).

3.5 t LHD (http://www.slideshare.net/smhhs/lhd) 17 t LHD (Cadia Valley Operations February 2013) Figure 26 LHD capacities from 3.5 tonnes to 21 tonnes

The two LHD types, currently used are diesel and electrics as shown in Figure 27. The diesel LHDs are the most commonly used and the electric, which was introduced in the last 10 to 15 years, are more suited to the herringbone layout and are becoming more popular given the mining environmental restrictions (i.e. diesel particulate). Electric LHDs are currently all tethered but there is active research to develop untethered machines.

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Keynote Speakers

Figure 27 Diesel 21 t and electric 14 t Sandvik LHDs (www.miningandconstruction.sandvik.com)

2.3.2

Automation

The idea of automated LHDs (semiautonomous) was introduced in the early 2000s in order to reduce the exposure of underground workers to severe and unsafe mining environment or conditions including hotter, wet muck or mudrush conditions, dusty, noisy and seismic prone areas (Gustafson et al. 2013; Schunnesson et al. 2009; Metsänen 2004; Schweikart & Soikkeli 2004; Varas 2004). In addition, automated LHDs were introduced as a potential means of achieving consistent productivity however this remains an active area of R&D. The application of fully automated LHDs is however yet to be achieved and remains an area of active research by different suppliers. With respect to underground automated trucks, there is only one known and documented case (Burger & Cook 2008; Cook et al. 2008). Figure 28 shows the semiautonomous LHD and truck used in cave mining.

Figure 28 Semiautonomous underground LHD and truck (Burger 2006; Cook et al. 2008)

2.3.3

Drawpoint secondary breakage

A number of incremental changes have been made in this area and have ranged from the use of a single boom jumbo in combination with either explosives or penetrating cone fracture (PCF), specialised high reach drill rig, water cannon to mobile rock breakers (see Figure 29). Additional research is been carried out on the use other more exotic techniques such as plasma rock breakage and pulse water jet. The goal in this area is to develop systems, which can be deployed rapidly with minimal evacuation, ventilation and therefore much less production interruptions (Singh 1998; Moss et al. 2004).

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Jumbo with a single boom

Commando

Water cannon

Mobile rock breaker

Figure 29 Drawpoint secondary breakage systems used in caving operations

2.3.4

Crushing and tipping arrangements

The conventional primary crushing systems in hard rock cave mining are gyro and jaw (Calder et al. 2000; Casten et al. 2000; Botha et al. 2008). In the last 10 years, jaw-gyro crushers (Duffield 2000; Manca & Dunstan 2013) and mineral sizers have been introduced as shown in Figure 30 (Arancibia et al. 2012; Fuenzalida et al. 2012). With respect to mineral sizers, the biggest change has been the ability to crush rock above 200 MPa. In addition, developments are being made to achieve throughputs higher than 2,500 tph using jaw-gyro crushers.

Gyratory crusher

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Jaw crusher (Flores et al. 2007)

Keynote Speakers

Jaw- gyratory crusher (Cadia Valley Operations April 2013)

Sizer (L’Estrange 2009)

Figure 30 Crushing systems

The tipping arrangements have evolved from single to multiple as shown in Figure 31 and from tipping into a hopper-feeder-crusher to tipping directly into the crusher (Krek et al. 2008; Manca & Dunstan 2013).

Figure 31 Tipping arrangements at Cadia East mine with 4-tipping points (Manca & Flores 2013; Cadia Valley Operations September 2013)

2.3.5

Draw Rate

Draw rate is the rate at which caved ore is drawn from individual drawpoints or a group of adjacent drawpoints and it is expressed in millimetres per unit time or tonnes per area per time period (mm/day or t/m2-day). The incremental change has been an increase of draw rate from 25 mm/day to 115 mm/day at cave initiation. The draw rates established for Cadia East during the cave initiation (up to 30% of the block height) vary from 115 mm/day to 280 mm/day with an average of 190 mm/day. Higher than 30% to the top of the block, the draw rates vary from 280 mm/day to 400 mm/day with an average of 320 mm/day. This increase in draw rates is being attributed by some to impact of preconditioning (Manca & Flores 2013). 2.3.6

Main Material Handling System

The main material handling systems to the surface stockpile or to the mill used in cave mining are trucks, trains, shafts and more recently conveyors as shown in Figure 32 (Tyler et al. 2004; Botha et al. 2008; Brannon et al. 2008; Ferguson et al. 2008; Pinochet, et al. 2012; Sinuhaji et al. 2012). The associated

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Caving 2014, Santiago, Chile incremental changes with respect to the main material handling systems have been mainly in the areas of capacity, depth and speed (Taljaard & Stephenson 2000; Brannon et al. 2012).

Train system (Flores et al. 2007) Truck system (Willcox 2008)

Conveyor belt system (Cadia Valley Operations April 2013)

Shaft system (Moss 2004)

Figure 32 Material handling systems used in cave mining

2.4.

The impact of the incremental changes

The incremental changes that have occurred in the last 30 years and discussed above, have arguably been significant in terms of increasing safety, mining efficiencies and productivity as well as reducing costs during cave mining of hard rock. Collectively, these changes have also enabled the industry to effectively mine in conditions that would otherwise have been uneconomic using conventional methods and practices. In addition, the moderate depths orebodies where current mechanised caving techniques have been developed are now being exhausted and new orebodies are increasingly been found at depth much greater than current. Such orebodies are bringing with them new challenges. The incremental changes designed to get more efficiently out of the 1970-80s step change have nevertheless not resulted in a generational transformation in cave mining. The future challenges constitute a radical shift from current experiences and therefore necessitate radical changes in order to effectively mitigate the risks that the cave mining business may face in the future. These challenges can be categorised under the broad topics of technical, economical, licence to operate and human capital. Now is the time to make fundamental and dramatic changes.

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Keynote Speakers 3

Future Challenges

The future high risk environments or challenges identified in this paper and with the potential to significantly impact on the efficiency, economic and sustainability of cave mining include the following: 3.1.

Challenge 1: technical

The technical challenges that need to be overcome include those associated with greater depths, lower average grade deposits and meeting the demand for increased productivity. 3.1.1

Greater depths

The issues impacted by depths and that need to be overcome are acquisition of reliable deposit knowledge, access to the orebody, harder rock, higher rock stresses, extreme work environment (e.g. ventilation, temperature and humidity), higher demand for power, longer distances for material transport to surface and effective working hours due to the transport the personnel from and to surface. Engineering solutions are required in order to be able to mine under these new conditions which are outside current practices. 3.1.2

Lower average grade deposits

It is widely recognised that the future will predominantly be associated with the mining of lower grade deposits. Cave mining operating costs are often not reported in the public domain, however these costs have arguably been escalating in recent times using current practices and estimated to be in the range to USD 7/t to USD 12/t. In order to economically mine future lower grade deposits, will necessitate the development and application of technologies, such as discuss later, in order to ensure that operationally, cave mining remains low costs (e.g.
Demand for increased productivity

Cave mining will continue experiencing the demand for increased productivity (Ernst & Young, 2013). This will be exacerbated by the mining of lower grade deposits. In order to be economical under these conditions will require the industry to at least double its current best productivity from a single footprint as discussed in paper by Wellman et al. (2012). Such productivity levels can only be achieved through application of new technologies for continuous and automated production systems. Broadly speaking, the technical challenges associated with depth, low grades and demand for increased productivity; suggest that cave mining cannot be business as usual. Fundamental changes are required in order to effectively address these challenges. As such, it is proposed that: 1 Methods are developed to enable safe and rapid access to the deep orebodies as well as rapid footprint establishment. With respect to single heading access development, the current mining industry’s best using drilling and blasting techniques assisted by long run drilling jumbos has been reported to average 265 m/month (Willcox 2008). During the same time, tunnels constructed using civil engineering mechanical excavators have reported averages of up to 670 m/month (Cigla et al. 2001). Whilst the conditions and requirement associated with civil tunnels are significantly different from the mining tunnelling requirements, considerable effort needs to be made to develop mechanical excavation technology for mining to approach similar development rates as civil tunnels. It should be possible to also adapt the same technologies for rapid footprint establishment purposes.

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Caving 2014, Santiago, Chile 2 The current industry’s practices to enhance cavability and fragmentation include the use of hydrofracturing and confined blasting techniques and more recently the combination of the two (Catalán 2012). The true effects of these techniques continue to be a subject of research in mining. More effective preconditioning methods should be developed in order to achieve unconstrained caving rates, better fragmentation and therefore better cave drawpoint flow with much higher degree of confidence. These methods could include ultra preconditioning and unconfined blasting. 3 The current production systems are batch in nature because of the use of LHDs as both a digging and tramming machine. In addition, with current layouts, the LHD spends more time tramming than digging. This results in a batch process with low productivity (e.g. 120 to 150 tph). To achieve the demand for higher productivity, continuous rather than batch systems need to be developed. This could include the use of compact, flexible and mobile machines integrating the loading and crushing processes linked to conveyor systems. Alternatively, systems such as the rock flow continuous production system could be adopted (Steinberg et al. 2012). 4 The current layouts such as El Teniente and Herringbone were developed to improve LHD productivity (Ovalle 1981, Chacón 2004). Alternative layouts to enable effective implementation of continuous mining systems need to be developed. The above changes combined are shown in Figure 33 and they should help reduce escalating mining costs thereby improving the cave mining business of the future in spite of the new technical challenges discussed.

Figure 33 Conceptual future cave mining – continuous mining system 3.2. Challenge 2: economical

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Keynote Speakers Both, capital and operating cave mining costs have been escalating in the last few years and this trend is expected to continue in the future. The result has been a reduction in the margin due to the lower commodities prices and the increasing mining costs. This challenge needs to be given priority if large scale and low grade caving operations are to be viable. Ernst & Young 2013 discuss strategies to manage cost escalation that could equally be considered for the cave mining business. The suggested strategies include reviewing the soundness and probabilistic risk of different cave mining projects, better definition of orebody characteristics, greater analysis of the return of investment from individual projects, building mines in phases and in smaller volumes when commodity prices are low and then scaling up as demand fundamentals shift, and shifting from asset expansion to operational excellence thereby maximizing cash flow from existing assets. 3.3.

Challenge 3: social license to operate

Public focus on mining activities and the manner in which community and environmental concerns are addressed continues to increase. This increases the importance of including social licence to operate considerations in the development and assessment of mining project proposals. A successful project should include community and environmental considerations in an enterprise risk management framework with clear and proactive risk mitigation strategies. A balanced assessment of a project, which identifies both the potential impacts of a project development and the project benefits, combined with early and continuing engagement with stakeholders at all levels, will have a significant impact on project outcomes. 3.4.

Challenge 4: skills shortage

In the last 10 years there has been an increasing number of experienced cave mining technical and operators either at or nearing retiring age. At the same time, there has been an increasing number of young people pursuing careers other than in mining (e.g. information technology, health, business, social services). These two trends have resulted in mining skills shortage in almost all major mining countries. This is an issue that the industry needs to address not only in term of filling the current skills shortage but also to prepare the new generation of mining talents equipped to adequately address the challenges discussed earlier. Some of the strategies discussed in the literature include better training or reskilling of existing talents, retaining modern talents, flexibility and mobility of the workforce, adopting new technology and closer links with educational and training institutions.

4

Conclusions

Cave mining will continue being an attractive method for quite some time mainly due to the fact that the method can be low cost and high productivity. However, a number of the future challenges, which are significantly outside past and or present experience, dictate that significant changes to current practices are required. It is argued in this paper that the number of incremental changes introduced to the industry in the last 30 years may not necessarily be adequate in themselves to effectively address the future challenges. Some of the envisaged step changes for future challenges are discussed in the paper. Whilst some major mining companies may have sufficient resources to address a number of the challenges in isolation, a global and collaborative approach is required in order to accelerate and more effectively develop new technologies, practices and skills required to successfully manage the challenges thereby sustaining the cave mining industry globally.

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Caving 2014, Santiago, Chile Acknowledgements The author would especially like to acknowledge Andrew Logan of Newcrest, Professor Gideon Chitombo of Bryan Research Centre, The University of Queensland, Australia and, Jock Macneish and Robert Black of Strategic Images who have contributed to this paper.

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Keynote Speakers Brunton, I, Sharrock, G & Lett, J 2012, ‘Full Scale Near Field Flow Behaviour at the Ridgeway Deeps Block Cave Mine’, Proceedings of MassMin 2012, Sudbury, Paper 6826, Canadian Institute of Mining, Metallurgy and Petroleum: Ontario. Burger, D and Cook, B 2008, ‘Equipment automation for massive mining methods’, Proceedings MassMin 2008, Lulea, (Eds: H Schunnesson and E Nordlund), pp. 493-498, Luleå University of Technology: Sweden. Butcher, R 2000a, ‘Block cave undercutting - aims, strategies, methods and management’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 405-411, Australasian Institute of Mining and Metallurgy: Melbourne. Butcher, R 2000b, ‘The role of mass concrete in soft rock block cave mines’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 422-428, Australasian Institute of Mining and Metallurgy: Melbourne. Cadia Valley Operations, 2013. Across the valley magazine. Newcrest Mining Limited, Internal magazine, Edition February 2013. Cadia Valley Operations, 2013, Across the valley magazine, Newcrest Mining Limited, Internal magazine, Edition April 2013. Cadia Valley Operations, 2013, Across the valley magazine, Newcrest Mining Limited Internal magazine, Edition September 2013. Calder, K, Townsend, P & Russell, F 2000, ‘PC-BC: a block cave design and draw control system’,Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 469-484, Australasian Institute of Mining and Metallurgy: Melbourne. Casten, T, Golden, R, Mulyadi, A & Barber, J 2000, ‘Excavation design and ground support of the gyratory crusher installation at the DOZ mine, PT Freeport Indonesia’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 295-299, Australasian Institute of Mining and Metallurgy: Melbourne. Casten, T, Rachmad, L, Arkadius, T, Osborne, K & Johnson, M 2008, ‘P.T. Freeport Indonesia’s Deep Ore Zone mine - expanding to 80,000 tonnes per day’, Proceedings MassMin 2008, Lulea, (Eds: H Schunnesson and E Nordlund), pp. 265-274, Luleå University of Technology: Sweden. Castro, R, Vargas, R & Huerta F 2012, ‘Determination of drawpoint spacing in panel caving: a case study at the El Teniente Mine’, The Journal of the Southern African Institute of Mining and Metallurgy, vol. 112. Catalan, A, Sinaga, F & Qudraturrahman, I 2010, ‘The role of geotechnical engineering during the prefeasibility studies and early works of Cadia East panel caving project, New South Wales, Australia’, Proceedings of Caving 2010 Conference, Perth, Australia. Catalan, A, Dunstan, G, Morgan, M, Green, S, Jorquera, S, Thornhill, T, Onederra, I & Chitombo, G 2012, ‘How can an intensive preconditioning concept be implemented at mass mining method? Application to Cadia East panel caving project’, Proceedings 46th Congress, US Rock Mechanics/Symposium, Paper No ARMA 12-681, July 24-27, Chicago, IL, USA. Chacón, J, Göepfert, H, Ovalle, A 2004, ‘Thirty years evolution of block caving in Chile’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), pp. 387-392, Chilean Engineering Institute: Santiago.

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Caving 2014, Santiago, Chile Chacón, E, Barrera, V, Jeffrey, R & van As, A 2004, ‘Hydraulic fracturing used to precondition ore and reduce fragment size for block caving’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), pp. 529-534, Chilean Engineering Institute: Santiago. Chitombo, GP 2010, ‘Cave mining - 16 years after Laubscher’s 1994 paper ‘Cave mining – state of the art’’, Caving Conference 2010. Cigla, M, Yagiz, S, Ozdemer, L 2001, ‘Application of tunnel boring machines in underground mine development’, 17th International Mining Congress, Ankara, Turkey. Cook, B, Burger, D, Alberts, L & Grobler, R 2008, ‘Automated loading and hauling experiences at De Beers Finsch mine’, Proceedings Tenth Underground Operators’ Conference 2008, Launceston, 231238, Australasian Institute of Mining and Metallurgy: Melbourne. Deloitte, 2013, ‘Tracking the trends 2014, The top 10 issues mining companies will face in the coming year’, Accessible at http://www.deloitte.com/assets/Dcom-Australia/LocalAssets/Documents/ Industries/Energyandresources/Mining/Deloitte Tracking_the_trends_2014_final_Dec2013. pdf. Dolipas, R 2000, ‘Rock mechanics as applied in Philex block cave operations’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 395-404, Australasian Institute of Mining and Metallurgy: Melbourne. Duffield, S 2000, ‘Design of the second block cave at Northparkes E26 Mine’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 335-346, Australasian Institute of Mining and Metallurgy: Melbourne. Dunstan, G & Popa, L 2012, ‘Innovative cave establishment practices at Ridgeway Deeps’, Proceedings MassMin 2012, Sudbury, Paper 6792, Canadian Institute of Mining, Metallurgy and Petroleum: Ontario. Ernst & Young, 2013, ‘Business risks facing mining and metals 2013–2014. The business risk report. Mining and metals 2013–2014’, Accessible at http://www.ey.com/Publication/vwLUAssets/ Business_risks_facing_mining_and_metals_2013–2014_ ER0069/ $FILE/Business_risks_ facing_mining_and_metals_2013–2014_ER0069.pdf. Ferguson, W, Keskimaki, K, Mahon, J & Manuel, S 2008, ‘Henderson 2000 conveyor update’, Proceedings MassMin 2008, Lulea, (Eds: H Schunnesson and E Nordlund), pp. 575-584, Luleå University of Technology: Sweden. Flores, G, Karzulovic, A & Brown, ET 2004, ‘Current practices and trends in cave mining’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), pp. 83-90, Chilean Engineering Institute: Santiago. Flores, G, Logan, A & Cuthbert, B 2007, ‘Codelco November 2006 Visit Lessons Report’, Newcrest Technical Visit report to Codelco operations, April 2007. Flores, G & Logan, A 2008, ‘Caving technology development and its application at Cadia East project’, Proceedings 3rd International Conference on Innovation in Mine Operations, Minin 2008 (Eds: J Arias, R Castro Tadeusz Golosinski), pp. 257-269, Santiago. Fuenzalida, P, Baraqui, J & Castro, R 2012, ‘Mining with low profile cruhsers: The experience at Codelco Chile’, Proceedings 5Th International Conference on Innovation in Mine Operations, Minin 2012 (Eds: R Kuyvenhove, J Morales and C Vega), pp. 90- 91, Santiago.

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Keynote Speakers Fuenzalida, P & Baraqui, J 2012, ‘Precast structure for draw point in block/panel caving’, Proceedings 5th International Conference on Innovation in Mine Operations, Minin 2012 (Eds: R Kuyvenhove, J Morales and C Vega), pp. 94-95, Santiago. Golden Jr, R & Fronapfel, L 2008, ‘Evolution of ground support practices on Henderson’s lower levels’, Proceedings MassMin 2008, Lulea, (Eds: H Schunnesson and E Nordlund), 717-728, Luleå University of Technology: Sweden. Gustafson, A, Schunnesson, H, Galar, D & Kumar, U 2013, ‘The influence of the operating environment on manual and automatic load- haul-dump machines: a fault tree analysis’, International Journal of Mining, Reclamation and Environment, vol. 27 Iss 2, 2013, pp. 75-87. Haley, W 1982, ‘Adaptation of surface mining machines to underground mining’, Design and Operation of Caving and Sublevel Stoping Mines, (Ed: D R Stewart), 1198-1219, Society of Mining Engineers, AIME: New York. Jofre, J, Yáñez, P & Ferguson, G 1992, ‘Evolution in panel caving undercutting and drawbell excavation, El Teniente Mine’, Proceedings Massmin 1992. Krek, R, Leonforte, A, Pratt, A & Dunstan, G 2008, ‘Underground infrastructure requirements for underground cave mining operations’, Proceedings Tenth Underground Operators’ Conference 2008, Launceston, pp. 205-217, Australasian Institute of Mining and Metallurgy: Melbourne. Kvapil, R, Baeza, L, Rosenthal, J & Flores, G 1989, ‘Block caving at El Teniente Mine, Chile’, Trans Instn Min Metall, Sect A: Min Industry, 98: A43-56. Laubscher, DH 1994, Cave mining – the state of the art. J S Afr Inst Min Metall, 94(10): pp. 279-293. Leach, A, Naidoo, K & Bartlett, P 2000, ‘Considerations for design of production level drawpoint layouts for a deep block cave’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 356-366, Australasian Institute of Mining and Metallurgy: Melbourne. Leiva, E and Duran, L, 2003. Pre-caving, drilling and blasting in the Esmeralda sector of the El Teniente mine. Fragblast 2003, Vol 7, No. 2, pp. 87-104. L’Estrange, H 2009, ‘Cadia East Technology, Chile Visit Report – Sizer Investigation’, Newcrest Technical Visit report to Codelco operations, October 2009. Madrid, A & Constanzo, H 2013, ‘Operational definition of three undercutting fronts for the New Mine Level project’, Proceedings of 3rd International Seminar on Mine Planning, Santiago (Eds: J Beniscelli, C Bottinelli, J Cárdenas, H Constanzo, H Göpfert and E Henríquez), pp. 171-179, Chile. Manca, L & Dunstan, G 2013, ‘Cadia East – a case study in applied innovative design’, Proceedings of 3rd International Seminar on Mine Planning, Santiago (Eds: J Beniscelli, C Bottinelli, J Cárdenas, H Constanzo, H Göpfert and E Henríquez), 181-190, Chile. Manca, L & Flores, G 2013, ‘Modern Planning Practices for Cave Mining’, Proceedings of 3rd International Seminar on Mine Planning, Santiago (Eds: J Beniscelli, C Bottinelli, J Cárdenas, H Constanzo, H Göpfert and E Henríquez), pp. 191-204, Chile. Metsänen, A 2004, ‘Supplier as solution provider for the mining industry, Sandvik Mining and Construction vision of the future in mining’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), pp. 659-661, Chilean Engineering Institute: Santiago.

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Caving 2014, Santiago, Chile Moss, A, Russell, F & Jones, C 2004, ‘Caving and fragmentation at Palabora: prediction to production’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), 585-590, Chilean Engineering Institute: Santiago. Music, A & San Martin J 2012, ‘Great volume draw bells blast at El Teniente’, Proceedings MassMin 2012, Sudbury, Paper 6779, Canadian Institute of Mining, Metallurgy and Petroleum: Ontario. Ovalle, AW 1981, ‘Analysis and considerations for mining the El Teniente ore body’, Design and Operation of Caving and Sublevel Stoping Mines, (Ed: D R Stewart), pp. 195-208, Society of Mining Engineers, AIME: New York. Ovalle, AW & Albornoz, HR 1981, ‘Block caving with LHD equipment at El Teniente’, Design and Operation of Caving and Sublevel Stoping Mines, (Ed: D R Stewart), pp. 355-361, Society of Mining Engineers, AIME: New York. Pinochet, A, Constanzo, H & Larraín, M 2012, ‘Value generation at El Teniente mine by using the main transport system flexibility’, Proceedings MassMin 2012, Sudbury, Paper 6979, Canadian Institute of Mining, Metallurgy and Petroleum: Ontario. Rojas, E, Cuevas, J & Barrera, V 1992, ‘Analysis of the wear in drawpoint at El Teniente mine’, Proceedings Massmin 1992, Johannesburg, (Ed: H Glea), pp. 303-310, SAIMM: South Africa. Rojas, E, Molina, R, Bonani, A & Constanzo, H 2000, ‘The pre-undercut caving method at the El Teniente Mine, Codelco – Chile’, Proceedings MassMin 2000, Brisbane, (Ed: G Chitombo), pp. 261266, Australasian Institute of Mining and Metallurgy: Melbourne. Silveira, AC 2004, ‘Undercutting at E26 lift 2 Northparkes’, Proceedings MassMin 2004, Santiago, (Eds: A Karzulovic and M Alfaro), pp. 410-414, Chilean Engineering Institute: Santiago. Silveira, C, Lovitt, M & Hewitt, T 2005, ‘Off to a Good Start with Lift #2: Drawbell Extraction – Northparkes’, Proceedings Ninth Underground Operators’ Conference 2005, Perth, pp. 75-80, Australasian Institute of Mining and Metallurgy: Melbourne. Schunnesson, H, Gustafson, A & Kumar, U 2009, ‘Performance of automated LHD machines: A Review’, Proceedings of International Symposium on Mine Planning and Equipment Selection, Banff, Canada, pp. 773-782.

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Keynote Speakers

It’s Not Mine Safety But Mind Safety - A Henderson Approach GK Carlson Climax Molybdenum Company, USA

Abstract As all industries have come to realize the implications and value of a safe workplace, and while it continues to be a topic that dominates conversation, we continue to have injuries. Henderson is no exception. Safe Production is our number one priority and Henderson has improved its safety performance; but we still have not achieved the ultimate goal of no accidents and incidents. With all the rules, programs, tools, experience, and studies at our disposal today, the question of why they have not been eliminated comes down to one thing: not using the greatest tool at our disposal, OUR BRAINS! Henderson has been working for years to get all employees, top to bottom, to understand that Safe Production is truly the number one priority every minute of every day and tries to create an atmosphere that encourages people to make good decisions in all they do at work. To succeed at this, all employees must feel comfortable with the concept that safety has priority over production. It can also be summed up by a single statement from our General Manager “I want you all to be intensely selfish when it comes to making safe choices. After all, you and your family depend on you to exercise your intelligence to that end. You are the only one that has complete control.” We all know the weakest link in a chain will be the failure point. As in all workforces, from the top down, anyone not intensely selfish about safety becomes the weak link, and the chain will break. This paper will present some of the structure and belief systems Henderson is employing toward the ultimate goal of no accidents and incidents. To achieve this everyone must use the best tool available, their brains!

1 Introduction The Henderson Mine is a post-undercut, panel caving operation located near Empire, Colorado, USA. The mine produces high quality primary molybdenum from a complex of granitic intrusives. Mine safety is not a separate topic from general safe work practices. Maintaining a safe workplace is required of all industries and businesses. Pigeonholing ourselves into a specific industry rather focusing on the root causes and mitigation of injuries does not assist in elimination of accidents and incidents. Mining does have certain hazards that are somewhat unique, but as an industry we should be focusing on safe work practices in general. An open hole is an open hole, whether it is an ore pass or a catwalk with a section of grating removed. Electricity can kill if it is an energized cable in a mine or an improperly wired outlet in a house. Confined spaces are found in mines as well as a city’s sewer systems. Tripping hazards can be anywhere. Henderson, like all other mines and mills, works diligently to mitigate and minimize all hazards that may affect our workers. However, without the full and complete buy-in of employees into a true safety culture, injuries and incidents will continue. Henderson has rules, lots and lots of rules. Coupled with Henderson’s requirements are the government authorities’ rules and regulations concerning areas not necessarily directly involved with safety. How can anyone possibly keep all this information at the forefront of their minds and perform daily work? So why do we have so many rules? Obviously, these rules have been developed in order to give workers a reference and guidance on safe work practices as all were likely created due to some historic injury or fatality. The rules are intended to help prevent repetition of past incidents by providing training so people can learn, and hopefully, act accordingly.

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Caving 2014, Santiago, Chile Thus, how do we get workers to follow all these rules? Many programs, incentives, studies, and systems have been developed to accomplish this goal. The question is: do they work? The answer is: sort of. No matter how much businesses try, individuals act according to the way they think and their perception of the world around them. That perception and method of processing inputs has been developing in a person from the day they were born. It is influenced by the people, experiences, and beliefs they have been interacting with throughout their lives. Changing a lifetime of reasoning is a daunting task. However, in most cases, some facet of a person’s life learnings must be overcome in order to develop a worker who consistently makes safe choices. It is vital that we diligently pursue the re-education and training of people to become safe and productive employees. Although one would think just going home to our loved ones uninjured would be incentive enough, it appears to need reinforcement. Furthermore, positive feedback or recognition for good decisions can be a more powerful incentive to reinforce a behavioural change. Henderson has found that accountability for one’s actions is a key element to changing behaviour. If people are not held accountable for making poor decisions, there is little external incentive for them to change their behaviours. This must be consistent for all or the system will fail. In other words, we need to do all we can to get people to use the best tool available to them, their brains. Henderson continues to re-examine its safety culture, programs, systems, and behavioural evaluations. Henderson utilizes an OHSAS 18001 certified safety system to manage all training documents, all hold Safe Operating Procedures (SOP’s), develop and classify all compliance documentation and assist Henderson in auditing system compliance. Sub-categories of this system include; task risk assessment, consequence thinking, and engineered or administrative controls to reduce reliance on behavioural decision making for any job. All of these are elements that must be used as training, enforcement and reward opportunities. Henderson believes it has the appropriate procedures and controls in-place for all our employees to complete all tasks safely, as long as they follow the “rules.” More importantly, however, Henderson is attempting to redirect and give positive influence to the way a person thinks, reasons and reacts. Until people believe passionately in their own wellbeing as the number one priority, fully understand the combined knowledge that has contributed to all the rules that are in place, and use that knowledge to think before acting, we will continue to have accidents. We must strive for all employees to exercise their minds and use their brains, the best tool out there.

2

Beyond the System

Henderson’s OHSAS 18001 certified safety system is the basis for its safety program. This system is used to ensure Henderson does what it says it is doing as well as track all relevant documentation. The scope of this paper though is not, however, to discuss the OHSAS system. This paper is intended to go beyond the rules and focus on other attributes that Henderson believes are the core of a successful safety culture. Let’s first focus is on some of the very basics. A clean, well-organized workplace: To this end, Henderson has embraced a higher level of housekeeping compliance and workplace inspections, making it incumbent upon frontline supervisors and employees to adhere to this principle. Personal accountability is paramount in maintaining this high standard. Throughout the operation, there is no tolerance for saying “it’s not my area, so not my problem.” All personnel are to report any issues observed and if they can fix it, then fix it! This includes simple things like picking up any stray trash; making sure all equipment is kept clean, cleaned for inspection, and ready for the next user; keeping all storage area orderly and supplies secured; and barricading any areas that may have safety issues (and promptly reporting such issues). In general, Henderson encourages its employees to take pride in the operation. In order to achieve such high standards, the workforce must act as a cohesive unit. Individuals cannot be working in multiple directions based on their own attitudes and pre-conditions. Henderson relies on its frontline supervisors to be fully engaged with the operation and their own crew to make sure the company goal of Safe Production is at the forefront of all we do, and is not compartmentalized. This requires constant communications and further makes the frontline supervisors have to think and understand their roles and

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Keynote Speakers responsibilities, as well as how they interact with those downstream. Henderson further conducts “mock audits” in which the Senior Supervisor of each department routinely audits their work areas, trying to think as a Federal inspector might. This allows supervisors to find any issues and mitigate them before the issues become enforcement citations. All supervisors carry cameras for documenting observations (both good and bad) to share with others. Equipment pre-operational checks are examined to prove they have been properly done and all supervisors and employees are held responsible for their work areas. The benefits of this effort have been impressive. Employees are taking ownership and the level of issued citations from Federal inspections, despite increased scrutiny, is falling (Figure 1). Equipment availabilities have risen as employees take greater care of their machines and subsequently so has productivity. This did not happen overnight. It has taken many years of perseverance and enforcement by all levels of supervision to “re-train” themselves and the workforce to reach these levels.

Figure 1. Henderson Quarterly Issued Citations (Mine and Mill)

3 Accountability It is probably well accepted in our industry that we have lots of rules. So many so that it is beyond the human capacity to remember all of them. However, most rules are a result of some poor individual being injured or killed before the rule became a rule. Certainly, these rules are good, but if they are not followed or fully understood, it can lead to accidents. Accountability and impartial enforcement is mandatory if an effective safety culture is to take root. The excuse ”I didn’t know” is not acceptable at Henderson. If a worker does not know the procedures, they should not be doing the task. If they are unsure, they should get confirmation and/or training before proceeding. Henderson takes the approach that individuals must be fully engaged in their own personal safety to appreciate the gravity and dimensions of the decisions that govern their work ethic. Concurrently, how that individual responds to their supervisor’s directions and how the supervisor reflects the safety attitude will dictate how a choice is made by each individual. Henderson has placed particular emphasis on the supervisors’ roles and responsibilities to ensure that they are fully engaged and that communications to the workforce are in line with Safe Production. This continues up the chain of command to the very top.

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Caving 2014, Santiago, Chile In this way, all are held accountable and responsible for the actions of those below them. Continued discussions between supervisors and subordinates have slowly begun to move the pendulum towards the safety culture Henderson wants. It is now considered alright for an employee to refuse to do a job they feel is unsafe, but it is not acceptable to just say something is unsafe. The employee must identify the issues that they believe make it unsafe, whether it is insufficient training, specific controls are not in place, the equipment to be used is unsatisfactory for performing the task, or whatever the issue is. Supervisors work with employees to the degree necessary to ensure that the task has been properly vetted and risk has been minimized to an acceptable level. If this cannot be agreed upon, the task will be reviewed by an appropriate team until all agree the risk has reached acceptable levels. This rarely occurs, however, since the OHSAS 18001 system dictates that crews have reviewed their tasks, assessed risks, apply proper controls, and have a safe procedure in place before any work begins. These are the SOP’s that have been developed and supply the “rules” to be followed in order to complete a task safely. Again, this is using brain power upfront as an effort to stem the possibility of unintended consequences in the future. Accountability does not always have to be negative. When we observe people making good choices (like stepping back and thinking when things are not proceeding correctly) and publicly acknowledging it as a desired behaviour, this can potentially be more powerful and lasting than punishment for an undesirable behaviour.

4

The Frontline Offense

Safe Production is not a defensive strategy and should be proactive rather than reactive. It is all offense. We must make the first moves, be relentless in our efforts, and continually communicate the expectations and goals to be achieved. Henderson is very successful in safely bringing large projects to completion, yet has tended to have lessfavorable experiences on normal day-to-day tasks. This may be due to the recognition that large projects have large potential for accidents. We focus our minds and efforts during these large projects in a higher fashion to ensure that the project is done safely. The human brain has a tendency toward running in autopilot. Thus, an individual’s attention tends not to be focused to the degree necessary to produce the same results as large projects. Obviously, the people who do the work are those with the best knowledge of how to do it safely and efficiently, but for some reason may make poor choices. Henderson’s approach is to place a high degree of responsibility on the frontline supervisors to have a more thorough knowledge of their personnel to properly engage them, know what motivates or demotivates them, what life issues might affect their work focus, and direct them so that their choices are in line with the appropriate goals. It is incumbent on the frontline supervisors to have the greatest awareness of those under them to ensure that they are the right people for the job, are focused and ready to perform the work, and are not being distracted by other factors that could put them or others at risk. Henderson requires the frontline supervisors to spend 80% or more of their time visiting their work areas and communicating with their personnel, keeping a pulse on anything that might detract from the work focus. To assist in this effort, Henderson sets limits on crew sizes to allow the frontline supervisor to have the extra time to truly engage their subordinates. This engagement fosters communications, thinking (using the mind) and examining and analysing any pertinent issues to create the intended result of working in a safe and productive manner. Similarly, this allows more one-on-one interaction between the supervisor and subordinate, giving each a better understanding of expectations and distractions that might be barriers to reaching the desired results.

5

It’s Not Just Us

Another aspect that Henderson has changed in recent years is how we work with and manage our contractors on-site. Contractors are considered an addition to the workforce and as such must abide by all the values and rules of Henderson. There are no exceptions and contractor work hours and incidents are

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Keynote Speakers compiled with the sites’ reported safety numbers. Not only do contractors have to submit a site safety plan prior to commencing any work on the property, but Henderson has placed additional requirements and accountability on project managers to ensure full compliance with their plan and Henderson’s regulations. Since contractor safety performance impacts the Henderson safety statistics, every employee is empowered to question a contractor’s perceived unsafe acts to ensure we are all moving forward in a safe manner. It is not just the project manager’s responsibility; everyone in the area is responsible to ensure all work is being performed safely. Accountability, as mentioned before, is key for both Henderson and contractor personnel, and those people who fail to meet the standards have been removed. Again, this has taken time and diligence, and the improvement is demonstrated by the incident graph (Figure 2) showing a progressive decline in recordable incidents. One normally thinks of contractors as those doing “heavy” work, but at Henderson it applies to all contractors, including, for example, the janitorial staff to freight haulers. All are subject to the same rules and standards. This also requires additional diligence as many smaller companies may not have taken measures to ensure safe work practices to meet Henderson’s expectations. The educational and communication demands to get everyone utilizing their brains and acting accordingly is enormous, but must be done if we expect to have all workers making safe choices on a routine basis.

Figure 2 Henderson Total Recordable Incident Rate(TRIR)

6 Incentives Over the years many ideas have been tried to incentivize the safety culture. Historic approaches usually included extrinsic motivators such as awards, bonuses and gifts. These motivators only seemed to have worked for some. The question we need to ask is: What better incentive is there than to not be hurt, disabled or fatally injured while at work? How do you teach someone that the end of a finger, or a toe, or a leg, or an arm is worth thinking about every minute of every day? Henderson has come to recognize that intrinsic motivation is the key. We are trying to help people understand that they need to be focused on the task at hand for their own wellbeing and their loved ones rather than to earn some trinket or to prevent disciplinary actions. Most would agree that when a task becomes routine, the person(s) doing the work will become complacent in performance since they have done it many times without injury. This is one of our greatest challenges. When the mind goes into autopilot, the consequences of actions and choices become less at the forefront of the work, until something goes wrong.

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Caving 2014, Santiago, Chile What is the meaning of “Safe Production?” Is it just words or is there a true commitment? Henderson believes that Safe Production is just as it says. Henderson’s bonus structure is based on the entire operation’s performance and corporate goals. Initially, this was structured as a breakdown of 35% of the total bonus (paid quarterly) based on the operation’s safety performance. This placed the burden on all employees to work safely or all could potentially lose a significant percentage of the bonus. This may seem unfair to some, punishing the innocent, however it is also meant to act as an incentive to have employees pay attention to each other’s work practices, speak up and correct inappropriate behaviours and actions. How would you feel if you chose to ignore an action you observed, knowing it was unsafe, only to find out later that the person was injured or killed? Getting the workforce to accept critical coaching from peers and/ or subordinate as well as overcoming the intimidation one may feel in approaching a peer or a contractor, is challenging. This requires getting all individuals to accept coaching not as a criticism but rather as an opportunity to think and analyse what they are doing and make sure it is the safest and best approach. This has taken a lot of time and effort to change the way the workforce behaves and is by no means complete. However, there is noticeable positive change taking place and Henderson is committed to pursuing this act as we believe the new culture is taking root. Recently, the bonus structure was modified to allow for 50% of the bonus pay be based on the operation’s safety performance, with the caveat that any employee injured in an accident and found culpable in a safety violation forfeits their safety portion of the bonus. In other words, Henderson is holding people accountable for poor decisions. Some might say this will make employees not report injuries and, in some relatively minor incidents, this might be true. However, a major accident with injuries will get reported. The subsequent investigation will also reveal the decision processes leading up to the occurrence and this information is then shared throughout the company. Additionally, contractor hours and incidents are included in the calculations for bonus pay. Although they are not participants in the program, their safety record can affect the bonus calculations. Supervisors are also rated on their crews’ performance which can have implications towards advancement and merit pay, and on up the chain.

7

Consequence Thinking

Now that we have rules, controls, accountability, enforcement and incentives, why do we continue to have accidents and incidents? As the old saying goes, you can lead a horse to water but you cannot make him drink. Each person has a different perspective and attitude about the world around them. The way they process information, weigh alternatives and make decisions are all different. Likewise, the way each individual communicates desires or concerns is equally unique. It is impractical and impossible to have a supervisor with every employee at every moment. We must rely on the employees to understand what is expected of them and trust them to act accordingly. Enter the demon; the human capacity to not use the best tool in our arsenal, our brains. Consequence thinking involves using one’s brain to analyse what might happen and weigh the risks if a particular choice is made before proceeding with the action/decision. Henderson calls it the 15 second rule – what could happen in the next 15 seconds of work. This sounds simple but as a matter of practice it goes against human nature to engage the brain that much. Since the human brain prefers to run in autopilot, for each individual to think before each action with hundreds of actions taking place over the course of a workday is challenging. However, this is what must be done in order to truly have Safe Production. No one wants to be injured and consciously decides to proceed with a task knowing they will be hurt. Unfortunately, people faced with a choice can gamble that they will not be hurt (“it won’t happen to me”). This may be due to several factors: it is a routine task done many time before without injury (complacency),

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Keynote Speakers they ignore or disregard some aspect of the procedure thus the job will be done more quickly (shortcuts), it is more of a bother to get the right tool than use what is at hand (improvising/laziness), they believe they can handle the load without assistance (bulletproof), and on and on. In each instance, there is probably some small voice in the brain telling them not to proceed, but it is often ignored, sometimes with serious consequences. In the injuries Henderson has suffered over the past few years, the follow up investigation demonstrates a common element of human behaviour that has caused a poor choice to be made. How do we get people to engage their brains and really listen to that little voice?

8

Managing Production Pressure, Frustration, Upset Conditions

In keeping Safe Production as the priority, Henderson tries to convey to employees that production pressure is really an individual choice. Although there may be emphasis communicated to get a task done, it is Henderson’s policy that it is not to be construed as an order to shortcut any process and put oneself at risk in order to complete the task quickly. What it is meant to be conveyed is that all efforts should be taken to ensure the task is completed correctly, in the shortest possible timeframe. Production pressure at this point becomes an individual’s personal decision. Knowing that the task is to be done correctly, employees need to exercise their minds, use their brains, to determine the most effective and safe manner in which to proceed to minimize task completion time. It is also required that the supervisor be engaged with, and understand, their people to make sure all work is being done appropriately. Likewise, if things are not going well as tasks are being performed, it is better to take the time to stop, reexamine the issues, get additional help if needed (more brains), develop a plan moving forward, and then begin again. Taking the time to incorporate all these steps should, if the brain is used, result in the issue being resolved and the task being completed safely and correctly in a practical timeframe. It is when frustration is allowed to overshadow consequence thinking, ignoring that little voice and forcing a resolution, that accidents are likely to occur.

9

Core Values

What Henderson wants is for safety to become a core value of each person’s life. If a person fully embraces safety as part of his or her true self, then this attitude is carried beyond the workplace to home life as well. An employee seriously injured at home, suffering lost work time, impacts the company just as much as one injured at work. The result is the same, lost time and productivity. A true belief in safety is not checked in and out at the gate to work. What does it take to make good behaviours become good habits? Constant and concise training, reminders, and continual re-enforcement of good behaviours are a key element. Training is not just how to operate or work on a piece of equipment, it is also the safety aspects of that operation. We must begin by taking the time to fully train our workforce in the safety aspects of their jobs. We must have constant reminders of that training and on a day-to-day basis follow up to make sure these practices are being utilized. This cannot be done by only a few individuals, but must be a part of the day-to-day work by all. Henderson has been cultivating a culture where all workers can and should question another when they see something that does not appear to be safe. Likewise, it is obligatory for the employee being questioned to not be intimidating or dismissive, but instead to accept and examine or explain their own practices. Call it forced re-focusing, a reminder of consequence thinking, using the brain. Henderson also helps people focus on safety by having any group meeting begin with a safety share. This may be a management meeting, crew tailgates, or any other meeting. People are expected to come to meetings with something to share on safety. Management also holds quarterly supervisor meetings in order to bring all frontline supervisors and senior supervisors together with the superintendents to discuss safety values and issues in addition to production matters so all can share experiences and learn from each other. This helps drive, from the top down, the core value and expectation that Safe Production is the first priority. It helps

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Caving 2014, Santiago, Chile break down the barriers to a true safety culture that can then be communicated further down to the workforce. It also reinforces the attitude that supervisors will support good decisions with regards to safety, even if it disrupts or hampers production. Good decisions are recognized and shared throughout the operation.

10 Conclusions As a business, we lead people toward the results we desire and must utilize our best efforts to influence and positively redirect a person’s thoughts, attitudes and actions to consistently make safe choices. Henderson is continually trying to engage each employee, from top to bottom, to encourage the belief that Safe Production is not only in the company’s best interest, but in each individual’s best interest. We provide guidance and set examples for others to follow. Holding people accountable for their poor decisions, as well as holding supervisors accountable for the actions of their employees, is mandatory if an effective safety culture is to take root. If a person does not hold safety as a core value in their life, then true safety is not an intrinsic value and the person is gambling with their body and/or life and potentially the lives of their fellow employees. Recognizing and communicating the safe choices that have been made is a positive and potentially greater motivator that all must share. Safety is a mind game. We must continually focus on each individual, day by day, to reinforce the belief that safety is a core value they need to adopt. We cannot let up; to do so invites the inevitable accident. This is not easy and will never be complete because the brain wants to run on autopilot. But in order to have Safe Production it must be continually active and focused. Safety is uncompromising, repetitious and demands lots of continuous effort, and the very best safety tool provided to everyone is their brain. We must be relentless in the pursuit that all need to use it!

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Case Studies

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Case Studies

Fracturing in the footwall at the Kiirunavaara mine, Sweden M Nilsson Luleå University of technology, Sweden D Saiang SRK Consulting (Sweden) AB, Sweden E Nordlund Luleå University of technology, Sweden

Abstract The Kiirunavaara mine is a large scale sub level caving (SLC) mine located near the city of Kiruna in northern Sweden. It is owned and operated by LKAB (Luossavaara-Kiirunavaara AB). The mine produces approximately 28 million tonnes of iron ore annually. Over the last 30 years the mine has experienced a slow but progressive fracturing and movement in the footwall rock mass induced by the SLC operations. The footwall contact which assumes a “slope-like” geometry is partially supported by the caved material from the hangingwall. However, since the late 1980s damage has been observed on the footwall crest as well as within the footwall. Progressive rock mass movement in the footwall is indicated by surface subsidence and visual observations underground. The extent of the damage has traditionally been estimated using empirical relations. Most of the current long term underground infrastructure within the footwall is located at a considerable distance from the ore contact. However, for new developments on deeper levels it is imperative to predict the future extent of the damage volume. Approximating the position of the damage boundary in the footwall at the current state of mining would assist in predicting the extent and characteristics of the damage volume as the mine deepens. LKAB and LTU (Lulea University of Technology) have therefore initiated a joint research project to study the long term stability of the footwall at the Kiirunavaara mine. This paper constitutes part of the work in this research. The paper describes a damage mapping campaign and subsequent analysis of the Kiirunavaara mine footwall to approximate the outer boundary of the damage. The footwall was systematically mapped on 6 levels between 320 and 800 m. The mapping results were then used to interpolate damage lines on the respective levels. The damage lines were used to construct a continuous damage surface between the studied levels. Existing records of damage mapping, monitoring and predictions were reviewed and compared to the results from the current campaign. The new results show that, the outer damage surface appears to remain stationary on the upper levels while new damage was observed on the deeper levels. At levels above 740 m the damage is judged to be mainly controlled by movements along natural discontinuities. At levels below 740 m the majority of the damage seems to be stress induced.

1 Introduction The Kiirunavaara mine, located near the city of Kiruna in northern Sweden, is a large scale sublevel caving operation producing 28 Mt (million metric tons) of iron ore per year. Originally an open pit operation the mine later transited to underground operations in the late 1950-s. Today the main orebody is mined using SLC and the mine is currently transitioning from a main haulage at level 1045 to the new one at level 1365 situated at a depth of roughly 1100 m (actual depth from ground surface). The main host rock in the footwall is the Precambrian aged low quartz syenite porphyry. The porphyry is subsequently replaced by other rock types farther into the footwall. The footwall porphyry is subdivided into 5 categories; denoted SP1-SP5 according to strength properties. The RMR for the footwall was compiled and referenced by Sandström (2003) to range between 49 and 68.

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Caving 2014, Santiago, Chile In principle the mine is oriented north-south, with the footwall on the west side and the ore body dipping around 60˚ towards the east (see Figure 1). Orientation and naming of objects and infrastructure are referenced to a local 3D coordinate system with vertical z-axis originating at the pre-mining peak of the Kiirunavaara mountain. The local y-axis is roughly oriented north to south and follows the general strike of the orebody, the x-axis is oriented roughly west to east, z-coordinates increases with depth, y-coordinates increases southwards and x-coordinates increases eastwards into and beyond the hangingwall.

Figure 1 Coordinate orientation of the Kiirunavaara mine

The surface and underground mining infrastructures at Kiirunavaara are located on and within the footwall. The general layout of the underground infrastructure is aligned parallel to the strike of the orebody. Most of the permanent infrastructures such as crushers, skip shafts and workshops are located at a relatively large distance from the footwall-ore contact. The infrastructure close to the contact consists mainly of roads, ore passes and footwall drifts. Over the last 30 years the mine has experienced a slow but progressive fracturing and deformation in the footwall rock mass. This movement of the rock mass is directly related to the sequential sub-level caving (SLC) operations. As the orebody is removed through SLC the footwall contact becomes de-stressed and assumes a slope-like geometry, see Figure 2. The footwall “slope” is partially supported by the caved rock masses from the hangingwall (Villegas & Nordlund 2008; Stöckel et al. 2013). Damage has been observed since the late 1980s both on the footwall crest and at the underground infrastructure. Surface cracks have been systematically mapped and tracked in varying regularity since 1992. The mapping was first focused within the extent of the open pit area but has later shifted to the northernmost parts of the crest where cracks have been observed outside the open pit area (Lupo 1996).

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Figure 2 Concept of the footwall slope

Even though most of the production critical infrastructure (skip shafts, crushers, etc.) is located at a considerable distance from the ore contact a large scale movement/failure in the footwall could drastically impede the mining operations due to damage on roads, ore passes and footwall drifts. It has thus been a focus of LKAB since the early 1990s to accurately forecast the global stability of the footwall with increasing mining depth. Several short studies have already been published with this aim. However, these studies have produced inconsistent and inconclusive results. LKAB and LTU (Lulea University of Technology) have therefore initiated a joint research project to study the long term stability of the footwall at the Kiirunavaara mine. This paper constitutes the initial phase of this research project and aims to determine the present extent of the footwall fracturing (damage line) and, if possible, confirm the assumed failure modes.

2

Kiirunavaara footwall fracture studies

2.1

Previous studies

In the 1990s several studies were published on the large scale footwall stability at the Kiirunavaara mine. These studies were aimed at identifying the footwall failure mechanisms. Dahner-Lindkvist (1992) analysed the observed damage by using the slope stability charts developed by Hoek & Bray (1981). Singh et al. (1993) evaluated the progressive failure of the hanging and footwall at Kiirunavaara mine and Rajpura Dariba SLC mine in India. They interpreted tensile failure as the main mechanism driving the footwall instability and henceforth predicted formation of tensile cracks at the ground surface. Lupo (1996) assumed that the failure surface in the footwall was planar, either comprising of a pre-existing structure or a combination of geological structures and failure surfaces through intact rock in combination with a surface tension crack. With this assumption the surface deformations and the correlated underground damage were predicted to follow the inclination of the ore/host rock contact and not to propagate significantly westward as the mining depth increased.

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Caving 2014, Santiago, Chile Sjöberg (1999) performed numerical and limit equilibrium analyses of the Kiirunavaara footwall and identified circular shear failure as the primary failure mechanism. The subject of footwall stability was revisited by Henry & Dahner-Lindqvist (2000) in which the subject of predominant failure mechanisms was briefly discussed but not examined in depth. Later, Villegas & Nordlund (2008) modelled the progressive failure in Kiirunavaara using the code PFC2D. Both the rock mass and the caved material were modelled, but major structures were not considered and the size of the particles constituting the caved material had a radius of 1 m. The model showed that the caved material supports the footwall even during ore draw. The effect of the traction from the caved material on the footwall induced only local failures on the footwall face close to the undercut level. These local failures did not progress significantly into the wall. This contradicts some indications by Lupo (1996) that the caved masses added to the shear forces in the footwall during ore draw.

3

Current monitoring

3.1

Surface monitoring

Quantitative deformation monitoring is performed almost exclusively on the ground surface. Both the footwall and hangingwall are monitored by GPS along predetermined monitoring lines (Stöckel et al. 2013. The footwall monitoring lines includes a total of 84 measurement hubs of which the majority are measured once a year. Some specific points of special interest where larger deformations are expected are measured quarterly. InSAR technology has been used since 2009 but is primarily evaluated only for the hangingwall ground surface (Stöckel et al. 2013). In addition, aerial photographs by helicopter over-flights have been captured yearly since 2008. So far the footwall crest suffers only minor and continuous deformations. 3.2

Underground monitoring

Deformations are more apparent in the underground infrastructure than they are on the surface. Large scale footwall fracturing was first observed underground in the 1980s. To monitor this deformation Time Domain Reflectometry (TDR) cables were utilized. However, due to the early installation many of the measuring points are now inside the failure volume and can no longer be accessed. The high cost of installing new TDR cables led to LKAB not replacing this system as measuring points were lost. Instead a macro-seismic system from CANMET was brought in around the year 2000 to track volumes suffering rock mass deformation. This system was subsequently replaced by a local micro-seismic system from IMS in 2003 constituting around 10 geophones. The 2003 system was later expanded to a mine-wide system in 2008 which included around 220 geophones in late 2013 (Stöckel et al. 2013). The majority of the current IMS geophones are installed in the footwall close to the active mining areas, while only a few are located at the higher levels. Due to the resulting low azimuthal coverage of the upper levels the system has of yet not been evaluated for monitoring the footwall failure in any larger extent using the current analysis methods. The seismic system is currently the only method that is quantitatively monitoring the footwall underground. Qualitative measurements are performed by routine damage mapping concentrated near the production areas. The upper decommissioned areas are mapped only in relation to specific projects, meaning that several years may pass between the mapping campaigns. Internal LKAB memos document several of these campaigns, the two most recent were performed in 2004 and 2012 respectively but they only covered the mine section Y22 to Y24.

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Case Studies 3.3

Quantitative damage mapping

In the campaign reported in this paper damage mapping was performed between the coordinates Y15 and Y45 (roughly 3 km along the strike of the orebody), see Figure 3. The aim was to update the underground observations on decommissioned levels and to present them in the context of large scale footwall fracturing. To achieve the best overview a number of evenly spaced levels were studied. Old haulage levels were found to be both quite easily accessible on most Y-coordinates and evenly spaced in depth. The levels 320, 420, 509, 540 and 775 m were therefore initially considered for mapping. In addition, level 740 m was included as the 2004 and 2012 campaigns (between Y22 and Y24) indicated a break in trend of the location of damage on the overlying levels. In order to single out specific regions to investigate previous damage mapping protocols for the respective levels were used. A line was drawn on each level following the outlined contour of the outermost previously mapped damage. Areas where the contour lines intersected existing drifts running semi-perpendicular to the ore strike were marked as candidates for mapping. Areas hosting known large scale discontinuities where movement had previously been recorded were also included.

Figure 3 Sketch of the footwall damage mapping area (adapted with courtesy of LKAB)

In some areas, such as parts of level 740 m, damage was already mapped in the outermost (farthest from the ore contact) parts of the infrastructure. In these areas, the position of the outer damage surface cannot be explicitly determined utilizing damage mapping alone but needs to be interpolated between mapped points.

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Caving 2014, Santiago, Chile 4 Results By interpolating damage contour lines between observed points on the respective levels a damage surface geometry was approximated (Figure 4). The damage contour lines were then updated based on new observations when damage was confirmed outside the lines derived from the previous campaigns (i.e., farther into the footwall).

Figure 4 Plan map showing contour lines for mapped damage on the respective levels

The observed fallouts associated with footwall damage were predominantly structurally controlled above level 740 m. At level 740 m the majority of the observed damages were stress induced. At this level the initiation and propagation of new local fractures also became more apparent. Figure 4 outlines a failure surface that roughly follows the general dip and strike of the orebody. For further analysis all parts of the contour lines were given the same credibility regardless of if the points were derived from direct observation or from interpolation. On the basis of this assumption a damage surface can be interpolated also between levels. An “averaged” damage plane was generated by tracing the projected edges of the damage contour lines at Y12 and Y49 between levels 320 m and 740 m using straight lines. The “average damage plane” obtained was a flat surface dipping 56˚ and striking parallel to the orebody. The plane was used as a reference for setting the colour scheme of a refined damage surface generated from the full contour lines. The refined damage surface is hereafter referred to as simply “the damage surface”. Dark colour means that the damage surface lies east from the “average plane”, while light colour means that it lies west from the “average plane”, black line indicates the reference “average plane” (Figure 5). The reason behind the different colouring was to simplify visualisation of the complex surface geometry.

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Figure 5 Profile view of the damage surface with colour reference plane (left) and with levels 320 and 775 m (right)

From the colour scheme in Figure 5 it is clear that the shape of the damage surface is complex. The earlier postulated circular shear surfaces are not discernible nor can any other clear mechanism be identified from the delineated surface. Domains are established to prepare for future numerical models of the footwall. The volume is separated into domains with similar damage surface geometry. This means that a 2D numerical model can be calibrated with respect to each domain using the trace of the damage surface and an assumption of plane strain. In Figure 6 the damage surface has been divided into 7 domains. An arbitrary vertical profile within a domain should capture the representative behaviour of the damage surface within that domain.

Figure 6 Footwall domains with respect to the damage surface geometry, front view from the east (upper image) and top view (lower image) along the “average plane”

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Caving 2014, Santiago, Chile The footwall was divided into domains based on the orientation of the major joint sets by Rådberg (1991). In comparison with the joint domain for level 795 m presented by Rådberg (1991) a number of similarities appear to the domains proposed in this paper, see Figure 6 and Figure 7.

Figure 7 Joint set domains on level 795 m by Rådberg (1991), top view

The results from both Rådberg (1991) and the current investigation indicate domain boundaries at roughly Y33, Y39 and Y44. The agreement between the two studies is an indication of a change in the rock mass properties at these sections. The northern part of the mine i.e. between Y15 and Y33 was not divided into domains by Rådberg (1991).

5 Conclusions Using damage observations connected by contour lines on multiple levels a 3D representation of a large scale damage zone can be made. A study of this 3D representation allowed a number of conclusions related to the damage behaviour of the footwall to be made:

• A continuous but complex damage surface can be approximated from mapped underground damage.

The movements in the footwall causing this damage does not directly transfer to the ground surface as surface deformation measurements by GPS indicate small and continuous deformation only.

• Comparing with previous mapping it is clear that the progression of the damage surface into the footwall does not follow the mining depth linearly. The rate of progression seems to be lower on shallow levels than on levels closer to the excavation level (i.e. deeper levels).

• At levels above 740 m the observed fallouts appeared to be predominantly structurally controlled. Below 740 m the damage seemed to be mainly stress induced.

• No single failure mode could be discerned from the damage surface. The derived geometrical shape was complex which would indicate that two or more mechanisms are acting in combination.

The surface approximation shows a plausible position of an estimated damage surface. That is, it indicates the boundary between mobilised damaged de-stressed rock mass and non-mobilised load bearing rock mass. The fact that the delineation results in a relatively smooth surface suggests that this boundary represents an explicit zone of fracturing. The zone of fracturing is indicated to be continuous and subparallel to the ore strike and dip. Perpendicular to the ore strike the zone seems to be limited in width with the activity concentrated in a narrow region. It might thus be detectable through deformation monitoring. A monitoring/measuring system should be designed to confirm and monitor the position of the damage boundary in-situ by.

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Case Studies Acknowledgements The authors acknowledge the funding and data access granted for this study by LKAB. Thanks are also due to Centre of Mining and Metallurgy (CAMM) at LTU. The authors also acknowledge the on-site contributions during mapping by LKAB staff, especially Karola Mäkitaavola, Håkan Krekula and Åke Öhrn. Finally the authors wish to thank Jonny Sjöberg (Itasca/LTU) for valuable comments during the study duration.

References Dahner-Lindkvist, C 1992, Liggväggstabiliteten i Kiirunavaara (in Swedish). Bergmekanikdagen, pp. 3752), Stockholm: BeFo. Henry, E, & Dahner-Lindkvist, C 2000, ‘Footwall stability at the LKAB’s Kiruna sublevel caving operation, Sweden’, Massmin 2000, pp. 527-532, Brisbane, Queensland, Australia: The Australasian institute of mining and metallurgy. Hoek, E, & Bray, JW 1981, Rock slope engineering. London: Institution of Mining and Metallurgy, 358 p. Lupo, JF 1996, ‘Evaluation of deformations resulting from mass mining of an inclined orebody’, Colorado School of Mines: Doctoral Thesis. Rådberg, G 1991, Strukturkarteringar i Kiirunavaaragruvans liggvägg, Nivå 795m avv (in Swedish). Technical Report: Tekniska Högskolan i Luleå. Sandström, D 2003, ‘Analysis of the virgin state of stress at the Kiirunavaara mine’, Luleå University of Technology: Licentiate Thesis 2003:02. Singh, UK, Stephansson, OJ, & Herdocia, A 1993, ‘Simulation of progressive failure in hangingwall and footwall for mining with sub-level caving’, Transactions- Institution of Mining and Metallurgy, vol A102; pp. A188-A194. Sjöberg, J 1999, Analysis of large scale rock slopes. Luleå University of Technology: Doctoral Thesis 1999:01. Stöckel, BM, Mäkitaavola, K & Sjöberg, J 2013, ‘Hanging-wall and footwall slope stability issues in sublevel caving’, Slope Stability 2013, pp. 1045-1060, Brisbane, Australia: Australian Centre for Geomechanics. Villegas, T & Nordlund, E 2008, ‘Numerical simulation of the hangingwall subsidence using PFC2D’, Massmin 2008: Proceedings of the 5th International Conference and Exhibition on Mass Mining, pp. 907-916, Luleå: Luleå University of Technology.

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Draw control strategy at the New Gold New Afton Mine A Chaudhary New Gold, Canada K Keskimaki New Gold, Canada S Masse New Gold, Canada

Abstract The New Gold New Afton mine is a 4.5 Million tonne per year operating block cave mine located 8 km outside of Kamloops, British Columbia. The current production level at New Afton is the 5070m level and has a mineable reserves base of 48.8 Mtonnes at 0.56 g/t Au and 0.84% Cu, with a lower production level in the planning stage. At its size, New Afton is one of the smaller producing block caving operations in the world, with a long and narrow footprint atypical of most existing caving operations. In 2012, to further optimize the project’s economics, the decision was made to separate the New Afton cave into three distinct areas; west, east and central. The first drawbell in the east cave was blasted in June 2013 forcing the transition from a panel cave mentality in the west cave to a self-contained block cave. This transition was necessary to manage the cave back profile, minimize the potential for rilling of broken cave material and to establish an even draw profile to reduce or delay the effects of dilution entry. Key tools used at New Afton to manage this change in philosophy and to monitor results include: extensive draw point grade sampling and trend analysis, cave monitoring systems such as micro-seismic system and time-domain reflectometry (TDR) cables, and weekly height of draw (HOD) sections developed from daily drawpoint production reporting. A key challenge encountered while transitioning to a block cave draw strategy was the balancing of grade and production tonnes to ensure consistent mill feed material, as well as maintaining draw focus to minimize stress generation on the production level. Verification of cave performance compared to PCBC modeling, tracking drawpoint grade changes vs. column height, and evaluating for dilution entry is still in its infancy at New Afton. These areas continue to be studied to verify cave performance and for model calibration. New Afton’s draw strategy and adjustments so far have proven to be successful. Learnings from the west cave are currently being applied to the east cave in order to ensure healthy cave growth, as well as maximizing resource recovery and project value.

1 Introduction New Gold’s New Afton mine is a 4.5 Million tonne per year operating block cave mine located 8 km outside of Kamloops, British Columbia. The 2013 year end reserve base is estimated at 48.8 Mtonnes with average grades of 0.56 g/t Au and 0.84% Cu. New Afton is a traditional rubber tired caving operation with 4 CAT R1600 scoops operating on the extraction level. Material from the cave is dumped down ore passes to the haulage level where 7.6 m3 scoops and 45 tonne trucks transfer it to the underground gyratory crusher. Material is then conveyed by a 4.2 km long conveying system to the surface stockpile. In 2012 the New Afton cave was separated into three distinct areas, the west, east, and central caves, each having 130, 203, and 56 planned drawpoints respectively. The separation of the three caves required the adaptation of draw strategy from a panel-caving scenario in the west cave where the oldest drawpoints are pulled the hardest, to a block caving strategy where the cave is drawn down evenly. In order to achieve this, a complete switch in draw strategy was required to catch up the east portion of the cave to promote cave propagation and locate the height of draw (HOD) peak closer to the center of the footprint.

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Case Studies 2 Cave development The west cave at New Afton was constructed from west to east (Figure 1), and as drawbells were blasted the tonnes produced from each drawpoint steadily increased over time to reach suitable production targets. This method of cave advancement naturally produces a skewed cave profile and an inclined advancing cave face. This can be beneficial and is preferable for larger caves as older drawpoints are depleted sooner. Therefore, as the cave advances there is not an oversupply of drawpoints that cannot be maintained. It was realized at New Afton that a continued uneven draw profile for smaller caving footprints can promote rilling of material down the steep cave face, and early dilution entry by drawing overlying waste zones sooner than other parts of the cave. Uneven draw could potentially affect expected cave performance and recovery of the ore deposit. A proactive approach was taken to continuously monitor and manage the cave back profile as well as having outside consultants analyze draw control strategies to ensure optimum cave growth.

Figure 1 New Afton cave footprint (as of April 1, 2014)

3

Draw control strategy

The decision to adjust draw strategy to a block cave scenario was made to balance the cave back shape and promote even vertical draw down of columns. The decision was made after the cave had broken to surface and subsidence was observed on surface to confirm cave breach. At this point the entire west cave footprint was constructed and actively caving. To increase cave growth in the east, even out the steep cave face, and minimize potential dilution entry, the decision was made to balance the cave back by focusing draw on the east side. At typical draw rates ranging from 15 to 20 buckets/day (126 to 168 tonnes/day) per drawpoint, several months of draw would be required to balance the cave shape. Key rules followed during draw strategy adjustment included: 1) ensuring even grade and tonnage distribution between shifts 2) ensuring the differential in daily draw between adjacent drawpoints does not exceed 50%, and 3) maintaining draw emphasis on higher than normal convergence drawpoints Even grade distribution was managed by utilizing data from the extensive draw point sampling program in place at New Afton. Grab samples are taken from drawpoints at varying frequencies to develop a database

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Caving 2014, Santiago, Chile of assay results. These grades are tonnage weighted between samples to provide actual drawpoint grade performance each month. Draw rates were adjusted to balance grade and provide consistent mill feed material. This was a key request from the mill as a consistent feed material is beneficial to maintain steady state operations and improved recovery rates. A key focus at New Afton is ensuring neighboring drawpoints do not have large daily draw rate differentials. High draw differentials between adjacent drawpoints can result in local problems of uneven mixing and has the potential to promote packing and convergence in weaker ground. Therefore, maintaining a strict rule for a maximum daily draw differential of 50% between adjacent drawpoints helps alleviate such problems and ensures the progression of a consistent cave shape. A group of 6 drawpoints at New Afton have experienced greater than normal convergence rates at 1 to 1.5 mm per day of movement. It was seen that vertical convergence in drawpoints can be controlled with increasing the rate of draw by a magnitude of 50%-100% in that particular drawpoint for a week. However, the presence of horizontal convergence and very minor vertical convergence was seen in these 6 drawpoints. Experimentation with maintaining steady draw rates, increasing draw in the affected area for varying periods of time, and continuous draw on both day and night shifts was conducted for this scenario. It was noted that continuous draw proved to be the most effective in controlling horizontal convergence.

4

Cave profile tracking

At New Afton, visual interpretations of the cave back are completed on a weekly basis by utilizing cave growth data from TDR cables and seismic activity detected by micro seismic sensors installed around the cave. Data from these instruments gives indications of where the seismogenic and fracture zones of the cave are located. The plotting of instrumentation data along with HOD profiles allows for visual interpretation of the cave back and cave growth rates can be determined as shown in Figure 2.

Figure 2 Cave profile section view

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Case Studies Through analysis of this data, it was found the New Afton cave follows a 4:1 growth rate. This means the cave back grows at roughly 4x the HOD height. Locally this cave growth rate may vary, however on a global scale this relationship gives a good indication of expected cave growth, approximate timing to breach surface, and timing for interaction with any infrastructure located above the growing cave. A 4:1 cave growth factor is equivalent to 25% swelling of the cave material as it is broken and is indicative of fractured and fragmented cave material. New Afton has a rockmass rating (RMR76) of 35 -50 with some areas significantly weaker due to clay infill. The main rock types encountered include BXF, Mozonite, and Diorite. The fine fragmentation has been observed through the operation of the New Afton cave as drawpoint availability has been excellent. It is very seldom a drawpoint is unavailable for production due to oversized material requiring remediation work lasting longer than a single shift. This high availability has made it possible for New Afton to reach higher than planned production rates as the ability to move material from the cave has not been the limiting factor. In addition, the Cave Management System (CMS) application in PCBC allows for the utilization of daily draw data to develop cave HOD maps. Sections cut along each strike drive are analyzed for HOD progression, cave back estimations, as well as calculations for potential air gap generation. These metrics are analyzed weekly and draw is adjusted in areas of the cave that has been under or overdrawn. Weekly draw compliance to plan is also measured and adjustments are made based on performance. Height of draw contours and percent of total column drawn contours are valuable tools to assess overall cave shape and to plan long-term draw strategies. Figure 3 shows the progression of the New Afton HOD profile over time.

Figure 3 HOD progression tracking

5

Reconciliation process

The reconciliation process is still fairly young at New Afton after only one year of full production. The cave has produced as planned in its first year which can be expected in younger caving operations as there is little opportunity to see dilution entry. Copper grades have tracked well against expectations while gold grades have outperformed expectations in 2013. The reconciliation process going forward will become more crucial to track cave performance as the potential for mixing and dilution entry may affect cave performance. PCBC is used to reconcile mine assay grades to the predicted block model grades and mill feed grades shown in Figure 4. To accomplish this reconciliation process, accurate records of tonnes produced per drawpoint and assay data are required to have a good understanding of actual performance over time.

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Figure 4 Cave grade performance over time

Overall cave grade performance can be analyzed against plan and mill performance. A close relationship between the three measures is ideal to confirm cave performance to plan. If these measures begin to diverge than further investigation will be required. Issues such as unplanned mixing, block model uncertainty and grade sampling procedures are some of the issues that can lead to diverging trends. Another key graphic developed to analyze drawpoint performance is a grade differential to plan vs. column height graph shown in Figure 5. These graphics are developed to track drawpoint performance over time and observe trends as drawpoints mature and climb to higher heights of draw.

Figure 5 Drawpoint grade performance vs. HOD

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Case Studies 6

Lessons learned

Through the operation of the west cave at New Afton, many lessons have been learned that will be applied to the operation of the east cave. The achievement of the minimum number of drawpoints required to reach maximum production rates from the cave is a key learning to be transferred to the east cave. 84 drawpoints (42 drawbells) has been determined as the critical values to produce 9,000 to 11,000 tonnes per day from the cave. At an average expected drawbell construction rate of 3 bells per month, earlier blasted drawpoints will have a ramp-up period of 14 months to achieve peak production rates of 15 to 20 buckets per day (or 128 to 170 tonnes per day). This draw rate is equivalent to 450-500 mm of column draw down per day. As the cave is developed from west to east, earlier blasted drawbells will have a higher HOD than the center of the cave and the newly blasted bells in the east. The draw rates will be reversed to catch-up the HOD in the east once the newer blasted bells reach the ramp-up period. This strategy will continue until a balanced cave angle is reached.

7 Conclusion The New Afton block cave has been very successful through its first full year of production. The cave has performed as expected based on metal production and as operational efficiencies continue to increase, greater than nameplate daily throughputs are being achieved. The New Afton west cave was transitioned from a panel cave to a block cave draw strategy in order to balance the cave back profile. This was a key measure to maintain long-term cave performance and to work on ensuring cave growth across the footprint. The operation of the west cave has provided valuable experience on how the New Afton rock mass responds to caving and has proved to be very suitable for block caving. East cave development continues as planned and a mill expansion to 14,000 tonnes per day is underway to better match the mine production capabilities.

References Diering, T 2004, ‘Combining long term scheduling and daily draw control for block cave mines’, in Proceedings MassMin 2004, A Karzulovic and M Alvaro eds, 22–25 August, Santiago, Chile, Instituto de Ingenieros de Chile, Santiago, pp. 486–490. Laubscher, DH 1994, ‘Cave mining – the state of the art’, Journal of the South African Institute of Mining and Metallurgy, vol. 94(10), pp. 279–293.

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Caving experiences in Esmeralda Sector, El Teniente Mine M Orellana Codelco, Chile C Cifuentes Codelco, Chile J Díaz Codelco, Chile

Abstract Collapse processes occurred in the Esmeralda sector, particularly those located ahead of the undercutting front during years 2009-2010, did not allowed the mining advance. A new exploitation strategy was created, and two new sector Block 1 and Block 2 were developed as virgin caving. Mining method was defined as conventional Panel caving with hydro-fracturing preconditioning. To deal with the collapse and rockburst risk due to the stress redistribution during the connection stage, a new operational strategy was designed. New rates for drawbell opening, a new extraction policy and the undercut front advanced taking into account the geological features were established. Currently, Block 1 is in permanent caving regime and Block 2 is in the connection process. From these two experiences, it is possible to highlight some results related to the mining management under different geological settings. The seismic response and the duration of the connection processes have been modified by the different mining strategies. For instance, a distinct result is the different seismic response in both blocks due to the differences in geological setting and stress field. 1

Introduction

The Esmeralda Mine currently extracts a total of 25,000 tpd from the exploitation of three main areas: Block 1, Block 2 and Panel 1. Blocks 1 and 2 were started as part of a new exploitation strategy designed for Esmeralda after the most recent collapses in 2010, and Panel 1 is being worked to recover the reserves from the central collapse area. Blocks 1 and 2 are independent sectors designed to start new caving processes away from the old Esmeralda cavity (Figure 1). The exploitation sequence starts with Block 1, which covers roughly 43,000 m2, and then continues with Block 2, with an area of 41,000 m2.

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Figure 1 Historic collapses in Esmeralda Mine. Block 1 and 2 locations

Conventional panel caving is the exploitation method used in both areas, with preconditioning of the first 100 m of rock above the Undercut Level. The method first completely develops production and undercut levels, followed by the firing of drawbells and, finally, advancing by blasting at the undercut level (Figure 2).

Figure 2 Conventional Panel Caving sequence

Exploitation of Block 1 began in June of 2011 with the first drawbell blasting. Caving started approximately one year later after connection was made with the upper level of Teniente 4. Exploitation of Block 2 started in July of 2012, and its connection process is currently being completed. The two blocks, being of different geological and structural conditions and with different stress fields, produce different seismic responses at each stage of the connection process. These responses will be explained in this paper.

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Caving 2014, Santiago, Chile 2

Geology and Geotechnical Conditions

The column height in the sector where Blocks 1 and 2 are located varies from 650 m at the west end to 1,000 m at the east end. This column height has first 160 meters of in situ column, and the rest is broken material up to the surface. The lithology of Block 1 is primarily competent rock mass made up principally of the El Teniente Mafico Complex (CMET) and diorite porphyry. The main structures of this Block 1 face NE and NNW; these structures are Fault P and Faults H and J, respectively. The quality of this rock mass is Regular-Good on the IRMR scale. Block 2, which consists mainly of a Brechas unit and CMET, has smaller structures. The Lamprofido dike crosses it and it faces NE. The geotechnical quality of the CMET portion of the rock is Regular, and the Brechas Complex portion is classified as Good. Table 1 shows the average value of the sector’s stress fields the major stress in pre-mining condition and rock mass characterization. Table 1 Geology and geotechnical conditions, Block 1 and Block 2 (Quiroz et al. 2010)

Parameter

Block 1

Block 2

Lithology

CMET-Teniente Mafic Complex (60%) Diorite Porhyry (40%)

CMET-Teniente Mafic Complex (55%) Breccia complex (40%) Tonalite (5%)

130

145

P Fault, J Fault, H Fault

Lamprofid Dike

0.28-0.29

0.37 in CMET 0.26-0.31 in Breccia

UCS [Mpa] Major geological structures Structure frecuency (ff/m)

Geotechnical quality (IRMR) Column height S1/S2/S3 Dip/dip direction S1

1-3 (Good-Regular)

3

Block cave parameters

3.1

Mine design

650-750 40/36/21 353/30

3 CMET (Regular) 2 Breccia (Buena) 800-1000 43/34/20 202/20

The two blocks are very similar in terms of design. The draw layout is 15x20 m throughout Block 1, while the north half of Block 2 has a 15x20 m and that of the south half is 15x24 m. The reason for this difference in design is to increase the safety factor and strength of the pillars. Both blocks have a similar footprint: 280x200 m in Block 1 and 240x200 m in Block 2. The undercut level is 14 m above the extraction level. The exploitation method used with the blocks, conventional panel caving, uses high 16 m undercutting and a 2 m burden. The drawbells are opened in two stages, and the undercutting blasts are done three rings at a time.

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Case Studies The preconditioning design uses ascending hydraulic fracturing on each block’s entire footprint to mitigate seismic risk and to promote caving propagation; the perforations are made in a 40x35 m mesh. Downward preconditioning is also being done on an experimental basis, with 70 m holes drilled from the production level downwards in order to diminish the seismic response during the caving process. 3.2

Mining sequence and extraction strategies

In Block Caving in primary rock, the initial exploitation phase is one of the most important in the project since it influences the macro sequence of the exploitation of the area, caving propagation, and the time needed to connect to upper levels. Exploitation of both blocks began at the NE end and progresses toward the NW so that the undercut front advances perpendicular to the structures and major faults (Fault P in Block 1 and the Lamprofido Dike in Block 2). As mining progresses, the strategy is to open 4x5 drawbells over an area of approximately 12,000 m2 that is open and available for extraction to begin of caving propagation. When Block 1 exploitation began, the average monthly advance rate was 1,800 m2/month, as shown in Figure 3. In June 2012, this rate dropped to the current average advance rate of 1,000 m2/month in order to minimize the seismic risk generated by the increase in seismic frequency and the high-magnitude, highenergy events that occurred at the beginning of the caving process. In Block 2, in contrast, areas were first opened at advance rates of roughly 700 m2/month since it was expected that greater stress fields would be produced in the area due to the higher column and the presence of lithological contacts. Since little seismic activity was recorded in the area, this advance rate was increased to 1,200 m2/month in April 2013. Different extraction strategies were established for the two blocks based on the caving stages: prior to reaching the critical area to start caving, and once the critical area has been reached. The drawbells are incorporated into production at a extraction rate of 0.1 or 0.2 tons per m2 per day. Once the critical area to start caving has been reached, the rate increases based on the height at which extraction is taking place. Daily extraction from both blocks increases as the amount of productive area available increases. Block 1 currently has a daily extraction rate of 16,000 tons, with an open area of some 40,000 m2 available, while Block 2 extracts 3,000 tpd with an open area of 10,000 m2 available. 3.3

Cave initiation

There are various theories as to how the different configurations of stress magnitude and direction cause caving (caving mechanisms). Coates (1981) suggests that there are two different mechanisms that, acting independently or together, cause caving to start: horizontal stress traction in the center of the undercut, and high subvertical compression stress at the corners of the undercut. Caving begins when these stresses exceed the rock’s resistance. Heslop and Laubscher (1981), on the other hand, propose that there are two fault mechanisms present in caving propagation. The first is known as “stress caving,” which involves the combination of flat dipping discontinuities that cave due to shearing in high compression stress fields. In the second mechanism, called “subsidence caving,” a solid rock mass quickly caves in due to shear stress near the vertical edges of the block. This occurs because the normal stress acting on the edges of the block is lower than the slide resistance created along the length of these edges and is not high enough to support the block. These phenomena are seen indirectly in seismic activity, as seismic events involve the breakage of rock masses due to concentration of stress. For example, when mining on these blocks began, intense seismic activity was seen near the production level and around the undercut front as stress accumulated at the base of the blocks when a small, unstable, dome-shaped cavity was formed.

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Figure 3 Cumulative area, monthly area and extraction per day, Block 1 and Block 2

Then, when high-level caving propagation began, the connection process was accompanied by an increase in seismic activity in the pillar, whose thickness decreased as the cavity grew. Stress was redistributed over the bases of the cavity as a result of the connection, making it impossible for the stress to continue to be transmitted through the rock mass. 3.4

Seismicity induced by the caving process

The stages of a caving process may be identified from the frequency of seismic activity, the location of the seismic events, and the characteristics of relevant events. The following topics detail the behavior of each of these parameters for the different stages of caving. 3.4.1

Seismic frecuency

There was relatively little seismic activity when mining began at Block 1, averaging 10 events per day from August 2011 to February 2012, as shown in Figure 4. The activity was located chiefly near the production level and around the undercut front as a result of abutment stress. As little seismic activity was recorded at high altitude, an extraction test was done once an area of 4,700 m2 had been opened up. Extraction rate was first increased to 0.4 m2/day and was later halted to avoid the risk of air blast since the slight seismic activity recorded, even with increased extraction, indicated that there was not yet enough open area to cause caving.

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Case Studies A seismic activation process began in April 2012, with frequency increasing to an average of 20 events per day and peaking at 30 to 40 seismic events per day. This activation was associated with an increase in the caved area and, therefore, to an increase in the destabilized area. During this pre-caving stage, which occurred from April to June 2012, the seismic activity was located at the cave front and, particularly, clusters began to appear in the western sector of Block 1, specifically between faults J and H, which manifested in high-altitude seismic events. The first overall peak of seismic frequency of 200 events per day was observed at the end of July 2012, with an extraction area of 13,500 m2 and a caved area of 16,000 m2. It was at this time that the first evidence of breakthrough with the upper level of Teniente 5 was observed, 90 m above the production level at Esmeralda. Seismic activity remained high after the block’s first breakthrough with Teniente 5, with some 100 events occurring per day. The second overall peak of 350 events/day was reached in October 2012 when the connection was made between Block 1 and the Teniente 4 level located 160 m above the Esmeralda production level. At that time, the seismic activity was occurring chiefly between the Teniente 5 and Teniente 4 levels. Block 1 was declared connected with a total of almost 24,000 m2 caved and an estimated connection time of 15 months. After the connection with Teniente 4 was made, seismic activity began to decline considerably, down to an average of 40 events/day during December 2012.

Figure 4 Total seismic frequency, seismic frequency above UCL and seismic frequency below UCL in Block 1 and Block 2

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Caving 2014, Santiago, Chile Mining at Block 2 began in July 2012 with the same seismic frequency as at the start of Block 1, an average of 10 events per day. However, in October of 2011, before mining started in this sector, seismic events reached a peak of 50 events/day as a result of the connection made in Block 1. This indicated that the two blocks are not completely independent from each other despite the fact that they are some 200 m apart. The first sign of high altitude caving propagation was a peak of 40 seismic events per day recorded at the end of July 2013. The first signs of connection with the Teniente 5 level were observed on this date, with approximately 10,000 m2 caved. Finally, the connection with the Teniente 4 level occurred in October 2013 with 14,000 m2 caved and an estimated connection time of 15 months. As shown in Figure 5, Blocks 1 and 2 underwent similar processes in terms of frequency of events, with both connection processes experiencing increased seismic activity throughout the breakup of the crown pillar. Afterwards, seismic activity decreased back to a state of equilibrium. However, seismic activity in Block 1 was much greater than in Block 2, probably because the high level of stress due to a greater column height and increased fracturing frequency gave rise to conditions favorable for caving. It took approximately four months to break up the crown pillar in each of the two blocks, starting with the time the first peak in seismic activity was recorded until the blocks’ seismic activity returned to equilibrium. 3.4.2

Rock bust and event magnitude greater than 1.

Regarding the rock bursts that occurred while connecting the two blocks, it is important to note that there were two rock bursts at Block 1: the first while expanding a cavity, and the second while connecting a cavity to the upper level of South Teniente 4. One rock burst occurred at Block 2 while connecting that block to the upper level. Table 2 presents a summary of the linear meters damaged by the rock bursts. The damage caused by rock bursts ranges from minor spalling of shotcrete to projection of rock mass. Block 1 suffered damage in the ventilation level and hauling level, while the Block 2 burst caused damage at the production level, but not at the lower levels. Table 2 Lineal Damage in meters by Rock Burst, Block 1 and Block 2

Level Undercut Production Ventilation

Begin caving experiences in Esmeralda Mine, Division El Teniente. Transporting

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Block 1 15-05-12

Heavy

Moderate

7m

Total

Block 1 29-09-12

Block 2 22-10-13

Minor Heavy Moderate Minor Heavy Moderate Minor 45 m 103 m

10 m

70,5 m

284 m

12 m

40 m

439 m

Total

133 m

22 m

103 m

Total

125 m

250 m

Case Studies The seismic events that damaged the tunnel’s support and rock mass were triggered by mining blasts. Table 3 shows the date and time of the blasts and the seismic events as well as the time lapse between the blasts and the seismic events connected with the rock bursts. Table 3 Date and time after blasting to triggers the rock burst, Block 1 and Block 2

Block 1 1

Date

15/05/2012 29/09/2012

Magnitude

Distance to blasting

Time since blasting

2

22/10/2013

1,9

29

0:00

1,7 2,1

29 71

0:00 3:38

Figure 5 show the blasting locations and the seismic events of each rock burst.

Figure 5 Event and blast location that triggers the rock burst

Events with magnitudes greater than 1 occurred in both blocks, 35 in Block 1 and 26 in Block 2, triggered by undercut and drawbell blasts and by caving propagation, 80% in Block and 46% in Block 2 was registered in the first 24 hours since the blast. Figure 6 shows plan and cross-section views of the seismic events equal to or greater than 1 in Blocks 1 and 2.

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Figure 6 Event magnitude great tan 1 Plant View and cross cut

3.4.3

Breakthrough and cave back

The cave back is estimated using information of seismic activity, inspections drillholes, subsidence inspections, and the lithology and granulometry at extraction points. The first evidence of expansion in Block 1 caving was recorded at the end of May 2012. This expansion was associated with an increased frequency of seismic events and was furthered by the fact that the H and J structures are situated semi-parallel to the undercut front of Block 1. The seismic activity recorded at Block 1 after the first evidence of growth in the cavity manifests increased collapse, spreading in altitude, and an increase in the collapsed area at the altitude of Teniente 5. Figure 7 shows the estimated cave back at the end of May 2012.

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Figure 7 Estimated cave back of Block 1 at May 2012.

After this expansion of the Block 1 cavity, seismic activity increased from 10 to 20 events/day. A first peak of 200 events/day was reached, followed by a new peak of 350 events/day, evidence of the increased height of the Block 1 cavity, which was connected in November 2012 (Figure 8).

Figure 8 Estimated Cave Back of Block 1 at November 2012

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Caving 2014, Santiago, Chile Exploitation of Block 2 began in July 2012, with the opening of the first drawbell. The block did not generally provide evidence of high-altitude caving through seismicity until the records began to increase in July 2013, when a variety of different seismic peaks occurred; this continued until November 2013. After the connection to the upper level was made, seismic activity returned to equilibrium (Figure 9).

Figure 9 Estimated Cave Back of Block 1 at November 2013

It took a total of 15 months for both blocks to be connected to the upper level, and the differences between the areas that caved in during this process are relevant. At Block 1, an area of 24,000 m2 was caved to make the connection with the upper level, while 14,000 m2 was caved at Block 2. These differences in rate of caving propagation have to do with the blocks’ stress conditions and the quality of their rock mass; Block 2 had more soft fractures and greater in situ stresses, resulting in caving propagation over a smaller area. Figure 10 show the differences between Blocks 1 and 2 regarding the cave back heights and the tonnage extracted, as well as the open area needed to reach cave back heights of approximately 160 m. They also reflect the differences between mayor and minor principal stress of 19 MPa to Block 1 and 31 MPa to Block 2.

Figure 10 Caving area and draw tones vs Cave Height, Block 1 and Block 2

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Case Studies 4

Conclusions

Seismic activity, in terms of frequency and relevancy of events, reflects the different features of the initial processes, breakthroughs, and the regimes for caving in virgin areas. There was seismic activity before the connection was made with the Teniente 4 and Teniente 5 levels, reflected in frequency peaks of roughly 200 events/day in Block 1 and 40 events/day in Block 2. Rock bursts also occurred prior to connection due to the concentration of stresses generated by the connection process in the area of the undercut front and underneath the production level. Therefore, it is important that, prior to and during the initiation of the caving connection, mining activity be decreased in terms of m2 blasted per month. An example of this is what occurred in Block 1, where the undercutting rate had to be reduced from 1,800 m2 to 1,200 m2 per month. Different area were needed for the two blocks to be connected due to the geological and structural characteristics of each sector. In Block 1, the H and J structures were instrumental in beginning the caving propagation process since the release of these structures produced a free face for material to collapse, thus generating a sort of “toppling failure” toward the east sector. These structures were activated and behaved as they did due to the effect of the front’s advance with respect to these failures (sub-parallel) combined with the direction of the main stresses that caused the release. The P fault, in contrast, did not influence the caving propagation process since the undercut front advanced perpendicular to the fault and, together with the direction of the largest main stress, kept this structure confined, which caused it to respond with significant seismic events. The caving process in Block 2 was more benign than that of Block 1 in terms of frequency and relevance of seismic events, probably because the intense stresses generated by the higher column and more frequent fractures created favorable caving conditions. Both caving processes took 15 months from the first drawbell blast until connection was made with the upper level, compared to the previous experience at North Esmeralda, which took 46 months. This time difference in connection may be explained by the fact that hydraulic fracturing was used with the blocks. In both Block 1 and Block 2, four months of seismic activity was recorded that reflected the increasing height of the cavity as the crown pillar was broken, with peaks of 200 to 350 events per day. Seismic activity later returned to a frequency of less than 30 events per day. The three rock bursts in the blocks were triggered by the caving or undercutting blasts, and two of the three rock bursts that occurred during the blocks’ caving connection process were recorded during the seismic event peaks and, therefore, while connection was being made with the upper cavity. While blasting was halted at Block 1 during this process, this was only done during two weeks and after the rock burst that had occurred in Block 1. During seismic peaks caused by caving propagation, the undercutting rate must be decreased to keep from triggering major seismic events.

References Quiroz, R, Vega H, Cuello D, Cifuentes C, Quezada O, Millán J & Barraza M 2010, ‘Esmeralda Sur definiciones de crecimiento’, Informe Interno DPL-I-2010. Coates, DF 1981, Rock Mechanics Principles. Monograph 874, pp. 5-1 to 5-37, Energy, Mines and Resources, Canada. Cuello, D, Cavieres P, Cifuentes C 2011, ‘Informe Lineamientos Geomecánicos para Planificación Minera Bloque 1 Módulo A, Proyecto Esmeralda Sur’, Informe Interno SGM-I-006/2011. Diaz, J, Cifuentes C, Orellana M 2013, ‘Back análisis conexión de bloque 1, mina esmeralda sur’, Informe Interno SGM-NI-10-2013. Diaz, J, Cifuentes C, Orellana M 2013, ‘TTAB, Esmeralda Bloque 1’, Presentación Interna.

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Caving 2014, Santiago, Chile Heslop, TG & Laubscher, D 1981, ‘Draw Control in Caving Operations on Southern African Chrysotile Asbestos Mines’, Design and operation of caving and sublevel stoping mines, NewYork, (Ed(s): D. Stewart), 755-774, Society of Mining Engineers -AIME. Millán, J 2010, ‘Antecedentes geológico-geotécnico entre XC-Acceso 3 y XC-Acceso 4, Mina Esmeralda’, SGL-I-083-2010. Millán, J & Gonzalez F 2011, ‘Antecedentes geológicos y geotécnicos del área a incorporar el año 2012 (P0)’, SGL-I-072-2011.

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Undercut advance direction management at the North 3rd Panel, Rio Blanco Mine, División Andina Codelco Chile L Quiñones Codelco, Chile C Lagos Codelco, Chile F Ortiz Codelco, Chile E Farías Codelco, Chile L Toro Codelco, Chile D Villegas Codelco, Chile

Abstract The Rio Blanco Mine, property of Codelco Chile and one of the largest copper deposits in the world began the mining operation of the 3rd Panel in 1997 by Panel Caving with conventional undercutting and LHD extraction. This panel has an overburden of 500 meters and a low-medium stress regime. The main topic treated in this document is the Undercut Advance Direction Management at the North 3rd Panel, where the Design and Ground Control of front cave are the key factors for a successful operation.

1

Introduction

The Río Blanco Underground Mine is located 50 km North of Santiago. The extraction process began in 1970 with the 1st Panel, which was mined until 1982 using Block Caving with grizzly treatment. Between 1982 and 1997 the 2nd Panel was extracted using the same mining method. Both panels were situated in secondary rock. The mine operation of the 3rd Panel started in 1997 by Panel Caving with conventional undercutting and LHD extraction. It is placed in primary rock, with an overburden of 500 meters and a low-medium stress regime. Subsequent to a southwest caving advance (1997 to 2004), the mining process stopped for eight years in the area, continuing at the North zone. There are plans to continue with the exploitation in this sector until 2017. The main topic treated in this document is the front cave orientation control at the Undercut Level and its usage as an instrument to reduce geotechnical problems, emphasizing Design and Ground Control of front cave as key factors.

2

Risk reduction in Panel Caving

With the acquired experience and the situations that occurred during the years of exploitation of the 3º Panel, there are facts that must be taken into account when it comes to reducing risk in the exploitation of a Panel Caving. These are:

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Caving 2014, Santiago, Chile 2.1

Front Cave orientation considering the most important geological structures.

In order to orientate the Front Cave in relationship with the geological structures, two aspects must be considered:

1. The Front Cave must not be orientated parallel to the main structures. In the case of the III Panel, an angle of N40ºE has been defined, as shown in Figure N° 1.

Figure 1 Front Cave orientation in relation to main structures, Production and Undercut levels

2. Faults or discontinuities must not be left immediately after the Front Cave because this could cause collapsing of the brow. In this case, two options could be considered; either the caving has to be stopped at a reasonable distance before the discontinuity to avoid activation, or it has to be continued beyond the discontinuity in order to have the problematic area blasted, thus preventing the activation of the structure (Figure 2).

Figure 2 Structural condition in the blast N°11, GH-81, March 2014

2.2

Principal stresses orientations

The main stress (s1) is orientated in an E-W direction, which is related to the present tectonic regime. Front Caving parallel to s1 has not been done and thus its maximum augmentation has not been observed. On the other hand, if blasting is done with long spacing between fronts, a concentration of stress in the edges may be observed. To avoid these concentrations, which could endanger the stability of the front, the spacing must be no more than 5 ring holes (Figure 3).

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Figure 3 Spacing between Ring Holes

2.3

Rate of the undercut advance

The undercut advance rate depends on the monthly area to be blasted. This rate varies between 1,000 m2 a 1,500 m2, allowing an annual undercut advance of 10,000 to 15,000 m2, always maintaining the defined angle (Figure 4).

Figure 4 Layout of undercut from January to December (2014)

2.4

Rock mass Preconditioning of primary rock

The use of Hydraulic Fracturing and Confined Blasting (DDE) methods, allows decreasing of the primary fragmentation size and increasing of the caving propagation speed.

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Safety zone built behind the front cave

In 2004, there was a collapse in the southern 3rd Panel. After analyzing the causes, a security band was defined at a certain distance from de Front Cave. The length of this band was determined by considering the abutment stress effect, which in this case has been estimated in 50 m. The band needs to be completely built and fortified before the beginning of the caving process and it must be maintained during the advance of the Front Caving. This procedure must be controlled monthly as shown in Figure 5.

Figure 5 Monthly safety zones in the Production Level

2.5

Support design for Production and Undercut Level

A support system design for the Panel Caving method is defined for the most critical condition, which are the Front Caving advance and the abutment stress effect on the drift in the Production Level. For this purpose, a standard fortification has been defined for the caving and Production Levels, as presented in Figure N°6. This fortification is determined based on the geomechanical evaluation of the behavior of the supporting structure during the caving process. Although the design considerations are important for successful caving in a Front Caving advance with controlled risk, ground control is of vital importance. For this parameter, the following aspects need to be considered: 2.6

Brow damage at the Undercut Level

The damage produced in the brow by the detonation is evaluated after every blast with the objective of eliminating the risk of falling blocks, which could endanger operators. This evaluation is informed accompanied by roof control and damaged supporting structure replacement recommendations.

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Figure 6 Support design for Production Level and Undercut Level

2.7

Detentions of the extraction in the front cave

Once an area has started its production, extraction must go on according to the production program. If the program is not followed, pressure points might be originated, which would result in collapse in the Production Level. To control this risk, the Production Level is continually inspected, a monthly control of extraction and extensometer monitoring are used as an alert system (Figure N°7).

Figure 7 Accumulated extraction rate, January 2014. The black line indicates the advance of the front cave

2.8

Extensometer monitoring.

Presently, the Production Level has a monitoring system based on extensometers, each one with measuring points at a distance of 1, 3, 5 and 7 m. The extensometers are installed at production drift. A total amount of 10 extensometers are distributed in approximately 10,000 m2. These are connected to a data logger for data transmission (Figure N°8).

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Figure 8 Displacement of extensometer by Undercut advance

2.9

Seismic activity in the front cave

Since 2005, induced seismicity is monitored by an ISS 30 channel seismic system. This method has been used to support operational decisions in front caving. The system is modified annually, as the front advances with the installation of new sensors at a maximum height of 40 m. Results indicate that seismicity is mainly associated to structure activation and caving propagation at the undercut, as shown in Figure N°9.

Figure 9 Seismicity in structures at the Undercut Level

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Case Studies The magnitude of the seismic moment measured up to now shows values in the range of -0.2 and 0 (Figure 9). 2.10

Crater advance

The crater advance is monitored every year by aerial photography. This system allows calibration of the subsidence model used to estimate the crater advance in the future years.

3 Conclusion Undercut management, with reduction of the associated risk, is a complex task that requires the consideration of diverse variables and disciplines, like geomechanics, geology and geophysics. These aspects contemplate from design to ground control and instrumentation monitoring. In División Andina, a methodology that considers all the previous aspects related to a successful caving operation has been implemented, assuring the viability of production and reducing associated risks.

Acknowledgements The authors would like to thank Codelco Chile División Andina for allowing the publication of this paper.

References Brady, BHG & Brown, ET 2004, Rock Mechanics for Underground Mining, 3nd edition, Chapman &Hall; London. Brown, ET 2003, Block Caving Geomechanics. The International Caving Study Stage I 1997-2000. JKMRC Monograph Series in Mining and Mineral Processing 3. Hoek, E & Brown, ET 1980, Underground Excavations in Rock. Institution of Mining and Metallurgy, London. Hoek, E 2006, Rock Engineering, Course notes. Available from . Institute Mine Seismology (IMS)– JMTS 2012, Análisis de información sísmica, Australia. Institute Mine Seismology (IMS)– JDI v 5.0 2012, Visualizador tridimensional de información sísmica, Australia. Lagos C, Toro A, Diario-SGEOM_MS_27Nov2012, ‘Informe geomecánico diario, Nivel 16 Hundimiento’. Merino A, Quiñones L 2009, ‘Plan de Instrumentación Geotécnica III Panel LHD. Años 2009-2016’, Nota Interna GRMD-SGEOT-063-09. Ortiz, F, Gallardo, G 2012, Caracterización Geotécnica áreas 18 y 19, Sector Norte III Panel. Soto, C, Merino, A, Quiñones, L, Ortiz F, 2009, ‘Revisión Geomecánica Programa Quinquenal de Obras 2009 – 2014, Mina Subterránea - III Panel Mina Río Blanco’, Nota Interna GRMD-SGEOT120 – 08. Villegas, D 2013, Cartilla No. 1, Primera Semana de Febrero 2013.

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New growth strategy in Esmeralda Mine N Jamett Codelco, Chile RQ Alegría Codelco, Chile

Abstract Esmeralda mine is the third major panel caving fully developed in primary ore at Mina El Teniente (CODELCO). Conceptual engineering was performed during the years 1992 and 1993, mainly based on the experience gained from the exploitation of former sectors along with geomechanical and mine design knowledge of that time. Esmeralda is one of the main productive sectors of the Mina El Teniente, with reserves of 205 Mt @ 0.92% Cu located on a footprint of 629,000 m2. Design for production goal of 45,000 tpd and applying mining method Advanced Panel Caving, CODELCO began its undercut process in 1996 and then drawbells blasting a year later in September 1997. At the beginning of 1999 with 12,000 m2 of open area, Esmeralda experienced the first connection process at higher levels, which added to the fulfillment of production targets for the year 2000 and which established a high quality in its operation and big expectations for the future. However, since 2001 an instability phenomena of collapse type began to manifest itself, which continued until late 2004 causing loss of galleries and infrastructure, totaling 26,600 m2 of collapsed area behind the undercut front. Given this situation in terms of active area and its own production capacity, it was impossible to achieve the production goal and another, new growth strategy of undercut process was put in place in August 2008. However, in December of that year, the instability phenomena began that continued until 2010, causing a full stop of the process and a loss of galleries and infrastructure equalling 30,605 m2 of loss area but this time ahead of the undercut front. Given this critical scenario, in 2010, a new task force generated a robust proposal allowing to resume and continue the growth of Esmeralda mine towards the production goal. As a result, a sequence of exploitation with smaller fronts (blocks) and a change in the mine design, mining method and growth macro-sequence commenced, that is, Conventional Panel Caving plus preconditioning (hydraulic fracturing) and an orientation of the undercut front as a function of relevant structures and lithology, including updates of the mine plan. In July 2011, growth activities commence based on this new strategy, giving good results and also continuity in the growing process, achieving the milestones and targets established in the mining plan. Currently, two blocks are being excavated simultaneously reaching 30,000 m2 of open area.

1 Introduction The collapse processes lasted from 2004 to 2010, particularly those generated ahead of the undercut front between 2009 and 2010, which compromised the mine plan due to the impossibility of mining in the affected sectors. This damage caused a full stop of the blasting process of new drawbells and stopped all the activities related to growth and sustainability of Esmeralda mine (open area and available reserves). In response to this critical situation, a project team composed of experts of the Division was established with the aim to define a plan of action to return to the growth trajectory initially generated. As a result, a new operating strategy that involved radical changes to the original project in terms of the minig method, mine design, macro-sequence and operational practices was generated. This is the “Esmeralda Sur Project”, which generally consists of a series of modules (blocks) that are operated independently by applying Panel

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Case Studies Caving Conventional plus hydraulic fracturing (FH) method to all rock mass being mined. The first block to be incorporate corresponds to Block 1, which has an area of 43,250 m2 and reserves of 29 Mt @ 1.04% Cut.

2 Objectives The objective of this paper is to describe the most relevant aspects of design criteria, scope, implementation and operation of the Project “Esmeralda Sur” to excavate Block 1.

3 Background 3.1

Geology

Esmeralda Mine is located in a sector composed mainly of andesite rock and a series of intrusive and geological structures with preferential trend NE and subordinately NS to NNW (Figure 1). Block 1 is mainly composed by rocks of three lithological units: Mafic Complex El Teniente (CMET) and Diorite Porphyry, and small bodies of igneous and hydrothermal breccias. The CMET corresponds to a volcano plutonic complex consisting of basalts, andesites, gabbros and joints. The Diorite Hw or Diorite Porphyry corresponds to a felsic intrusive intermediate composition, which has been associated with the development of magmatic breccias (igneous) and hydrothermal. Related to the location of felsic intrusive Hw, there are different bodies of igneous and hydrothermal breccias, some of which are associated with high grade copper mineralization. Two main structural systems are defined; the first corresponds to P Fault System and Lamprophyre Dike with the main branch of this system corresponds to the P Fault and has a persistence estimate of 900 m in the horizontal and fillings of low cohesion. The second system is NS-NNW system (J Fault and Latita Dike). The failures of this system have cloaks that tend to be subvertical and filled of anhydrite, molybdenite, carbonates, bornite and plaster. In this domain, the presence of the J Fault is dominant, which has an estimated extent above 300 m horizontally and 200 m vertically. 3.2

Stress State

Regarding the stress state, the information is integrated from in-situ stress measurements and the use of a three-dimensional numerical model to interpolate and extrapolate the stress tensor in those areas of interest, where no information is taken in-situ (Table 1).

4

Strategy & Macro-sequence

After a series of analyzes and studies the option to resume growth under the concept of operating modules (blocks ) in order to decouple from a large single front to smaller fronts (Figure 2) was determined. The mine method established was the Conventional Undercut Design with Hydraulic Fracturing (FH) applied to the entire rock mass to be incorporated. In addition, the orientation of growth in these blocks had to take into account the main geological structures to avoid or reduce the parallelism previously experienced on the single front. Finally, it was decided that all future mining will be “under shadow”, i.e. under Teniente 4 Sur, the old sector located above Esmeralda. In the case of Block 1, the direction front would be South-West in order to face directly the P fault system through a growth perpendicular to it. This strategy also sought to better address the diorite complex, together with the J and H fault, and thus ensure the connection of caving in the most complex area toward higher stability more favorable spread. In addition, for the first block, negative FH was made from production level on failures and relevant lithologic contacts (P fault, diorite Hw).

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Figure 1 Esmeralda Mine Geology and Location of Esmeralda Sur Block Table 1 Tensional pre-mining field for the first blocks in Esmeralda

Block Block 1 (Hw)

Block 3 (Central)

Block 2 (Fw)

5

Principal Stresses S1 S2 S3 S1 S2 S3 S1 S2 S3

Magnitude 40 36 21 43 34 20 50 31 19

Trend 353 ± 30 257 ± 25 123 ± 40 202 ± 20 112 ± 40 353 ± 8 191 ± 13 95 ± 16 327 ± 23

Plunge 1 ± 30 5 ± 25 81 ± 20 9 ± 23 5 ± 10 80 ± 20 10 ± 12 16 ± 17 65 ± 9

Mine Plan

The mine plan for Esmeralda Sur was defined for each block independently, integrating all information obtained from back analysis of Teniente 4 Sur as well as geological and geomechanical aspects, mining, mine design and planning. Each block was planned to use south and north access under both shadow and two lines of orepass systems (crosscut) for material handling (Figure 3), with a nominal capacity of production of 3,500 tpd, each drift at production level and 15,000 tpd for each crosscut at haulage level. Block 1 was planned to begin production in July 2011 and, the following year, the exploitation of Block 2 was planned to commence reaching, in total 25,000 tpd, in August 2014 (Figure 4).

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Figure 2 Macro-sequence blocks Esmeralda Sur

Figure 3 Blocks Design Esmeralda Sur

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Figure 4 Five Year Plan Esmeralda Sur 2011 -2015

6

Unit Operation

6.1

Activities Sequence

The developed sequence of activities relevant to the unit operations that were part of the mining operation and a Conventional Panel Caving preparation, considered developments of the undercut front to a variable distance between 100 to 150 m and a security zone or transition zone of 70 m from the front, where all the buildings and fortifications had to be made. For Block 2, the area was extended to 100 m. In parallel, while advancing the mining preparation, drilling drawbells in the production level and radial fan at undercut level began. This was followed by the first blast of drawbell (two stages), leaving a 3 m slab between the roof. Subsequently, the undercut level and material removal were completed until the void was generated necessary for the fan blasting at the undercut level towards the completion of the drawbell (approximately 10 fans). 6.2

Drilling and Blasting Design

The design used at the Teniente 4 Sur mine with some improvements to the length of the holes was used for this new production levels. The drawbell design (Figure 5) considered two stages corresponding to the raise and its north and south sides as the first blast phase. The lateral parts (W-E) were planned for the second phase. The design considered 58 holes of 15 m high leaving a pillar of 3 m between the roof and the undercut level. Total drilling length was 731 m, area was 588 m2 and total volume removed was 1.824 m3. At the undercut level, which corresponded to the radial fan drilling, 20 holes with 270 m total drilling length, burden and spacing of 2 m and undercut height 16 m were completed. This design also had negative holes for connecting drawbell to the undercut level and eliminating the pillar of 3 m (Figure 6).

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Figure 5 Drilling and Blasting Design Standard Drawbell Blocks Esmeralda

Figure 6 Drilling and blasting design: Standard Fan Undercut Blocks Esmeralda

6.3

Blasting

In March 2011, drilling on the north-east side of Block 1 began, given the launch schedule blasting in July of the same year, which was successfully achieved on the 27th of July and making it the first blasting in Esmeralda Sur. Subsequently, blasting along the entire area of this block was achieved (undercut rates 1500 m2/month) together with the fulfillment of production goals. In June 2012, the first evidence of caving connection was already achieved at higher levels, generating damages at the upper galleries located at Teniente 5 level under an active area of 16,000 m2 (5x5 drawbell). Together with this process, there

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Caving 2014, Santiago, Chile was a rise in seismic events, typically on every breakthrough process, reaching peaks of 300 events / day. Blasting activities and drawing continued normally until three months later (October 2012) with 19,000 m2 of incorporated area. The second process of connecting to the Teniente 4 level that caused the breakthrough process on Block 1 was then declared as officially achieved. 6.4 Drawing The drawing strategy was influenced by the evolution of the connection process. The extraction rate applied to the incorporated drawbells in production from the beginning until critical area was reached was 0.2 ton/m2-day. After the first connection process was reached, the strategy changed in order to accelerate the spread of the caving. Two distinct areas were defined: the first was on the periphery of the extraction area that included a line of drawbells and the other was a central zone composed of drawbells “inside” the active area, where higher extraction rate was applied. The release of the first draw point was performed in December 2012 in which 2,700 m2 were released. In terms of production and new drawbells incorporated, continuity and growth of both processes was achieved (Figure 7).

Figure 7 Monthly Production and New Area Block 1

7 Conclusions Based on the experience during the various stages of implementation, drawing and continued growth of active area and subsequent breakthrough process in Block 1, the new strategy and macro-sequence showed great promise towards achieving production goals. This conditions created high expectations towards, firstly, achieving the goals established in the original plan of Esmeralda and, secondly, towards suggesting that the application of Conventional Panel Caving was a viable alternative to be applied to future projects at Teniente or another Mine. These high expectations were based on improvements, such as, hydraulic fracture of the rock mass, small undercut front and orientation of growth taking into account the main geological structures.

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Case Studies Nevertheless, the operational discipline, quality and better control of the activities should always be used as an underlying robust standard towards successfully achieving the goals and milestones defined by the mine plan.

References Barraza, M, Quiroz, R, Vega, H 2010, ‘Esmeralda Sur Definiciones de Crecimiento’, DPL-I-009-2010. Cifuentes, C, Díaz, J & Orellana, M 2013, ‘Back Análisis Conexión de Bloque 1, Mina Esmeralda Sur’. Cuello, D, Gallardo, M, Díaz, S & Cavieres, P 2010, ‘Ingeniería Geomecánica Proyecto Esmeralda Sur’, SGM-I-029/2010. Rojas, E, Quiroz, R, Leiva, E, Gaete, S 2005, ‘Diagnóstico Mina Esmeralda’, SGM-I-024/2005.

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Caving Mechanics

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Assessment of broken ore density variations in a block cave draw column as a function of fragment size distributions and fines migration L Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman University of British Columbia, Canada A Aguayo Codelco, Chile

Abstract The broken ore density (BOD), commonly related to the swell or bulking factor, is an important parameter for block caving design. It is well known that the ore column density decreases (and swell factor increases) at the drawpoint due to the development of a loosening zone generated by ore extraction. However, the broken ore in the draw column also potentially experiences stress and density heterogeneities throughout, depending on the block properties (e.g., shape, aspect ratio and size distribution). Other important factors include the air gap thickness, draw rate and draw sequence. In addition, the blocks undergo abrasion and breakage (i.e., secondary fragmentation) which increases with draw column height. This generates rounder block shapes and smaller particles, enabling different block shape configurations and finer broken ore size distributions. These smaller particles migrate downwards into the draw column increasing the BOD. Important advances with respect to the calculation of minimum and maximum packing of coarse granular soils and rockfill makes it possible to estimate the ranges of draw column densities using as input the block size distribution. This information is used in this work to obtain the ranges of broken ore density and its spatial and temporal distribution in an ideal draw column. From this analysis, several broken ore density distributions are proposed for an ideal draw column and initial block arrangement based on data taken from the literature, and conceptual models regarding fines migration and broken ore size distributions.

1

Introduction

The swell of a caved rock mass plays a significant role in the planning and design of block and panel cave mines, especially in terms of cave propagation, subsidence extent and ore recovery (Van As & Van Hout 2008). Key parameters such as draw height and draw rate are influenced by the ratio of caved (Vcaved) and in-situ volume (Vin situ). Hence, the bulking factor is defined as B = Vcaved / Vin situ -1 and the swell factor is (1+B) x 100 (Brown 2007). With respect to the block/panel caving method, there is a lack of data regarding the swell factor. Laubscher (1994) recommends swell factors of 108%, 112%, and 116% for coarse, medium and fine fragmentation, respectively. In comparison, Gonzalez & Duplancic (2012) have suggested values of 130-140% based on experiences at the Teniente mine, Chile, and Alcalde et al. (2008) obtained values of 115% and 120% at the Andina mine, Chile. Regarding a maximum swell factor, Lorig & Pierce (2000) and Hancock et al. (2012) have suggested porosity values of 0.4 and 0.5, respectively, based on numerical studies, which correspond with swell factors of 166% and 200%. The swell of a caved rock mass depends largely on the rock mass characteristics and properties (e.g. strength). Important factors include the number of joints, their orientation, spacing and persistence, which control the in situ fragmentation. Other key factors include the in situ stress conditions and whether an air-gap is present. These, respectively, influence the primary fragmentation and fall height of the blocks

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Caving 2014, Santiago, Chile from the cave back, and subsequently, the initial block size and shape distributions. The resulting initial configuration of blocks then undergoes changes (secondary fragmentation) as the material moves down through the draw column, with further fragmentation and swell depending on the changing column height (Ross & Van As 2012) and changing gravity flow mechanisms with changing column height (Castro et al. 2014). Ross & Van As (2012) introduced the fact that the broken ore undergoes a change in the bulking factor along the ore column, as also suggested by Sharrok et al. (2012). Herein, the term broken ore density (BOD), which is the ratio between the bulk weight and caved volume, will be used together with swell or bulking factor.

2

BOD distribution along a draw column

The broken ore in a draw column can be expected to undergo changes that lead to a heterogeneous density distribution. Single blocks released from the cave back can experience different block arrangements depending on the initial block size and shape distributions, as well as air gap height. As will be discussed later in this paper, the initial broken ore density will subsequently undergo changes in response to compression and dilatancy. 2.1

Initial block arrangement

Single blocks released from the cave back can align to form numerous block arrangements. The air gap height is a relevant parameter in this regard. In the case of a negligible air gap, the blocks released from the cave back will have less chance to rotate and thus will retain their contact with adjacent blocks. This would lead to a tighter packing and smaller initial swell factor. In contrast, the presence of a sizeable air gap would facilitate a more disordered block arrangement, increasing the initial swell factor. Block shape and aspect ratio are also important in the initial block arrangement; for example a cubic block shape would allow a tighter packing compared to blocks with high aspect ratios. Research on the influence of block shape distribution, using characterization techniques presented by Kalenchuk et al. (2006), are currently ongoing. 2.2

Broken ore compression - increasing BOD with depth

After their initial arrangement, individual blocks will start to move down through the draw column. As the height of the draw column increases above a block in response to continued draw and caving, the broken ore will be subjected to increasing vertical load. The resulting stresses experienced will depend on its location within the draw column as shown in Figure 1. Experimentally, the different stress regimes present in an ore column can be represented by a triaxial compression test incorporating shear/compression and onedimensional compression zones. Using triaxial compression testing, Valenzuela et al. (2011) investigated density increases in waste rock under a range of high confining pressures (Figure 2). This data is from waste rock with an average specific gravity (Gs) of 2.7 and maximum block size of 0.2 m, which is 20 times less than a typical oversize of 4 m. However, the size distribution in terms of gradation and block shape distributions are similar, both of which are critical parameters controlling BOD. Thus, it is feasible to employ this chart to estimate increases in BOD. Based on this chart, a density increase from 1.85 t/m3 to 2.12 t/m3 is seen for an overload of 200 m height (effective mean stress of 2.25 MPa). Note that this chart is suitable to assess the BOD from the upper part of a muckpile moving down through the draw column, excluding the dilatancy zone and any potential arch effect close to the drawbells.

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Figure 1 Different stress regimes acting on broken ore in a draw column

Figure 2 Influence of confining stress on broken ore density (Modified from Valenzuela et al. 2011)

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Caving 2014, Santiago, Chile 2-3

Broken ore dilatancy - decreasing BOD near the drawpoint

The broken ore close to the drawpoint tends to dilate due to ore draw (Melo et al. 2008), likely as arching develops and ore passes into the drawbell. Without considering secondary fragmentation and fines migration during ore draw, the broken ore has a maximum dilation, which can be associated with a minimum packing of the broken ore. This minimum packing can be associated with minimum density tests performed for cohesionless soils (ASTM D 4254 – 00), together with geotechnical characterization of gravels and rockfill reported in the literature (De la Hoz 2007, Dorador 2013). These demonstrate that the minimum packing depends directly on the block size distribution (BSD) and block shape (assuming constant specific gravity).

3

Secondary fragmentation and fines migration

3.1

Impact of secondary fragmentation on ranges of BOD

Secondary fragmentation is commonly associated with comminution, point load fracturing (splitting), corner rounding and crushing of blocks due to shear and compressive stresses imposed during vertical movement of the broken ore. This fragmentation process is important for the BOD because the average size distribution decreases, impacting the ranges of broken ore densities. As shown in Figure 1, a broken ore zone moving down through an ore column can undergo a combination of two modes of stress. The first can be associated with compression within the central axis of the column, where the broken ore would predominantly experience a significant amount of splitting. The second involves shear/compression outside the central axis of the draw column, where the broken ore would generate more fines due to shearing and rounding along the block edges. Hence, the secondary fragmentation of broken ore will result in a dualmode weighted gradation curve from splitting and fines generation derived from two modes of induced stresses. 3.2

Fines migration impacting ranges of BOD

Fines migration is another key process influencing the BOD distribution within a draw column. Fines travel down through the column and fill the voids in between larger blocks, increasing in concentration towards the bottom of the column. In cases where there is a significant amount of fines close to the drawpoint, large blocks may be found floating in a fine matrix of sand and gravel. In order to study the evolution of the block size distribution at a drawpoint in terms of adding fines into the broken ore, four fluvial material gradation curves (G1, G2, G3 and G4) are presented in Figure 3 and Figure 4. In qualitative terms, the G-1 curve would correlate with the block size distribution at a drawpoint when caving starts and G-2 to G-4 would represent the transition to a finer gradation due to fines migration. Figure 5 shows the corresponding ranges of minimum and maximum densities for each of these gradations. This shows that the density increases from the uniform gradation (G-1) through G-2 and G-3. However, this then slightly decreases for G-4 producing a peak density with gradation G-3, consistent with the Fuller & Thompson (1907) curve of maximum density for aggregates. This analysis will be employed in a later section of this paper to study the broken ore density distribution. It should be noted that there is no standard procedure to evaluate fines migration through a draw column. Dorador et al. (2014) have investigated using segregation and internal erosion relationships derived for earth dams to approximate fines migration through a draw column. However, this requires further calibration using data from active block cave operations. Finally, as shown in Figure 6, the block size distribution along the ore column varies depending on the secondary fragmentation and fines migration, which generates a broken ore density distribution along the draw column. As follows, the ranges of BOD in a ore column can be discussed using the “void index” (e) parameter, typically used in geotechnical characterization, equivalent to the bulk factor B.

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Figure 3 Gradation curves for start of caving (G1) and transition due to increasing fines migration (G2 to G4) (Modified from Dorador 2010)

Figure 5 Minimum and maximum densities for gradation curves (Modified from Dorador 2010)

4

Figure 4 Gradation samples

Figure 6 Example of influence of fines migration and secondary fragmentation on the final BSD at the drawpoint

Ranges of BOD in a draw column

Several authors have observed that the ranges of minimum and maximum densities for gravels and sands, or in terms of void ratio emax and emin, depends primarily on the particle size and shape distributions. The former factor has been reported by Biarez (1994) and Dorador (2013) and correlates well with the maximum packing (or emin) using the uniformity index Cu, defined as the ratio between D60/D10 (Figure 7). The density ranges in sands and gravels have been found to be linear when plotting emax vs emin (Cubrinovski & Ishihara 2002, De la Hoz 2007). In order to extend these correlations, data from Kezdi (1979), Gesche (2002), De la Hoz (2007) and Dorador (2010) have been reviewed. This data consists of laboratory testing of minimum and maximum void ratios on subangular gravels (Figure 8). The minimum packing is typically determined by pouring the material into a cylindrical mould as suggested by ASTM standard D 4254 – 00. The maximum packing is carried out in the same mould by means of a compaction process using a vibratory table (ASTM standard D 4253 – 00). Hence, these two correlations (Figure 7 and Figure 8) can be used for estimating the minimum and maximum densities of the broken ore when it undergoes an initial loose packing (see section 5.1) comparable to the packing of rockfill and gravel materials.

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Figure 7 Correlation between BSD gradation (Cu) and maximum packing (emin). Modified from Dorador (2013)

5

Figure 8 Minimum and maximum packing in gravel

Estimation of BOD distribution into a draw column

Two scenarios have been proposed to analyse the BOD distribution in a draw column. The first refers to a loose initial packing of broken ore material, involving blocks that were free to rotate and fall through an air gap onto the muckpile surface. Due to the initial loose state of the material, correlations from Figure 2, figure 7 and Figure 8 can be applied. Conversely, the second case is where an air gap is not present and the broken ore released from the cave back remains in a dense packing. Because the blocks retain their contacts with adjacent blocks in a tight assemblage, the comparison between broken ore and rockfill or gravels is not applicable, and the correlations previously mentioned do not apply. 5.1

Loose packing of broken ore

Block Size Distribution (BSD) curves applicable for drawpoints derived by Dorador et al. (2014) have been applied here to study the loose packing of the broken ore in a draw column (Figure 9). The initial BSD corresponds to an ideal size distribution at the top of the muckpile (after primary fragmentation and rock fall impact). This has a Cu = 27, which is higher than several Cu values of primary fragmentation reported in the literature for operating cave mines (e.g., Salvador and Palabora mines; see International Caving Study II). Hence, this BSD will be used in a qualitative estimation of the broken ore density distribution. The scope of this work will be limited to the use of a block size distribution (Cu = 87) for a drawpoint under a 220 m high draw column (Figure 9). The procedure to obtain the range of BOD in a draw column for both initial and final BSD is based on the method explained in section 4. The “average value” curve in Figure 7 is used to obtain the maximum packing, which is emin = 0.26 or BOD = 2.14 t/m3 for the initial BSD (considering a specific gravity of 2.7), and emin = 0.22 or BOD = 2.21 t/m3 for the final BSD. To obtain the minimum packing, the correlation provided in Figure 8 is employed. Thus, the initial and final BSD produce an emax = 0.59 (BOD = 1.70 t/ m3) and emax = 0.53 (BOD = 1.76 t/m3), respectively. Hence, the BOD distribution for the two different initial densities is analysed in Figure 10 (alternatives A and B). “A” corresponds to an initial BOD of 1.80 t/m3, which increases with column depth following the dashed curves depicted in Figure 2, until it reaches its maximum value of 2.0 t/m3. At this point (50 m above the draw point), dilatancy is assumed to begin. Inside the dilatancy zone, the broken ore is hypothesized to decrease in density until it reaches its minimum packing (1.76 t/m3). Conversely, alternative B starts with a BOD of 1.92 t/m3 increasing in a similar manner up to a maximum of 2.12 t/m3 and then decreasing with increasing dilatancy until the minimum packing of 1.76 t/m3 is arrived at.

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Figure 9 Block size distribution at drawpoint for a column height of 200 m. After Dorador et al. (2014)

5.2

Figure 10 BOD distribution along a draw column of 200 m height, with loose packing

Dense packing of broken ore

Two cases have been considered to study the evolution of the BOD distribution for an initial dense packing of broken ore (Figure 11), for example where a negligible air gap may be present. The first is a wellarranged packing of blocks without any swell factor (Sf = 100%). In this case, the density does not undergo significant changes until the dilatancy zone where the broken ore exhibits a significant decrease in density splitting into minimum and maximum density scenarios at the drawpoints (Figure 12). The second is the case of a small swell factor of Sf = 113%. It is assumed that a slight reduction of the BOD with depth occurs until the start of the dilatancy zone, where a significant decrease in density then occurs.

Figure 11 Two examples of different initial block arrangements with dense packing

Figure 12 BOD distribution along a draw column of 200 m height, with dense packing

Hence, based on these qualitative BOD distributions, it is possible to observe the importance and impact of the initial block arrangement on broken ore density. It is believed that the rock mass characteristics and subsequent secondary fragmentation, as well as the air gap thickness play a key role in this initial arrangement of the blocks. Continued investigations are being carried out on these topics.

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Conclusions

This preliminary study addresses the density distribution of the broken ore along a draw column. The Broken Ore Density (BOD) undergoes changes with increasing depth, relative to the top of the draw column, and is strongly controlled by the initial block arrangement. This in turn is conditioned by the orientation, spacing and persistence of the discontinuities within the rock mass (i.e., in situ fragmentation), as well as the air gap thickness, presence of veining, rock mass strength, and other factors that influence the primary and secondary fragmentation. Preliminary research has shown that the discontinuity network and corresponding block size and shape distributions are dominant factors, for example, cubic shaped blocks tend to form a more ordered packing with respect to other block shapes. As noted, air gap is also a significant factor because negligible air gap will result in a tighter initial arrangement of blocks; with increasing air gap, the blocks released from the cave back will have enough space to rotate causing a more disordered packing, generating a higher bulking factor and smaller BOD. Numerical models as well as operational mine data can help to improve this understanding of the influence of the initial block arrangement on BOD distribution, and is the subject of ongoing research. The analysis presented includes the influence of secondary fragmentation and fines migration. Both of these factors reduce the average size of the block size distribution (BSD). Using correlations and charts from empirical relationships derived for coarse granular materials, the broken ore density was shown to increase until a critical BSD. For finer BSD, this produced a more gradual decrease in density. However, it is believed that the impact of secondary fragmentation and fines migration on BOD is not as significant as the initial block arrangement. The compression and dilatancy of the broken ore during caving and its movement through the draw column was also studied. In the case of an initial loose packing of broken ore, the BOD increases with depth (i.e., confining stress) down through the draw column until a point where the BOD exhibits a decrease when it enters into the dilatancy zone. Conversely, the initial dense packing in the broken ore undergoes a different BOD distribution. The BOD is assumed to experience minor changes along the draw column before entering into the dilatancy zone, but then exhibits a greater reduction in density at the drawpoints. These results reflect findings from the first phase of a detailed investigation for Codelco Chuquicamata (PMCHS), with further research planned to improve understanding of BSD tendencies and evolution down through the draw column.

References ASTM D 4254 – 00. Standard Test Methods for Minimum Index Density and Unit Weight of Soils and Calculation of Relative Density. ASTM D 4253 – 00. Standard Test Methods for Maximum Index Density and Unit Weight of Soils Using a Vibratory Table. Alcalde, F, Bustamante, M & Aguayo, A 2008, ‘Estimation of remaining broken material at division Andina’, In 5th International Conference and Exhibition on Mass Mining, Luleå, pp. 179-189 Biarez, J & Hicher PY 1994, ‘Elementary Mechanics of Soil Behaviour: Saturated Remoulded Soils’, A. A. Balkema, Rotterdam. Cubrinovski, M & Ishihara K 2002, ‘Maximum and minimum void ratio characteristics of sands’, Soils and Foundations, vol. 42, pp. 65-78. Castro, RL, Fuenzalida, MA & Lund, F 2014, ‘Experimental study of gravity flow under confined conditions’, Int J Rock Mech Min Sc, vol 67, pp. 164-169. De La Hoz, K 2007, ‘Estimación de los parámetros de resistencia al corte en suelos granulares gruesos’, Tesis de Magister en Ciencias de la Ingeniería, University of Chile, Santiago (in Spanish).

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Caving Mechanics Dorador, L 2010, ‘Análisis experimental de las metodologías de curvas homotéticas y corte en la evaluación de propiedades geotécnicas de suelos gruesos’, Tesis de Magister en ciencias de la ingeniería. University of Chile, Santiago (In Spanish). Dorador, L & Besio, G 2013, ‘Some considerations about geotechnical characterization on soils with oversize’. In Fifth International Young Geotechnical Engineering Conference - 5iYGEC’13, Paris, pp. 407-410. Dorador, L, Eberhardt, E, Elmo, D, Aguayo, A, 2014, ‘Influence of secondary fragmentation and column height on block size distribution and fines migration reaching drawpoints’, In Proceedings of the 3rd International Symposium on Block and Sublevel Caving, Santiago. Fuller, W & Thompson SE 1907, ‘The laws of proportioning concrete’. Trans. of the American Society of Civil Engineers; vol. 59, pp. 67-143. Gesche, R 2002, ‘Metodología de evaluación de parámetros de resistencia al corte de suelos granulares gruesos’, Thesis of Civil Engineering, University of Chile, Santiago (In Spanish). Gonzalez-Carbonell, P, Duplancic, P & Thin, I 2012, ‘A generic overview of the interaction of a block cave draw strategy and cave monitoring. In 6th International Conference and Exhibition on Mass Mining, Sudbury. Hancock, W, Weatherley, D & Chitombo, G 2012, ‘Modeling the gravity flow of rock using the discrete element method’. In 6th International Conference and Exhibition on Mass Mining, Sudbury. Kalenchuk, KS, Diederichs, MS & McKinnon, S 2006, ‘Characterizing block geometry in jointed rockmasses’, International Journal of Rock Mechanics and Mining Sciences, vol. 43, pp. 1212–1225. Kezdi, A 1979, ‘Soil physics – selected topics’, Elsevier Scientific Publishing Co., Amsterdam. Laubscher, D 1994, ‘Cave mining – the state of the art’. Journal of South African Inst. of Mining and Metallurgy, vol. 94, pp. 279-293. Lorig, L & Pierce M 2000, ‘Methodology and guidelines for numerical modelling of undercut and extraction level behaviour in caving mines’, Itasca Consulting Group Inc, Report to International Caving Study. Marsal, R 1973, ‘Mechanical properties of rockfill’, in Embankment-dam engineering: Casagrande Volume, Wiley, New York. Melo, F, Vivanco, F, Fuentes, C & Apablaza V 2008, ‘Kinematic model for quasi static granular displacements in block caving: dilatancy effect on drawbody shapes’, Int J. Rock Mech. Min. Sci., vol. 45, pp.248–59. Ross IT & Van As, A 2012, ‘Major hazards associated with block caving’, In 6th Int. Conf. and Exhibition on Mass Mining, Sudbury. Sainsbury, B, Pierce, ME & Mas Ivars, D 2008, ‘Analysis of caving behaviour using a synthetic rock mass - ubiquitous joint rock mass modelling technique’, in Proceedings, SHIRMS, Perth, vol. 1, pp. 343-252. Sharrock, GB, Beck, D, Capes, GW & Brunton, I 2012, ‘Applying coupled Newtonian Cellular Automata - Discontinuum Finite Element models to simulate propagation of Ridgeway Deeps Block Cave’, in 6th International Conference and Exhibition on Mass Mining, Sudbury. Valenzuela, L, Bard, E & Campaña, J 2011, ‘Seismic considerations in the design of high waste rock dumps’, in 5th International Conference on Earthquake and Geological Engineering ICEGE, Santiago. Van As, A, Van Hout, GJ 2008, ‘Implications of widely spaced drawpoints’, in 5th International Conference and Exhibition on Mass Mining, MassMin, Luleå, Sweden, pp. 147-154.

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Assessing the state of the rock mass in operating block caving mines: A review D Cumming-Potvin, University of Western Australia, Australia J Wesseloo, University of Western Australia, Australia

Abstract

The block caving mining process limits access to the orebody, in turn limiting opportunities to gauge the state of the rock mass once the rock mass degradation and caving process has initiated. Poor knowledge of the evolving state of the rock mass inside the column and of the propagation of the cave can lead to some of the key geotechnical risks in block caving, namely uncontrolled, dynamic large scale caving events, caveback hang-ups and undesirable cave propagation outside of the orebody. Due to this lack of access, geotechnical monitoring is often conducted to gain an understanding of the rock mass. Open hole and TDR monitoring give point measurements of the cave back which are usually reliable (Chen 2000). Extensometers give point measurements of displacement, however the results are not always entirely reliable (Brown 2003). These monitoring methods, while still important for gaining physical measurements of the cave propagation, are only point measurements. In order to monitor the entire cave volume, microseismic monitoring is commonly used (Lett and Capes 2012). While some attempts have been made to quantify the rock mass degradation process in the cave column or to identify the location and profile of the cave back, there is no accepted and proven method for doing so based on monitoring data. Therefore, there is no accepted scheme for assessing the state of the rock mass in a block caving setting. This affects the knowledge that mines have of their cave as it propagates, but can also affect calibration of numerical models. If there is no reliable data on the state of the rock mass, then any calibration will subsequently be unreliable. This paper reviews the existing literature on assessing the damage state of the rock mass in block caves, as well as other mining environments. The strengths and shortcomings of the different analysis methods are discussed. Based on this review it is proposed that proper systematic verification of the available suggested methods is lacking, as well as a systematic process of integrating the different methods into a single coherent system for the spatial and temporal evaluation of the damage state of the caving rock mass.

1 Introduction Block caving is a mining method which has been increasingly implemented in recent times due to its cost efficiency and high production rates. The lack of access to the orebody, which is characteristic of the block caving method, results in a poor knowledge of the state of the rock mass inside the ore column. This leads to some of the key geotechnical risks in block caving, namely, uncontrolled, dynamic large scale caving events, cave back hang-ups, poor fragmentation and undesirable cave propagation outside of the orebody (Hebblewhite 2007; Westman, Luxbacher & Schafrik 2012). In order to mitigate these risks, geotechnical monitoring is carried out in order to gain information on the initiation and development of caving. Brown (2003) defines four general types of monitoring methods for measuring the propagation of a cave in block caving mines: manual methods (such as depth measurement from open holes), Time Domain Reflectometry (TDR), microseismic methods and Cavity Monitoring Systems (CMS).

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Caving Mechanics Whilst some analysis methods for assessing cave development have been applied to caving, no review of the different methods used has been published. This paper will review the literature available on defining the location of the cave profile and the damage ahead of it. We will be a focus on the use of microseismic data and analysis techniques, as microseismic techniques provide time continuous three-dimensional data on the cave. A number of analysis methods have been used to quantify rock mass damage in open stoping mines (Falmagne 2001; Coulson & Bawden 2008), in laboratory tested samples (Eberhardt et al. 1998; Chang & Lee 2004) and in quantifying the effects of pre-conditioning (Reyes-Montes, Young & Van As 2012). These methods could be applied to caving mines for the purpose of identifying the damage state of the rock mass and cave profile, however the scope of this paper will be limited to analysis conducted on block caving mines.

2

Microseismic analysis methods

Duplancic (2001) divided the caving profile into five zones; the pseudo-continuous domain, seismogenic zone, zone of loosening, air gap and caved zone (Figure 1). This model for the caving front is widely accepted and has been adopted by the industry as the framework within which monitoring results are interpreted (Brown 2003).

Figure 1 Zones of ground behaviour in a block caving mine (Duplancic 2001)

A number of analysis methods have been used in caving mines to obtain information on the caving process from microseismic data. These are performed for two main objectives, with different analysis techniques being applied to each of them and can be summarised as follows: •

Finding cave/seismogenic zone geometry o Event location o Apparent stress/energy index o Passive seismic tomography

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Caving 2014, Santiago, Chile •

Identifying rock mass damage in the cave column o Passive seismic tomography o Shear wave splitting

3

Finding cave/seismogenic zone geometry

3.1

Event location

A number of authors have used the vertical location of seismicity in caving mines to estimate the location of the seismogenic zone (Duplancic 2001; Trifu et al. 2002; Glazer 2007; Hudyma et al. 2007a; Hudyma, Potvin & Allison 2007b; Hudyma & Potvin 2008; Dixon et al. 2010; Abolfazlzadeh 2013). The cave is often divided into different cross-sections to determine the seismogenic zone. The criterion for determining the limits of the seismogenic zone is usually a contour along the cross-section above or below which a certain proportion of the events lie (as seen in Figure 2). Some of these studies have been compared to open hole dipping results for validation. It should be noted that this approach implicitly assumes a zone of loosening with a constant thickness over space and time. As the zone of loosening does not have a constant thickness through space and time (Hudyma et al. 2007b), the two methods cannot be used to validate each other. Abolfazlzadeh (2013) created a detailed case study of tracking the seismogenic zone at Telfer mine. He suggested using a two-dimensional grid (in plan view) that each column’s event vertical location distribution is used to define the limits, with 10% and 90% vertical locations suggested as the effective limits of the seismogenic zone. This approach implicitly assumes that the cave back migrates only vertically. This is generally not the case. Abolfazlzadeh acknowledged that ‘ultimately, there is no ideal methodology to define the seismogenic zone’. The results of the method are dependent on the subjectivitely chosen to cutoff values and direction and spacing of the cross-sections used. Although this method generally corresponds with the framework presented by Duplancic (2001), the clear delineation of different zone remain uncertain.

Figure 2 Seismogenic zone for Northparkes lift 2; May-August 2003 (Hudyma el al. 2007b)

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Caving Mechanics 3.2

Apparent stress/energy index

Apparent stress is a seismic source parameter often used as an indicator of the stress level in the rock mass where the seismic event originated. Energy Index (EI) is conceptually similar but, in contrast to apparent stress, is a model dependent and scale independent measure of the stress levels (Van Aswegen & Butler 1993). Hudyma et al. (2007b) used the apparent stress of microseismic events to identify the seismogenic zone at Northparkes E26 lift 2. The hypothesis was that high apparent stress events (in this case apparent stress > 10 kPa) were more likely to appear on the edge of the seismogenic zone, which is expected to be an area of high stress. The high apparent stress events occurred in a tight spatial distribution in the upper bound of the seismogenic zone. Abolfazlzadeh (2013) came to similar conclusions. Although the premise of the approach is reasonable neither of these studies included any verification. Chen (1998) used EI to indicate the location of a de-stressed zone immediately above the cave back and a highly stressed zone above this de-stressed zone (i.e. a seismogenic zone). While this is broadly consistent with the caving zones of Duplancic (2001), the Chen noted that EI could not be used to infer fracturing. He also noted that a delineatiation of the cave profile using the event locations, could not be achieved. 3.3

Passive seismic tomography

Passive seismic tomography estimates the velocity structure of an area of interest using microseismic events. Although it shows great potential, passive seismic tomography is not often used to define the cave back or seismogenic zone. Its use seems to be limited to two separate studies performed at Ridgeway mine (Pfitzner et al. 2010; Westman et al. 2012). Some confidence in the potential of the method is provided by a theoretical study performed by Lynch and Lötter (2007). Lynch and Lötter (2007) used synthetic data to test the passive seismic tomography technique in finding the velocity structure and geometry of theoretical block caving mines. By using a given velocity structure and randomly placed events and sensors, they converged to velocity results by minimising the residuals of the travel times. They tested three different geometries; a simple homogeneous model, a single cave and a model with two caves of different heights. They were able to find the geometric parameters of the models within 5 - 7% of the true synthetic values. The study shows that it is technically feasible to use passive seismic tomography to find the geometry of the cave back. The geometries tested were, however, limited to simple parabolic shapes which do not accurately reflect the shapes of cave backs in reality. The synthetic seismic events are also a simplification of reality. Whilst the study included the bending of ray travel paths around the cave, it did not take into account the reflection and refraction often seen in true seismic events (Daehnke 1997). One of the studies that used double difference passive seismic tomography in order to identify the seismogenic zone at Ridgeway was performed by Pfitzner et al. (2010). In this study the velocity increase was used as an indicator of higher stress, which was interpreted as the location of the seismogenic zone (Figure 4). This zone was not compared to seismogenic zone limits defined by the event locations, such as those found in Hudyma et al. (2007b). The second study at Ridgeway using double difference passive tomography was performed by Westman, et al. (2012). They used passive seismic tomography over an 18 month period to investigate the changing rock mass and stress conditions. The location of the seismogenic zone was also identified as part of the study.

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Figure 4 Seismogenic zone inferred by velocity increase (Pfitzner et al. 2010)

Figure 5 shows examples of the results from the study. The event locations were found to mostly lie within the higher velocity zone above the cave. The study was limited to qualitative assessments and no attempt was made to correlate the seismogenic zone with the event locations or to quantify the boundary of the seismogenic zone.

Figure 5 Contour plot of velocity for March 2010 with both the block and sub-level caves. The low velocity isosurface represents 5400 m/s (Westman et al. 2012)

4

Identifying rock mass damage in the cave column

4.1


Passive seismic tomography, despite showing strong potential, has not been extensively used in block caving mines to determine rock mass damage. Glazer and Lurka (2007), Pfitzner et al. (2010) and Mercier et al. (2012) have used the technique to infer both rock mass damage and stress state from seismic velocity inferred through tomography.

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Caving Mechanics Glazer and Lurka (2007) used passive seismic tomography to quantify velocity change at Palabora block cave mine which they related to stress change. The results indicated an area of higher velocity towards the east of the mine which was interpreted as either being an area of higher stress, or being an area of compacted coarser cave material. The uncertainty of whether it indicates an area of solid rock under high stress or compacted cave material highlights the subjectivity of the interpretation involved with the passive seismic tomography method, as described by (Glazer & Lurka 2007). Glazer and Lurka (2007) performed a verification exercise by comparing the higher velocity areas to a crosscut with large convergence and areas where the seismicity had a higher energy index. Whilst this verification used independent methods for comparison, it was limited to a broad qualitative comparison. Pfitzner et al. (2010) used the drop in velocity to infer zones of damage at Ridgeway mine. The inferred limit of damage (Figure 6) roughly concurs with Duplancic’s (2001) caving model, however there was no reconciliation of the area with independent measurement techniques. Conventional cross-hole tomographic surveys were carried out with the aim of quantifying the change in rock mass modulus, however the survey results were inconclusive (Morgan 2009, in Pfitzner et al. 2010). Whilst the parameter used to assess rock mass damage (velocity drop) was quantitative, no cut-off values or guidelines for the quantification of damage were presented. The interpretation of the meaning of the different velocity values seems to be uncertain and the application of the method seems to be somewhat subjective without a means to validate the results.

Figure 6 Rock mass damage inferred by velocity decrease (Pfitzner et al. 2010)

Mercier et al. (2012) used double difference passive seismic tomography to investigate the changes in velocity at the Northparkes E48 block caving operation and the relationship of the velocity changes to stress changes in the rock mass. They built p-wave and s-wave velocity models for different time periods. An example of s-wave velocity tomography results for four different periods is shown in Figure 7. The four models correspond to the progressive undercutting (January-July 2010) and self-propagating (October 2010) periods in the cave progression.

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Figure 7 S-wave velocity models for Northparkes E48, 2010 (modified after Mercier et al. 2012). The approximate position of the undercut is given by the red line. The isosurface respresents a velocity of 3000 m/s.

The resulting velocity model shows the progression of the higher velocity zone across the orebody. They associated this high velocity zone with stress increase from the undercutting activity. For example, the overall decrease in velocity seen between July and October 2010 was associated with completion of the undercut along with the upwards migration of the high stress zone with the propagation of the cave column. The only validation conducted for the study was to note that “The results are internally consistent and in accordance with accepted views of the caving sequence”. The analysis conducted was largely qualitative and based on interpretation of broad trends in the velocity model. While this may improve overall knowledge of the cave behaviour, it does not aid in the identification and quantification of rock mass damage through space and time. 4.2

Shear wave splitting

Shear wave splitting is a seismic analysis technique for microseismic events which travel through isotropic media. As the shear wave enters the medium, it is split into two orthogonally polarized waves. The wave arrival times are separated by a delay which is proportional to the degree of anisotropy and the travel path length (Wuestefeld et al. 2011). Wuestefeld et al. (2011) used shear wave splitting to identify fracture evolution at Northparkes E26 block cave. The shear wave anisotropy was calculated for over 13 000 events. They found variations in anisotropy over time, which was attributed to the generation of new fractures, however the analysis was never conducted for different areas of the mine (only the whole cave column), and spatial changes in fracturing were not considered. There was no validation done for the analysis, however it is difficult to independently validate measures of fracturing without direct visual observation. 4.3 Discussion As previously mentioned, Brown (2003) described four different monitoring methods for block caving mines: manual methods, TDR, microseismic methods and CMS. Conducting a CMS in a block cave is usually impractical, due to the limited access inherent to the mining method. Manual methods and TDR monitoring give useful information on the location of the cave back, however they only give point measurements in space which have to be taken incrementally (i.e. they are not continuous monitoring methods). These methods only give an indication of the location of the cave back, without any information on the state of the rock mass. Microseismic methods have the advantage of being a continuous three-dimensional

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Caving Mechanics representation of the cave development in real time, however, they do not give direct information on the cave profile and rock mass damage, which necessitates further interpretation of the microseismic data. In order to get a complete picture of cave development with monitoring, microseismic methods need to be properly validated so that confidence can be placed in the results. In block caving, monitoring results and subsequent analyses are most commonly interpreted based on the framework provided by Duplancic (2001). Complicated analysis techniques such as passive seismic tomography have emerged to interpret stress change from velocity in order to find the seismogenic zone. The interpretation of these, however, is based on the framework of a model derived from event location and so the results may not be an improvement upon simply using the location of events to determine the seismogenic zone. There is a discord in the interpretation of velocity change in the passive seismic tomography technique. Some authors (Pfitzner et al. 2010) have interpreted velocity change as rock mass damage, whereas others (Glazer & Lurka 2007; Westman, Luxbacher & Schafrik 2012; Mercier et al. 2012) have used it purely as an indicator of stress. It is reasonable to assume the reduction in velocity to be a result of both reduction in stress and an increase in damage. The two phenomena happen concurrently. It seems that further understanding of the caving process is required before meaningful separation of these two effects can be achieved. Whilst some show promising results, none of the analysis techniques discussed have shown an ability to adequately describe the caving profile and damage zone across space and time. A system combining these techniques could give improved performance over any individual technique. In order to quantify any improvement, a verification method which can give an independent measure of rock mass damage and the three-dimensional location of the cave back is needed. None of the verification methods in the studies presented can produce this sort of independent measure. There is a systematic lack of quality validation through all studies and so there are no obvious criteria by which to judge which of the techniques is most successful in describing the caving profile. An independent method of verification which can identify the location of the cave profile (including damage above the cave back) across space and time is necessary to evaluate caving analysis techniques. This could potentially be achieved through the use of a physical model, where direct visual observation of the scaled representation of the cave can be tied with acoustic emission monitoring.

5 Conclusion Table 1 summarises the pros and cons of the analysis techniques which have been applied to block caving operations. Following a review of the different analysis techniques which have been used to quantify cave development, it is unclear which of these methods gives the best results. Each has strengths and weaknesses, and we suggest that better result could be achieved by combining these techniques into a single (calibrated) analysis system. Physical modelling may provide a means of furthering our understanding of the caving process and provide a strong empirical basis for verifying and improving current techniques for assessing the rock mass state throughout the caving process.

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Caving 2014, Santiago, Chile Table 1 Summary of pros and cons of analysis techniques

Analysis technique

Pros

Finding Cave/seismogenic zone geometry - Potential for high quality Passive seismic definition of all zones in the cave tomography profile Apparent stress /energy index

Event location

- Conceptually simple

- Conceptually very simple

Cons - Interpretation of velocity limits for seismogenic zone are subjective - Not routinely used - Unless further temporal information is used, can only give information on current seismically active areas - Unless further temporal information is used, can only give information on current seismically active areas - Definition of sections and proportion of events to use is subjective -Grid methodology assumes cave propagates vertically

Identifying rock mass damage in the cave column


- Ability to get quantitative information on velocity which can be related to fracturing - Can get information on aseismic zones of rock mass

- Geology or ‘virgin state’ must be known precisely, else only relative changes can be observed - Interpretation of damage from velocity is somewhat subjective - Not routinely used

Shear wave splitting

- Possibility to find orientation of fracturing

- Has not yet been used to define spatial changes in fracturing in block caves - Not routinely used

References Abolfazlzadeh, Y 2013, Application of Seismic Monitoring in Caving Mines - Case Study of Telfer Gold Mine, Thesis, Laurentian University. Brown, ET 2003, Block Caving Geomechanics, Julius Kruttschmitt Mineral Research Centre, The University of Queensland, Indooroopilly, QLD. Chang, SH & Lee, CI 2004, ‘Estimation of cracking and damage mechanisms in rock under triaxial compression by moment tensor analysis of acoustic emission’, International Journal of Rock Mechanics and Mining Sciences, vol. 41, pp. 1069-1086. Chen, D 1998, ‘Application of a microseismic system in monitoring E26 Block Cave at Northparkes Mines’, International Conference on Geomechanics and Ground Control in Mining and Underground Construction. Coulson, A & Bawden, W 2008, ‘Observation of the Spatial and Temporal Changes of Microseismic Source Parameters and Locations, Used to Identify the State of the Rock Mass in relation to the Peak and Post-Peak Strength Conditions.’, 42nd US Rock Mechanics Symposium. Daehnke, A 1997, Stress Wave and Fracture Propagation in Rock, PhD Thesis, Technischen Universitiit Wien.

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Caving Mechanics Dixon, RA, Singh, U & McArthur, C 2010, ‘Interaction between a propagating cave and an active pit at Telfer Mine - Part II: monitoring interaction’, 2nd International Symposium on Block and Sublevel Caving. Duplancic, P 2001, Characterisation of caving mechanisms through analysis of stress and seismicity, PhD Thesis, University of Western Australia. Eberhardt, E, Stead, D, Stimpson, B & Read, RS 1998, ‘Identifying crack initiation and propagation thresholds in brittle rock’, Canadian Geotechnical Journal, vol. 35, pp. 222-233. Falmagne, V 2001, Quantification of rock mass degradation using micro-seismic monitoring and applications for mine design, PhD Thesis, Queen’s University. Glazer, S 2007, ‘Applications of mine seismology methods in block cave mining’, 1st International Symposium on Block and Sublevel Caving. Glazer, S & Lurka, A 2007, ‘Application of passive seismic tomography to cave mining operations based on experience at Palabora Mining Company, South Africa’, 1st International Symposium on Block and Sublevel Caving. Hebblewhite, BK 2007, Management of geotechnical risks in mining projects, School of Mining Engineering, The University of New South Wales, Sydney, NSW. Hudyma, M & Potvin, Y 2008, ‘Characterizing caving induced seismicity at Ridgeway gold mine’, MassMin 2008. Hudyma, M, Potvin, Y & Allison, D 2007a, ‘Seismic monitoring of the Northparkes lift 2 block cave—Part 2 production caving’, 1st International Symposium on Block and Sublevel Caving. Hudyma, M, Potvin, Y & Allison, D 2007b, ‘Seismic monitoring of the Northparkes lift 2 block cave—Part I undercutting’, 1st International Symposium on Block and Sublevel Caving. Lynch, R & Lötter, E 2007, ‘Estimation of cave geometry using a contrained velocity model inversion with passive seismic data’, 1st International Symposium on Block and Sublevel Caving. Mercier, J, Mercier, J, De Beer, W & Morris, S 2012, ‘Beyond Coloured Balls: Passive Source Tomography of Microseismic Data for Block Caving’, MassMin 2012. Pfitzner, M, Westman, E, Morgan, M, Finn, D & Beck, D 2010, ‘Estimation of rock mass changes induced by hydraulic fracturing and cave mining by double difference passive tomography’, 2nd International Symposium on Block and Sublevel Caving. Reyes-Montes, J, Young, R & Van As, A 2012, ‘Quantification of preconditioning efficiency in cave mining’, MassMin 2012. Trifu, C, Shumila, V & Burgio, N 2002, ‘Characterization of the caving front at Ridgeway mine, New South Wales, based on geomechanical data and detailed microseismic analysis’, 1st International Seminar on Deep and High Stress Mining. Van Aswegen, G & Butler, A 1993, ‘Applications of quantitative seismology in South African gold mines’, 3rd International Symposium on Rockburst and Seismicity in Mines. Westman, E, Luxbacher, K & Schafrik, S 2012, ‘Passive seismic tomography for three-dimensional timelapse imaging of mining-induced rock mass changes’, The Leading Edge, vol. 31, no. 3, pp. 338-345. Wuestefeld, A, Kendall, J, Verdon, J & Van As, A 2011, ‘In situ monitoring of rock fracturing using shear wave splitting analysis: an example from a mining setting’, Geophysical Journal International, vol. 187, pp. 848-860.

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Influence of secondary fragmentation and column height on block size distribution and fines migration reaching drawpoints L Dorador University of British Columbia, Canada E Eberhardt University of British Columbia, Canada D Elmo University of British Columbia, Canada B Norman B. University of British Columbia, Canada A Aguayo Codelco, Chile

Abstract In block and panel caving projects, the secondary fragmentation and its effect on the final block size distribution (BSD) reaching the drawpoints are key considerations in the design and success of a caving operation. Although there are existing empirical methods to predict these (e.g., Laubscher’s size distribution chart, Esterhuizen’s ‘BCF’, etc.), these incorporate several rules of thumb that can be improved upon through a more mechanistic understanding of the complex processes involved. This paper first explores the techniques commonly used in practice to assess secondary fragmentation as well as the key influencing mechanisms: comminution, fines migration and BSD into a drawbell. Comminution originates from point load breakage, shearing, crushing, and abrasion between rock blocks as they migrate downward into a drawbell, increasing the finer broken ore size distribution with depth. A simple methodology is proposed to estimate an approximate range of fines migration for different draw column heights, based on the technical literature published on internal erosion and fines segregation in earth dams. In addition, the shape of the BSD curve into a drawbell as a function of column height and undercut depth will be examined. The latter will account for the influence of the in situ stresses on the primary fragmentation and initial BSD below the cave back as the cave propagates and the column height grows. Experimental data from the literature examining particle breakage under compression/shear will be considered in order to characterize the BSD curve as a function of column height and depth.

1

Introduction

Rock fragmentation is one of the most important factors in the performance of a block caving operation (Van As & Van Hout 2008; Moss 2012). In addition, it is well accepted that caving fragmentation incorporates three components: the in-situ fragmentation, representing the natural discrete fracture network distributed throughout the rock mass; the primary fragmentation, arising from stress-induced fractures propagating in the cave back; and the secondary fragmentation, resulting from block impact, comminution and other fragmentation processes occurring within the draw column (Laubsher 1994; Eadie 2003). In the context of secondary fragmentation, this involves a variety of mechanisms not all of which are well understood (Brown 2007). To date, empirical design charts produced by Laubscher (1994) as well as several numerical approaches described below are generally used to assess secondary fragmentation. The Block Caving Fragmentation (BCF) model devised by Esterhuizen et al. (1996) employs empirical relationships to assess the primary and secondary fragmentation as well as hang-up potential. Although this approach is able to quantify secondary fragmentation and has been calibrated using mine data, its reliability has been questioned (Butcher 2007). Experiences at Palabora found the BCF over predicted the percentage of oversized blocks (> 2 m3) and under predicted the number of hang-ups (Ngidi & Pretorius 2011). Pierce

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Caving Mechanics (2009) proposed an alternative methodology built on a particle flow model (REBOP) and laboratory testing with an annular shear cell. In addition, a hybrid approach was developed during MMT1, which includes empirical rules based on comminution theory (Kojovic 2010). Weatherley & Pierce (2011) compared the performance of these methods, but the predictions did not fully match the data collected at Ridgeway Deeps (RWD), underpredicting the fines production. Instead, they concluded that Pierce’s method (REBOP) performed better. Finally, Rogers et al. (2010) has proposed another methodology based on a stochastic Discrete Fracture Network (DFN) approach, which captures the breakage of blocks as they move through the cave, but lacks computing the fines production. Although there is significant interest in developing the basis and fundamentals of secondary fragmentation, some topics have received less attention, such as the influence of: fall height, block rotation and rockfall impact on the muckpile surface where an air gap is present; vein, rock strength and non-persistent joint distributions in the blocks; the initial arrangement of caved blocks and subsequent block interactions; the broken ore density and its distribution within a draw column; and the role of fines in cushioning block interactions. Several of these are influenced by BSD and its evolution down through the draw column height over time.

2

Secondary fragmentation assessment by means of large compression tests

Secondary fragmentation is commonly attributed to a combination of block splitting and rounding, with block movement being controlled through a combination of shear and compressive stresses occurring in the draw column zones (Pierce 2009). Replicating these conditions through laboratory testing provides a useful means to develop empirical rules of thumb or numerical model calibration. Accordingly, published results involving large triaxial compression tests CID (Consolidated Isotropically Drained) are a valuable source of data to evaluate secondary fragmentation of broken ore within a draw column. These are discussed below. 2.1

Large compression tests to evaluate secondary fragmentation

As shown in Figure 1, secondary fragmentation can be linked to two modes of stresses acting within a draw column. In the center, the broken ore undergoes anisotropic compression. This is similar to the load path conditions applied in an oedometer test. Adjacent to this, towards the outer periphery of the column, the broken ore experiences shear stresses. This is similar to the load path conditions applied in direct simple shear tests. Of interest are laboratory results involving a unique, large triaxial device capable of testing samples with 1 m diameter, previously applied to rockfill characterization studies (Marsal 1973; Verdugo et al. 2007). These serve as a proxy for the load path experienced in the draw column (Figure 2), which includes the development of both compression and shear zones (Figure 3). Maximum particle sizes of the rockfill and waste rock tested in this facility have reached 15-20 cm. These represent a valuable data source that can be extended to secondary fragmentation studies, given similarities in the intrinsic rock properties (block strength, angularity, aspect ratio), as well as block size distribution, initial material density, and confining pressure (i.e., column height). Two main contributions of these large scale tests is the shearing strength chart for rockfill by Leps (1970) and geotechnical characteristics of large waste rock dumps (Valenzuela et al. 2007). Large diameter triaxial tests offer a useful alternative to estimate the comminution of broken ore in a draw column under shear and compression stresses based on samples with a maximum particle size of 15 cm and load paths simulating a draw column of 200 m overload height. Results using this device for specific testing of secondary fragmentation are reported in a later section.

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Size scaling from large compression samples to broken ore sizes

Blocks in a draw column are much larger than the maximum particle size tested in the large triaxial compression tests discussed above; thus a scaling relationship is needed to apply test results to block size distribution relationships. The most popular technique to scale geotechnical properties from small granular samples to large broken rock is based on the parallel gradation method or parallel size distribution method first proposed by Lowe (1964). This technique involves shifting the size distribution curve (on a semi-log plot) by a factor “S” to scale the smaller sized triaxial compression tests to the larger scale in situ material (Figure 4). De la Hoz (2007) demonstrates that this technique is suitable in sands and gravels, but for larger sample sizes, the strength and stiffness tends to decrease (Frossard 2013). It is also well reported that the strength of individual blocks decreases as block size increases (Hoek & Brown 1980; Santamarina & Cho 2004). On the other hand, the block coordination number (i.e., number of contact points) resulting from the particle packing also influences block fragmentation. Particles with more contact points are generally subjected to a lower probability of secondary fragmentation due to the loads being more distributed (McDowell et al. 1996). Thus, a large block adjacent to a number of smaller blocks is less susceptible to fragmentation due to its higher coordination number but more susceptible to containing strength reducing defects (e.g., veining, non-persistent joints, etc.). Some authors agree that the coordination number is more significant than strength reduction due to block size; however there is not enough experimental data to confirm this assumption. Moreover, another effect related to the coordination number is the contact nature among adjacent blocks. Large block are susceptible to breakage depending on how its flaws are aligned relative to the contacts acting on it (e.g., corner-side or side-side). Research applying empirical and numerical techniques is currently underway to quantify the influence of the particle arrangement around large blocks.

Figure 1 Stresses within a broken ore zone assuming narrow flow width (Laubscher 1994) and interactive flow (Susaeta 2004)

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Figure 2 Representation of draw column stress modes to triaxial compression loading conditions

Caving Mechanics

Figure 3 Stresses developed in a triaxial compression test

3

Figure 4 Gradation parallel method

Methodology to assess fines migration in a draw column

3.1 Background Fines migration (Figure 5) has been investigated by several authors conducting empirical studies (Hashim & Sharrock 2012; Chen et al. 2009; Castro 2006) and numerical modelling (Leonardi et al. 2008; Pierce 2009). This segregation process has been identified as a key element in draw control and cushioning of large blocks (Laubscher 1994), and mudrush risk due to the presense of water (Jacubek et al. 2012). Although significant advances have been reported on this theme, there is a lack of a methodology to quantify the fines migration for different draw column heights in caving operations. In contrast, numerous studies exist investigating fines segregation in granular materials related to internal erosion, piping, suffusion and filter design in earth dams. These are reported in standard design manuals such as the Earth & Rock-Fill Dams General Design & Construction Considerations (2004) and Design and Construction of Levees (2000). Major advances in Dam Engineering by Kezdi (1979), Sherard (1979) and Kenney & Lau (1985) make possible the assessment of segregation potential of fines (< 4.75 mm size) from larger particles (> 4.75 mm up to 1,000 mm). These methods focus on fines segregation due to seepage through an earth fill dam, which is not fully comparable to the fines migration in a block cave draw column. However, the fines segregation from a broken ore zone is a dynamic process involving the continuous downward progression of blocks, including internal movements among blocks, facilitating the migration of fines from the broken ore. Hence, the fines migration in caving is somewhat comparable to the internal erosion in dams. Applying Kezdi’s (1979) method to an ore column, the initial gradation can be divided into a coarse and fine gradation as shown in Figure 6. The key hypothesis of this method is that the segregation of the fines gradation will occur if the ratio D15 /d85 is higher than 4, where D15 is the particle diameter for the 15% of mass passing of the coarse gradation and d85 is the particle diameter for the 85% of mass passing of the fine gradation. Here it is necessary to establish the initial gradation, which then allows the calculation of the fine and coarse gradations relative to a specific block size (black dashed line in Figure 6). The segregation potential can then be checked by applying this procedure to several block sizes. A complete explanation of this method can be found in Kezdi (1979), Chapuis (1992) and Li & Fannin (2008). 3.2

Fines migration and broken ore size distribution

It is well accepted that fines move more rapidly than coarser particles through the draw column (Laubscher 2000). This can be used to develop a conceptual fines migration sequence occurring down through an ore

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Caving 2014, Santiago, Chile column, guided by Kezdi’s method together with results from large diameter triaxial tests (Figure 7–9). The broken ore zones in the column are defined with roman numerals adjacent to the column, with each representing either the addition of broken ore to the top of the muck pile (through caving from the cave back) or subtraction representing removal of the zone through extraction at the draw point. Other nomenclature in these figures includes the numbers 0, 20, 40…until 200, which are related to the overload height of broken ore. Included with the overload notation are the letters “C and “F”, which refer to the “coarse” and “fine” gradations presented in Figure 6. Thus, a C40 is a coarse gradation under 40 m overload height and F120 is a fine gradation under 120 m overload height. Furthermore, the sequence takes into account 11 stages, which in turn includes 3 sub-stages (letters a, b, c). Letter “a” corresponds to the initial secondary fragmentation of the ore after a vertical movement of 20 m; “b” is related to the corresponding migration of fines (blackened gradations); and “c” corresponds to the broken ore’s response to the overload pressures. The sequence starts at stage 0 in the undercut level (Figure 7). The blasted rock is assumed to be mined, so that the cave initiates and broken ore falls into the undercut. Next, the procedure assumes that two new portions of broken ore are released from the cave back (Stage 1a), comprising a coarse and fine gradation (C0+F0). At this early stage, no fines migration is assumed for the “b” sub-stage because the draw column is not developed enough to permit significant internal movement of the broken ore. Thus stage 1b remains the same as stage 1a (this is the same for stages 2a and 2b). At stage 1c, an overload of 20 m is applied and the stresses are disproportionately concentrated on the coarse gradation. In response, C0 changes to C20 and some fines are generated (equal to F20 minus F0). Thus, the fines production is increased with every sub-stage “c” (i.e., F40 – F0, F60 – F0, through F200 – F0) due to overloading through the progression of the draw column (roman numerals “i” through “xx”). A similar sequence repeats for stage 2. In stage 3, the ore column is mature enough to consider the migration of fines. Thus, for stages 3 and 4, it is assumed that half of the fines will migrate downward to the broken ore zone below and, for stages 5 to 7, all of the fines are assumed to migrate into the underlying broken ore zone. For stages 8 to 10, it is assumed that the fines will migrate two broken ore zones down (or 40 m downward). Finally, this sequence is described until stage 10, which corresponds to a draw column height of 220 m.

Figure 5 Segregation process along a draw column

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Figure 6 Kezdi’s method to divide an initial gradation into a fine and coarse gradation. Thus, the initial gradation is the mixing product of both fine and coarse gradations. Fine and coarse particles are defined as particles smaller and larger than 20 mm, respectively

Caving Mechanics

Figure 7 Fines migration sequence (Stages 0 - 2)

Figure 8 Fines migration sequence (Stages 3 - 4)

Figure 9 Fines migration sequence (Stages 5 – 10)

4

Evolution of BSD at different ore column heights

In order to study the evolution of the BSD within an ore column, a large triaxial compression test on saturated waste rock material was carried out. The material tested corresponds to a granodiorite with a UCS of 140-150 MPa and specific gravity (Gs) of 2.77. The triaxial test was performed applying a confining pressure of 2.5 MPa, resulting in a deviatoric stress at failure of Dsf = (s1- s3) of 9.5 MPa and vertical deformation at failure of 18%. The specimen dimension was 100 cm diameter and 180 cm height, with a maximum particle size of 15 cm and specimen density of 19 KN/m3. Thus, this triaxial test approximates a broken ore zone with an overload of 200 m. Using the parallel gradation method, the initial gradation and gradation after testing were scaled to a maximum block size of 4 m, imitating block sizes within a draw column (Figure 10). Thus, the initial scaled gradation represents the primary fragmentation curve. Finally, it is possible to interpolate several

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Caving 2014, Santiago, Chile gradation curves (nine in total) representing different confining pressures (or overload heights) between the initial gradation (no stress) and gradation after testing (s3 = 2.5 MPa), as shown in Figure 10. Note that these interpolated curves can be used to characterize the coarse gradations (C0, C20…C200) as well as the fine gradations (F0, F20…F200) in the fines migration sequence (Figures 7-9). After scaling the gradations to the in situ block sizes, the fines migration sequence is applied. Figure 11 presents the evolution of the scaled gradation (or BSD) for three different column heights. Based on these results, it is possible to observe how the initial BSD curve transitions at stage 10 to a more linear trend than an “S” shaped curve. The magnitude of the fines migration on the final BSD at the drawpoint depicts a reduction in the average size of the material. For example, in Figure 11, the 50% of mass passing due to loading only (dashed curve) drops from 0.6 m to 0.3 m (two times less). However, this reduction considering both loading and fines migration (stage10 curve) drops from 0.6 m to 0.04 m (15 times less). Thus, the combination of large triaxial tests and the fines migration analysis is able to capture the evolution of the BSD from its initial gradation (primary fragmentation) to that mined at the drawpoints. Moreover, the impact of the fines migration on the BSD can be determined by comparing the initial scale gradation after testing curve (black dashed curve in Figure 10) and the “stage 10” curve (grey color). The former is representative of the fragmented ore close to the drawpoint with a muckpile overburden of 200 m but no fines migration, and the latter represents the same overburden but including the fines migration process.

Figure 10 Initial gradation (BSD) and gradation after testing, including scaled curves (parallel gradations)

5

Figure 11 Evolution of Initial gradation for three different stages. Stages 3, 6 and 10 represent a column height of 80, 140 and 220m, respectively

Discussion

The shape of the block size distribution has been addressed in terms of applying relationships drawn from large compression tests (triaxial CID test) and a fines migration sequence analysis based on techniques developed to study internal erosion in earth dams. Some assumptions included in this work are discussed as follows:

• Triaxial tests used as a proxy for simulating the shear and compression zones within a broken

ore column: As explained in section 2.1, a triaxial test combines elements of both simple shear and oedometer tests but this hypothesis needs to be corroborated with a conventional laboratory testing program involving simple shear, oedometer and triaxial CID testing, with special focus on particle breakage. For example, simple shear tests could involve significant splitting but much

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Caving Mechanics less fines generation compared to an oedometer test, which in turn could experience much more fines generation due to stress concentrations among particles but less splitting than simple shear.

• Fines migration sequence: This sequence applies segregation and piping criteria used for earth

dams. However, due to the influence of water flow on fines migration, this methodology could overestimate the fines arriving at the drawpoints. In addition, this sequence assumes different segregation rates, which still require validation from field data and percolation rate studies (e.g., Bridgwater et al. 1978; Cardew 1981; Pierce 2009).

• Block size scaling effects on broken ore fragmentation: This issue has not been addressed in this

study and requires more experimental and numerical studies to better understand the influence of veins and smaller discontinuities present in larger blocks on their overall fragmentation within a draw column.

6

Conclusions

A conceptual model has been developed accounting for the influence of secondary fragmentation and fines migration on the block size distribution of broken ore encountered at a drawpoint. Firstly, the broken ore fragmentation by shear and compression has been investigated using a large diameter compression test on waste rock (Triaxial CID). Secondly, the parallel gradation method is used to scale the standard particle size from the triaxial test sample to that representing the in situ block sizes. Thirdly, a simple method is proposed to estimate an approximate range of fines migration for different ore column heights. This suggests a more linear BSD than an “S” or exponential shape. Finally, more efforts are required to understand the fundamentals of secondary fragmentation and the prediction of the drawpoint BSD with a higher accuracy.

References Bridgwater, J, Cooke, MH & Scott, AM 1978, ‘Inter-particle percolation: Equipment development and mean percolation velocities’, Transactions of the Institution of Chemical Engineers, vol. 56, pp. 157-167. Brown, ET 2007, Block Caving Geomechanics, Indooroopilly: Julius Kruttschnitt Mineral Research Centre. ISBN 978-0-98003622-0-6, Queensland. Butcher, RJ & Thin, IGT 2007, ‘The inputs and choices for predicting fragmentation in block cave projects’, in Proceedings First International Symposium on Block and Sub-level Caving, Southern African Institute of Mining and Metallurgy, Johannesburg, pp. 35–49. Cardew, PT 1981, ‘Percolation and mixing in failure zones’, Powder Technology, vol.28, no. 1, pp.119-128. Castro, R 2006, ‘Study of the mechanisms of granular flow for block caving‘, PhD Thesis, University of Queensland. Chapuis, RP 1992, ‘Similarity of internal stability criteria for granular soils’, Canadian Geotechnical Journal, vol. 29, no. 4, pp. 711–713. Cheng, YM, Liu, ZN, Song, WD & Au, SK 2009, ‘Laboratory Test and Particle Flow Simulation of Silos Problem with Nonhomogeneous Materials’, Journal of Geotechnical and Geoenvironmental Engineering, vol. 135, no. 11, pp. 1754-1761. Eadie, B 2003, ‘A Framework for modeling fragmentation in block caving’, PhD Thesis. The University of Queensland. Australia.

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Caving 2014, Santiago, Chile Esterhuizen, GS, Laubscher, DH, Bartlett, PJ & Kear, RM 1996, ‘An Expert System Approach to Predicting Fragmentation in Block Caving’, Proceeding Massmin Mining Methods, SAIMM. Frossard, E, Ovalle, C, Dano C, Hicher, PY, Maiolino, S & Hu, W 2013, ‘Size effects due to grain crushing in rockfills shear strength’, Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering, Paris. Hashim, MHM, & Sharrock, GB 2012, ‘Dimensionless percolation rate of particles in block caving mines’, In: MassMIN 2012 Conference Proceedings. MassMin 2012: 6th International Conference and Exhibition on Mass Mining, Sudbury, Ontario, Canada. Hoek, E, & Brown, ET 1980, ‘Underground excavations in rock’, Instn Min. Metall, London. Kenney, T, & Lau, D, 1985, ‘Internal stability of granular filters’, Canadian Geotechnical Journal, vol. 22 pp. 215–225. Kezdi, A 1979 ‘Soil physics – selected topics’. Elsevier Scientific Publishing Co., Amsterdam. Kojovic, T 2010, ‘Application of the Hybrid Model to RWD’, Subproject Report submitted to MMT2 Secondary Fragmentation Project. Laubscher, D 1994, ‘Cave mining – the state of the art’, The Journal of The South African Institute of Mining and Metallurgy, pp. 279-293. Leonardi CR, Owen DRJ, Feng, YT & Ferguson WJ 2008, ‘Computational modelling fines migration in block caving operations’, Proceedings of the 5th international conference and exhibition on mass mining, Lulea, Sweden. Li, M & Fannin, RJ 2008, ‘Comparison of two criteria for internal stability of granular soil’, Can. Geotech. Journal, vol. 45, pp. 1303-1309. Lowe, J 1964, ‘Shear Strength of Coarse Embankment Dam Materials’, Proc. 8th International Congress on Large Dams, vol.3, pp. 745-761. Marsal, RJ 1973, ‘Mechanical Properties of Rock Fill Embankment- Dam Engineering’, (ed.) Hirschfelt and Poulos, John Wiley. New York. Mcdowell, GR, Bolton, MD, & Robertson, D 1996, ‘The fractal crushing of granular materials’, Journal of the Mechanics and Physics of Solids, vol. 44, no. 12, 2079-2102. Ngidi, SN & Pretorius, DD 2011, ‘Impact of poor fragmentation on cave management’, In 6th Southern African Base Metals Conference. The Southern African Institute of Mining and Metallurgy, pp. 111-122. Pierce, M 2009 ‘A Model for Gravity Flow of fragmented rock in Block Caving Mines’, PhD Thesis, The University of Queensland. Rogers S, Elmo D, Webb, G, & Catalan, A 2010, ‘A discrete fracture network based approach to defining in situ, primary and secondary fragmentation distributions for the Cadia East panel cave’, In Caving 2010, Proceedings of the 2nd International Symposium on Block and Sublevel Caving, Perth, (Edited by Y. Potvin), Australian Centre for Geomechanics. Santamarina, JC & Cho, GC 2004, ‘Soil Behaviour: The role of particle shape’, Proc. Skempton Conference, London.

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Caving Mechanics Sherard, JL 1979, ‘Sinkholes in dams of coarse, broadly graded soils’, Transactions, 13th International Congress on Large Dams, New Delhi, India, vol. 2, pp. 25-35. Susaeta, A 2004, ‘Theory of gravity flow (part 1)’, In Proceedings of the 5th international conference and exhibition on mass mining, Santiago, Chile, pp. 167-178. U.S. Army Engineer Manual 2004, ‘Earth & Rock-Fill Dams General Design & Construction Considerations, EM 1110-2-2300’, available from: . [1 April 2014]. U.S. Army Engineer Manual 2000, ‘Design and Construction of Levees’, EM 1110-2-1913, available from: . [1 April 2014]. Valenzuela, L, Bard, E, Campana, J & Anabalon, ME 2008, ‘High waste dumps - challenges and developments’, In: Rock Dumps 2008, Fourie, A. (Ed.), Australian Centre for Geomechanics, Perth, pp. 65-78. Verdugo, R, Peters, G, & Bejarano, I 2007, ‘Evaluación de parámetros geomecánicos de suelos gruesos’, VI Chilean Geotechnical conference, Valparaíso. Weatherley, D, & Pierce, M 2011, ‘Progress Report - Fundamentals of Caving Fragmentation’, Report to the Mass Mining Technology 2 Project, February.

137


Analysis of hangup frequency in Bloque 1-2, Esmeralda Sur Mine E Viera Codelco, Chile E Diez Codelco, Chile

Abstract Monitoring and analysis of secondary breakage is relevant in productivity of Block- Panel Caving mines, especially in mines where production area with low percentage of draw column (<30% of primary column) is majority in comparison to an area without geomechanical constraints. A smaller capacity of secondary breakage can generate deviations in draw strategy, which may compromise production plans in the short and the long term. Bloque-1 of Esmeralda mine (located in the Sub -5 level El Teniente mine ) reflects the condition of extraction described, because 35% of production area has draw rate constraints (percentage of primary draw column <30%) and 42% of the open area has no draw rate constraints. In this particular sector, a low capacity of secondary breakage (S.B) can generate wrong practices of draw rate strategy that may impair considered planning strategies (dilution control, angle control and regularization of draw heights), due to the reduced availability of area in state of caving propagation process (which has higher hanging frequencies) and increase of extraction in area without draw rate constraints (lower hanging frequency). The main goal to this work is to perform a quantitative analysis of the hanging frequency to Bloque-1, correlating this variable with the extraction percentage of primary column and granulometry curves. For Bloque -1 a record of hangings and activities related with secondary breakage of draw points for the year 2013 has been made, which we have calculated the rate of extraction between hanging (REH) of production drifts throughout the detailed period. This index has been related to granulometry curves and the average percentage of primary column in each production drift. It is aimed to establish a pattern that allows including the frequency of hanging by draw point in short-term plans, considering the predominant granulometry curves and the percentage of average primary column for future projects to be undertaken in Esmeralda Mine.

1.

Introduction

A greater understanding of rock fragmentation is vital for Block/Panel Caving mines design and planning stages, as well as the variables that depend on the rock fragmentation. They go from design of layout (spacing of drawpoints), to the daily secondary breakage requirements. Because of its importance, it is also necessary to perform back analysis studies that allow to estimate fragmentation predictive models, and management tools, in order to be able to monitor the predominant granulometry behavior. The following study performs a back analysis with the data recorded in Esmeralda Sur, which have the observed granulometry reports in ranks, and the secondary breakage (SB) made in 2013. Currently, Esmeralda Sur is formed by Bloques 1 and 2, which, until March 2014, showed an incorporated area of 30,589 m2 and 13,163 m2 of production, respectively. Esmeralda’s Mine Sector Sur has a volume of mafic rocks, known as El Teniente Mafic Complex (Figure 1), intrusioned by subvertical matters of felsic rocks. Felsic rocks present in Bloque 1 are located between

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Caving Mechanics production drifts 23 and 33, and trenches 30 to 35 to the mine, where dioritic porphyry can be found in contact to hydrothermal breccias of dioritic porphyry in the NE. On the other hand, Bloque 2 had one intruded of tonalite that occupied half of the surface, cutting it diagonally from NW to SE, which suffered later intrusions from matters of anhydrite hydrothermal breccias, biotite hydrothermal breccias, and finally from a microdiorite porphyry:

Figure 1 Esmeralda Sur Lithology

2

Objectives

The following study aims to analyze the Esmeralda Sur hanging drawpoints frecuency, by considering the secondary breakages made, and the recorded granulometry during 2012 and 2013.

3

Methodology

3.1

Information record

The hang drawpoints condition information is recorded in 2 databases:

•

Control Production Mine database: the mentioned database is hold by analyst’s routes that record relevant information about the drawpoints condition, such as humidity, hangs and observed granulometry conditions. Ideally, the most relevant productive areas must be checked daily.

•

Esmeralda Mine Operational database: the draw area records every secondary breakage performed in the drawpoints. This database provides the secondary breakage date (day and shift), amount of explosives used, drift and trenches in which the breakage labors are made.

When comparing both databases, which shows a greater reliability analyzing the hanging drawpoints frequency is the second one, since it covers a greater temporality (the first one fairly shows 1 daily shift with information, the second one shows 3 daily shifts with records).

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Caving 2014, Santiago, Chile In order to quantify the effect generated in production by the secondary breakages, made at drift level, the Secondary Breakage Index is defined as it follows:

This index must be defined on a mine unit (from mine scale size to drawpoints level) and at a certain time interval. 3.2

Granulometry information record.

This information is compiled by a team of eight production control analysts, at least once a day in major areas. During their routes, they pick up information related to drawpoint condition, granulometry, humidity, lithology and dilution. The information used for this work was the drawpoints granulometry, according to the following overlaps: fragments lower than 5 cm (A), between 5 and 25 cm (B), from 25 to 50 cm (C), from 50 to 100cm (D), and rocks greater than 100 (E). When classifying rocks into one of the overlaps, the three right-angled sizes arithmetic mean to the volume it should hold it is obtained. The granulometry information is a visual appreciation to the percentages of 5 granulometry overlaps on the slope surface made in the drawpoints and it is calibrated by point measures made on ground with a tape measure. The information obtained on ground and stored into a database has continuity from the beginning of Mina Esmeralda. Note that, additionally the drawpoints can be hang, which makes the data collect not to be safe for the field personnel, thus not collecting granulometric information. This lack of information creates a bias within the database, which affects the thicker granulometry representativeness. For this condition with no granulometry surveys, a 100% of the granulometry greater than 1m is assigned. The analyzed data for this article were the thicker granulometry information (>50cm), obtained from January 2012 until March 2014, which covers almost the entire study area productive life so far. Finally, the scheme of stages to consider for the hang drawpoints and granulometry analysis is detailed bellow: All of the secondary breakage information recorded between January-November 2013 will be considered. During 2012, the database information was not 100% completed, therefore it is not considered within the analysis. Note that the secondary breakage analysis only considers the drawbells in which the caving heading has completely passed (production drawbells). The previous criterion leaves out of the analysis the drawbells with swelling condition, which do not show hanging conditions. As for a greater understanding concern, the analysis will be developed at production drift level. This consideration allows adding the secondary breakage variable within the short term plans in an easy way.

4

Results

When considering the secondary breakages labors made for drift and the draw during 2013, the REH index is calculated for every month, which is compared to the average primary column draw, obtaining the following:

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Caving Mechanics

Figure 2 Information process for analysis scheme

Figure 3 REH as a function of extraction

As the primary column draw rate increases, the REH index increases proportionally, thus showing a greater dispersion of amounts above the 30% of the average primary column draw. The previous behavior can be explained by the following points:

•

Increasing geomechanical draw rates: the continuous geomechanical increasing draw rates occurs as certain limits, previously defined, are exceeded. Therefore, the greater extraction of primary column, the greater productive capacity per area (no operational restrictions).

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Caving 2014, Santiago, Chile •

Variations in the material handling system: as for the Esmeralda Sur case, this variation occurs when ore passes are incorporated, which becomes into a more efficient material handling system, since it reduces the ore hauling mean distance.

•

Reduction of hanging drawpoints and/or rock sizes: when reducing the hangings and/or rock sizes, it reduces the operational interferences, which increases the material handling system productivity (more operational continuity).

In the following section, the reduction of hanging drawpoint and/or rock sizes will be explained in detail, by analyzing if there is a grater granulometry record reduction (type E), as the extraction of percentage of primary column increases. The Figure 4 shows the granulometry evolution according to the primary draw percentage, by considering a data record from 2012 and 2013 in Esmeralda Sur:

Figure 4 Fragmentation observed at drawpoints as a function of extracted column

According to the Graphic 2 information, it is evident that the coarse fragments remains between 50% and 60% from the beginning of the draw up to the 80% of the primary rock column, getting to its maximum in this point, and decreasing to the 30% in drawpoints with higher extraction percentage of primary column (120%). Regarding the two granulometry overlaps behavior, it can be ensured that it is despair, as the “D” granulometry remains relatively stable, on the other hand, coarse fragments varies according to the draw. The aimed granulometric curve for this mining method foresees a high raise in fine graining, after it exceeds the column’s 100%, the primary rock is replaced for fragmented material. Such decrease is mitigated at Bloque-1, where the points having 120% of draw remain an average of 20% of size E rocks with the capability of blocking the ore flow. Starting at 80% of primary column extraction, a decrease in performed secondary breakage should be seen, by which it should raise the REH index defined above. This result is important from the short term planning point of view, since it is possible to take this value as a reference to increase the productivity per drift, within the short term. Linking the Figures 1 and 2, we can see that the REH rate increases proportionally with the extraction, however reports of fragmentation type “E” no evidence of variation up to 80% extraction of the primary column, whereby the increasing of geomechanic draw rates and improvements in the mineral handling system are more relevant in that stretch.

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Caving Mechanics When reducing the plotted values (in Figure 2), calculating the REH index within the entire period (JanuaryNovember 2013 period and not monthly as performed in the first case), the obtained results are as shown in Figure 5.

Figure 5 Average extraction of primary column vs REH

Figure 5 shows the same behavior seen when plotting all of the monthly data. However, the difference is that in this graph some regression function can be found. The logarithmic regression (explained in the graph) shows an acceptable correlation, and then it is possible to use the regression equation within the monthly planning exercises. In order to exemplify this analysis usage in the short term planning, the defined parameters for production method during March at Bloque-1 are considered as listed in Table 1. Table 1 Parameters Bloque-1 March 2014.

Production Drift

Productivity (tpd)

Amount of drawpoints

21

301

3

25

1.244

10

29

2.579

15

23 27 31 33 35 37 53 55 57 59

810

2.153 2.778 2.817 2.879 1.379 348 544

1.095 1.280

7

13 16 16 16 8 5

Primary column average extraction (%) 14 18 19 46 58 73 76 57 38 6

8

13

11

11

10

15

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Caving 2014, Santiago, Chile With the average percentages we can obtain the REH index for each one of the production drifts detailed above (in the regression formula). Considering that we have average planned productivity, it is possible to estimate the entries amount in production drifts, to perform daily secondary breakage labors (according to REH formula, Table 2): Table 2 Amount of overlaps per secondary breakage labors

Production Drift

REH

Production drift entries to S.B labors per day

Amount of points in S.B per day (*)

21

826

0,36

1

25

1.244

1,26

4

29

2.579

23 27 31 33 35 37 53 55 57

959

2.153 2.778 2.817 2.879 1.379 412 789 867

0,84 1,55 1,73 1,74 1,74 1,94 1,06 0,84 0,69 1,26

59 725 1,77 (*) in base to 3 points in S.B per production drift entry.

3 5 5 5 5 6 3 3 2 4 5

For the demanded rate during the March program and for the average primary column draw level per drift, it is necessary to perform secondary breakage labors in 50 distributed points, as shown in table 2. When considering the full month and the time this labors per drift take, it is possible to estimate the monthly required time for secondary breakage labors, which can be included in the monthly plan. Note that to satisfy these high productivity rates, is necessary the detailed quantity of secondary breakage per production drift. Significant deviations in the distribution of secondary breakage impact in draw strategies in the short term planning (for example humidity control, dilution control, extraction angle control, etc.). This is exemplified in production drift C-25 and C-27 of Bloque-1, which has completely different conditions of granulometry (Figure 6). In production drift C-25, the hangings frequency is higher as compared to C-27, whereby the requirement of secondary breakage in C-25 is increased. However more resources are allocated to reduce hanging drawpoints on production drift C-27, which helped to create a difference between the two extraction areas (due to increased availability of area in production drift C-27), causing a difference in extraction height in both areas:

3

Discussion and conclusions

A correlation is seen between the primary draw percentage and the REH index, which is directly proportional to the primary column draw percentage.

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Caving Mechanics

Figure 6 Extraction height between production drift C-25 and C-27

It is possible to state that starting at 80% of primary column extraction, a change was seen in the granulometric distribution reports as the recorded ranks, decreasing in a progressive way the fragments percentage reports above 1 m, starting at average primary column draw level. This topic development will be useful for planning and control of the following mine Esmeralda Sur productive block. It is possible to estimate the secondary breakage level to be made considering the primary column draw percentage and the planned productivity in the short term programs (at productive drifts level). Differences in the secondary breakage requirements make deviations in the short term program, which could affect draw strategies (draw angle increase, dilution control, humidity control, etc.).

Acknowledgement Contributions from Gabriel Tapia (Universidad de Chile) and Boris Leal (Universidad de Santiago de Chile) are gratefully acknowledged.

References Montecino, N, Castro, R 2013, ‘Modelo de mezcla de fragmentación secundaria en minería de block/panel caving’, Laboratorio de Block Caving, Santiago, Chile. Moss, A, Russell, F & Jones, C 2004, ‘Caving and fragmentation at Palabora: prediction to production’, Proceedings MassMin 2004, Santiago, Chile.

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A 3DEC-FLAC3D models to predict primary fragmentation distribution in Cave Mines T V Garza-Cruz Itasca Consulting Group, Inc., USA M Fuenzalida Itasca Consulting Group, Inc., USA M Pierce Itasca Consulting Group, Inc., USA P Andrieux Itasca Consulting Group, Inc., USA

Abstract Prediction of primary fragmentation is of great importance in cave mining. Numerical models provide an opportunity to link caving induced stresses at failure with associated fragmentation of rock masses. In this paper, the results of a set of 3DEC simulations, based on a methodology developed by Itasca to model the fragmentation of massive veined rock masses, are used to relate induced stresses at failure to percentage of large fragments produced. A new approach is described in which such fragmentation relationship is applied to predict the primary fragmentation distribution in a FLAC3D cave model based on computed stresses at failure.

1 Introduction The spatial distribution of primary fragmentation as a result of mining operations becomes critical in the context of cave mining. The ability to predict an approximate size distribution and likelihood of generating large fragments as a function of rock mass strength and caving induced-stresses can provide significant insight into the mine design and risk assessment. Discontinuum approaches, such as DEM, can realistically simulate mechanisms such as spalling and bulking, but are too computationally expensive to be implemented in a large-scale caving model. For this reason continuum approaches are required to ensure reasonable computational times. A methodology is presented here that combines the characterization of a heavily veined massive rock mass (by construction and testing of a synthetic rock mass), with results of a continuum cave-scale model (informed by the SRM-derived rock mass strength) to predict the distribution of fragment sizes as a function of rock mass strength and cave back stress as illustrated in Figure 1 .

Figure 1 Diagram illustrating the proposed methodology for primary fragmentation prediction

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Caving Mechanics 2

Synthetic rock mass (SRM) modeling

A synthetic rock mass was developed following the methodology described by Garza-Cruz & Pierce (2014) using 3DEC to model a heavily veined massive rock mass. The rock mass was represented by a collection of interlocked tetrahedral blocks bonded at their contacts. The block contacts represent a network of low persistence veins as well as unveined intact rock. The advantage of 3DEC (Itasca, 2013) is that it allows for the construction of samples with zero initial porosity and highly interlocked irregular shapes that provide resistance to moment after contact breakage. This allows one to mimic the high uniaxial compressive strength to tensile strength ratios and friction angles typically exhibited by hard rock. Virtual testing of SRM samples populated with real field data allows us to characterize the mechanical behavior of a rock mass and, when subject to caving stress paths, provides information about the associated fragment size distribution, both of which are emergent results. 2.1

Sample construction

Two different samples (a 8x8x8-m and 18x18x18-m) were constructed by assembling a collection of highly interlocked tetrahedral blocks with approximate edge length of 0.5 m using KUBRIX-Geo and importing them into 3DEC to be populated with pertinent material properties (Garza-Cruz and Pierce 2014). Block contacts were populated with three different tensile strength distributions (accounting for intact rock and veins) to examine their impact on rock mass strength. The three distributions correspond to three different geological domains and include a certain percentage of zero strength veins that constitute 12%, 13% and 22% of all strength measurements for the “strong,” “medium” and “weak” domains, respectively (Figure 2). In order to populate a sample, each block contact was assigned a tensile strength value randomly selected from the cumulative distribution of rock tensile strength of interest (Figure 2), and its local cohesion was set to be 2.5 times such tensile strength. The used cohesion-to-tensile-strength ratio was based on a sensitivity study in which such ratio produced macro UCS/tensile strength ratio in the order of 10-20, which is consistent with typical observations. In all models, the blocks were defined as elastic and zoned with an approximate edge length of 0.5 m. The blocks and block-contacts micro-mechanical properties used are summarized in Table 1.

Figure 2 Synthetic rock mass sample generation procedure. 3DEC 8x8x8-m sample after trimming 1 m off all sides (left). Tensile-strength cumulative distribution for the “strong,” “medium” and “weak” categories of rock used in the 3DEC models (center). Vertical cross-section of the sample showing the block contact tensilestrength distribution based on the “medium” strength category (right). Block contacts in black have zero tensile strength and cohesion value, and account for 13% of contacts.

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Caving 2014, Santiago, Chile Table 1 Elastic block and contact mechanical properties used in the model

Block Properties Young’s Modulus 51 GPa Poisson’s Ratio 0.25 Density 2650 kg/m3 Contact Properties Normal stiffness 163.2 GN/m Shear stiffness 81.6 GN/m Peak friction angle 21° Residual friction angle 42° Dilation angle 10° Peak tensile strength Variable (see Figure 3) Residual tensile strength 0 Peak cohesive strength 2.5*tensile strength Residual cohesive strength 0 An additional 8-m cubic sample with more persistent veins was constructed following the same procedure outlined and superposing a discrete fracture network (DFN) in the sample by cutting the blocks intersected by the DFN. The contacts formed by the introduction of the DFN were randomly populated with a strength distribution from samples that failed on a structure for the “weak”, “medium” and “strong” subdomains (Figure 3). Such sample was used to address the impact more persistent joints have in primary fragmentation.

Figure 3 DFN with fractures colored by area used to cut the SRM sample (left). Tensile strength distribution of vein strength for the “strong”, “medium” and “weak” subdomains (center). Vertical cross-section of the SRM sample with DFN showing the block contact tensile-strength distribution based on the “medium” strength category and DFN populated with the “medium” vein strength distribution (right)

2.2

Emergent rock mass strength

Failure envelopes were estimated for the weak, medium and strong subdomains by subjecting samples to a suite of virtual numerical triaxial tests under different confinement levels, as well as to direct tension and uniaxial compression. The uniaxial compression test was performed by compressing the sample at a constant strain rate until it failed. The model represents the compression of a rock mass sample between frictionless roller boundary conditions which is relevant to spalling conditions. In an analogous way, a direct tensile test was performed by pulling each sample apart at a constant rate in a quasi-static fashion until it failed. The results of the UCS and direct tensile tests as well as the approximate failure envelopes for

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Caving Mechanics the different synthetic veined rock mass samples analyzed are shown in Table 2 and Figure 4 respectively. Corresponding Hoek-Brown failure envelopes were estimated, resulting in an average mi = 13. This information was used as input to model the different rock masses in a cave-scale FLAC3D model. Table 2 Summary of UCS and Tensile Strength Results for the Different Veined Rock Mass Samples Tested

Strength Distribution

SRM Sample “Strong”

SRM Sample “Medium”

SRM Sample “Weak”

Tensile Strength [MPa]

1.45

1.15

0.45

UCS [MPa]

20.5

16

8.7

Figure 4 Approximate failure envelopes derived from strength testing of veined rock mass samples with different vein strength distributions

2.3

Primary fragmentation under caving-induced stress path

A series of SRM samples were tested under caving-induced stress paths to predict primary fragmentation. The 3DEC model allows the blocks forming the sample to break at their subcontacts as a result of stress concentrations, mimicking the initiation of cracks that can coalesce and/or propagate to fracture the rock mass. This results in an emergent fragment size distribution. The testing environment for fragmentation studies consisted of either an 8-m or 18-m veined rock mass cube embedded in an elastic boundary as shown in Figure 5 (Garza-Cruz & Pierce 2014). The top and sides of the synthetic rock mass cube were bonded to the elastic boundary. The elastic boundary facilitated the application of stress boundary conditions to represent the induced stresses in the cave back and would provide a way for stresses to shed upwards during failure (as would be expected in situ). Boundary conditions were applied to the model as shown in Figure 5. The model was cycled to equilibrium, and the vertical stress on the sample bottom-face relaxed in ten increments, while allowing the model to reach equilibrium each time, simulating the upward advance of the cave back from below.

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Figure 5 8-m edge-length cubic sample embedded in an elastic boundary for fragmentation testing. a) and c) show the boundary conditions applied to the model. b) Elastic boundary made transparent to aid visualization

As the vertical stresses at the stope back decrease, tensile fractures develop sub-parallel to the face. A wide range of SRM tests were conducted to examine the impact of cave back stress, block size, sample size and vein persistence on predicted fragmentation. Figure 6 shows selected results for the case of 8-m cubic samples of the “weak”, “medium” and “strong” domain (the different colors represent fragments, defined as any collection of blocks that were still bonded together at one or more subcontacts). Figure 7 and Figure 8 show example results for the case of 18x18x18m “strong” sample and 8x8x8m “medium” sample with a superposed DFN, respectively. The models revealed that for a given stress state, the volume of stope back that undergoes spalling decreases as the rock mass strength increases. In order to characterize the spalling behavior in a quantitative way, the cumulative percentage by volume as a function of approximate fragment volume for all the “strong,” “medium” and “weak” samples were calculated (results corresponding to the three cases presented in Figure 6 are shown in Figure 9 and Figure 10). The fragmentation test indicated that as the strength of the rock mass increases, so does the size of fragments that can be expected to result from the rock mass failure and redistribution of stresses, which in turn results in a larger size distribution. It is important to mention that the size of the smallest fragment that can be created is limited by the size of the elemental tetrahedral forming the modeled rock mass. Figure 9 also summarizes the 25th, 50th and 75th percentile fragment sizes by volume for the different strength samples subject to a maximum cave back stress of 70 MPa. Figure 10 lists the percentage of the modeled synthetic rock mass that underwent fragmentation, as well as the percentage of fragments with volume in excess of 1.3 m³. It is interesting to note that weaker rock would fragment finer, and in this specific example would produce no large fragments, while stronger rock masses would tend to produce more large fragments than medium strength rock masses. Therefore, the probability of generating large fragments increases as the quality of the rock mass increases.

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“Weak” SRM

Vertical plane through center of the model

Fragmentation at stope back

“Medium” SRM

“Strong” SRM

Figure 6 Fragmentation of a “weak” (top), “medium” (center) and “strong” (bottom) SRM samples after the vertical stress was fully relaxed from below. SRM sample dimensions: 8x8x8m. Views along a vertical section (left) and looking up from below (right). Stress state tested: σv = 23.4 MPa, σH = 3*σv = 70.2 MPa, σh = 1.6*σv = 37.44 MPa

151


“Strong” SRM



Figure 7 Fragmentation of a “strong” SRM sample after the vertical stress was fully relaxed from below. SRM sample dimensions: 18x18x18m. Views along a vertical section (left) and looking up from below (right). Stress state tested: σv = 15.6 MPa, σH = 3*σv = 46.8 MPa, σh = 1.6*σv = 25 MPa.

“Medium” SRM with DFN



Figure 8 Fragmentation of a “medium” SRM samples with superposed DFN (see Figure 3) after the vertical stress was fully relaxed from below. SRM sample dimensions: 8x8x8m. Views along a vertical section (left) and looking up from below (right). Stress state tested: σv = 15.6 MPa, σH = 3*σv = 46.8 MPa, σh = 1.6*σv = 25 MPa

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Caving Mechanics Edge Length

Strong

Medium

Weak

25th percentile [m3]

0.04

0.03

0.02


0.12

0.07

0.03


0.27

0.17

0.07

Fragmented Volume

10.2%

13.7%

23.4%

Fragments >1.3m3

5.37%

0.0%

0.0%

Figure 9 SRM-derived primary fragmentation distribution for cave back stress of 70 MPa.

Weak

Medium

Strong

Fragment Diameter Distribution Assuming Disk Shape (Cave Back Stress = 70 MPa)

Figure 10 SRM-derived primary fragmentation distribution for cave back stress of 70 MPa, assuming disk shape for fragments

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Caving 2014, Santiago, Chile A wide range of SRM tests were conducted to examine the impact of cave back stress, block size, sample size and vein persistence on predicted fragmentation. The results of all tests are summarized in Figure 11, which relates the fragmentation to the strength of the domain and the induced stress at failure in the cave back. In general, it can be seen that the weak and moderate domains generally produced fragments <1.3 m3 under both low and high stress, whereas the strong domain has the potential to produce large slabs (several meters in diameter) when failing under low stress. The SRM-derived fragmentation prediction chart was combined with the results of cave-scale modeling to estimate how primary fragmentation might vary through the column as a result of differences in cave back stresses alone for a given rock mass strength.

Figure 11 Primary fragmentation prediction chart the “strong”, “medium” and “weak” domains generated from SRM sample testing

3

Numerical approach to analysis of caving

Five key geomechanical zones are associated with block and panel caving, as shown in the conceptual model sketched in Figure 12. This builds on the conceptual model developed by Duplancic and Brady (1999). The following are defining characteristics of each of the five zones.

• Elastic zone — Induced stresses may be high here, but are insufficient to induce measurable microseismicity.

• Seismogenic zone — Where microseismicity occurs within the jointed rock via joint slip and fracture

extension. This is commonly defined via an empirical damage threshold criterion that is a function of the deviatoric stress and intact UCS [0.3 < (σ1 – σ3) / (UCSintact) < 0.5 ].

• Yielded zone — Where the rock mass has disintegrated and lost all of its cohesive and/or tensile strength, but has not moved a significant distance yet. The outer limit of this zone generally coincides

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Caving Mechanics with the fracture limit, where visible fractures are evident in intersected openings or on ground surface, significant offset occurs in open boreholes and TDR cables break.

• Air gap — An air gap can exist if the overlying rock mass retains some level of cohesive and/or tensile

strength. As an air gap expands in size, the overlying rock mass may weaken further, causing advance of the yielded zone and collapse into the air gap.

• Mobilized zone — This is where the disintegrated rock mass has moved a significant distance and is

starting to dilate and bulk as a result. The criterion depends on the scale of the cave and the modulus of the rock mass; a total or vertical displacement of 1-3 m is generally employed.

The caving and stress-redistribution process inherently involves large deformations, shear along preexisting joints and bedding surfaces, fracturing of intact rock blocks and fragmentation of the rock mass above the undercut level. Ideally, one would model this process using a discontinuum (e.g., 3DEC or PFC3D) approach in which pre-existing fractures are explicitly represented in the model. However, the computational size and time requirements to solve mine-scale problems currently still make it impossible to attack a problem completely with the discontinuum approach. Instead, an algorithm to simulate caving within the concept of a continuum-based model has been developed over the past 15 years during the industry-funded International Caving Study (ICS I & II) and Mass Mining Technology (MMT I & II) projects. The constitutive rock mass response required to represent caving (i.e., the rock mass yield, dilation and bulking) was developed using strain-softening material models, with strain-dependent properties that are adjusted to reflect the dilation and bulking that accompany caving. The caving algorithm as implemented in FLAC3D (Itasca, 2012) attempts to predict the limits of these zones as a function of production from the cave. In addition to these cave limits, the results of cave-scale modeling are used to derive estimates of the following:

• Caveability; • Abutment and cave stresses; • Bulking factors, caving rate and breakthrough timing; and • Subsidence. Successful comparisons between predicted (via FLAC3D) and actual cave behavior have been achieved at a number of operations worldwide (e.g., Northparkes E26, Ridgeway Deeps, Palabora, Grace Mine).

Figure 12 Conceptual model of caving

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Caving 2014, Santiago, Chile Within the caving model, a rigorous mass-balance routine is implemented to ensure that the tonnesbased production schedule is represented accurately within the numerical model. Although the routine is computationally intensive, and can lead to relatively long model run-times (two to three weeks), the numerical approach is required to accurately capture the mechanisms of damage, yield, dilation and bulking necessary to correctly reproduce the evolving cave shape and propagation rates in response to a specific production schedule. 3.1

Numerical modelling of caving and primary fragmentation predictions based on SRM

A series of heterogeneous models were constructed in FLAC3D using the strength of domains derived from SRM testing (UCS = 8.7, 16 and 20.5 MPa for weak, medium and strong domains, respectively and mi=13) along with brittleness derived from back-analysis of stope overbreak (not presented here). The modelled undercut layout consists of 350 m N-S and 140 m E-W, with a hydraulic radius = 50 m. The in-situ stress state used is shown in Figure 13 (σH applied at 058°).

Figure 13 In-situ stress regime used in the FLAC3D cave model

The production process was simulated assuming “weak”, “medium” and “strong” rock everywhere. The model results suggested that it would be possible to sustain caving all the way to ground surface. Figure 14 shows the stress redistribution around the mobilized and yielded zones as well as the predicted shape of the inclined cave after two years of full production (“strong” case).

Figure 14 Predicted shape of the inclined cave two years after start of full production. Contours show the major principal stress redistributing around the mobilized and yielded zones

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Caving Mechanics As the cave is propagated, the model records the induced stresses at failure. Such stresses at failure for the case of “strong” domain after caving to ground surface took place are shown on the left side of Figure 15. The SRM-derived fragmentation prediction chart (Figure 11) was combined with the results of cave-scale modeling to relate induced stresses at failure to percentage of large fragments produced and estimate how primary fragmentation might vary through the column as a result of differences in cave back stresses alone (Figure 15).

Figure 15 Caving-induced stresses at failure assuming “strong” rock everywhere (left). Associated primary fragmentation as a percentage of fragments larger than 1.3 m3 for given stresses at failure (right)

At the beginning of cave life, brittle spalling is promoted by the large hydraulic radius, which limits arching and the associated buildup of confinement in the cave back, leading to a higher percentage of large fragments as derived from the SRM fragmentation analysis (~50% of fragments could be >1.3m3). As caving progresses, finer fragmentation should be expected at the column mid-height, where the cave back stresses are highest (~30% of fragments could be >1.3m3). As the cave reaches ground surface, stresses are low and fragmentation relies more on gravitational pull, leading to a higher percentage of large fragments (~60% of fragments could be >1.3m3).

4 Conclusions A methodology has been developed using 3DEC and FLAC3D to predict primary fragmentation as a function of rock mass strength and stresses induced at failure in a cave mine. SRM samples of heavily veined massive rock masses can be constructed using 3DEC by assembling a collection of tetrahedral blocks bonded at their contacts; while contact strength heterogeneity is introduced based on field data. Emergent SRM strength is used to inform a FLAC3D caving model. SRM samples were also tested under cave-like stress paths to predict primary fragmentation. The SRM-derived fragmentation prediction chart was combined with the results of cave-scale modeling in FLAC3D to relate induced stresses at failure to percentage of large fragments produced and estimate how primary fragmentation might spatially vary through the column. The results suggest that primary fragmentation could be quite coarse where medium and strong domains cave under low stresses, which is expected at the bottom and top of the ore column. This methodology can be applied to models where the spatial variation of geologic domains is known, to predict primary fragmentation as a function of changes in rock mass strength and induced stresses.

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Caving 2014, Santiago, Chile Furthermore, the estimated primary fragmentation size distribution can be used as an input to predict cave drawdown and secondary fragmentation using REBOP (Fuenzalida et al, 2014). Further studies are warranted to refine the strength characterization of rock masses using SRM as well as their associated fragmentation under caving stress paths.

References Duplancic, P, & Brady, BH 1999, Characterization of caving mechanisms by analysis of seismicity and rock stress, Proceedings 9th International Congress on Rock Mechanics (Paris), vol. 2, pp. 10491053. Balkema, Rotterdam. Fuenzalida, M, Garza-Cruz, TV, Pierce, M & Andrieux, P 2014, ‘Application of a methodology for secondary fragmentation prediction in cave mines’, Proceedings 3rd International Symposium on Block and Sublevel Caving, Santiago, Chile. Garza-Cruz, TV, & Pierce, M 2014, ‘A 3DEC Model for Heavily Veined Massive Rock Masses’, Proceedings 48th US Rock Mechanics / Geomechanics Symposium. Minneapolis, USA. Itasca Consulting Group, Inc. 2013, 3DEC – Three-Dimensional Distinct Element Code, Ver. 5.0 User’s Manual, Minneapolis: Itasca. Itasca Consulting Group, Inc. 2012, FLAC3D – Fast Lagrangian Analysis of Continua in 3 Dimensions, Ver. 5.0 User’s Manual, Minneapolis: Itasca.

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ALCODER, challeges of paradigms in caving methods Gl Krstulovic Geomecánica Ltda., Chile GA Bagioli Tetra Tech Metálica, Chile

Abstract More than 90% of the Chilean metal production from underground mining sites is extracted through collapse mining methods. The experience gained in these mining sites has originated a series of assumptions, which largely respond to popular belief and have no sufficient analytical support to be considered as true to the behavior of in-situ rock. The reformulation of old concepts in classic rock mechanics has allowed establishing new criteria in order to explain the behavior of rocks excavated thus, which include the “deterioration criterion” as an alternative to the “rupture criterion”; this allows reviewing the most frequent paradigms in mining operations by caving. In our case, the Janbú-Kulhawi-Krstulovic concepts, which are alternative to the traditional MohrCoulomb-Hoek, allow anticipating the orientation that the collapsing rock will adopt, including the resulting seismicity, among other things. It may be assumed from the foregoing that this review concludes on geometric configurations that favor collapse, including the geometry of the anomaly that is currently known as pre-conditioning of rock, in order to also favor collapse. The analytic formulation of this deterioration criterion has been incorporated in the ALCODER computer simulator. The necessary input data for these processes require identifying the behavior of the deformation module of the rock, and the variation with their surrounding tectonic confinement. Complementing the foregoing, the maximum deformation energy (DE) tolerated by such rock based on lab tests, constitutes a comparative pattern for establishing seismicity and potential rockburst in-situ.

1

Brief introduction to ALCODER

More than 90% of the Chilean metal production from underground mining sites is extracted by collapse mining methods. ALCODER is a Fortran simulator by finite state-of-the-art elements, expressly designed to adequately respond to mining problems in relation with collapsing rock. The ALCODER algorithm is based on original computer programs from Utku (1968) and Kulhawy (1972). Both algorithm well validated as per references:

• ELAS-A., Senol Utku et al, 1968. • Kulhawy, Fred H., 1972. These original concepts are modified according to the references indicated below:

• Krstulovic G.,2004 • Bagioli G., Krstulovic G., 2008. The recent uses of ALCODER in Caving and SLS Mass Blast are the following:

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Caving 2014, Santiago, Chile • CODELCO Salvador, Geomecánica Ltda. / Metálica Ing., 2000, Panel Caving. Basic Eng. for Inca Oeste.

• Vasante Metals Inc. Geomecánica Ltda. / Metálica Ing., 2004, SLS Mass Blast (Brasil). • Free Port Mc Moran Santos, Alcaparrosa and Candelaria Ore Deposits, Geomecánica Ltda., 2000-2009, Internal project reports and design results for Mass Blast in SLS stoping.

• CAP Romeral, Geomecánica Ltda. / Metálica Ing., 2010, SLC Project Under Current Open Pit. • Yamana Gold Inc., Geomecánica Ltda. / Metálica Ing., 2010, Mining Projects for QDDL (Argentina), Jerónimo (Chile) Ore Deposits for Mass Blast in SLS Stoping.

• IM2, Geomecánica Ltda., 2010-2011, Internal Consulting Reports (In House Consultant) for Rock Pre Conditioning in Caving.

• CODELCO Chuquicamata, Geomecánica Ltda., 2011-2012, Caving Propagation Estimation (PMCHS).

• CODELCO Andina, Geomecánica Ltda. / SKM Ing, 2012-2013, Support Analysis for Haulage III Excavation Alternative.

• CODELCO El Teniente, Geomecánica Ltda. 2013-2014. ALCODER Validation of 7 RockBurst at Pilar Norte Orebody

More information on ALCODER results for full scale trial can be obtained from Freeport McMoran for MassBlast in SLS stoping , and from CODELCO El Teniente for RockBurst.

2

The “deterioration in rock” criterion that rules the ALCODER algorithm

Since its beginnings, Rock Mechanics for Geomechanics in Mining has inherited, from its civil “pair,” and according to classic mechanics, the Mohr-Coulomb-Navier Rock Rupture Criterion concept. This concept was modified some years ago by an empirical equivalent: the Hoek Rupture Criterion. In both cases, the Criterion aims to explain the conditions that rule the rupture phenomenon in rock. As a result, the software that are commercialized for rock stability evaluations during mining excavations invariably contain these rupture criteria to explain the behavior of such rock. The Output of these software invariably provides:

• Safety conditions of the remaining rock after mining excavations through a Safety Factor. • Deformation conditions of this same remaining rock. Both outputs require interpretation and validation, which are not always sufficiently achieved for the purposes of the study under evaluation. Alternatively, in mining excavations (unlike civil excavations), one must coexist with deteriorated rock that are still capable of supporting the “mining building.” Consequently, the expected deterioration degree in this mining excavation becomes a relevant factor for the design in Caving. Here we formulate that “deterioration in rock” is directly associated to the deformation that these materials could suffer in a confinement / deconfinement process resulting from mining excavations. Rock taken from virgin (raw) conditions to deconfined conditions (near an excavated wall) suffer micro fractures which compensate (in volume) the deformation of such walls towards the excavated space. The occurrence of these micro fractures implies a reduction of the competence capacities of such rock, i.e., a reduction that responds to a “deterioration” of the competence indexes that define the quality of this rock.

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Caving Mechanics The competence index that is well detected by this “deterioration,” is the Deformation Module (E). It is empirically proven that rock with lower (E) have lower competence qualities, i.e., there are widely accepted empirical formulas that relate (E) / RMR, (E) / GSI, (E) / RQD, where: RMR, GSI, RQD are quality indexes collected through geotechnics in-situ. In other words, (E) is a good indicator of in-situ rock quality i.e., (E) can suffer modifications according to the confinement / deconfinement process resulting from mining excavations in-situ according to the tectonics of the area. The law that rules this variation of (E) in rock was initially formulated by (Kulhawi 1972). The ALCODER incorporates this Law of Variations of (E) under confinement, thus concluding the resulting new (E) in the remaining rock at the excavation. The variations of (E) are transferred to variations of RMR, GSI, RQD, as appropriate, according to the aforementioned empirical formulas. Therefore, the ALCODER Output is user-friendly in RMR, GSI, or RQD indexes, which allow configuring the “deterioration” experienced by rock undergoing an excavation process.

3

Collective imagination myths regarding the caving process

Mining by caving has the following unchallenged paradigms:

1. After causing collapse over the Undercut, Caving progresses according to dome geometry. 2. The advance perimeter or “caving face” in Panel Caving must be concave towards the collapse. 3. For Caving simulator effects, Modules (E) in rock can be assumed as invariable. 4. Pre-Conditioning (PA) of in-situ rock always helps to accelerate collapse. 5. The caving policy from “extraction points” can be made independently from the collapse process.

6. The resulting granulometry in “extraction points” is independent from the collapse process. 7. Computer simulators cannot detect faults that have not been pre-established. 8. Rock Burst can only be anticipated with seismic records. In the following Sections, we present results of the ALCODER simulator with the deterioration criterion in rock, which challenge the accuracy of these myths. 3.1

Myth 1

After causing collapse over the Undercut, Caving progresses according to the dome geometry. This as schematically described in Figure 1. INCORRECT. The geometric configuration of collapse in progress depends on the quality of in-situ rock and the tectonics of the area. Under normal conditions, collapse tends to advance towards a geological discontinuity or slopes in mountain topography. Figure 2 shows the deviation of the advance in collapse. Example of an ALCODER simulator Output after 7 iterations of collapse in Caving under an Open Pit topography and without an express extraction policy. In other words, retraction according to spontaneous collapse. The black line in figure 2, is the main fault. Collapse Criterion: Spontaneous by Hydraulic Radius (HR) in rock with MRMR index lower than 40.

161


Figure 1 Collective Imagination Regarding Caving Process

Figure 2 Deviation of the Advance in Collapse

3.2

Myth 2

The “caving face” perimeter in Panel Caving must be concave towards the collapse. INCORRECT. The geometric configuration of the collapse cavity in progress / in-situ rock outlined in plant projection and in vertical projection makes it look like a vault which auto-supports itself with the concavity of its walls. To facilitate collapse, both vertical and horizontal projections must be as straight as possible, so that the abutment effort is minimized with the straight faces. Figure 3 shows an isometric view of ALCODER simulator for Caving. Figure 4 shows ALCODER result of the variation in the abutment Stress according to the geometry of the face in collapse. In other words, the closer the face gets to the vertical, the abutment Stress will diminish.

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Caving Mechanics

Figure 3 Isometric View of ALCODER Simulator Devoted to Results on Figure 4.

Figure 4 ALCODER Result of the Variation in the Abutment Stress

3.3

Myth 3

For Caving simulation, Modules (E) in rock can be assumed as invariable. INCORRECT. Module (E) varies according to the confinement / deconfinement of the location. Figure 5 shows the variation of (E) according to lab tests for the case of Porphyry in Chuquicamata. The Law that rules the variation of (E) according to (Kulhawi 1972) is function of the experimental constants (K) and (n), where Pa is the atmospheric pressure and Sigma 3, the lower confinement in-situ.

163


Figure 5 Variation of (E) According to Lab Test on Porphyry Rock 𝜎𝜎

Where:

𝑛𝑛

𝐸𝐸 = 𝐾𝐾 ∙ 𝑃𝑃𝑃𝑃 ∙ �𝑃𝑃𝑃𝑃3 �

(1)

E = Young`s Elastic Modulus Pa = Atmospheric pressure expressed in the same units as E σ3= Minimum principal stress n = Modulus Exponent , K = Modulus Number K, n= are pure numbers Figure 6 shows, according to (Barragan & Krstulovic 2013), a recent compilation with the empirical relation (K) / (n). It is estimated that in rock with RMR under 60, the mistake of not considering the variation of (E) can seriously affect the Simulator results.

Figure 6 Empirical Relation (K) / (n)

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Caving Mechanics 3.4

Myth 4

Pre-Conditioning (PA) of in-situ rock always helps to accelerate collapse. INCORRECT. The (PA) aims to cause a “quality” anomaly in the in-situ rock. In such condition, in order to achieve the acceleration of collapse, the (PA) must satisfy a requirement of adequate geometric shape and “intensity” in the reduction of the quality of in-situ rock. Figure 7 shows an ALCODER example of a (PA) geometry 20% - 40% deterioration in (E), which successfully accelerates (compared with Figure 2) Caving under mountain topography. The black line is the main fault.

Figure 7 Example of (Pa) Geometry Deterioration in (E)

Figure 8 shows an ALCODER example of an insufficient application of (PA) with deterioration in 20% in (E) to accelerate the collapse process in a tectonic environment. This Figure 8 includes (%) of the collapsed material according to RMR index. In this case, the quadrant with 20% deterioration in (E) does not accelerate rock collapse in the object sector of (PA).

Figure 8. ALCODER Example of Insufficient (PA)

165


Myth 5

The mining production policy from “extraction points” can be made independently from the caving process. INCORRECT. Ideally, mining from the extraction points should be made with previous knowledge of the way in which Caving collapses spontaneously. If a mining policy that coincides with the spontaneous collapse of Caving is not maintained, this could cause extreme conditions at the production level:

• If mining happens faster than the collapsed material in Caving, this could cause a space between

the ground in-situ and the already collapsed material, originating an abutment condition, or else, the detachment of wedges as in-situ rock.

• If mining happens slower than the collapsed material in Caving, the non extracted column

is compacted and serves as temporal support for Caving and causes a collapse option at the production level.

Figures 9 and 10 are records of collapse due to inconsistency between mining / Caving according to the ALCODER Output. The dark bodies located in the collapsed material, are wedges incorporated spontaneously in the Caving. In other words, the ALCODER allows forecasting the spontaneous collapse process, and thus allows adjusting the mining policy in-situ.

Figure 9 Collapse at the Production Level

Figure 10 Wedges Incorporated in the Caving

3.6 Myth 6 The resulting granulometry at the “extraction points” is independent from the collapse process. INCORRECT. During the collapse process, the confinement / deconfinement conditions in the in-situ rock cause spontaneous collapse of various granulometries. Thus, it is incorrect to assume that initially there is a coarse granulometry that subsequently is reduced along its transit towards the extraction point. The spontaneous granulometry produced by Caving depends on the rock quality, tectonic stresses and the geometry of the spontaneous collapse, which could include extreme cases:

166

Caving Mechanics • Abundant spontaneous coarse granulometry at the beginning that subsequently limits itself to spontaneous collapse of fine granulometry.

• Alternatively, only fine granulometry at the beginning, and/or subsequently, large boulders due to collapse in higher elevations.

Figure 11 describes an ALCODER Output with granulometries differentiated by colors as caving outcrops under an open pit slope. ). Note that fragmentation is result of different RMR/ E values. Validation by correlation RQD/E/RMR /Blok Size is in progress.

Figure 11 ALCODER Output with Differentiated Granulometries

Figure 12 describes the configuration of the collapsed material by Caving stages according to ALCODER Output in 14 extraction columns over the undercut.

Figure 12 Extraction Columns Over the Undercut After 6 Caving Stages

3.7

Myth 7

Simulators cannot detect faults that have not been pre-established in the simulation model. INCORRECT.- ALCODER detects the spontaneous displacement in rock bodies as Caving progresses, and sheds light on subsidence Faults/Cracks or Faults/Cracks in the mining infrastructure near the collapse.

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Caving 2014, Santiago, Chile Figure 13 schematically shows an SLC project under an exhausted Open Pit. In Figure 14, the ALCODER simulator identifies cracks in the SLC mining sequence. These cracks are from subsidence in overturn, and cracks in ramp infrastructure of the SLC as mining operations progress.

Figura 13 SLC Project Under Exhausted Open Pit

Figure 14 Cracks in the SLC Sequence After ALCODER Out Put

3.8

Myth 8

Rock Burst can only be anticipated with seismic records. INCORRECT. There are no bibliographic records regarding Rock Burst forecasts in mining sites. Although more than 40 years have passed since micro seismic hearing systems were implemented in mining operations, the Rock Burst issue has still not been solved. According to Classic Mechanics, Rock Burst occurs in rock when the Deformation Energy (DE) per unit volume exceeds the tension resistance in the rock. To determine this (DE) per unit volume, rock is assumed as a block with differential dimensions, which undergoes the action of the main normal stresses (S). (ED) is the work executed by these stresses when deforming the cube one magnitude (dl) (Obert L. & Duvall W. 1967).

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Caving Mechanics In other words, Magnitude (ED) is equal to the work necessary to deform the rock cube in (dl). Rock Burst occurs when the work exceeds the tension resistance of the rock (Example: Resistance at Atmospheric Confinement). The (ED) can be estimated according to formulas (1) and (2).

(2)

Where: E =Young`s Elastic Modulus υ = Poisson`s Ratio σ= 1, 2, 3 Principal Confining Stresses Figure 15 shows an ALCODER simulator with Output in collapse after 10 iterations. For each of the iterations, the ALCODER identifies the (ED) that exceeds the maximum value accepted by this rock according to (ED) verifications in lab. In this case, points 1 to 5 present numbers for (ED) Rock Burst.

Figure 15 ALCODER Out-Put with Maximum (ED)

In other words, the ALCODER can anticipate the opportunity and the place where Rock Burst would occur during the caving process. Magnitude of Rock Burst anticipated by ALCODER can be estimated from Krstulovic (1977).

4 Conclusions The Rock Deterioration Criterion based on the (E) Module deformation index suggested here has analytical grounds in classic mechanics, and empirical verifications in Figure 6. Taken to applications of the “mining business” through the ALCODER algorithm, this criterion is adequate for addressing a series of typical caving issues, i.e., problems that range from abutment stress, collapse in production levels, to Rock Burst.

169

Caving 2014, Santiago, Chile Acknowledgements The authors thank Tetra Tech Metálica and Geomecánica Ltda. for the considerations granted to support this document.

References Bagioli, G, Krstulovic G 2008, ‘An ALCODER for Computer Monitoring of Slopes Stability During WTI Program in Open Pit Mining’, ISRM Congress, Lima, Perú. Barragan, JL, Krstulovic G 2013, Lab. data compilation from different authors. Duvall, W & Obert, L 1967, Rock Mechanics and the Design of Structures in Rock, John Wiley & Sons, Inc. Fred, H. Kulhawy, 1972, Finite Element Modeling Techniques for Underground Opening in Rock. Contract Nº H0210023. Advanced Research Projects Agency, Washington USA. Krstulovic, G 1977, Métodos y Técnicas Micro Sísmicas en la Evaluación de Estabilidad Dinámica de Macizos Rocosos. RI-77-1 Centro de Investigaciones Minero y Matalurgica CIMM- Chile Krstulovic, G 2004, ALCODER A New Method for Evaluating Stability of Rock Excavations, Mass Mine Chile. Senol Utku et al. 1968, General Purpose Computer Program for the Equilibrium Problems of Linear Structures. TR 32-1240. Jet Propulsion Lab. CALTEC Pasadena, California.

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Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente Mine, Chile. Part I A Brzovic Codelco, Chile P Schachter Codelco, Chile C de los Santos Codelco, Chile JA Vallejos, University of Chile, Chile D Mas Ivars Itasca Consultans AB, Sweden

Abstract A comprehensive geotechnical characterization has been undertaken at the El Teniente mine to describe and determine the rock mass behaviour strength properties of the primary copper ore. This type of rock can be considered as a heavily veined massive and unfractured rock mass. This compressive work is focused sequentially on; 1) an intensive structural data collection campaign from several oriented core placed within main geotechnical units, 2) discrete fracture network modelling, 3) laboratory testing of the intact rock and veins materials combined with scaling procedures, and 4) application of the Synthetic Rock Mass (SRM) approach to study the strength and deformation behaviour of the main geotechnical units. The Synthetic Rock Mass (SRM) modelling approach, based on particle mechanics, has been developed to simulate the mechanical behavior of jointed rock mass. This technique uses the bonded particle model for rock to represent intact material and the smooth-joint contact model (SJM) to represent the in situ joint network. The macroscopic behavior of an SRM sample depends on both the creation of new fractures through intact material and slip/opening of pre-existing joints. SRM samples containing thousands of nonpersistent joints can be submitted to standard laboratory tests (UCS, triaxial loading, and direct tension tests) or tested under a non-trivial stress path representative of the stresses induced during the engineering activity under study. This paper describes the first part of the study, with focus on structural data collection campaign (points 1) and laboratory testing (point 3).

1 Introduction The primary copper ore at the El Teniente mine is described as very competent and massive, due to it exhibits a brittle behavior, often violent failure under high stress conditions (Rojas et al 2001). This description is coherent with the geological description of the rock mass, which does not have discontinuities match as the definition provided by International Society of Rock Mechanics (ISRM, 1981). Only faults can be classified as discontinuities, but they are widely spaced. The primary copper ore has a high frequency of veins, where the cooper mineralization is hosted, these vein network structures are known as stockwork (Figure 1). Soft veins containing weak minerals as infill (chalcopyrite and anhydrite) control the disassembling of the rock mass during caving (Brzovic & Villaescusa 2007; Brzovic 2011). Nowadays, there are two traditional methods used to estimate the strength of the rock mass: 1) determination of the strength envelope of the rock mass using scaling parameters from laboratory tests, for example HoekBrown’s failure criterion and 2) using numerical modeling based on back analysis of previous experiences of failure observed and measured in mining or civil excavations.

171


Figure 1 Panel caving method currently used at the El Teniente Mine (a), Intense vein network stockwork at a development ahead of the cave front (b), and Weak Veins as faces of caved rock blocks (c) (modified from Brzovic & Villaescusa 1997)

The primary copper ore of El Teniente mine practically has no fractures or joints; therefore it is difficult to determine a rating able to scale laboratory data, such as RMR (Laubscher 1977) or GSI (Hoek 1994). Therefore, the way number 1 cannot be used properly unless the RMR´s or GSI´s input parameters are manipulated or adjusted in order to obtain reasonable results. The second option requires, in general, a good characterization of a previous failure event in the rock mass, not always available. In recent years, new techniques of numerical modeling associated with the concept called “Synthetic Rock Mass” have been developed (PFC3D, ELFEN, Abaqus) aiming to capture the real behaviour of the rock mass. Those methodologies are the third way to estimate the strength of the rock mass, but they are still in development. This paper is composed by two parts. It is aimed to implement the concept of SRM developed by Itasca (Pierce et al. 2007; Mas Ivars et al. 2011). The methodology is divided in making a geotechnical characterization (mapping and laboratory tests), developing scaling laws and applying the SRM approach. This part describes the results of comprehensive effort to characterize the El Teniente rock masses, which include; core logging, field work, structural data analysis, Discrete Fracture Network (DFN) modeling, and laboratory testing. In a following paper (Vallejos et al. 2014), the strength and deformation behaviour of four rock mass domains from the El Teniente mine are studied.

2

Intensive structural data collection

An intensive structural data collection campaign from several oriented core placed within main geotechnical units were undertaken as part of this study. In order to avoid orientation bias during data collection at each location or geotechnical unit, three oriented cores were drilled in three almost orthogonal orientations to each other. Those groups of cores, 9 in total, are called “triada” and represent more than 2000 meters of structural mapping (between 240 and 300 meters each triada). Geological and full structural core logging (similar to scanline mapping) was undertaken to determine the intensity of weak veins at each

172

Caving Mechanics location. Quantitative mineralogical assemblage, orientation and geometric features of veins were the main characteristics obtained during core logging. Weak veins and fault lineal intensity then were determined for each geotechnical unit, the mean value measured of the lineal frequency (P10 according to Dershowitz & Einstein 1988) at each triada location, range from 1m-1 to 12m-1 (excluding the low grade central brecha braden). On the other hand, fault lineal intensity measured on the same cores, range from P10 0.05 m-1 to P10 0.4 m-1. Those intensity values agree with the geological description of primary rock mass by Brzovic & Villescusa (2007). Additional core logging (more than 10,000m from un-oriented cores) and historical structural drive mapping were used to generate a new geotechnical zoning of primary rock mass, which agree with the alteration and genetic geological model of the El Teniente porphyry copper. The new geotechnical zone are shown in Figure 2, detailed sequence of zoning building was presented by Brzovic & Schachter (2013).

Figure 2 Plan view of the geotechnical model at the El Teniente mine (level 2121 and 2210) based on weak veins intensity measured in oriented cores

3

Discrete fracture network modelling

Structural data analysis were also undertaken to build Discrete Fracture Network (DFN) as the best way to represent the rock structure of the stockwork veins nature (Figure 3). The methodology followed to build a DFN from each geotechnical unit is fully described in Brzovic & Herrera (2011). Discontinuity size, for small scale geological structures were obtained from scanline data collected in mine drives, and for faults, from general plan view of fault interpretations. Based on the structural data analysis, it was possible to obtain the weak veins volumetric intensity P32 (Dershowitz & Einstein, 1988) of each geotechnical unit. DFN in Figure 3, were built using commercial FracMan software, which allowed to readily determining in Situ Fragmentation of primary rock mass. The P32 of weak veins determined at the El Teniente mine range from 2m2/m3 to 15m2/m3, which represent a small percentage of the stockwork veins of the primary rock mass. On the other hand, fault intensity P32 were determined from 0.15m2/m3 to 0.40m2/m3.

173


Figure 3 DFN model as the way to better represent the nature of the stockwork veins at the El Teniente mine (Adapted from Brzovic & Herrera 2011)

4

Laboratory testing results

Rock and veins samples of primary ore were tested in several laboratories to determine strength properties of intact rock and geological structures. Laboratory testing included; UCS and triaxial test of the standard rock sample size, UCS of large rock specimen to develop scaling law relationship for main rock types, direct tensile and shear test of all vein types. That information was complemented with data analysis of the historical lab information from the mine site. 4.1

Intact rock properties

In general, at the El Teniente mine, there are two main factors that control the strength of a laboratory specimen that represent the intact rock material of primary rock mass: the proper intact rock material and the veins features contained on the small rock sample. This aspect describe a fundamental characteristic of the primary ore, stockwork veins intensity are so high that even a small core sample contain several veins within. Based on that fact, Marambio et al. (2000) suggested a classification of rock sample failure (or failure mode) during testing, which is summarized in Figure 4. The same figure also presents the standard values of UCS and triaxial testing from main geological units of the El Teniente mine. In addition, Figure 5 presents historical values of Young Modulus.

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Caving Mechanics

Figure 4 Historical values of Uniaxial and Triaxial tests undertaken for main rock types at the El Teniente mine, which were classified according the failure mode suggested by Marambio et al (2000)

Figure 5 Historical values of Young Modulus and UCS to different rock sample sizes undertaken to main rock types at the El Teniente mine

175

Caving 2014, Santiago, Chile Figure 5 also presents UCS values for different rock sample sizes undertaken to find out scaling law relationship. In this figure only failure mode A, B and C were included. As would be expected, large variability of “intact rock” strength properties are found out due the vein influence during rock sample failure. 4.2

Vein strength properties

Standard direct shear tests non-conventional direct tensile tests were undertaken for several vein types in order to obtain strength properties of the El Teniente geological structures. Those tests included full geological and geometrical description of each veins typed tested. More than 40 samples were undertaken for direct shears test, most of them at the SP Technical Research Institute of Sweden, and more than 50 samples were undertaken for direct tensile test at both the SP Technical Research Institute of Sweden and the IDIEM Laboratory of University of Chile. Historical information from the mine site was also included during data analysis. Description of the methodologies used can be seen in: De los Santos and Brzovic (2013), Baraona (2012), and De los Santos (2011). Figure 6 present some shear and tensile strength values of the El Teniente veins correlated to the main mineralogical assemblage as infill.

Figure 6 Shear and tensile strength values of veins at the El Teniente mine (adapted from: De los Santos & Brzovic 2013; Baraona 2012; De los Santos 2011)

176

Caving Mechanics 5 Conclusions From the comprehensive geotechnical characterization undertaken at the El Teniente mine in order to describe and determine the rock mass strength properties of the primary copper ore, it was found that:

• A new geotechnical zonation was achieved based on the concept of weak veins occurrence. Average

lineal vein intensity (P10) of main geotechnical unit of primary rock mass range from 1m-1 to 12m1. Average fault lineal intensity also range from P10 0.05m-1 to P10 0.4m-1. These new concept/ zonation are in more agreement with rock mass behavior at the mine site than previous, for instance Fragmentation and Seismicity.

• Structural data analysis allowed building Discrete Fracture Network model that honored core logging

of main geotechnical units at the mine site. DFN output provide veins volumetric intensity of the primary rock mass (P32), which range from 2m2/m3 to 15m2/m3. Fault volumetric intensity P32 range 0.15m2/m3 to 0.40m2/m3.

• Intensive laboratory testing and historical mine site data were used to determine strength properties of

both intact rock and geological structures of the El Teniente mine. Data analysis also included scaling law relationship. All those basic information gathered were used to apply the Synthetic Rock Mass approach to study the strength and deformation behaviour of primary rock mass.

Acknowledgement The authors acknowledge to The El Teniente Division of Codelco-Chile for their permission to publish the data and for supporting this work. This study was commanded by API T10E202 of Codelco-Chile. FONDECYT Initiation Grant #11110187 also financed this study.

References Baraona, K 2014, ‘Comportamiento de Vetillas sometidas a Ensayos de Tracción Directa, mina El Teniente’, Internal report of the Superintendence Geology, CODELCO-Chile El Teniente Division, API T10E202. [in Spanish]. Brzovic, A & Villaescusa, E 2007, ‘Rock mass characterization and assessment of block-forming geological discontinuities during caving of primary copper ore at the El Teniente mine, Chile’, International Journal of Rock Mechanics and Mining Sciences’, vol. 44, pp. 565-583. Brzovic, A 2009, ‘Rock mass Strength and Seismicity during Caving Propagation at the El Teniente Mine, Chile ‘, In Proceedings of 7th International Symposium on Rockburst and Seismicity in Mines (RaSiM07). Tang, C.A. editor. Dalian University. (2) 838-52. Brzovic, A & Herrera, S 2011, ‘Assessing Geological Vein Size and Intensity using Discrete Fracture Network Modeling at the El Teniente Mine, Chile’, InProceedings of the 45th US Rock Mechanics / Geomechanics, ARMA Symposium, San Francisco, EEUU. 11-252. Brzovic, A & Schachter, P 2013, ‘Rock Mass Geotechnical Characterization based on the Weak Stockwork Veins at the El Teniente Mine, Chile’, In Proceedings of 3th International Seminary of Geology for the Mining Industry, GEOMIN. Santiago, Chile. De los Santos, C 2011, ‘Efecto de la Mineralogía, Alteración, y Geometría en la Resistencia Mecánica de las Vetillas, Mina El Teniente’, Región del Libertador Bernardo O’Higgins, Chile. Memoria Para Optar Al Título De Geólogo. Universidad de Concepcion. [in Spanish]

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Caving 2014, Santiago, Chile De Los Santos, C & Brzovic, A 2013, ‘Geotechnical Properties on Cemented and Healed Stockwork Veins at the El Teniente mine, Chile’, In Proceedings of 3th International Seminary of Geology for the Mining Industry, GEOMIN. Santiago, Chile. Dershowitz, W & Einstein, H 1988, ‘Characterizing rock joint geometry with joint system models’, Rock Mechanics and Rock Engineering, vol. 21, pp. 21-51. Hoek, E 1994, ‘Strength of rock and rock masses’, ISRM News Journal 2, pp. 4–16. Hoek, E & Brown, E 1988, ‘The Hoek–Brown failure criterion – a 1988 update’, in: Proceedings of the 15th Canadian Rock Mechanics Symposium, pp. 31–38. ISRM 1981, ‘Suggested methods for the quantitative description of discontinuities in rock masses’ in Rock characterization, testing and monitoring, ISRM Suggested methods, (edited by ET Brown), Pergamon Press, pp. 3-52. Laubscher, D 1977, ‘Geomechanics classification of jointed rock masses – mining applications’, Trans. Inst. Min. Metall., 86, A1-A8. Mas Ivars, D, Pierce, M, Darcel, C, Reyes-Montes, J, Potyondy, D, Young, P & Cundall, P 2011, ‘The Synthetic Rock Mass approach for jointed rock mass modeling’, International Journal of Rock Mechanics and Mining Sciences, vol. 48, pp. 219–244. Marambio, F, Pereira, J & Russo, A 1999, ‘Comportamiento Estudio Propiedades Geotécnicas Proyecto Pipa Norte’, Internal report SGL-280/1999 of the Superintendence Geology, CODELCOChile El Teniente Division [in Spanish]. Pierce, M, Mas Ivars, D, Cundall, P & Potyondy, D 2007, ‘A synthetic rock mass model for jointed rock’, In Proceedings of the 1st Canada-US Rock Mechanics Symposium, Vancouver, Canada, vol. 1, pp. 341-349. Rojas, E, Cavieres, P, Dunlop, R, & Gaete, S 2000, ‘Control of Induced Seismicity at the El Teniente Mine, Codelco Chile’, In Proceeding Massmin, Chitombo, G, editor, Brisbane, Australia, AusIMM, 777-781. Vallejos, J, Brzovic, A, Lopez, C, Bouzeran, L & Mas Ivars, D 2013, ‘Application of the Synthetic Rock Mass approach to characterize rock mass behavior at the El Teniente Mine, Chile’, Continuum and Distinct Element Numerical Modeling in Geomechanics: Proceedings of the 3rd International FLAC / DEM Symposium, Hangzhou, China, paper: 07-02. Vallejos J, Suzuki, K, Brzovic, A & Mas Ivars, D 2014, ‘Characterization and Synthetic Simulations to Determine Rock Mass Behaviour at the El Teniente Mine, Chile. Part II’, In: Proceedings of the 3rd International Symposium on Block and Sublevel Caving, Santiago, Chile.

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Characterization and synthetic simulations to determine rock mass behaviour at the El Teniente mine, Chile. Part II JA Vallejos University of Chile, Chile K Suzuki University of Chile, Chile A Brzovic Codelco Chile, Chile D Mas Ivars Itasca Consultans AB, Sweden

Abstract A comprehensive geotechnical characterization has been undertaken at the El Teniente mine to describe and determine the rock mass behaviour strength properties of the primary copper ore. This type of rock can be considered as a heavily veined massive and unfractured rock mass. This comprehensive work is focused sequentially on; 1) an intensive structural data collection campaign from several oriented core placed within main geotechnical units, 2) discrete fracture network modelling, 3) laboratory testing of the intact rock and veins materials combined with scaling procedures, and 4) application of the Synthetic Rock Mass (SRM) approach to study the strength and deformation behaviour of the main geotechnical units. This paper describes the second part of the study, with focus on point 4. The Synthetic Rock Mass (SRM) modelling approach, based on particle mechanics, has been developed to simulate the mechanical behaviour of jointed rock mass. This technique uses the bonded particle model for rock to represent intact material and the smooth-joint contact model to represent the in-situ joint network. The macroscopic behaviour of an SRM sample depends on both the creation of new fractures through intact material and slip/opening of pre-existing joints. SRM samples containing thousands of non-persistent joints can be submitted to standard laboratory tests (UCS, triaxial loading, and direct tension tests) or tested under a non-trivial stress path representative of the stresses induced during the engineering activity under study. The micro-parameters of the bonds and the smooth-joint contacts between the particles have been calibrated against the mechanical properties and scaling laws for intact rock and veins, so that representative virtual SRM samples of the four different geotechnical units could be generated and tested. Results from the SRM simulations include pre-peak properties (modulus, damage threshold, peak strength, etc.) and post-peak properties (brittleness, dilation angle, residual strength, fragmentation, etc.). Of particular interest is the ability to obtain predictions of rock mass scale effects, anisotropy, and brittleness, properties that cannot be obtained using empirical methods of property estimation.

1 Introduction Presently, there are mainly two traditional methods used to estimate the strength of the rock mass: 1) determination of the strength envelope of the rock mass using scaling parameters from laboratory tests, for example Hoek-Brown’s failure criterion, and 2) using numerical modelling based on back analysis of previous experiences of failure observed and measured in mining or civil excavations.

179

Caving 2014, Santiago, Chile The primary copper ore of El Teniente mine has no fractures; therefore, it is difficult to determine a rating that would enable to scale laboratory data, such as, RMR (Laubscher 1977) or GSI (Hoek 1994). This is the reason why the first option is not valid unless the entry data is manipulated or adjusted in order to obtain reasonable results. The second option requires, in general, a good characterization of a previous failure event in the rock mass, which is not always available. In recent years, new techniques of numerical modelling have been developed (PFC3D, ELFEN, Abaqus) aiming to capture the real behaviour of the rock mass. Those methodologies are the third way to estimate the strength of the rock mass, but they are still in development. This paper is composed of two parts. The first one is included in Brzovic et al. (2014). The second part includes results from numerical modelling in the primary copper ore in El Teniente mine, particularly it has been implemented the concept of SRM developed by Itasca (Pierce et al. 2007; Mas Ivars et al. 2011). The methodology is divided into making a geotechnical characterization (mapping and laboratory tests), developing scaling laws and applying the SRM approach. This paper aims to study the strength and deformation behaviour of four rock mass domains from the El Teniente mine (Dacite, Diorite and CMET) and compares these results with the estimations based on classification systems and other numerical models. This study endeavours increased knowledge based on a previous work with this technique in El Teniente veined rock mass (Vallejos et al. 2013).

2

Synthetic rock mass components

The SRM method is based on the generation and testing of three-dimensional synthetic rock mass samples in order to simulate the mechanical behaviour of jointed or veined rock masses. SRM is implemented in PFC3D 4.0 software (Itasca 2008) and uses the interface SRMLab 1.7 (Itasca 2012). Figure 1 summarizes the main components of the model, which represents the intact rock as an assembly of bonded particle (Figure 1a), using the Enhanced Bonded Particle Model (BPM), and an embedded Discrete Fracture Network (DFN) to represent joints (Figure 1b). Each joint is represented explicitly using the smooth-joint contact model (SJCM).

(a)

(b)

(c)

Figure 1 Sample constructed with PFC3D particles (a), DFN superimposing onto the previous sample (b) and Synthetic Rock mass sample (c) (Board & Pierce 2009)

Two models compose the Enhanced Bonded Particle Model (BPM), which represents intact rock; the particle contact and the parallel bond model. A more detailed explanation of the standard and enhanced BPM for rock can be found in Potyondy & Cundall (2004) and Potyondy (2011). The smooth-joint contact model (SJCM) represents joints in the SRM samples simulating the behaviour of a smooth interface, regardless of the local particle contact orientations along the interface. This model makes possible the creation of large

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Caving Mechanics volumes of synthetic rock containing thousands of non-persistent joints. A more detailed explanation of SJCM can be found in Mas Ivars et al. (2008b). The most logical choice to represent explicitly a vein network is by using a Discrete Fracture Network (DFN) (Dershowitz & Einstein 1988). Each DFN represents different rock mass domains, and are characterized by fracture intensity and orientation of each set of veins. The data and the procedure used to develop each DFN model is described in Brzovic et al. (2014).

3

Calibration of the SRM model

In order to calibrate a SRM model and to create a large-scale model, laboratory tests and fields observations are needed. A summary of data for intact rock and veins properties and scaling procedures are described in Brzovic et al. (2014). 3.1

Intact rock

Laboratory data for UCS tests is adjusted using the relation proposed by Yoshinaka et al. (2008), defining a scaling power law for each lithology:

(1)

Where: σc: is the uniaxial compressive strength of a cylindrical specimen with a diameter , k: is a material constant. Even though exists a calibration procedure, the basic way to define a set of micro-parameters is by a trial and error approach (Itasca, 2008). The size of calibration is defined by the intact block within each DFN. The size of particle is selected equal to four particles along the average intact block size, and the aspect ratio of the calibration sample is 2.1:1. The assumptions taken for the intact rock calibration are detailed in Vallejos et al. (2013). To sum up, there are assumptions to reproduce better the brittle behaviour of El Teniente rock masses and other ones suggested on previous studies for reproducing hard rock behaviour (Potyondy & Cundall 2004; Potyondy 2011). The model cannot reproduce Poisson’s ratios larger than approximately 0.10 if a reasonably brittle response is desired. Due to the rock mass response being more influenced by the veins behaviour, Poisson’s ratio is not considered in the calibration. The rest of macro-parameters were matched with less than 1% of error. 3.2 Veins Brzovic & Villaescusa (2007) suggest that veins with thicknesses greater than 2 mm and with less than 1/3 of hard minerals play a relevant role controlling fragmentation and in the seismicity during caving propagation. The present study includes only soft veins with thicknesses greater than or equal to 1 mm, assuming that veins with thickness between 1 and 2 mm affect the rock mass behaviour. Macro-parameters have to be scaled to represent the average in-situ conditions of each rock mass unit. In this case the average length of veins is 1 m, therefore all macro-parameters are scaled to this length. It is considered that friction angle is not influenced by scale effect. The procedure to calibrate the model considers estimating microparameters based on results of previous simulations. The calibration procedure is detailed in Vallejos et al. (2013). The assumptions taken for veins calibration consider peak and residual friction angles of veins to be 40° and dilation angle to be 0°. It is assumed that most of the rock mass dilation comes from block rotation and the relative large size of the particles.

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Characterization of rock mass units and behaviour

4.1

Geometry and test configurations

The tested geometry is a cylinder of width equal to ten times the intact block size with an aspect ratio height to width of 2.1:1. Each DFN is a statistical representation of the network of veins forming the stockwork in a 30 m x 30 m x 30 m volume, but the SRM specimen widths are set to only 10 DFN average spacing for each lithology and associated DFN in order to minimize simulations time. Table 1 summarize the geometry used in the modelling. It has to be taken into account that the SRM sample sizes used in this study are not large enough to reach the REV (Esmaieli et al. 2010). Considering all lithological units, P32 in function of a cubic sample width converges to a mean value in samples with over 10 m width. Table 1 DFNs and SRMs dimensions for the block geometrical analysis

Lithology Dacite

DFN Average spacing (m)

SRM Width (m)

SRM Particle diameter (m)

SRM Number of particles

0.16

1.6

0.040

166,845

0.70

Diorite

CMET HW

0.22

CMET FW

4.2

7.0 2.1

0.13

0.174

169,738

0.052

1.3

171,704

0.033

159,375

Characterization of rock mass behaviour

For each test, the pre-peak rock mass parameters and stress-strain behaviour are registered. Direct tension tests and triaxial tests are performed to characterize the SRM response of the rock masses. The testing directions include two orthogonal horizontal directions (direction 1 refers to the E-W direction, direction 2 refers to the N-S direction) and the vertical direction (direction 3) for each lithology. Figure 2 presents the stress-strain behaviour for triaxial tests in the three testing directions for the four lithologies. It is observed that Dacite presents a degree of anisotropy. As expected, the peak strength increases with confinement. However, the post-peak behaviour tends to be more brittle as confinement increases in Dacite, while in the others lithologies post-peak behaviour is brittle only in low confinements. 4.3

Characterization of rock mass parameters and comparison with classification systems

Table 2 summarize the main geotechnical parameters of each lithological unit. These data is used to compare SRM results with estimations based on the classification systems. Table 2 Geotechnical parameters for each lithology (Brzovic 2001)

Lithology

Ei (GPa)

UCS (MPa)

Diorite

45

140

Dacite

CMET FW

CMET HW

182

43 55 55

mi

GSI

D

70 - 90

0

167

10.6

75 - 90

97

12.1

70 - 85

121

9.2

12.1

70 - 90

RMR

0

72 - 77

0

66 - 72

0

68 - 72 66 - 74

Caving Mechanics

Figure 2 Axial stress–strain and volumetric–axial strain curves for triaxial tests in three directions

SRM envelopes are compared with the resulting envelopes obtained from a previous investigation in El Teniente, where a numerical model based in back analysis of documented collapses at Esmeralda was developed (Pardo et al. 2012). Also, the peak strength envelope of the rock mass is compared with the one proposed by Hoek et al. (2002):

(2)

Where (3) GSI: Geological Strength Index, D: factor of disturbance (blast damage and stress relaxation), σc uniaxial compressive strength of the intact rock material and, mi material constant. Figure 3 presents a comparison of peak strength envelopes estimated with different methodologies. The strength envelopes estimated with the SRM technique are non-linear and they are not consistent with the envelopes estimated with the Hoek-Brown criterion based on the parameters of Table 2. None of the envelopes are close to the envelope estimated with the minimum GSI. However, SRM envelopes are similar to the envelopes estimated for the mine scale elastic-plastic numerical modelling study (Pardo et al. 2012). Table 3 shows a summary including Hoek-Brown and Mohr-Coulomb parameters adjusted in RocData. The range of values includes results in the three tested directions. These values indicate that the GSI resulting from SRM modelling is between 41 and 60; therefore all rock masses have a fair quality. Geological information of El Teniente mine indicates that El Teniente rock masses have a good to very good quality.

183


Figure 3 Comparison of peak strength envelopes for each lithological unit Table 3 Geotechnical parameters adjusted from SRM modelling for each lithological unit

Lithology

GSI

c (MPa)

φ (°)

Dacite

19.4 – 22.9

55.5 – 57.7

3.7 – 4.1

50.0 – 53.2

CMET FW

12.8 – 15.7

51.8 – 54.5

2.6 – 2.9

42.6 – 45.0

Diorite

CMET HW

11.3 – 13.1 11.0 – 13.7

45.3 – 47.3 48.6 – 52.2

2.6 – 2.7 2.7 – 2.8

42.6 – 44.7 43.2 – 44.5

Uniaxial compressive strength and Yong’s modulus of the rocks masses are compared with empirical formulas based on classification systems:

1. Uniaxial compressive strength of the rock mass (Table 4). Empirical formulas proposed by Hoek

et al. (2002) and Hoek & Brown (1988) are used to compare modelling results. These formulas are based on s and a defined in equation (4), and RMR, the rock mass rating of Bieniawski (1974).

2. Young’s Modulus of the rock mass (Table 5). Empirical formulas proposed by Serafim & Pereira (1983) and Hoek & Diederichs (2006) are used to compare modelling results. These formulas are estimated using the intact rock modulus (Ei). The Young’s modulus estimated with SRM approach has a slight dependence on the minor principal stress and no significant evidence of anisotropy.

184

Caving Mechanics Table 4 Comparison of uniaxial compressive strength of the rock mass using different methods

Table 5 Comparison of Young’s Modulus of the rock mass using different methods

4.4

Effect of preconditioning by hydraulic fracturing

To study the effect of the hydraulic fracturing on the rock mass behaviour, sub-horizontal fracture planes were included explicitly in the SRM sample. Figure 4 presents the stress-strain curves resulting from triaxial tests in directions 1 (E-W), 2 (N-S) and 3 (vertical direction). Comparing these results with stress-strain curves in Figure 2, it is clear the impact on elastic parameters of the sample tested in the vertical direction. These results can be complemented with other studies that have shown the relevance of preconditioning by hydraulic fracturing in cave propagation and primary fragmentation (Sánchez Juncal et al. 2014).

Figure 4 Stress-strain curves resulting from simulations with fractures due to preconditioning

5 Conclusions The SRM approach has been used to characterize the behaviour of four lithological units from El Teniente mine. The results are promising and show an improvement compared to those reported in the previous papers (Vallejos et al. 2013; Mas Ivars et al. 2013). The main advantage of the SRM approach is that it allows estimating the behaviour of a synthetic sample as a result of a geotechnical and geological characterization, and not as a result of a back analysis or

185

Caving 2014, Santiago, Chile empirical formula. Results are consistent with the fundamental principles of rock mechanics. Nevertheless, some limitations have to be overcome, such as the fact that it is not possible to fit the Poisson ratio keeping a brittle post-peak behaviour. Furthermore, the particle assembly is calibrated to the strength and elastic behaviour of the average rock blocks in the rock mass; therefore the rock block scale effects is not captured explicitly. SRM standard tests show acceptable results scaling veins macro-parameters to the average length within the DFN. Due to all veins having the same macro-parameters, the only differences between models for each lithology are intact rock micro-parameters and the influence of the vein network geometry (DFN). These assumptions results in envelopes comparable with another numerical modelling estimation. The effect of the hydraulic fracturing on the rock mass behaviour, resulting from SRM modelling, show a high potential for the SRM approach to evaluate the effect of including new fractures due to pre-conditioning in the field. It is recommended to further investigate the effect of increasing the SRM sample size and also make additional effort in complementing laboratory and field data to support the numerical modelling results.

Acknowledgement The authors acknowledge The El Teniente Division of Codelco-Chile for their permission to publish the data and for supporting this work. This study was commanded by API T10E202 of Codelco-Chile (contracts 4501127645 and 4501142662) and by FONDECYT Initiation Grant #11110187. Caroline Darcel, Romain Le Goc and Lauriane Bouzeran from Itasca Consultants SAS are also acknowledged for their contribution to this work.

References Board, M & Pierce, M 2009, ‘A Review of Recent Experience in Modeling of Caving’, International Workshop on Numerical Modeling for Underground Mine Excavation Design: Proceedings of the 43rd US Rock Mechanics Symposium, Asheville, United States. Brzovic, A, Schachter, P, de los Santos, C, Vallejos, J & Mas Ivars, D 2014, ‘Characterization and Synthetic Simulations to Determine Rock Mass Behaviour at the El Teniente Mine, Chile. Part I’, Proceedings of the 3rd International Symposium on Block and Sublevel Caving, Santiago, Chile. Brzovic, A & Villaescusa, E 2007, ‘Rock mass characterization and assessment of block-forming geological discontinuities during caving of primary copper ore at the El Teniente mine, Chile’, International Journal of Rock Mechanics and Mining Sciences’, vol. 44, pp. 565-583. Brzovic, A 2001, ‘Fundamentos geológicos para un sistema de clasificación geotécnico del macizo rocoso primario, mina El Teniente’, Internal report SGL-187/2001 of the Superintendence Geology, CODELCO-Chile El Teniente Division [in Spanish]. Brzovic, A 2009, ‘Rock mass Strength and Seismicity during Caving Propagation at the El Teniente Mine, Chile ‘, In: Proceedings of 7th International Symposium on Rockburst and Seismicity in Mines (RaSiM07). Tang, C.A. editor. Dalian University. (2) 838-52. Dershowitz, W & Einstein, H 1988, ‘Characterizing rock joint geometry with joint system models’, Rock Mechanics and Rock Engineering, vol. 21, pp. 21-51.

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Caving Mechanics Hoek, E & Diederichs, M 2006, ‘Empirical estimation of rock mass modulus’, International Journal of Rock Mechanics and Mining Sciences, vol. 43, pp. 203-215. Hoek, E, Carranza-Torres, C & Corkum, B 2002, ‘Hoek-Brown failure criterion-2002 edition’, Proceedings of the fifth North American Rock Mechanics Symposium, Toronto, Canada, vol. 1, pp. 267– 273. Hoek, E 1994, ‘Strength of rock and rock masses’, ISRM News Journal 2, pp. 4–16. Hoek, E & Brown, E 1988, ‘The Hoek–Brown failure criterion – a 1988 update’, Proceedings of the 15th Canadian Rock Mechanics Symposium, pp. 31–38. ISRM 1981, ‘Suggested methods for the quantitative description of discontinuities in rock masses’ in Rock characterization, testing and monitoring, ISRM Suggested methods, (edited by ET Brown), Pergamon Press, pp. 3-52. Itasca Consulting Group, Inc. 2012, ‘SRMLab version 1.7’, Minneapolis, United States. Itasca Consulting Group, Inc. 2008, ‘PFC3D – Particle flow code in 3 dimensions, Version 4.0’, Minneapolis, United States. Laubscher, D 1977, ‘Geomechanics classification of jointed rock masses – mining applications’, Trans. Inst. Min. Metall., 86, A1-A8. Machuca, L & Villaescusa, E 2011, ‘Summary of intact rock property values for Codelco Chile–División El Teniente’, Western Australian School of Mines - Geomechanics laboratory report to División El Teniente, Codelco Chile, API T10E202. Mas Ivars, D, Bouzeran, L, Le Goc, R & Darcel, C 2013, ‘Final report on Synthetic Rock Mass (SRM) Fragmentation Analysis – El Teniente March, 2013’, Itasca Consulting Group report to División El Teniente, Codelco Chile, API T10E202. Mas Ivars, D, Pierce, M, Darcel, C, Reyes-Montes, J, Potyondy, D, Young, P & Cundall, P 2011, ‘The Synthetic Rock Mass approach for jointed rock mass modeling’, International Journal of Rock Mechanics and Mining Sciences, vol. 48, pp. 219–244. Mas Ivars, D, Pierce, M, DeGagne, D & Darcel, C 2008a, ‘Anisotropy and scale dependency in jointed rockmass strength—A synthetic rock mass study’, Continuum and Distinct Element Numerical Modeling in Geomechanics: Proceedings of the 1st International FLAC / DEM Symposium, Minneapolis, United States, paper 06-01, pp. 231- 239. Mas Ivars, D, Potyondy, D, Pierce, M & Cundall, P 2008b, ‘The smooth-joint contact model’, Proceedings of the Eighth World Congress on Computational Mechanics and Fifth European Congress on Computational Methods in Applied Sciences and Engineering, Venice, Italy, paper a2735. Pardo, C, Villaescusa, E, Beck, D & Brzovic, A 2012, ‘Back Analysis of intensive rock mass damage at the El Teniente Mine’, CRC-Mining Conference, Brisbane, Queensland University. Pierce, M, Mas Ivars, D, Cundall, P & Potyondy, D 2007, ‘A synthetic rock mass model for jointed rock’, Proceedings of the 1st Canada-US Rock Mechanics Symposium, Vancouver, Canada, vol. 1, pp. 341-349. Potyondy, D 2012, ‘The bonded-particle model as a tool for rock mechanics research and application: Current trends and future directions’, The Present and Future of Rock Engineering, Proceedings, of the 7th Asian Rock Mechanics Symposium, Seoul, Korea, pp. 73-105.

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Potyondy, D 2011, ‘Parallel-bond refinements to match macroproperties of hard rock’, Continuum and Distinct Element Numerical Modeling in Geomechanics: Proceedings of the 2nd International FLAC / DEM Symposium, Melbourne, Australia, Paper 08-04, pp. 459-465. Potyondy, D & Cundall, P 2004, ‘A bonded-particle model for rock’, International Journal of Rock Mechanics and Mining Science, vol. 41, pp. 1329-1364. Rojas, E, Cavieres, P, Dunlop, R, & Gaete, S, 2000, ‘Control of Induced Seismicity at the El Teniente Mine, Codelco Chile’, Proceeding Massmin, Chitombo, G, editor, Brisbane, Australia, AusIMM, 777-781. Sánchez Juncal, A, Mas Ivars, D, Brzovic, A & Vallejos, J 2014, ‘Simulating the effect of preconditioning in primary fragmentation’, to be published in Proceedings: Eurorock 2014, Vigo, Spain. Serafim, J & Pereira, J 1983, ‘Considerations on the geomechanical classification of Bieniawski’, Proceedings of the International Symposium on Engineering Geology and Underground Construction, Lisbon, Portugal, vol. 1, pp. 33-44. Vallejos, J, Brzovic, A, Lopez, C, Bouzeran, L & Mas Ivars, D 2013, ‘Application of the Synthetic Rock Mass approach to characterize rock mass behavior at the El Teniente Mine, Chile’, Continuum and Distinct Element Numerical Modeling in Geomechanics: Proceedings of the 3rd International FLAC / DEM Symposium, Hangzhou, China, paper: 07-02. Yoshinaka, R, Osada, M, Park, H, Sasaki, T & Sasaki, K 2008, ‘Practical determination of mechanical design parameters of intact rock considering scale effect’, Engineering Geology, vol. 96, pp. 173-186.

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Fragmentation estimates using BCF software – Experiences and pitfalls J Jakubec, SRK Consulting Ltd., Canada

Abstract Fragmentation estimates are one of the key inputs for cave mine design. Block cave fragmentation (BCF) software has been the industry standard for decades. However, experience from several mines has shown poor correlation between the initial fragmentation estimates and reality. Realistic input parameters in the BCF software are key to realistic fragmentation estimates. Often, such parameters are based on drill core data, but correct assessment of rock mass parameters based on such data could be a challenging task. The author of this paper discusses some of the reasons for poor reconciliation and shares his experience and methodology with using BCF software. Experience has shown that BCF software may overestimate fragmentation because of conservatism during the feasibility stage, drill core bias, ignoring fines and weathering, and inadequate accounting of rock block defects. The quality of fragmentation predictions using BCF software can be improved significantly through careful evaluation of these factors.

1

Introduction

The BCF software was developed and introduced to the mining industry in the 1990s (Esterhuizen 1994; Esterhuizen et al. 1996). There have been several changes to the software code since then and currently the most up to date version is BCFV305. Although there are other techniques to assess block caving fragmentation, BCF software remains a proven and practical method that enables the rapid evaluation of different scenarios. However, the current general experience in the mining industry is that BCF software predicts coarser fragmentation than the actual fragmentation present. The author of this paper uses his experience from a number of operating cave mines to analyze and discuss potential reasons for such a discrepancy and to suggest solutions.

2

Fragmentation in block caves

During the rock mass caving process, the rock blocks are formed by four mechanisms: • Gravity liberation of existing blocks bounded by open or weakly healed joints. • Stress fracturing via intact rock or via rock block defects. • Dynamic impact breakage due to rockfalls. • Breakage during the communition processes in the cave. When describing the rock mass fragmentation in caving mines, three types of fragmentations are recognized: • In situ fragmentation. • Primary fragmentation. • Secondary fragmentation.

191


In situ fragmentation

In situ fragmentation is the block size distribution of naturally formed blocks that are bounded by open or weakly healed joints. This block size distribution would be formed by gravity as if the rock mass simply fell apart. No new stress fractures would be induced or considered. 2.2

Primary fragmentation

Beside gravity, the rock mass caving processes typically involve induced stresses acting in the cave envelope. Such stresses cause rock fracturing through intact rock or along the pre-existing defects, further reducing the in situ fragmentation. Laubscher (2000) defines primary fragmentation as the fragmentation of the rock as it parts from the surrounding rock mass. 2.3

Secondary fragmentation

Secondary fragmentation is the further break down of the rock as it moves down the draw column (Laubscher 2000). The primary blocks are subjected to communition processes within the cave, further reducing the primary blocks and generating fines. In a cave where an air gap is present, the primary blocks could also be reduced by dynamic breakage during rockfall and drilling.

3

Rock mass characterization

Two rock mass parameters that are critical to define for realistic fragmentation estimates are rock mass defects and rock strength. 3.1

Rock mass defects

Geologic processes prior to mining such as brittle deformation of and/or sedimentation can introduce defects that have variable geometry, continuity, shear strength, and cohesion. Such defects could significantly reduce the rock block or rock mass strength, especially in an unconfined situation (Jakubec 2013). Rock mass defect characterization should include a range of different scale structures, from large-scale structures through joints to rock block defects. 3.1.1

Large-scale structures

Large-scale structures in the context of this paper include all types of large rock mass structures such as faults, shear zones, or closely spaced joint clusters. Although large-scale structures do not typically influence rock fragmentation processes, they are sources of fines (typically, fragments are smaller than 0.001 m3) and hence should be defined for fragmentation analysis. Typical rock mass in caving operations includes 3–15% of in situ fines contained within the largescale structures (Figure 1). 3.1.2

Open and cemented joints

It is typical industry practice, when describing a drill core or during the mapping of tunnel walls, to describe defects that do not have bonding cement as open joints (Figure 1). In the context of fragmentation analysis, the open joints should have sufficient continuity that they form an in situ block — in other words, they should be block-bounding joints. Often, the joints are cemented with mineral infill of variable strength ranging from very weak to strong with strength similar or occasionally exceeding the intact rock strength

192

Fragmentation (Figure 2). Fragmentation analysis using BCF software for rock masses that do not have open or weakly cemented block-bounding joints is not reliable. In such cases, a more sophisticated analysis such as a synthetic rock mass (SRM) approach should be used (Jakubec et al. 2012).

Figure 1 Example of fines generating fault (left) and open joints (right) in the drill core

3.1.3

Rock block defects

A special category of defects are small discontinuous fractures and veins or micro-fractures (Figure 2). Such defects have limited continuity and are contained within the in situ blocks (Laubscher & Jakubec 2001).

Figure 2 Example of cemented joint (left) and rock micro-fractures (right)

The description of such defects and the challenges in their characterization have been discussed in several papers (Jakubec 2013). To avoid “double dipping” and incorrect material strength characterization, the micro-defects contained within the hand specimen and affecting laboratory unconfined compressive strength (UCS) tests should not be taken into the subsequent IRS strength reduction process. The defects that do not affect UCS tests must be included in rock block strength reduction. 3.2

Rock strength

Both primary and secondary fragmentation are influenced also by rock strength, stresses acting on the rock in the cave back, and point loading in the cave column. The following categories of rock strength are recognized: • Intact rock strength. • Rock block strength. • Rock deformation.

193

Caving 2014, Santiago, Chile The MRMR classification system developed by Laubscher & Jakubec (2001) recognizes and addresses the need to include all important defects in the rock mass classification and rock mass strength. 3.2.1

Intact rock strength (IRS)

Intact rock strength in terms of BCF analysis is defined as the unconfined compressive strength of the rock specimen that can be directly tested, including all internal micro-defects contained in the specimen. 3.2.2

Rock block strength (RBS)

Rock blocks bounded by open or cemented joints will have a lower strength than the intact rock strength if their dimensions exceed approximately 50 mm. Discontinuous joints, fractures and veins that terminate within rock blocks and do not take part in the formation of blocks will further reduce the rock block strength. The intact block strength in the BCF software is equivalent to rock block strength in terms of the Laubscher-Jakubec MRMR classification. The concept of strength adjustment to rock block and rock mass, and strength reduction of rock block strength is illustrated in Figure 3.

Figure 3 Example of RBS concept (left) and RBS reduction from the Chuquicamata mine in Chile (after Jakubec et al. 2012)

3.2.3

Rock failure criterion

Rock mass failure characteristics are also included in the BCF software approach via Hoek & Brown mbvalue for rock mass. The Hoek & Brown criterion is used to determine the triaxial strength of the rock and the value may be estimated using published tables for typical materials or determined by laboratory tests.

4

Block caving fragmentation

The BCF software is a commercially available computer program authored by Dr. G. Esterhuizen and has been used by the cave mining industry for the past two decades. “The program is based on analytical and empirical rules describing the fragmentation processes and factors that play a role in block cave fragmentation. “ (Esterhuizen 2005) The program consists of three main modules: • Primary fragmentation module. • Secondary fragmentation module. • Hang-ups module.

194

Fragmentation The latest available version is BCFV3.05 from 2005. It is not an objective of this paper to describe in detail the BCF engine or algorithms and the author of this paper recommends that the reader refers to the BCF Technical Reference and User Guide (Esterhuizen 2005). 4.1

BCF software input data

A comprehensive data set is required for all three BCF software modules and the input data are organized in three main groups: • Geology (or rock mass) input. • Cave input. • Draw input. The first two inputs are used to calculate primary fragmentation and the last input is used to calculate secondary fragmentation and conduct the hang up analysis. 4.1.1

Geology input

The primary fragmentation module requires input from two areas: geology and cave information. The geology input consists of: Rock mass information • Rock type – simple rock type name or abbreviation. • Rock mass rating MRMR – (Laubscher 1990). • Hoek & Brown m value for rock mass (mb) – from published tables or calculated from laboratory tests. • Intact rock strength (UCS in MPa). • Fracture/veinlet frequency/m, ff/m, (rock block defects). • Fracture/veinlet conditions (Laubscher 1990 joint conditions equivalent). • Intact block strength (same as rock block strength) – this value is calculated or can be manually inputted. In situ block bounding joint information • Joint set number – three sets define the block, an additional set will shape it; there is typically no material benefit to inputting more than five sets. • Joint dip – average and range. • Joint dip direction – average and range. • Joint spacing – average and range (minimum and maximum). • Joint condition – average and scatter (Laubscher 1990 joint conditions, 1-40). Joint orientation is typically obtained from stereonet analyses of joint data.

195


Cave input

The cave input table is the second input group of information that has to be completed in order to calculate primary fragmentation. The cave input data include: • Cave orientation – dip and dip direction of the caving face. • Stresses – dip and strike direction, and normal stress of the induced stresses in the cave back (values could be estimated using the BCF software manual guidelines or obtained from numerical modelling analyses). • Stress spalling and fracturing – stress fractures may form in the cave face if the stresses are high enough to cause compressive failure of the rock. Spalling provides a second option to model the effects of stress fracturing. The amount of spalling is entered as a fixed percentage of the volume of rock. 4.1.3

Draw input

Draw input data are required to model secondary fragmentation and hang up analysis. The data include the following: •

Primary fragmentation file.

•

Draw data: o Draw height. o Maximum caving height. o Draw width. o Swell factor. o Rock density. o Additional fines. o Rate of draw.

•

5

Draw bell size.

Experiences of using BCF software

As mentioned before, the general experience in the mining industry is that the BCF software predicts coarser fragmentation than the actual fragmentation present. This view is supported by observations from several caving operations that the author has been involved with. Experience shows that there are several potential reasons for this discrepancy: • Focus on an oversize portion (+2 m3) of the block distribution curve during the feasibility study, hence a coarser, more conservative curve is selected as the base case. • The quality of the input data, which are often based on drill core with orientation bias and under sampled joints populations.

196

Fragmentation • Ignoring fines sourced from faults, shear zones, and joint clusters. • Not considering the weathering effect on fragments in the cave zone. • Ignoring portion of the joint populations when selecting major joint sets and not reconciling with the ff/m value measured in the core. • Ignoring rock block defects. 5.1

Focus on course blocks

The range of input data results in a range of fragmentation curves for the same rock mass. It is often the tendency to select the more conservative analysis to acknowledge uncertainty during the feasibility study stage because coarse blocks during the cave mining operation can cause operational difficulties that result in costly delays, due to hang ups and secondary blasting. Although this is a valid concern, inevitably coarse fragmentation distribution curves are selected as the base case scenario and this can potentially result in a discrepancy with the actual experience during production. Typically, finer fragmentation realization is not considered as important. However, it is important to consider both sides of the possible range: a coarse curve for material handling and production rates and a fine curve mainly for drawpoint spacing. 5.2

Orientation bias of the drill core

Preferred drill hole orientation can under sample joints populations resulting in the underestimation of the ff/m value and/or the joint set number, see Figure 4. It is important to orient drill holes in different directions to capture a complete family of joints. It is also important to apply a Terghazi correction in the stereoned analysis to minimize orientation bias. Acoustic or optical televier data could also improve understanding of the joints populations.

Figure 4 Example of drill hole orientation bias

5.3

In situ fines

In situ fines are sourced from large-scale structures such as faults, shear zones, and joint clusters. Generally, fragments smaller than 10 cm3 are to be considered as fines, which means that rock with a ff/m value of 10 can produce significant amount of fines. It is relatively easy to estimate amount of in situ fines if core logging did not ignore large-scale structures. Experience shows that typical caveable rock masses have 3–15% in situ fines.

197


Weathering of the caved rock

Weathering susceptibility and oxidization of the rock has to be considered. Broken rock in the cave zone usually resides in the cave for several years with constant attrition and often water migration. Some rocks, such as kimberlites and serpentinite, are susceptible to weathering, while oxidization is typical for sulphidic ore. Both processes could generate considerable amount of fines that is not accounted for in the fragmentation analysis. 5.5

Under sampling joints populations

The under sampling of joints populations is considered as one of the leading causes of discrepancy between analysis and reality. Joint sets are often selected graphically on stereonets using “fences” around the joint pole concentrations (Figure 5a). Plotting poles revealed waste number of joints outside fences.

Figure 5 Selected joint sets based on stereonet without poles (a) and with poles (b)

The problem is that a large amount of more randomly distributed and oriented joints is not accounted for. If a fracture frequency check as discussed above is not undertaken, the fragmentation curves could be very coarse and far from reality. Depending on the structural character of the rocks there are two main ways to rectify this. One way is to decrease the joint spacing for one or more joint families to match the expected ff/m value. The other way is to create a new joint set and assign the spacing to match the ff/m value. The BCF software tool for verifying the ff/m value is powerful and should be used to make sure that all joints are accounted for. 5.6

Ignoring rock block defects

Another common reason for underestimating fragmentation distribution curves is ignoring rock block defects. Currently, the only classification system that includes rock block defects is Laubscher & Jakubec MRMR (2001). If logging is done for example using Beniawski RMR or Hoek’s GSI, rock block defects are ignored and rock block strength will be overestimated. A comparison of primary fragmentation results with and without defects is illustrated in Figure 6.

198

Fragmentation

Figure 6 Comparison of fragmentation curves with and without defects

6

Conclusions

The BCF software remains one of the leading practical tools to assess block caving fragmentation. Despite its limitations, it enables the rapid assessment of different scenarios and investigation of sensitivity to individual parameters. Because of the complexity of fragmentation analyses, it is very difficult to guess the outcome by changing various parameters. For example, for a strong rock mass in a low stress environment the stress level variation will probably not produce realizations that are materially different. On the other hand, for rock masses where the stress and rock block strength values are close, a small change in stress level could produce significantly different outcome. It is difficult to comment on the accuracy of the fragmentation analysis. The accuracy is different for each case and dependent on input data quality, stress strength relationship, etc. In the case of the Chuquicamata underground study, two approaches, BCF and SRM combined with numerical analysis were used and yielded similar results (Figure 6). One of the biggest challenges for realistic fragmentation estimates is to correctly define block bounding joints. In cases where such joints are cemented with strong infill, a more sophisticated approach such as SRM should be used. For examples, the SRM approach in fragmentation was discussed in papers by Jakubec et al (2012) and by Jakubec (2013). As in any analysis, it is important to have realistic and complete data for input in order to produce realistic fragmentation estimates. This paper illustrates some of the reasons for the discrepancy experienced by the mining industry between the estimated and actual fragmentation. However, caution has to be exercised always to manage expectations. None of the techniques can and most like will not predict fragmentation with the often expected high degree of accuracy. The geological nature of the orebodies is too complex and it is not realistic to capture such complexity in the analysis without calibration to the real data. However, by considering all the available information we can produce estimates that are roughly right and not precisely wrong.

Acknowledgements The author would like to thank Dr. Gabriel Esterhuizen for his valuable comments, and Ms. Van Ngo and Ms. Sophia Karadov for their assistance in preparing the document for publication.

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Caving 2014, Santiago, Chile References Esterhuizen GS 1994, ‘A model for predicting block cave fragmentation’, in Application of Numerical Modelling in Geotechnical Engineering, South African National Group of the ISRM, Pretoria, South Africa, September 1994, pp 147-151. Esterhuizen, GS, Laubscher, DH, Bartlett, PJ & Kear, RM 1996, ‘An expert system approach to predict fragmentation in block caving’, Massive Mining Methods, South African Institute of Mining and Metallurgy, Colloquium, pp. 2-11. Esteruhisen, GS 2005, A program to predict block cave fragmentation - Technical reference and user’s guide. Hoek, E and Brown, ET 1997, ‘Practical estimates or rock mass strength’, Int. J. Rock Mech. Min.g Sci. & Geomech. Abstr., vol 34, Nº8, pp. 1165-1186. Jakubec, J, Board, M, Campbell, R, Pierce, M, Zaro, D 2012, Rock mass strength estimate—Chuquicamata case study, in Proceedings MassMin 2012, June 10-14, Canadian Institute of Mining, Metallurgy and Petroleum (CIM), Sudbury, Canada, CD-rom only. Jakubec, J 2013, ‘Role of Defects in Rock Mass Classification’, Ground Support 2013 Conference, ACG, Perth, Australia. Laubsher, DH, ‘A geomechanics classification system for the rating of rock mass in mine design’, J.S. Afr. Inst. Min. Metall., vol. 90, no. 10. Oct. 1900. pp, 257-273. Laubscher, DH 2000, ‘A practical manual on block caving’, International Caving Study (1997-2000), University of Queensland, Brisbane, Australia. Laubscher, DH and Jakubec, J 2001, ‘The MRMR Rock Mass Classification for Jointed Rock Masses’, in Underground Mining Methods: Engineering Fundamentals and International Case Studies, eds. W.A. Hustrulid and R.L. Bullock, Society of Mining Metallurgy and Exploration, SMME, pp. 475–481.

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Fragmentation

An alternative approach to verifying predicted fragmentation in weak rock RN Greenwood SRK Consulting Inc., Canada BN Viljoen SRK Consulting (Canada) Inc., Canada

Abstract Fragmentation of ore and waste rock in block cave operations influence several aspects of mine design including draw-point spacing, dilution entry, secondary blasting, and material handling systems. Current accepted practice to estimate fragmentation and expected block size distribution includes empirical and numerical analyses, such as Block Cave Fragmentation software (BCF), discrete fracture network, and particle flow code. BCF is the most widely used and generally accepted numerical method to determine potential fragmentation within hard rock environments with high stress levels. However, the considered greenfields caving project, which is in a highly variable and relatively weak rock mass, has required an alternative approach to be compared with fragmentation estimates from the BCF program. A 5 x 5 observational matrix combines observations from drill core photographs of brokenness/breakability with weakening alteration/rock strength. The result is a fragmentation point estimate o for the percentage of rock not passing 0.3 and 1.0 m3 (mine-specific requirements for the materials handling system). The evaluation results show the spatial distribution of the fragmentation estimates across the project area as compared to the fragmentation estimates based on the more conventional geotechnical inputs in the BCF analyses.

1 Introduction Prediction of rock mass fragmentation is used, among other things, to select mobile equipment, design material handling systems and to estimate draw-point spacing, attrition, and draw control. It is also used to budget for secondary rock breaking. Primary fragmentation is related to the rock fabric, condition of in situ rock blocks, and induced stresses, while secondary fragmentation considers comminution of the primary blocks as they are drawn down through the cave and finally report to the production level draw-point. A range of industry accepted methods are available to predict primary and secondary fragmentation. These typically include software packages developed from analytical and empirical rock engineering principles and refined through benchmarking studies and visual assessments. As with many early stage projects, data sources are typically limited to drill core logging, downhole geophysics and laboratory testing of core samples. Mapping in early stage or exploration drives is not always available. This paper reviews an alternative fragmentation assessment conducted for a greenfields caving project in a highly variable and relatively weak rock mass, where the BCF fragmentation prediction was coarser than expected based on the observed drill core condition. Predicted oversize ore negatively impacted on the material handling system design and an alternative assessment was developed to predict the expected the size distribution. This approach is considered suitable as an alternate estimation methodology in datalimited projects where core photographs are the primary data source used.

2

Numerical fragmentation prediction

In most established methods of fragmentation prediction, generalized rock mass properties are used as inputs to predict the primary and secondary fragmentation. Data from core logging or mapping is processed

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Caving 2014, Santiago, Chile to identify the type and orientation of discontinuities present (e.g., joints, cemented features, veins, and micro-defects), joint properties (e.g., frequency or spacing, roughness, infill, and persistence) and the intact rock strength. Data is consolidated to produce a set of inputs that is reasonably representative of the entire caving area (at the coarsest level) or individual geotechnical domains. Drill core logging data is typically the most common source of information and can be collected to a high level of detail including many parameters used as input to various classification schemes, such as in Laubscher RMR (1990), Jakubec IRMR (2000), Call Nicholas Core2Frag (2004), and Bieniawksi RMR (1989). Early stage projects typically do not have this luxury, and despite the large volume of data points potentially available, core logging data is limited in value providing small, three-dimensional samples of the rock along many one-dimensional lines through the rock mass. This data is useful to establish dominant joint orientations and spacing; however, properties such as joint persistence, primary block size, and block aspect ratios can only be inferred but not directly measured. The presence of micro-defects is often misinterpreted especially if drill core does not separate during the drilling and handling process. Specific tests have been developed to estimate the intensity and strength of micro-defects in core. Stresses within and around the cave can also influence the caving process and fragmentation. During the early stages of a project the virgin stress state is often uncertain and assumptions are based on global stress databases (e.g., World Stress Map, Heidbach et al, 2008) or estimated based on the regional tectonic and structural setting. Induced stresses are then derived for the perceived current stress state and the planned cave geometry. 2.1

Block cave fragmentation

BCF software (Esterhuizen, 2005) is a widely used fragmentation prediction tool with generally accepted results. Primary fragmentation is derived from joint spacing, orientation, and joint conditions. Intact rock strength, primary block dimensions, micro defects, and induced stresses determine the secondary fragmentation. BCF was developed and calibrated for caving mines in hard rock environments where joints, stress, and rock strength are the main contributors to fragmentation. The algorithms were not calibrated for weaker rock caves where altered weak rock tends to break up with minor disturbance. The software is most useful for analysis in cases where poor fragmentation is expected. This is, when rocks are highly jointed, fragmentation is unlikely to be an issue, and BCF will simply confirm that fine fragmentation can be expected (Esterhuizen, 2013). BCF requires explicit inputs with limited allowance to account for variability. Inputs to the fragmentation prediction are normally a generalization of the rock mass and geotechnical properties with a set deviation. Mine wide data is applied and localized variability is often overlooked. The unique properties associated with certain rock types or geotechnical conditions are not considered in the evaluation. An additional review of the BCF fragmentation results was requested to re-assess fragmentation predictions and over size.

3

Alternative fragmentation assessment

At the project site, a large number of diamond drill holes were completed during the exploration phase of the project to define the extent of the deposit and develop a resource model. The recovered core was logged geologically and geotechnically by on-site staff according to the various owners’ procedures. This procedure has resulted in data of variable quality, with geotechnical parameters related to multiple rock mass classification schemes. High quality core box photographs, and more recently in split-tube photographs, have also been collected for the majority of the project drilling.

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Fragmentation Figures 2.1 and 2.2 provide a comparison of core degradation through separation along pre-existing cemented features or discontinuities. The samples also show formation of new mechanical breaks. The core in Figure 2.1 was considered representative of the in situ rock, while the core in Figure 2.2 was considered more representative of fragmented ore in an established cave after being subjected only to standard triple tube drilling and handling practices.

Figure 2.1: Photo of core in split tubes during geotechnical logging

Figure 2.2: Photo of core in the core box after geotechnical logging

3.1

Core photograph re-logging

The difference in core condition highlighted in Figures 2.1 and 2.2 presented an opportunity to re-evaluate the expected fragmentation and size distribution. A concept was developed in which the core photos were individually reviewed and used to predict the perceived condition of the ore at the drawpoint based on the developed parameter descriptions. Re-logging was based on the assumption that all core was handled in a similar manner and potential inconsistencies had no insignificant influence on the interpretation. The following conditions were applied for the re-logging:

• Brokenness/breakability is the degree to which the rock was broken, irrespective of whether the discontinuities were open joints, drilling-induced fractures, or mechanical breaks. Brokenness/ breakability was defined by the intensity and spacing of discontinuities and the length of intact core.

• Alteration/hardness is the degree of core weathering or deterioration and the perceived hardness. The

rating was quantified based on the visual weathering or weakening of the rock, the amount of fines, and the rock hardness, based on experience with the core and similar material.

• Fragmentation is the extent to which caved rock was expected to fragment, based on the core brokenness, degree of alteration, and fines content.

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Caving 2014, Santiago, Chile The photo re-logging was not based on estimating measurable numbers; rather than attempting to derive an accurate fragmentation analysis, the process was used to qualify the fragmentation distribution throughout the deposit. The size distribution was considered in terms of the percentage of core that would not pass specific dimensions of 300 or 1,000 mm. These dimensions were guided by the infrastructure design of grizzlies, conveyors, and crushers. The core from 10 selected holes was evaluated and the brokenness/breakability, alteration/hardness, and fragmentation were rated individually. The results with core box photographs were used as case examples to establish the relationship between the input fields (brokenness/breakability and alteration/hardness) and fragmentation in a 5 × 5 matrix (Figure 2.3). This illustrates the expected core condition for the various ratings. The matrix was used as a template to guide further re-logging and to maintain standardized ratings using the brokenness/breakability and alteration/hardness ratings as the input fields to predict the expected fragmentation and size distribution.

Figure 2.3 Core photographs representing brokenness/breakability, alteration/hardness, and fragmentation ratings

3.2

Fragmentation assessment

The re-logging procedure was then applied to assess fragmentation in and around the deposit. The selection of drill holes to be reviewed was guided by the position of the cave footprints and the caving influence zone. The core photographs from 75 drill holes (8,727 core box photographs) were re-logged. Each core box was considered as a logging interval (a single rating represents the entire box) and the interval was spatially referenced by depth and drill hole. This allowed any further assessment to be spatially constrained to a horizon of interest (e.g., a production level) or to a particular domain or lithology within the cave footprint. The distribution of ratings (Table 3.1 and Figure 3.1) shows the number of boxes for each rating and the percentage of all the logged boxes.

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Fragmentation Table 3.1 Summary of photo re-logging results

Rating

Brokenness/Breakability Number of Boxes

Percentage

106

1%

2

365

3

Alteration/Hardness

Fragmentation

Number of Boxes

Percentage

784

9%

4%

4469

2155

25%

4

4640

5

1461

1

Number of Boxes

Percentage

200

2%

51%

367

4%

2526

29%

1589

18%

53%

842

10%

4986

58%

17%

106

1%

1585

18%

The re-logging provided an overview of the core condition and individual rating distribution, but no measurable output in terms of size distribution or fines content. The experience gained during the relogging made it possible to relate the assigned ratings to a modified size distribution and a percentage of contained fines. 3.3

Size distribution

The size distribution analysis produced for this project was not the typical S-curve but rather indicated the percentage of ore expected to exceed specific dimensions. The assigned percentages (Table 3.2) are subjective estimates based on the percentage of core expected to exceed the 300 and 1,000 mm limits. The evaluation was based on the actual core length. Block aspect ratio was not considered as it cannot be reliably estimated from drill core. Table 3.2 Core size distribution for the range of brokenness ratings

Brokenness/ Breakability Rating 1

Percentage Not Passing 300 mm 90%

Percentage Not Passing 1000 mm 30%

2

70%

10%

3

40%

0%

4

0%

0%

5

0%

0%

The modified size distribution for the deposit was estimated based on the distribution of fragmentation (Table 3.1) and the estimated percentage of core that would not pass 300 mm and 1,000 mm (Table 3.2). A weighted average method was used to calculate the total percentage core that was expected not to pass the 300 or 1,000 mm limits. The values were calculated for each fragmentation rating and expressed as a percentage of the total core volume (Figure 3.2). The lines show the cumulative percentage of core that would not pass 300 and 1,000 mm, respectively.

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Figure 3.2 Percentage of rock not passing 300 and 1,000 mm

The results from the core photo review were then used to indicate the expected fraction of core not passing the two specified dimensions of 300 mm and 1,000 mm. These results were related back to size distribution curves by assuming an aspect ratio of one, and applying the calculated percentage passing to the respective block volume. Figure 3.3 presents the BCF distribution curves resulting from varying the input parameters within the identified limits of the rock mass (i.e., fine, average, and coarse) and the two points derived from the core photo review. The points estimated from the core photo review predict much finer fragmentation when compared to the BCF size distribution curve. 3.3

Fines estimation

For the purpose of this study, fines were defined as any material consisting of small pieces in which the volume of the individual fragments did not exceed 1 cm3. The percentage of fines content was estimated based on the alteration/hardness ratings, where more intense weakening alteration typically results in an increase in fines content. The alteration/hardness ratings do not specifically consider the fines content, but rather on the overall condition and appearance of the core. This resulted in varying percentage fines for core with the same alteration/hardness rating. A sensitivity analysis approach was adopted to test the potential range of contained fines, and the upper and lower limits (Table 3.3) were derived from visual assessment of the core photographs. These represent the likely maximum and minimum percentage fines material for each alteration/hardness rating. Table 3.3 Estimated fine material associated with alteration/hardness rating

Alteration/Hardness Rating 1 2 3 4 5

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Lower Fine Material Limit 0% 0% 5% 10% 50%

Upper Fine Material Limit 0% 5% 10% 50% 90%

Fragmentation

Figure 3.3 Comparison between BCF and core photo review predicted size distribution

The calculation of the expected fines was similar to the calculation of core size distribution. The upper and lower fine material limits were combined with the alteration/hardness ratings distribution to calculate the fine material volume. The expected amount of fines for each of the alteration/hardness ratings is a percentage of the total core volume (Figure 3.3).

Figure 3.3 Fine material distribution based on alteration/hardness ratings

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Further evaluation

Incorporation of the fragmentation ratings into the previously established geological model prevented the evaluation from being an isolated product. Each drilling interval contained data on the rock and alteration type, alteration intensity, fragmentation ratings, and spatial locations. Review of this information highlighted the relationship between the different rock properties and the expected fragmentation. Block modelling the fragmentation data creates a three-dimensional representation of the expected size distribution and fines content throughout the cave footprint, which can better manage potential bias resulting from drill hole spacing, orientation, and depth. Improved fragmentation distributions can be predicted for the entire mine, individual horizons, or specific locations within cave areas.

4 Conclusions A visual evaluation of core photographs was used to re-log drill core to determine both the brokenness/ breakability and the alteration/hardness of the rock. The observations were then applied to a rating system to determine the expected fragmentation. The percentage of material exceeding the specified size limits was based on the rating of the individual core boxes and the generalized physical characteristics of the specific brokenness/breakability classification. The method estimates fragmentation from the condition of actual core rather than an empirical estimate based on generalized properties of the rock mass. The re-evaluation outcome provided size distribution point estimates for comparison with the BCF analysis. The detailed review of the core after drilling and handling indicated the fragmentation predictions made by the BCF software may be biased towards “coarse fragmentation”. Upon comparison of the two analyses (photo re-logging and BCF), the fragmentation results determined from the photo re-logging are expected to be a more reliable predictor of secondary fragmentation in a well-established cave within a similar weak rock mass.

Acknowledgements The authors would like to thank Messrs. Chris Page and Jarek Jakubec for their input during the development and review of the alternative approach to fragmentation estimation. As well, thank you to Gabriel Esterhuizen for his availability to discuss the BCF program.

References Bieniawski, ZT 1989, Engineering rock mass classifications, New York: Wiley. Esterhuizen, GS, BCF Version 3.04 – A Program to Predict Block Cave Fragmentation - Technical Reference and User’s Guide, 2005. Esterhuizen, GS, Personal Communication, April, 2013. Heidbach, O, Tingay, M, Barth, A, Reinecker, J, Kurfeß, D, and Müller, B, 2008, The World Stress Map database release 2008 doi:10.1594/GFZ.WSM. Rel2008. Laubscher, DH 1990, ‘A geomechanics classification system for the rating of rockmass in mine design’, Journal of the South African Institute of Mining and Metallurgy, 90, pp. 257-273. Laubscher, DH & Jakubec, J 2000, ‘The IRMR/MRMR Rock Mass Classification System for Jointed Rock Masses’, SME 2000. Nicholas, DE, & Srikant, A 2004, ‘Assessment of primary fragmentation from drill core data’, In Proceedings of MassMin 2004, A. Karzulovic & M.A. Alfaro (Eds.), Santiago, Chile: Instituto de Ingenieros de Chile, pp. 55-58.

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Block Caving using Macro Blocks S Fuentes Codelco, Chile F Villegas Codelco, Chile

Abstract This paper presents the Block Caving Macro Blocks concepts and genesis. Chuquicamata Underground Project has considered using this configuration as an underground exploitation method in order to improve the management of mining operations and control of geomechanical problems.

1

Introduction

After more than 100 years of operations, the Chuquicamata open pit will reach the end of its economic life at the end of this decade. However, the geological exploration program that Codelco undertook some years ago showed a large amount of remaining resources beneath the final shell pit. Due to the great depth, experience suggested that the only feasible exploitation method could be an underground operation. The Chuquicamata Division commenced studies in early 2000 to assess the technical feasibility and economic potential of a massive underground mining operation, which could maintain the historical production level. As a result, the Chuquicamata Underground Mine Project has been designed to recover approximately 1,760 million tonnes of ore, with an average ore grade of 0.71% Cu, 512 ppm of Mo and 492 ppm of As, over a 39-year time horizon, preceded by a period of 8 years of construction and commissioning. Today, the feasibility study is completed. The construction of the permanent infrastructure (main access tunnels, intake tunnels, exhaust and shafts) commenced in 2012. The project´s master schedule considers intensive drifting and construction until 2018, followed by a 7-year ramp-up period to achieve the 140,000 tonnes/day design capacity. 1.1

Project location

The Chuquicamata Mine is located 1,240 kilometers north of Santiago, the Chilean capital, at 2,900 meters above sea level. The site is very close to the city of Calama; it can be reached by highway and the nearest airport, Aeropuerto El Loa, is only 20 kilometers (Figure 1). The mine is located in the heart of one of the most important copper-producing districts in the world. It started its current operations in 1910, although the high quality ore deposit has been well-known since preHispanic times.

2

Block caving macro blocks origin

The concept of Macro Blocks was analyzed and developed as an extension of the classical Block Caving method, incorporating the latest of the Codelco’s experiences in Block and Panel Caving exploitation, especially, in the mining operations management, geomechanics and ore body geometry related topics. 2.1

Necessity of new production area

The Chuquicamata Underground Mine planning considers a high production rate, 140,000 tonnes/day. To achieve this target, it is necessary to prepare a very large area of 102,000 m2 (400 draw-points) for the first

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Caving 2014, Santiago, Chile productive year and an average of 70,000 m2/year (270 draw-points/year) for the rest of the production horizon (Figure 2). Considering these huge preparation requirements and the experience at El Teniente Mine on how to deal with the interferences produced by simultaneous preparation and exploitation activities (Araneda & Sougarret 2007), the project team analyzed and defined the implementation of an exploitation configuration method, which facilitates the management of interferences during the mining cycle. This method divides or separates the area under development from the area being undercut/blasted and from the area under exploitation. Each area is independent and is called a “Macro Block” (Figure 3). Additionally, this productive configuration is perfectly suitable for the geometry of the Chuquicamata ore deposit (long and thin).

Figure 1 Project location – Chuquicamata Underground

Figure 2 New production and drawpoints number

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Figure 3 Macro block method (Fuentes 2009)

Figure 4 shows the sequencing of the mining activities, as considered by the Chuquicamata Underground Mine Projects, using the Macro Blocks (MB) configuration. The central MB is in its productive stage, while the surrounding Macro Blocks have initiated the undercutting process. It appears to be an easy definition, but actually the discussion with internal and external experts was hard and took a very long time to reach an agreement. Today, Codelco is starting to mine many other areas with this modular configuration approach, following the same principles considered for the Macro Blocks development.

Figure 4 Macro block method in Chuquicamata underground

2.2

Management of geomechanical problems

The main geomechanical problem in Block and Panel Caving is the production level collapse. The most used strategy to resolve this type of issue has been to abandon the collapsed area and re-initiate the caving

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Caving 2014, Santiago, Chile (Pardo & Villascusa 2012), which means to leave a separation “pillar” between the affected area and “fresh” production area (Figure 5). This concept has been incorporated into the MB design. A 30 m pillar has been left between each MB in order to prevent any geomechanical problem and, also, to have the option to isolate this sector to continue with the exploitation. Pillar dimensioning criteria consider the estimation of the local abutment stress, which means that the pillar is big enough to avoid the effects of the abutment stress generated by the previous Macro Block on the production drifts located in the following MB in the mining sequence.

Figure 5 Boundaries of production drifts collapsed 2001 – 2010

3

Macro Block, an exploitation unit

In order to maximize the productivity of each MB, each MB was defined as an independent exploitation unit with its own ore passes, crushing and accessing system. In other words, each MB is considered as “an independent mine” that produces mineral and delivers it to the main transportation system. In addition, the Chuquicamata Underground Mine Project has considered the pre-conditioning of the rock column in each MB to improve the caving propagation and extraction, maximizing the production capacity of each MB system. Figure 6 shows an MB unit. Each MB should have enough area to initiate a new caving, and also, due to the possible dilution of the neighboring blocks, each new MB has considered a special draw control policy, based on a strict planning and production operation, to avoid or control the possible lateral dilution.

4 Conclusions The outcomes of the analysis of using Macro Block in Block Caving operations can be summarized as follows: •

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Block Caving using Macro Block configuration as mining method gives flexibility in production planning, development of mining infrastructure and other operations, improving the likelihood of success.

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Figure 6 Macro Block exploitation unit, Chuquicamata Underground Project

•

Macro Blocks are suitable for the geometry of the Chuquicamata’s ore body (long and thin).

•

The modular design allows the incorporation of technological changes more easily.

•

Major collapses in the production areas could be easily isolated.

•

The activities of development, construction, and undercutting are separated from the production processes with the possibility of achieving an improved productivity compared to all other caving configuration.

Acknowledgement The authors would like to thank Codelco Chile for sponsoring the presentation of this paper and all those who helped us in some way to properly write this paper.

References Araneda, O & Sougarret, A 2007, ‘Lessons learned in cave mining: 1997 - 2007’, International Symposium on Block and Sub-Level Caving Cave Ming Keynote address. The Southern African Institute of Mining and Metallurgy, South Africa, pp. 57-71. Chitombo, GT 2010, ‘ Cave mining - 16 years after Laubscher’s 1994 paper ‘Cave mining state of the art’”, Sustainable Minerals Institute The University of Queensland, Australia, Perth, pp. 45-61. Fuentes, S 2009, Key Decisions Document. Pre-feasibility study, Vice-presidency of project CodelcoChile. (Internal Report, Codelco Chile). Jofre, J. Yanez, P & Ferguson, G 2000, ‘Evolution in panel caving underground and drawbell excavation El Teniente Mine’, MassMin 2000 Brisbane, Australia, pp. 249 – 260.

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Caving 2014, Santiago, Chile Laubscher, DH 1994, ‘Cave mining - The state of art’, The Journal of the South African Institute of Mining and Metallurgy. Moss, A, Diachenko, S & Townsend, P 2006, ‘Interaction between the block cave and the pit slopes at Palabora Mine In Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations, Johannesburg, SAIMM, Symposium Series S44, pp. 399–410. Pardo, C & Villascusa, H 2012, ‘Methodology for back analysis of intensive rock mass damage at the Teniente Mine’, 6th International Conference & Exhibition on Mass Mining, Sudbury, Canada.

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La Encantada: An inclined cave design J Valencia NCL Ingeniería y Construcción, Chile P Paredes NCL Ingeniería y Construcción, Chile F Macías First Majestic Silver Corporation, Mexico

Abstract Massive caving methods offer low cost and high productivity alternatives when they are able to fit the orebody’s geometry and geomechanical characteristics. Nevertheless, when the orebody does not fulfil the typical caveable characteristics and stopping methods are not applicable, difficulties appear in finding a caving method that suits the orebody’s geometry and competence. An alternative to this problem is the use of inclined caving methods. This paper presents the methodology and main results of the preliminary study for the exploitation of the Breccias sector in First Majestic’s La Encantada mine. Some parts of this sector have been previously mined with Cut & Fill methods, leaving several excavations in the orebody. Difficulties related to the low competence of the rockmass and lower grades have led to the need of exploring alternatives to the traditional Cut & Fill mining methods. Attending the orebody’s geometry in the sector, the use of low cost and non selective mining methods, such as caving methods, has been considered. The use of sublevel caving implicates the construction of several excavations in the orebody, attending to the orebody’s conditions related to its low competence and representing the higher costs within the caving methods. Two horizontal Block Caving layouts were proposed: (1) a 3.5 yd3 LHD operated offset herringbone layout and (2) a scrapper operated regular layout. Attending to recovery considerations, both layouts consider a small drawpoint spacing, which results in small pillars that would cause stability issues and high excavation density. Thus, an inclined caving layout is proposed, solving recovery, costs, productivity and stability problems.

1 Introduction La Encantada Silver Mine, from First Majestic Silver Corporation (FMS), consists of silver/lead/zinc oxidized mineral deposits located in the State of Coahuila, México, 708 kms northeast of Torreon (Figure 1). The mine comprises numerous mineral concentrations within the underground development area, including some exhausted deposits and additional geologic potential in other areas. The Breccias Sector consists of two breccias (Milagros and San Javier) and a partly mineralized magmatic intrusive. This sector has been mined by FMS and previous owners using selective mining methods and contains, therefore, several remaining tunnels and mine openings (Figure 2). Difficulties related to the low rock mass competence have led FMS to seek an alternative for the current cut & fill methods applied in the mine. Attending the massive shape of the orebody in the sector and lower grades, FMS has considered the use of non-selective massive mining methods, such as, caving methods. The following paragraphs describe the caving alternatives and the final inclined cave design proposed by NCL for La Encantada mine’s Breccias Sector.

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Figure 1 La Encantada mine site location

2

Background and considerations

The Breccias sector has the following characteristics (Table 1):

• Massive shaped and low silver graded orebody. • Low rockmass competence in the Breccias units (MRMR ranging from 21 to 34). • Regular to fair rockmass competence in the foot and hanging walls. • Orebody limited by topography, no waste overload. Table 1 Geotechnical parameters for the geological units

RMR Bieniawski

MRMR Laubscher

Milagros Breccia

39

21

Milagros Intrusive

60

34

Limestone

61

35

San Javier Breccia

49

23

Figure 2 a) Isometric view of remaining mine openings and orebodies (in red San Javier Breccia, in blue Milagros Intrusive and in green Milagros Breccia); b) schematic plan view of the geological units

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Future Projects These characteristics allow the use of caving methods given that:

• Self-supported methods require high rockmass competence in the orebody, in order to obtain fair recovery.

• Cut & Fill methods are oriented to high graded orebodies, due to their high costs and low productivities.

Attending the above mentioned, sublevel and block caving designs were proposed for the sector in order to define which method better complied the following:

• Represent a low cost and high productivity alternative. • Minimize the volume of mine openings in the low rockmass competence.

3 3.1

Method selection Sublevel caving alternative

The Sublevel caving method (SLC) consists of drilling and blasting the orebody in several superimposed levels and of caving. As a consequence, the waste overload from the roof and hanging wall occurs. This method has, therefore, an intensive excavation density in the orebody and, in the particular case of La Encantada mine, would implicate safety hazards for both people and equipment. On the other hand, the cost of a SLC alternative is higher than a Block Caving (BC) operation. Thus, due to safety hazards, related to the excavation density in the orebody, the fact that the operational cost of a SLC is the highest among caving methods and higher dilution potential; the SLC alternative was discarded for the Breccias sector. 3.2

Block caving alternatives

Block caving and its variants consist of generating a drawpoint base at the bottom of the orebody and undercutting its base in order to allow natural caving of the rockmass. Considering the previously exposed characteristics of the deposit, this method was selected due to the following reasons:

• The general shape, grade distribution and structural characteristics of the deposit fit the main characteristics of the caving (undercutting) variants.

• Minimizes the volume of excavations inside the orebody, reducing them to only the extraction and undercut levels.

• Represents the lowest cost and highest productivity option. Considering the rockmass classification (Table 1), and Laubscher’s abacus for drawpoint spacing (Figure 3), the suggested spacing for a horizontal BC layout should be between 7 and 13 m, considering a 3 m drawpoint width. Taking into account the fact that the MRMR of the Breccias orebodies (San Javier and Milagros) are in the lower limit of the rock mass class (4), a horizontal 10 m x 10 m layout was proposed, considering two tramming options: 1. Using 3.5 yd3 LHD, which is the largest loader that fits the geometry 2. Using scrappers

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Figure 3 Laubscher’s abacus for drawpoint spacing (Laubscher 1994)

Figure 4 Horizontal layout using LHD

Figure 5 Horizontal layout using scrappers

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Future Projects Figure 4 and 5 show the horizontal layout design for LHD and scrappers, respectively. It is possible to appreciate that high excavation density is needed to satisfy a 10 m x 10 m spacing draw pattern. Previously mentioned stability problems in the excavations of the Breccias sector make the horizontal BC layouts a high risk alternative in terms of the stability, due to the high excavation density that they implicate. Therefore, the horizontal layout BC alternatives were also discarded for the Breccias Sector. Once the typical caving variants had been discarded, the evaluation of an inclined cave layout was considered, due to the higher robustness that it represents, by reducing the excavation density at the same elevation, using several levels to build drawpoints. Despite the fact this is not a common arrangement, inclined cave alternatives have been proposed and implemented in several mining countries, such as, Australia, Canada, South Africa and Zimbabwe (Jakubec 1992; Carew 1992; Hangweg et al. 2004, Laubscher & Jakubec 2000; Jakubec & Laubscher 2012) resulting in successful experiences in some cases. Figure 6 shows the inclined cave layout proposed for La Encantada mine. This layout was selected due to the lower excavation density per level and, therefore, the higher stability that it represents. It is worth noting that, in contrast to the conventional BC, this design does not consider an undercut level. This relies on the fact that the poor rockmass quality would enable the orebody to cave with a long drawbell blasting configuration.

Figure 6 Inclined cave layout

4

Mine Design

A single lift inclined cave design was proposed for the deposit using the internal tool “Block Cave” for footprint elevation definition, based on Laubscher’s vertical mixing algorithm (Laubscher 1994). Figure 6 shows the general mine design, which considers four drawpoints’ lifts separated 10 meters in vertical and main drifts at every drawpoint elevation that connect these drawpoints with production drifts. The higher level is located 300 m below surface. Drawbells of 17 m height connect the different production levels. Production drifts have a total length of 26 m, 14 m of which are drilled to generate the drawbell. Ore is dumped in an ore-pass, transferring it to existing haulage levels. Tramming is performed by 3.5 yd3 LHD’s and haulage is divided between a conventional trucking system (800 tpd capacity) and a railway-shaft system (1,200 tpd capacity). Ventilation shafts located at the north east side inject fresh air from surface at the end of every main drift.

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Figure 6 Breccias sector general mine design

Figure 7 Breccias sector general mine design isometric views

5

Mining Sequence

An initial area of 4,800 m2 (within a total of 11,200 m2) allows a 17 m Hydraulic Radius (HR) footprint, which is enough to initiate caving at the mine, considering the caveability assumed for the Breccias sector. On the other hand, a 2 drawbells per month incorporation rate is proposed. This would allow the Breccias sector to achieve full production at 2,000 tpd during the second year of mine life. This 2,000 tpd production rate is limited by the processing plant capacity, further production rate increase could be studied for the same design if a plant capacity expansion is evaluated. Finally, the mining sequence proposed goes from the top north-east to the bottom south-west drawpoints, obeying the geometrical restrictions in order to avoid undercutting a non-opened drawpoint.

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Future Projects 6

Industrial scale test

In order to identify possible risks and further issues without significantly compromising capital and mine production, an industrial scale test is proposed for the inclined cave design. This test consists of building 2 drawpoints in the upper level and operate them for a given period of time in order to capture as much experience as possible to be used in the engineering project for the rest of the area. This test is not intended to produce any caving at all, but it will generate valuable information related with the construction of the drawpoints, support requirements and drilling and blasting procedures for undercutting, providing FMS with some experience in the subject.

Figure 8 Breccias sector industrial scale test proposal plan view

7 Conclusions Inclined caving methods represent a viable solution to low graded deposits with weak rock massesl as they are a flexible alternative when traditional caving methods’ implementation presents technical difficulties. The use of an inclined scheme allows defining an adequate drawpoint’s pattern and at the same time account for a reduced excavation’s density in a single level, improving stability conditions. In particular, Breccias sector’s technical challenge can be overcome by a simple solution using inclined caving methods. The implementation of an industrial scale test would be of great utility to improve both technical and economical information in order to develop the project. Finally, the use of an inclined caving layout represents a valid alternative for the application of caving methods in medium scale mining.

References Carew, TJ 1992, Footwall drawpoint caving at Cassiar Mine: In proceedings MASSMIN 92, Johannesburg, South Africa, pp. 295-301. Hannweg, L et al. 2004, Koffiefontein mine front cave – Case History: In proceedings MASSMIN 2004, Santiago. Chile, pp 393-396. Jakubec, J 1992, Support at Cassiar underground mine: In proceedings MASSMIN 92, Johannesburg, South Africa, pp. 111-123. Janelid, I 1978, Method for mining of rock or ore according to the block caving principle in massive formations. U.S. Patent 4,072,352.

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Caving 2014, Santiago, Chile Laubscher, DH 1994, “Cave mining - the state of the art”, The Journal of the South African Institute of Mining and Metallurgy, vol 94, Nº 10, pp. 279-293. Laubscher, DH 2012, Incline Cave Mining – A Viable Alternative to Horizontal Layout: In proceedings MASSMIN 2012, Sudbury, Canada. Laubscher, DH & Jakubec, J 2000, Block Caving Manual – Incline Cave, ICS 2000 internal document. NCL SpA 2013, “Proyecto de explotación sector brechas, Mina La Encantada. Estudio preliminar”, Study report for First Majestic Silver Corporation (In Spanish).

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Geomechanic Design

225

Geomechanic Design

Considerations for designing a geomechanics monitoring plan for each engineering stage AE Espinosa Codelco, Chile P Jorquiera, Codelco, Chile J Glötzl, Glötzl GmbH, Germany

Abstract Frequently, plans for monitoring mining geomechanics are designed with the aim of measuring deformations or displacements that allow early identification of the onset of potential instability. This approach is suitable for the control of risks pertaining to the field of geomechanics. For this purpose a methodological support is necessary to link with a systemic functional approach, the process from the definition of purpose to the performance evaluation and compliance targets. From the experience of the last ten years in the development of implementation plans and monitoring geomechanics in the El Teniente Division of CODELCO - Chile, this paper proposes a methodology that guides the development of a plan for implementation and monitoring geomechanics. The methodology applies particularly for each project with a clear focus on the applicability of the records obtained in stages of conceptual design, functional implementation, procurement start to evaluation of results and fulfillment of objectives. All this will be done to finally close the loop with a stage design which fits oriented to the utility for operation over the life of the mine. The methodology proposed here uses, as a structure for defining purposes and objectives, the different stages of the mining project engineering, ranging from all engineering stages and then binding steps with the start of production operations. Finally the result is a map that identifies the processes required to develop an implementation plan and monitoring geomechanics to be considered as a necessary requirement and functional utility for the safe performance of mining operations activities.

1 Introduction Normally, in relation to the geomechanical monitoring in underground mines, the plans, in such technical application, are indicated as tools to control losses caused by geomechanical instabilities which occur along the mining working. For the appropriate design, the objective has been mainly centered in the acquisition of proper data. The main focus should be on the use and the evaluation instead of just collecting data. One paradoxical aspect is that the design is directed form a model of behavior previously estimated and the expectation resides in the confirmation of the assumptions of the original design. If this occurs, it necessarily would be an invitation to modify the model in which such design was elaborated. However, understanding that monitoring (as a control) is only made once the processes are initiated, this could mean to implement modifications. In that way, the design/setup of a geotechnical instrumentation and monitoring plan is a constant process, in which the main requirement is to define the main purpose for each engineering stage, considering the geotechnical, geomechanical mining designs and most of all the tolerance records, according to the associated geomechanical risks.

227

Caving 2014, Santiago, Chile The development of this working action plan respectively project has been motivated by the final results obtained by evaluation of monitoring of geomechanical plans, implemented in the last ten years in División El Teniente CODELCO – CHILE. In that period it was possible to identify that the focus on the design is very helpful, getting very detailed records which can be used and applied in the mining process. The previous consideration has driven to check the purpose of the implementation of these monitoring and geomechanical systems as well as its contribution to the mining development. This project proposes the considerations to elaborate a methodology that can guide properly toward the development of a geotechnical instrumentation, monitoring and in the end take geomechanical actions. This can be done for each individual case, with a clear guidance to the use and application of the different registrated files obtained. For the control of geomechanical risks or losses (also developed among the stages of conceptual and detailing engineering) properly evaluated after the different mining jobs. The geomechanical instrumentation in underground mines and particularly the available experience in El Teniente de Codelco Chile, has played a major role as a supporting tool for the understanding of caving processes in general, with regards to the comprehension of mechanical behavior of the surrounding massif and exploitation. However, applications on decisions in an emergency were more an instance inspired by previous experiences than using actual measurements. In principal this situation arose from historical cases and had an unknown consequence. The results simply need to be linked to the records with the final conclusions and also with unknown conditions along the different stages of the project and more important at the start of production. For this diagnosis it appears as a relevant fact, to become properly acquainted with a structure, allowing the definition of objectives which direct to the fulfillment of the expectations. According to the stage of engineering that will be developed during a determinate instance, making geomechanical monitoring coherent with mining design. Also taking into account the geomechanical vulnerability implied to the exploitation and the plans for the mining development. A very good example for the application of a system for geomechanical monitoring is the seismic system available in El Teniente (ISS – Mina). Actually here Analysis of the record allows taking concrete decisions with regards to blasting and the isolation of sectors (for instance, personnel entering and leaving). Besides the contribution of data for making progress evaluating seismic menacing or dangers, among other relevant effects it is important for long term planning for the mining development. At present we are working on a summary of a methodological proposal, to approach functional conceptual aspects as well as an instrumentation and geomechanical monitoring plan, with the general purpose of including it in different stages of engineering, according to the project expectations and the contribution to control geomechanical risks. At least four relevant stages are considered in the project:

• IP&GM expectations • Method to focus on points of each engineering stage • Designs for engineering and ground implementations • Evaluation of the results and fulfillment of the expectations From this perspective design and evaluation stages will occur, but previously expectations need to be defined that allow a decision carry out a IP&GM. All this implies that geomechanical monitoring is a

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Geomechanic Design means but not the purpose. In the end it depends on the project owner, his personal attitude to face the risk and the available alternatives and measurements in the particular case. 1.1

Background consideration

Checking the background that contain the different plans of geomechanical instrumentation and monitoring, developed by the mine of EL TENIENTE de CODELCO – CHILE in the last twelve years, it shows an emphasis orientated to get measurements mostly with regard to the application. This is evident in the limited documents which lead to the post evaluation and due to this actions were imposed by the results of the application of geomechanical monitoring plans.

2

Methodological development

Following this exposition the methodological focus including each stage will be described, in order to elaborate an Instrumentation Plan and Geomechanical Monitoring (IP&GM), that illustrates its contribution to value the mining process and that inserts itself in each one of the four geomechanical engineering stages. 2.1

Expectations of IP&GM for each engineering stage

The methodology considers one first step where the expectations for each engineering stage will be indicated. These expectations are reduced to the following definitions: Purposes, Objectives, Goals and Products. Table 1 Expectations on the IP&GM

Profile

Pre-feasibility

Feasibility

Details

Purpose

Identify geomechanical potential risks

Define monitoring requirements according to the geomechanical model

Estimate costs of IP &GM

Insert into the mining plan

objective

Develop work plan (time and costs) for the next steps

Size requirements and evaluate technologies

Determine type, amount and use of instruments (CAPEX and OPEX)

Develop location plans and monitoring plans

GOAL

Internal benchmarking and background available

Product

Report of descriptive scenarios and potential risks

Consider available technologies and shorter range of innovation Report to size requirements in time and costs as technologies

Design with technologies commercially available and accessible in terms defined by engineering Design and implement a plan for monitoring geomechanics.

Planes with all locations and monitoring frequency

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Methodological design for each engineering stage

The fundamental objective of this work is to materialize tasks, activities and/or concrete products. The aspects that are going to be developed in each engineering stage and the goal to implement an instrumentation and geotechnical monitoring plan have a valuable contribution to the mining process. 2.2.1

Profile of engineering stage

This stage defines the business potential, it describes the principal risks in qualitative terms and uses bench marking records for the IP&GM. This is necessary to get an overview of the level of costs and the magnitude of works, using similar experiences and present identified risks of mayor relevance. Table 2 Key contents for IP&GM in the Profile engineering stage

IP&GM Engineering stage

Delivery details

Process

1.- review of measurements available: It consists of a compilation of instrumentation records made around the area of interest or comparable geomechanical conditions (geotechnical, mining, environment stress). This background is useful to support new requirements if necessary.

2.- Budget and business plan: Must be considered costs associated with the construction of conceptual geomechanical model and plan that the proposed implementation is a product that is made after the availability of the model

Offer process:

Background required

Start a plan for developing a system to monitor the impact of geomechanical instabilities, adding value to define the true dimension of the requirements that meet the stated objective.

1.- Geographical location and timing of the operation. 2.- Security Policy, Standard fatalities control and risk classification

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Geomechanic Design 2.2.2

Pre-feasibility engineering stage

In this stage different exploitation alternatives, mining designs and stages of exploitation are evaluated. The work ends with the selection of the best alternative that will be focused in the following stage. The instrumentation and geomechanical monitoring plan has been defined conceptually according to the technology and the type of measurement normally used for that kind of activity.

• Extensometers and periodical measurements in a manual way. • Local deformation measurements according to non-systematic requirements. • Seismic activity records, remote and continuous automatic monitoring 2.2.3

Feasibility engineering stage

For each useful stage a definition about the method and the dimension of the exploitable sector is available. It corresponds to development of parameters that allows elaborating the mining plan. For this stage, where the temporality is defined and where the constructions are realized, results are very important to incorporate the installation works that develop mining activity. Table 3 Key contents for IP&GM in prefeasibility engineering stage

Process

IP&GM to prefeasibility engineering

Delivery details

1. - Report with Geomechanical instrumentation plan that incorporates definitions: the kind of device and the amount estimates (in the range of 25%)

2.- Report with the qualitative assessment of the expected risk control: is defined the scope and range of functionality for design, should be clearly established “for what” is the IP&GM 3.- Report with the evaluation of the expectation value contribution of IP&GM in mining development indicators are definite to assess the effectiveness of the design

Offer process:

Background required

Identify relevant aspects of the expected geomechanical behavior and incorporate them into a rational design of IP&GM

1.- Mining method. 2.- Sizing of mining

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Detailed engineering stage

In this stage plans for installation are elaborated as well as technical bases for allowances (permissions). Practical ground plans are designed and the strategy to use the available resources in construction and different developments (availability for perforations, constructions, electric installations).

3

Performance Indicators

As a manner to identify the necessity of modifications and corrections of the parameters that define instrumentation, monitoring and record analysis it is proposed to use indicators that inform about efficacy and the use of the system. Like any evaluator to give the final value to the final product of the system. The premises for the works on the comportments of the geomechanical monitoring systems are:

• Matrix results: the options of the final results are reduced to four stages that depend on whether the alert was right or not.

• Modifications of the instrumental monitoring parameters amend the original costs of the system. Quantifications of these modifications are an available economic indicator of the system. Table 4 Possibilities on the final results of the geomechanical system

Consequences

No consequences

Alert

Success

Minor fail (cost)

No Alert

Major Fail (Safety)

Monitoring

Modifications of the system are realized fundamentally on the base of acceptable criteria of the negative/ positive decisions registered. In this sense the cost indicator depends on the system work. Regarding the operative implementation, each case will be specially analyzed in order to take decisions on the modification of the available system.

4 Conclusions • The described methodology allows a structured focus headed to identify the exact values contributed by a system for a geomechanical monitoring mining project.

• The inclusion of appropriate indicators, gives great reliability of the present initiative. • The advantages of identifying the role of a geomechanical monitoring system for mining processes

allows to plan deliverable products, value engineering at each stage, satisfying expectations from technical and also economic perspectives.

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Geomechanic Design Table 5 Key contents of IP&MG in feasible engineering stage.

Process

PI&GM to engineering feasibility

Product details

Report (conceptual Plan) description the kind of the geomechanical instrumentation for monitoring excavation and for control of the process of mining on the rock mass. Report (Instrumentation) technical specifications and cost estimate for the purchase and installation of the required instruments.

Report (Monitoring) definitions of the frequency of measurements, data analysis, threshold values and actions in case of deviations in the expected response.

Offer process:

Background required

Design geomechanical monitoring system for the control of major excavations (caves) and for control over the response of the rock mass against the advance of mining

1.- Geomechanical Conceptual model 2.- Plan developments in mining

References Espinosa, Cornejo, Fuentes 2012, ‘Geomecánica proyecto Dacita – Enlace ingenierías básica y detalles’, SGM-I-052. Morrison, RGK 1976, ‘A philosophy of ground control’, Department of mining and metallurgical engineering McGill University, Montreal.

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Integrated support quality system at El Teniente Mine MS Celis, Codelco, Chile RA Parraguez, Codelco, Chile E Rojas, Codelco Chile, Chile

Abstract The improvement of the design and installation of underground support requires an integrated quality control system. It rises from the relevance of the support quality in its response to loadings associated to mining (especially rock burst and collapses), as support quality can achieve a better damage control, protecting personnel and mining infrastructure. The integrated quality support system has been implemented during year 2013 and it is based on the legal framework. It considers 3 main aspects: design - monitoring - post evaluation. Ground control engineers check the quality of the installed support and compare it to accepted standards. Considering the limited resources, monitoring is focused in some critical areas defined according to seismic activity, mining and stress field. Technical reports have been prepared including the main aspects surveyed in the field for each support system. Post-evaluation reports are generated including lessons that should feedback designs, establishing a support improvement cycle. The resulting information is monthly sent to the areas of interest (Critical Risks, Mine Operations, construction companies). Depending on the critical level of findings causality analysis or corrective actions were undertaken.

1 Introduction In Teniente´s mine the Geomechanics Superintendence has the following mission: “To contribute to maximization of economics value in long term of El Teniente Division and Corporation, support mining explotation with Geomechanical application, with emphasis in rock burst risk control”. To control rock burst risk, 3 points are involved:

• Source: mining control or rock mass pre conditioning can reduce seismic event magnitude. • Damage control: installation of support with better response to dynamics loadings can control the damage´s level in a better way.

• Personnel exposition: definition of exclusion criteria, abutment stress zone and use of remote-

controlled equipments looking for reducing exposition of personal and equipment in higher risk zones.

In accordance with that, we deduce the importance of an Integrated Support Quality System, considering the relevance of the support quality in its response to loadings associated to mining and because it’s necessary to improve our management of findings and interactions with others Geomechanics areas.

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Geomechanic Design 2

Component of System

The integrated Support Quality Index (ISQS) includes the main components as indicated in Figure 1: DESIGN considers as inputs the mining design variables: gallery sizes, lifespan, use, dynamic loadings, corrosion, lithological and structural conditions, stress field among others. This data is used to produce drawings, calculation logs, technical specifications, quality standards. Those products are the inputs for monitoring support behavior. MONITORING: in addition to the survey of the installed support elements, it includes also the testing of other innovative support systems that could be used in some specific underground conditions and daily solutions to operational requirements for underground singularities. POST EVALUATION. It allows to put together information notes, loading characteristics (rock bursts, collapses, abutments stress levels, falling wedges) and the expected support behavior. Post evaluation notes includes the lessons that should feedback designs, establishing a support improvement cycle.

Figure 1 Components of the Integrated Support Quality Index (ISQS)

The 3 components, design, monitoring and post evaluation, define a working cycle. It includes continuous improvement of designs. But besides, it produces an interaction with the Critical Risk area responsible for the management of areas considered critical due to their impact in the personnel´s safety and for the mining business. This allow implementing corrective actions in a faster and effective way.

3 Monitoring 3.1

Definition of attention focus

Ground control engineers check the quality of the installed support and compare it to accepted standards. Considering the limited resources, monitoring is focused in critical areas that have been defined as seismic

235

Caving 2014, Santiago, Chile activity, mining and stress field. Notes are prepared including the main aspects surveyed in the field for each support system.

Figure 2 Parameters to define attention focus

Abutment Zone: Zone in the vicinity of caving front, where rock mass show evidences of the concentration, variation and rotation of stresses. The width of this zone is defined for each sector depending on the applied caving method and geomechanical and geotechnical conditions. Table 1 Analysis criteria for Abutment Zone

Variable

Evaluation

ABUTMENT ZONE

Criteria: to include ZT ahead of extraction limit

Energy Index: ratio between event radiated energy v/s expected radiated energy. EI is calculated for each event (Mendecki, 1997). Besides for seismic activity the criteria involve cluster seismic magnitude (Table 2 and 3).

236

Geomechanic Design Table 2. Analysis for Energy Index

Variable

Evaluation

ENERGY INDEX Criteria: to include zones with IE>0,8

Table 3 Analysis criteria for Seismic Activity

Variable

SEISMIC ACTIVITY

Evaluation

Criteria: to include event cluster magnitude ≥0,5 in last year

Fractures Pressures: characterization with geo–statistical models of the spatial distribution of propagation fracture pressures induced by hydraulic fractures. In this case the critical are defined as these that reaches more than 30 MPa of pressure to break a fracture (Table 4).

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Caving 2014, Santiago, Chile Table 4 Analysis criteria for Fractures Pressures

Variable

Evaluation

FRACTURES PRESSURES

Criteria: to include fractures pressures greater than 30 MPa

Peak Particle Velocity: maximum vibration velocity estimated from seismic sensor records. In this case a critical zone is defined as these where the ppv reaches xx mm/s as shown in Table 5. Table 5 Analysis criteria for Peak Particles Velocity

Variable PEAK PARTICLE VELOCITY Criteria: to include the greatest values of PPV

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Evaluation

Geomechanic Design The above indexes are considered to define a critical zone when all criteria are meet. That is when conditions described from Table 1 to 5 are reached.

Figure 3 Identification of Attention Focus

3.2

Survey Information

Technical reports are prepared including the main aspects surveyed in the field for each support system (survey date, name of geomechanical engineer, identification of evaluation site, contractor company responsible of the site and evaluated items).

4

Management of Critical Findings

Geomechanics group send monthly all the surveyed information. Depending on the critical level of findings, Critical Risk area request causality analysis or corrective actions that have to be sent to Mine Operations or Construction Companies. Reports include critical findings with their impact in the productive process, verification of correctives actions obtained from these findings and inform the unfulfilment of support system quality of surveyed galleries (summary of technical reports).

239

Caving 2014, Santiago, Chile This is the evaluation scale:

: Agree with the design : Possibility of improvement : Major flaw Table 6. Example of Technical report. Bolt – plate – nut and mesh system

Technical Report

Evaluation Point

100

98-99

0-97

100

86-99

0-85

100

98-99

0-97

Saw grouting outside drilling

100

Mesh without corrosion

100

<100

Overlap mesh with one bolt line

100

<100

Mesh from floor to floor

100

Intact mesh without rock slabs

100

Figure 4 Actual reduction of major flaw

240

% compliance

Bolt length agree with design Spacing between bolts agree with desing All bolts installed with plate and nut Plate in correct position (relative to dome) Good connection between plate, nut, bolt and mesh 30 cm maximum height from floor to first bolt

100

<100

100

<100

100

<100 90-99

90-99 <100

0-89

0-89

Geomechanic Design

Figure 5 2013 Evaluation sites in Production Level (Esmeralda, Reno – Dacita and Diablo Regimiento sectors)

5 Conclusions As a conclusion, we have identified some contributions of this Integrated Support Quality System:

• It improves the management of findings in areas considered critical due to their impact in the personnel safety for the mining business, executing corrective actions in a more rapid and effective way.

• It gives simple solution to some operational problems and includes learned lessons of post evaluation of the support designs.

• It allows the same standard for evaluating the installation quality of underground support in all the Mine sectors.

• A database can be created to feed post evaluations. • It is an indicator for management regarding the installed support quality. It is a warning in relation

to practices that impact negatively the quality of installation and motivates the searching of solutions for them.

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Caving 2014, Santiago, Chile For this year, we expect to extend design and post evaluation aspects, to implement this System in the new projects of the Division and to structure a computational platform with all the system information.

References Cornejo, J, Muñoz, A & Rojas, E 2012, ‘Estimación de presiones de propagación de hidrofractura en volúmenes pre acondicionados PQ2013, Mina El Teniente’, Internal report SGM-I-056/2012. Dunlop, R, Gaete, S & Rojas, E 1999, ‘Sismicidad inducida y estallidos de roca en Mina El Teniente’, Internal report PL-I-099/99. Juran, M, Gryna FM 1995, Análisis y Planeación de la Calidad, ed. McGraw-Hill. Mendecki, A 1997, Seismic Monitoring in Mines. Chapman & Hall.

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Geomechanic Design

Management indicators for the cave geometry control, El Teniente mine J Cornejo Codelco, Chile C Pardo Codelco, Chile

Abstract The fulfillment of production targets for massive cave mining is achieved by a successful application of cave back management. In this context, the planning tools used to identify the quality of extraction process are essential. For panel caving operations, the cave back could be managed through the extraction process and the incorporation of drawbells into production. He main geomechanical hazards, such as, rock bursts, collapses and early dilution could be related to unfavourable cave geometry. Therefore, the constructions and follow up of cave back management indicators are essential for an effective planning of caving operations. In this article, the authors present the results from the application of the angle of draw as an indicator of the performance of the cave back at Reservas Norte sector of El Teniente. The results indicate that there is need to improve the draw control over the sector as the indicator is underperforming. This would help to identify and to reduce the probability of occurrence of major geomechanical hazards.

1 Introduction Many geotechnical risks have been associated with unfavorable conditions of cave back geometry. Some rock bursts and collapses experienced at different operations have been facilitated by deviations from planned draw process. Normally, the geotechnical guidelines set out the requirements that must be met; however, it is crucial to look for some geometry indicators for evaluating the conceptual models used as “caving rules”. In the present study, management indicators were developed as key process indicators (KPIs), reflecting the general state of the cavity regarding the geotechnical guidelines. Based on the key process indicators, it is possible to anticipate geometries issues that increase the plan vulnerabilities reducing the occurrence of major detentions and risk of personnel exposure. The cave back geometry would be strongly associated the stress variations around the cave front during complete extraction steps in any block caving mine. It is crucial to keep a geometry control at any panel caving, because any deviation could lead to unfavorable conditions. Figure 1 shows (Flores et al. 2004) representative phases for connecting process by block caving. During the firsts stages the flat undercut induce an active zone located upper this cavity. This affected volume of rock mass corresponds to a combination of seismogenic zone and loosening area which induce stress changes towards edges of the cavity mean cavity progress (Duplancic & Brady 1999). Although one of the main geotechnical aspects of caving process is the crown pillar breaktrough and connecting to upper cavities, there are other parts of the process that would be almost as strategic as the previous ones. The exploitation method considers incorporating area after connections, increasing the extension of the undercut, advancing with the cave front and increasing the rock mass affected by mining advance. Therefore, for wider undercutting fronts, such as, at Reservas Norte case, the progressive process of the breaking the ore column properly becomes a very important issue (Landeros et al. 2012).

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Caving 2014, Santiago, Chile (a) Initial undercutting, the main mechanisms for propagation is the unconfinement of rock mass.

(b) Cave propagation induces a curve shape increasing the effect of stress caving.

(c) Additional upward caving propagation increases the curvature of the cave back, and makes the stress caving mechanism predominant

Figure 1 Evolution of the cave back and caving mechanisms through time due to the upward propagation of caving (Flores 2005)

Experience at El Teniente Mine allows conclude that unfavorable caveback geometries increase potential geotechnical hazards, such as rockbursts, collapses, hang-ups and airblasts and dilution entry. In this context, there is evidence of seismic activity which has induced rock-bursts and also collapses experienced in several levels, affecting recovery of reserves and staff safety working in different operations (Landeros et al. 2012). In order to identify unfavourable geometry condition of cave back, a number of direct and indirect methodologies for measuring and/or estimating the cave back shape have been implemented. One of the most widely used indirect relations is the ratio of extraction surface (angle) with the cave back. This concept is a simplification defined as the average angle between the effective extraction height and distance to a point reference to a specific direction of advance (Araneda & Gaete 2004); in Figure 2 a scheme of this concept is shown. From the above concept, it is assumed that the extraction rate is linked to the drawbell incorporation rate by the extraction surface (simplified as an angle). This geometrical relationship between the extraction rate (Pi+1, Pi, Pi-1) and caving rate is defined by the following equation:

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Geomechanic Design

Figure 2 Simplified caving model

ve = tan(x) vn

Where:

ve

: Extraction rate [t*m2/month].

vn

: Incorporation rate [m2/month].

x

: extraction angle for a drawpoint

(1)

Thus, there is a lineal relationship between the drawbell incorporation rate and the extraction rate. Therefore, in order to get to a constant draw angle, any increase of the extraction rate will have to be accompanied by an increase on the drawbell incorporation rate. One of the research consideration is that an accelerated incorporation rate relative to the effective extraction rate would mean a low extraction angle and a potential unfavorable stress condition around the cavity. On the other hand, a higher extraction rate with respect to the incorporation rate could induce potential or dilution (mud or dilution) resulting in loss of reserves. Furthermore, the activation of geological faults could be facilitated by a higher angle of extraction.

4

Cavity control indicators for Reservas Norte Sector

An indicator could be defined as a number that describes the performance of a specific activity. Key performance indicators (KPI’s) are defined as indicators of strategic significance, which are perceived as critical under current business circumstances (Tomkins 1988). In this study, a methodology based on the current practice of calculating profiles extraction was used. This methodology consists of calculating the height draw at specific reference lines along the mine. In this study, reference planes were used to calculate the indicators and the state of each point is measured against these planes of reference, these points are grouped into zones according to the preferential growth of the cavity. In Figure 3, an outline of the methodology and the description of how to calculate the indicator are shown.

245


Figure 3 Plan view of zones defined to calculate the angle of draw (zones 1 to 6)

The criterion to define the categories took into account the influence of the distance from the height of draw of a given drawpoint to the edge of the cavity, i.e., it was considered that the closer a point on the edge of the cavity, the greater its influence on stability condition in the surrounding infrastructure, while its influence decreases as the distance increases. In Figure 4 the categories associated with different conditions of points with respect to the reference planes, where the critical conditions are given by the category “red” and “magenta”. In this four categories are defined:

1. Low angle of draw, that is when the calculated angle is smaller than α° (magenta). 2. Good angle of draw, when the draw angle is within α-β° range (green). 3. Intermediate angle of draw (yellow), when the angle od draw is larger than β and smaller that a critical angle (CA).

4. Large angle of draw, this is when angle of draw is larger than the CA (red). From this classification, sectors under critical conditions were 1) and 4). In these cases, actions are conducted:

1) In the case of areas reaching 1), they are considered as “priority extraction areas”. In this case, the

continuity of the operation suggests to increase the extraction and/or decelerates the incorporation of new drawbells.

2) In the case of areas reaching 4), these were termed “Areas of over extraction”. In this case, there is a need to accelerate the incorporation of new drawbells and not to over-extract drawpoints from what has been planned.

As example, the methodology was applied in Reservas Norte sector at the El Teniente mine (Figure 5), where the following features were considered:

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Geomechanic Design

Figure 4 Angle of cave back and categories used in this study

• The indicator was calculated only for active fronts. • Based on the empirical relation that indicates connection to upper cavities when 33% of column height of primary rock has been extracted, a distance of 60m for evaluation has been calculated. This evaluation distance is fixed between the incorporation front (first draw bell incorporated) and the last point used to calculate the angle of the cave back.

•

The sector has been divided in zones to improve the interpretation(Table 1 and Figure 5):

a. Panel caving variants. b. Lithological and structural condition. c. Column height of primary rock. It should also be considered that in Reservas Norte there are different mining methods taking place as indicated in Table 1. The results of the analyses are indicated in Figure 6 which shows that Zone 1 has the largest numbers of drawpoints with overdrawn. Zone two and four have the largest number of drawpoints with under drawn. Finally, through the use of the proposed tools, it is expected to maintain control of geometry cavity, which allows improving consistently the results of the indicators. In particular, expected to the estimated compliance from the five-year plan for 2014 through management in the short and medium term, it is possible to reach levels that allow non-stop operation.

247

Caving 2014, Santiago, Chile Table 1 Zoning used mina Reservas norte

CATEGORY

ZONE 1

ZONE 2

ZONE 3

ZONE 4

- Advanced panel caving Variation of panel caving

- Advanced panel caving

- Conventional panel caving

- Conventional panel caving from XC5N hacia el Norte

Lithological and structural condition

Column height of primary rock

-Faults G and East-West System

-Faults G y F. -Dacita

-CMET

-Breccia Anhidrita

160 to 180 meters.

160 to 180 meters.

-Falla de Agua y And 1. -CMET

160 to 180 meters.

- Conventional panel caving

-Faults N1 AND2, AND3 y AND 4. - CMET -Brecha Anhidrita - Pórfido Diorític Porphiric 180 to 360 meters.

Results

Figure 5 Plan view of zoning used to cavity control at Reservas Norte Sector

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Geomechanic Design

Figure 6 Results of the application of performance indicators, November 2013

5 Conclusions Surface extraction (draw angle) is one of the key indicators of effective cave back control at El Teniente mine. The results so far have identified some specific spots, where the extraction does not follow expectations on the draw angle. Based on that finding, this tool has allowed improving the operational control over the draw strategy, reducing the possibility of early mud entry and instability problems at the mine. In the future, to achieve continuous improvement goals, an attempt will be made to use this criterion selectively.

Acknowledgement The authors would like to thank Codelco Chile, El Teniente, to authorize the publication of this document and, in particular, to all the people who make the Superintendency of Geomechanics GRMD.

References Araneda, O & Gaete, S 2004a, ‘Continuous modelling for caving exploitation’, MassMin 2004, A Karzulovic & M Alfaro eds., Chilean Engineering Institute, Santiago, Chile. Duplancic, P and Brady, BH 1999, ‘Characterisation of caving mechanisms by analysis of seismicity and rock stress’, Proc. 9th Congr., Int. Soc. Rock Mech., Paris (eds G. Vouille and P. Berest), vol. 2, pp. 1049–53, A. A. Balkema: Rotterdam. Flores, G, Karzulovic, A and Brown, ET 2004, ‘Current practices and trends in cave mining’, MassMin 2004, A Karzulovic and M Alfaro (eds), Chilean Engineering Institute, Santiago, Chile,. Landeros, P, Cuello, D & Rojas, E 2012, ‘Caveback management at Reservas Norte Mine, Codelco Chile, El Teniente Division’, 6th Conference and Exhibition on Mass Mining, Massmin 2012, Canada. Tomkins, J 1988, The warehouse management handbook, Mc Graw Hills eds, pp 513-559.

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Geomechanical issues and concepts associated with scoping study and prefeasibility stage of a Block/Panel Caving Project J Díaz DERK Ltda., Chile P Lledó DERK Ltda., Chile F Villegas Codelco, Chile

Abstract Considering the amount of underground mining projects in development that have studied the application of the caving methods together with other ore bodies that will also evaluate the feasibility of a future underground caving, this document summarizes the main geomechanical issues associated with the stages of a scoping study and a prefeasibility study for the Block/Panel Caving operation that must be considered. In addition, this technical paper will present the usual practices adopted and other considerations during the development of this type of studies. Equally, this material could be used as a general reference for engineering professionals in technical offices and/or mining companies interested in caving issues as well as in education and professional training.

1 Introduction The development of the first engineering stages in a Block or Panel Caving underground project, as any other project, require the definition of various key geomechanical aspects, which scope and depth must be in accordance with the stage of the development of the project. The terms and targets of each engineering stage play a relevant role in the adequate supply of resources in Geomechanics during the project design and planning, avoiding excessively advanced or unnecessary definitions, which will probably be reviewed and modified again in the following stages of the project. This work is focused on the Scoping and Prefeasibility stages of Block or Panel Caving underground mining projects because, according to the authors, occasionally, the scope and depth of the geomechanical issues tend to be confused between the above mentioned engineering stages.

2

Stages of a mining project

In general terms, a mining project has five major stages: 1. Planning Stage: It considers the period since the conception of the project, at the level of “idea” or “profile”, up to the demonstration of its economic feasibility at the level of scoping engineering.

250

The Scoping Engineering identifies the project’s business potential, key factors, fatal risks, investment order of magnitude and operating costs together with the relevant technical issues.

The Prefeasibility (Conceptual) Engineering studies the possible alternatives of the project to establish the most favourable case. The technical and economic feasibility of the mining configurations, technologies, capacities, and others are determined. In this stage, the investment sums (CAPEX) and operating costs (OPEX) are established.

Geomechanic Design 2. Design Stage: This is the stage where the project is “DESIGNED”, including the activities that belong to the Basic and Detailed Engineering stages or contexts. Together with the former stage, this one corresponds to the project category.

The alternative selected in the Prefeasibility stage is developed in this stage to demonstrate its technical and economic viability.

3. Development Stage: This stage corresponds to the construction or development of the project, according to the design specified in the former stage. However, there can be design modifications during this stage without changing or altering the core concepts. 4. Operational Stage: This stage corresponds to the project operation during its life, including the pre-production or ramp-up stage. 5. Closure Stage: This stage corresponds to the project closure after the end of its life. To frame these general stages with the project’s engineering phase, Figure 1 presents a scheme with these relationships.

Figure 1 Relationship between the general stages of a mining project with the engineering stages and the category or stat

As explained previously, this work is framed within the planning stage and addresses the geomechanical issues to be defined in the Scoping and Prefeasibility Engineering stages of a Block and/or Panel Caving underground project. A geomechanical problem can have a significant impact on an underground project; thus, it is necessary to reduce or minimize the risk of making errors in the project’s early stages (Scoping and Prefeasibility Engineering).

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Geomechanical issues of a Block/Panel Caving mining project

The major geomechanical issues in a Block/Panel Caving project to be addressed are the following: •

Geological, Structural, Hydrogeological and Geotechnical characterization.

•

Intact rock, geological structures and rock mass properties.

•

Stress field characterization.

•

Caveability assessment.

•

Fragmentation (Primary and Secondary).

•

Induced Seismicity.

•

Geotechnical assessment for design.

•

Geotechnical assessment for planning.

•

Subsidence analysis.

•

Geotechnical hazards.

Although these issues can vary depending on the characteristics of each project and type of deposit, it’s possible to cluster these issues in the following categories: 1. Geometry: Geometry establishes the factors that define the shapes, sizes and distributions in space. It can be divided in two sub-groups: Natural and Unnatural. The natural geometry considers the geologic, structural, hydrogeological and geotechnical models in addition to the fracturing level of the environment (primary fragmentation) and the surface topography (geomorphology). The Unnatural geometry involves the mining method scenario or context, greatly measured by the geomechanical issues for design and planning. 2. Geomechanical Context: The geomechanical context is specific for each deposit and project; it can be divided in two large groups: Pre-mining properties and Induced Loads. Pre-mining properties consider the material characterization and contact zones in the geological, structural, hydrogeological and geotechnical models, in particular, the parameters to evaluate the strength and deformability of the rock mass and its components (intact rock and geological structures). This group also characterizes the in situ or pre-mining stress state. On the other hand, Induced Loads correspond to the effects that will be produced by caving on the environment, with the review of aspects associated to caveability, cave propagation, abutment stress, secondary fragmentation and induced seismicity, among other issues. 3. Interaction: Interaction considers the impacts that the project can create on the environment, with its subsidence phenomena and the occurrence of geotechnical events. Figure 2 shows a diagram that summarizes the issues involved in each one of the major groups or categories that the Geomechanical Discipline must manage in a Block/Panel Caving project.

4

Geomechanical concepts and parameters in the scoping and prefeasibility engineering stages

In practice, during the development of engineering projects, it has been possible to observe that there are requirements and contributions made to Geomechanics that mostly don’t have enough and reliable information to respond according to the project’s engineering stages. In accordance to this, it is always highly necessary to define which subjects and parameters must be addressed, both in terms of their scope

252

Geomechanic Design and depth from the geomechanical caving methods viewpoint in the Scoping and Prefeasibility (Conceptual) Engineering stages.

Figure 2 Geomechanical Issues and Parameters considered in a Block / Panel Caving Mining Project

Table 1 provides a summary of the issues or subjects of interest and the geomechanical parameters the authors suggest to be addressed during the Scoping and Prefeasibility Engineering stages in a Block / Panel Caving Mining Project. Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages

Issues of interest / Parameter

Characterization

Geological

Structural

Scoping Engineering

Plan views and sections with Lithology, Alteration and Mineralization, preferably systematically spaced. Plan views must consider nearness to undercutting and production levels. Sections must be oriented in the geographic NS and EW and/or mina (local); spaced between 100 and 200 m. Plan views and sections with major and/or principal structures traces and projections. Plan views near the undercutting and production levels. The plan views and sections must spatially coincide with the geological information.

Prefeasibility Engineering

Geological model (3D) in a computer platform that includes at least the lithologies, alterations and mineralization attributes. The first geomechanical model (or geotechnical model) corresponds to the geological model; that’s why it’s important to have it as early as possible. Structural model (3D), in a computer platform with the major structures identified, including structural domains with the interpretation of the information about the minor structures surveyed from drillholes and/ or outcropping or underground exploration works.

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Caving 2014, Santiago, Chile Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages (Continued)

Issues of interest / Parameter

Characterization

Hydrogeological

Geotechnical

Scoping Engineering

Recording of water levels in probe holes and/or drillholes. Water table and recharge resulting from precipitation (water and/or snow). Knowledge of surface run-offs or presence of lake deposits.

Geotechnical characterization, at least one rock mass quality method (i.e., RMR Laubscher) in the geotechnical database. Plan views near the undercutting/ production levels and sections with the geotechnical quality distribution. In case there is not geotechnical characterization, there at least must be the spatial distributions and/or RQD values and/or Fracture Frequency, FF. Uniaxial Compressive Strength (UCS) estimations and Point Load Tests (PLT) in major units.

Stress State

Primary Fragmentation

Mine Design

Surface Topography Mining Method

Levels elevation

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Preliminary estimation of in situ stresses according to topography and lithostatic column, benchmarking of the region and/or mining district, use of the world stress map, etc.


Conceptual Hydrogeological Model, identification of permeable and impermeable units.

Geotechnical model (3D) able to display at least two rock mass quality methods (for example RMR Laubscher, GSI f(RMR Bieniawski) or Q). Development of intact rock samples’ lab test campaigns for the principal units; uniaxial tests must be included determining the elastic constants (15 tests per unit), triaxial tests (5 tests for each confinement level, preferably 5 levels), indirect tensile tests (15 tests per unit); in addition to the index properties (unit weight, porosity, P and S wave propagation velocity). Carry out preliminary stress measurements, preferably through hydraulic fracturing to obtain the deep stress distribution and using some mine-scale numerical models (3D).

It is suggested to review the technical literature (Hoek & Brown (1980); Amadei & Stephansson (1997); Díaz & Lledó (2005); Lledó & Díaz (2006)).

It must be considered that these numerical models could reach error levels between 25% and 50%.

Updated surface topography.

Updated surface topography.

Fracture frequency FF and RQD analysis Primary fragmentation estimation from benchmarking and preliminary simulations for different rock qualities, structural domains and stress states.

Analyzing the possible mining alternative(s), development of decision matrices for the undercutting variants comparatively evaluating geomechanical concepts. It must be possible to detect possible fatal flaws as major focus of attention.

Knowing preliminary elevations of the main levels (undercutting and production) of the mining alternative(s).

Primary fragmentation simulation for the geotechnical units of interest for each structural domain under the expected stress states and applying tools such as BCF, Size, etc. (Díaz, Lledó, Aguilar & Sepúlveda (2013)). Analyzing the possible mining alternative(s), development of decision matrices for the undercutting variants comparatively evaluating geomechanical concepts with the highest detail and information possible. It is necessary to know the elevations of the undercutting, production, ventilation and haulage levels of the mining alternative(s).

Geomechanic Design Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages (Continued)

Issues of interest / Parameter Mine Design

Levels Layout

Extraction grid

Crown Pillar

Draw point

Undercut type and height

Levels Ground Support

Scoping Engineering


The nominal thickness of the crown pillar can be defined the support of benchmarking and expert judgement. In addition, it must be in agreement with the undercutting variant and geotechnical scenario.

The thickness of the crown pillar proposed can be analysed through available 3D numerical models.

Carrying out a comparative analysis of the different types of extraction grids, benchmarking support and expert judgement.

The draw point design must consider the expected geotechnical scenario.

Carrying out a comparative analysis of undercut height (low, medium or high), benchmarking and expert judgement are used.

Undercutting Production

Ventilation Reduction

Transportation Temporary Infrastructure

Definition of general ground support criteria, usually from geomechanical classifications, experience obtained at the site and/or other companies, in addition to the specialist’s expert judgement.

The alternative(s) for selected drawpoint spacing must be analysed, hopefully with 3D numerical models. This model must include excavations and pillars from the Production level, ore pass system, draw bells and undercutting in the undercutting level.

The thickness of the crown pillar proposed can be analysed through available 3D numerical models and must comply with the geomechanical design criteria. Defining the undercut type and height alternative, it must consider the expected geotechnical scenario, the use of caving assistance methods (preconditioning), geomechanical design criteria and drilling and blasting criteria.

Generation of major drawings with support recommendations, support systems and elements arrangement in addition to technical specifications. It is possible to make a support zoning from the additional information available. Verification of the support recommendations through 2D numerical modelling for different geotechnical scenarios.

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Caving 2014, Santiago, Chile Issues of interest / Parameter Mine Planning

Block Height

Advance Sequence

Scoping Engineering

The in situ and extractable block height proposed must be analysed from empirical relations, benchmarking and expert judgement to make caving propagation analyses, to estimate the in situ and induced stress state and the subsidence. Parametric 2D numerical models can be developed. (Karzulovic et al. 2004)). The advance sequence must consider the principal stress orientation and the orientation of major geological structures.


A deeper analysis is made to the assessment of block height impact in a similar manner as the Scoping stage, including the draw point support design and draw bell design in the analysis. It is possible to develop 2D and 3D numerical models depending on the complexity of the project (Díaz & Lledó 2008). The definite advance sequence must consider the orientation of major stresses, the orientation of major geological structures, the undercutting front geometry, the undercutting and production levels layout and the distance between levels. Possible it will be necessary to make 3D modelling in the definite sequence.

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Geomechanic Design Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages (Continued)

Issues of interest / Parameter Mine Planning

Caving Rate

Draw Rates

Secondary Fragmentation

Caveability Assessment

Caveability

Caving Propagation

Scoping Engineering

The definition of the average caving rates must be made through benchmarking and/or expert judgement.

The definition of the average draw rates must be made through benchmarking and/or expert judgement.

Normally based on benchmarking and project requirements for the expected productivity of the draw point and production rate.


It is necessary to define the operationally reasonable caving rate ranges considering differentiated rates for design, planning and geomechanical conditions. This tries to mitigate the eventual occurrence of geotechnical hazards. It is recommended to use expert judgement and the experience obtained in sites with similar scale and geomechanical context.

It is necessary to define the operationally reasonable draw rate ranges considering differentiated rates for different design, planning and geomechanical conditions. It is recommended to use expert judgement and the experience obtained in sites with similar scale and geomechanical context.

A secondary fragmentation analysis is required to establish the expected productivity in the draw point and production rate. Sensitivity analysis is made for the different geomechanical units, structural domains and stress state.

Use of empirical methods It is recommended to use empirical (Laubscher’s caveability chart (1990). methods (Laubscher’s caveability chart (1990) or Laubscher & Jakubec (2001) including the data of sites using caving with a geotechnical scenario similar to the project’s one. Use of empirical relations and benchmarking (Karzulovic et al. 2004).

In addition to empirical relations, make use of numerical modelling if an unfavourable condition is detected.

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Caving 2014, Santiago, Chile Table 1 Geomechanical Issues and Parameters in the Scoping and Prefeasibility Engineering stages (Continued)

Issues of interest / Parameter Geotechnical Hazards

Collapses

Hang-ups (Air Blast)

Water/Mud Rushes

Subsidence

258

Rockburst occurrence potential analysis based on the rock’s mechanical properties and stress state in order to identify if it corresponds to a fatal flaw or a serious condition for the project.

Rockburst risk analysis; seismic risk analysis.

Collapse risk analysis. Identification of potential collapse occurrence zones. Recommendations on design and planning measures for mitigation purposes.

Recommendations on design and planning measures for mitigation purposes.

Hang-ups (air blast) occurrence potential analysis in order to identify if it corresponds to a fatal flaw or a serious condition for the project.

Hang-ups (air blast) risk analysis. Identification of mitigation measures.

Water/mud rushes occurrence potential analysis based on hydrogeological information in order to identify if it corresponds to a fatal flaw or a serious condition for the project.

Water/mud rushes risk analysis.

Subsidence Preliminary estimation of the final Crater extent of the subsidence crater on the surface, use of empirical methods and/ or benchmarking. General subsidence angles are defined.

Zone of Influence Major Infrastructure


Collapse occurrence potential analysis to identify if it corresponds to a fatal flaw or a serious condition for the project.

Rockbursts

Geotechnical events

Scoping Engineering

Preliminary estimation of the zone of influence around the final subsidence crater, use of empirical methods and benchmarking.

Orientation and location of excavations with respect to the principal structures, stress state (in situ and induced), geotechnical quality of the rock mass and subsidence development.

Recommendations on design and planning measures for mitigation purposes. Recommendations on design and planning measures for mitigation purposes. Preliminary estimation of the final extent of the subsidence crater on the surface, use of empirical methods and 3D numerical modelling. Angles of subsidence are defined by period and by crater wall, considering the geotechnical scenario.

Estimation of the zone of influence around the subsidence crater by period, use of 3D numerical modelling.

In addition to deepen the work made in the Scoping Engineering, the geometries, sizes and level of stability of the excavations must be analysed. Preparing the construction sequence of the excavations and defining systematic ground support recommendations.

Geomechanic Design 5 Conclusions The input information in each one of the engineering stages must be considered as strategic information. Consequently, the quality and reliability of this information define the quality and the reliability of the analyses made to support the project’s geomechanical recommendations. It is relevant for the client to have a team of at least one geotechnical geologist and a geomechanical engineer with experience in underground caving. This will substantially improve the quality of the geomechanical outputs associated with the scoping and prefeasibility engineering. This becomes increasingly relevant when there is a transition from open pit mining to underground mining.

References Hoek, E & Brown ET 1980, Underground Excavations in Rock, Institution of Mining and Metallurgy, ISBN 0 419 160302, E & FN SPON. Amadei, B & Stephansson, O 1997, Rock Stress and its Measurement, ISBN: 0 412 44700 2, Chapman & Hall, Londres - Inglaterra. Díaz, J & Lledó, P 2005, Estado Tensional In Situ y/o Preminería en Mina Chuquicamata - División Codelco Norte, Informe Técnico IT-DCN-E01-01-05, División Codelco Norte de Codelco Chile por Derk Ingeniería y Geología Ltda. Lledó, P & Díaz, J 2006, ‘Mediciones de Esfuerzo In Situ Mediante Técnicas de Hidrofracturamiento’, in Proceedings of Mining 2006 II International Conference on Mining Innovation, May 23 to 26, Santiago Chile. Lledó, P & Díaz, J 2008, ‘Modelos Numéricos 2D para Diseño de Soporte, Ingeniería Conceptual Chuquicamata Subterráneo Codelco Chile – VCP’, Informe Técnico IT-VCP_CHS-E09-01-08 emitido por Derk Ingeniería Limitada al Proyecto Chuquicamata Subterráneo. Laubscher, DH 1990, ‘A Geomechanics Classification System for the Rating of Rock Mass in Mine Design’, South African Journal of Mining and Metallurgy, vol. 90, no. 10, Oct. 1990, pp.257-273. Díaz, J, Lledó, P, Aguilar, J & Sepúlveda, J 2013, ‘Geotechnical Pre-Feasibility Study Carrrapateena Project’, Technical Report IT-NCL-E01-02-2013, Derk Ingeniería y Geología Ltda. To NCL. Karzulovic, A, Flores, G & Brown, T 2004, ‘Current Practices and Trends in cave mining’, Massmin 2004, Codelco Norte Division, Codelco Chile. Laubscher, DH & Jakubec, J 2001, The MRMR Rock Mass Classification for Jointed Rock Mass, eds. Hustrulid, W. A. & Bulock, R. L. Underground Mining Methods. Engineering Fundamentals and International Case Studies, 718 p. SME: Littleton, Colorado. Codelco 2014, Available at http://www.codelco.com/el-abc-de-un-proyecto/prontus_codelco/2013-10-24/114451. html.

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Geotechnical Characterization

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Ciresata geotechnical evaluation and caving study, Romania N Burgio Stratavision Pty Ltd, Australia

Abstract The Ciresata deposit is located in the Golden Quadrilateral Mining District of the South Apuseni Mountains in west-central Romania. Carpathian Gold Inc. commissioned a prefeasibility study to determine a suitable bulk scale underground mining method to achieve a production capacity of 30,000 t/day. Geotechnical investigations focused on the sublevel and block cave mining options. Material property testing of diamond drill core was undertaken to provide essential information for caveability, fragmentation and stress modelling analysis. Anomalous rock strength information required a second testing laboratory to be utilised for data verification. Initial predictions of fragmentation were highly sensitive to rock strength and local stress field information which impacted undercutting strategies for the block cave option. This paper outlines how engineering judgements were applied to cover data uncertainty in the early stages of the prefeasibility study and how the ongoing geotechnical evaluation was managed as new information became available.

1 Introduction The Ciresata deposit is located in the Golden Quadrilateral Mining District of the South Apuseni Mountains in west-central Romania (Figure 1). Historic gold production from the Golden Quadrilateral exceeds 55 MM Oz. Ciresata is one of the three main deposits occurring within the Rovina Exploration license, managed by Carpathian Gold Inc. The Rovina and Colnic deposits are planned as open pit operations whilst Ciresata is undergoing studies for a bulk underground mining technique. The license is located approximately 20 kms southwest of Rosia Montana. A prefeasibility study concluded that Ciresata was amenable to the sub-level caving or block caving based on a production capacity of 30,000 t/day.

Figure 1 Ciresata Location Map

263


Ciresata geology & geotechnical domains

Gold-copper mineralization is hosted by subvolcanic intrusions of Neogene age and adjacent hornfelsed Cretaceous sediments (Nebauer et al. 2005). The porphyries comprise of small, vertically attenuated, coarsegrained hornblende-plagioclase intrusives. Mineralisation occurs as pyrite-chalcopyrite disseminations associated with sheeted and stockwork quartz veining. Alteration types range from magnetite progressing outwards to potassic, phyllic, and propylitic assemblages. Mineralisation commences between 50 m and 150 m below the present day surface due to a hornblende porphyry which caps mineralisation near the surface. The host sediments dip at moderate angles to the northeast. Geotechnical domains were defined based on lithology, structure, alteration and rock mass properties (Figure 2). Several intrusive phases were grouped into a single domain referred to as the Intermineral Porphyry. Weaker geotechnical conditions occur closer to surface due to increases in argillic alteration and elevated fracturing. The rock mass was characterised based on Laubscher’s RMR system of classification (Laubscher 1990) and summarised in Table 1. Preliminary fault interpretations were prepared to identify structural features that could assist caveability and cave propagation behaviour. Sub-vertical faults strike along a north to north-westerly direction. Haloes of carbonate wall rock alteration are often observed next to faults zones. Milled breccias, representing thrust faults, dip sub-parallel to bedding at moderate angles to the north and northeast. The thrust faults appear to be offset by earlier vertical faults. The fracture frequency below the Argillic Cap is generally low and typically ranges from 1.5 to 2 fractures per meter. There appears to be no preferential development of jointing along the bedding planes, despite sediments occasionally displaying laminated textures.

Figure 2 Ciresata Geotechnical Domains

264

Geotechnical Characterization Table 1 Average Rock Mass Ratings for Geotechnical Domains

Domains

Description

Surface fracture Zone

Oxidation (1-5m) and elevated fracturing to 50m depth

Sediments

Sediments dipping moderately towards the northeast

Argillic Cap

Argillic-carbonate alteration extending to 150m depth

Intermineral Porphyry

Sub-vertical intrusive pipes and dykes

Western Porphyry

Eastern Capping Porphyry Lower Bedded Zone

Lower Fracture Zone

3

Northwest striking intrusive located west of the ore zone Intrusive sill which caps mineralisation

Sedimentary zone with occasional laminated bedding

RQD

RMRL90

75

55

38 83 85 73 82 71

43 60 60 57 58 55

Zone of increased fracturing due to faulting 67 51

Caveability

A conceptual block cave layout was positioned 650 m below surface.The hydraulic radius (HR) of the production area (HR=92) is well in excess of that required to initiate caving (HR=42). By contrast, a minimum span of 120m (HR~28-30) was established for the primary SLC level within the narrower carapice of the mineralised zone approximately 120 m below surface (Figure 3). The rock mass above the SLC level has an MRMR range from 42 to 60, hence, the minimum span offers a reasonable balance between proximity to surface whilst enabling a progressive stepout for the expansion of subsequent SLC production levels at depth.

Figure 3 Ciresata SLC and Block Cave Caveability Chart

265


Stress field estimation

The likely stress field conditions were estimated based on a review of local and regional tectonic information. Several geologically recent extensional basins and over-thrusting combined with ‘current’ earthquakes, associated with subducting remnant tectonic plates, has generated a tectonically complex area. The generalised map of European stress trends implies there is little stress data near the Ciresata deposit and it is not certain if the local stresses are European (NW-SE) or related to the Anatolian Province to the southeast. In addition the European stress trends are based on deep earthquake and hydraulic fracturing data rather than shallow in-situ rock stress measurements in mines or civil constructions (Lee 2012). Deposit scale structures (i.e. dykes, contacts, faults shears, bedding etc) are thought to influence local stress orientations and principle stress ratios, rather than regional tectonic considerations. As the lithologies at Ciresata are geologically recent it can be argued that the styles, and offsets of the main faults, are likely to be good indicators of the current regional stress field. The surrounding over-thrusting implies that high horizontal stress conditions cannot be sustained within the ringing Carpathians. The interpreted steeply dipping NNW-SSE faults and have a similar orientation to the regionally developed structures and may imply N-S orientation for the local maximum horizontal stress. There have been no observations of drilling difficulties such as squeezing or core discing. Two acoustic televiewer surveys were completed, however these were shallow and borehole breakouts or drilling induced fracturing would not be expected at these depths. Using current interpretations, it was concluded that the most likely orientation for the maximum horizontal stress at Ciresata is NNW-SSE i.e the bisector of the acute angle between the interpreted fault structures. Estimated rock stress relationships were developed for the most likely scenario as well as more and less deviatoric options (Table 2). Table 2 Estimated Rock Stresses for Ciresata

266

Geotechnical Characterization 5

Rock strength testing

Field observations and point load testing indicated hard rock strengths, yet initial UCS tests were conspicously low at almost half the expected value. A decision was made to apply the higher rock strength parameters inferred from field observations, for preliminary geotechnical analysis. Further UCS testing from an alternate laboratory (Laboratory B) generated much higher results which more closely matched field observations and verified engineering assumptions (Table 3). Table 3 Initial UCS Test Results

Rock Type Porphyry Sediments

Min

Laboratory A (MPa)

30 22

Average

Max

Min

42

69

40

56

118

28

Laboratory B (MPa) Average

Max

76

120

116

180

An independent suite of samples were sourced and supplied courtesy of Newcrest’s Cadia Valley operation in Australia to check the performance of both laboratories. Sample duplicates were prepared including a ‘standard’ comprising of dental plaster. The results confirmed earlier observations that Laboratory A was almost consistently generating lower than expected results. There remains some uncertainty if the lower results were due to sample preparation, equipment, or some other systematic error. Table 4 Comparative UCS Testing using Cadia Valley Drill Core

Laboratory A

Laboratory B

(MPa)

(MPa)

Volcanoclastic rock

116

171

Volcanoclastic rock with epidote

90

68

Pyroxene phyric volcanoclastic rock

64

102

Pyroxene phyric volcanoclastic rock

61

184

Dental plaster mould (15MPa standard)

8

12

Cadia Valley Sample Descriptions

6 Fragmentation Fragmentation predictions are highly susceptible to the degree of natural fracturing and also the ratio between induced stress and rock strength. The Ciresata rock mass has a widely spaced jointing system, hence, the degree of stress induced fracturing becomes an important factor in reducing fragmentation provided the stress/strength ratios are favourable. The analysis was undertaken using the Block Cave Fragmentation program (BCF v3.05) developed by Esterhuizen (2005). Fragmentation profiles were generated for low and high strength scenarios for the sedimentary and intrusive host rocks (Figures 4 and 5).

267


Figure 4 Fragmentation Domains

Figure 5 Primary Fragmentation for Sediments and Intrusives

268

Geotechnical Characterization There is a substantial difference in primary fragmentation for each scenario based on the rock strength inputs. The intrusives are expected to generate very coarse fragmentation albeit over a relatively small production area. Secondary fragmentation analysis suggested that 50 m to 75 m of draw would be required before more manageable levels of oversize were encountered from the sediments for the block cave option. The design of a high undercut would mitigate the initial coarse fragmentation to more manageable levels (Figure 6).

Figure 6 Undercut Design and Secondary Fragmentation

7 Conclusions Comparative laboratory testing became necessary to address and confirm discrepancies in UCS results. Sample standards and duplicates offered an opportunity to test the quality and reliability of laboratory results and supports the engineering judgments applied to the base case scenarios for Ciresata. The decision to use an alternate material testing laboratory was significant as lower rock strengths would have underestimated fragmentation and overestimated the rock mass deformation response. The Ciresata rock mass and geometry is amenable to block cave and sub-level cave mining methods. Minimum dimensions were established for the primary SLC level to initiate caving. The block cave footprint far exceeds the hydraulic radius required for caveability, however, cave propagation could be slow due to the competent rock mass and limited faulting. A high undercut design was incorporated into the block cave option to reduce secondary breakage requirements and improve early productivity. An improved understanding of the rock mass will be achieved once underground exposures become available and site stress measurements are undertaken. Opportunities also exist to consider preconditioning to reduce fragmentation and assist cave propagation.

Acknowledgement I wish to acknowledge Mr Randy Ruff of Carpathian Gold Inc. for permitting the publication of this paper.

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Caving 2014, Santiago, Chile References Esterhuizen, G 2005, BCFV3.05 A program to predict block cave fragmentation, Technical Reference and User´s Guide. Laubscher, DH 1990, ‘A geomechanics classification system for the rating of rock mass in mine design’, Transactions A. Afr. Inst. Min. Metall, vol.90, Nº10. Lee, M 2012, Ciresata Rock Stress Estimate, Internal Memo, Carpathian Gold Inc. Neubauer, F, Lips, A, Kauzmanov, K, Lexa, J & Ivascanu, P 2005, ‘Subduction, slab detachment and mineralization: The Neogene in the Apuseni Mountains and Carpathians’, Ore Geology Reviews, Elsevier, pp. 13-44.

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Identification of different geomechanics zones in panel caving- application to Reservas Norte El Teniente P Landeros Codelco, Chile J Cornejo Codelco, Chile J Alegría Codelco, Chile E Rojas Codelco, Chile

Abstract The identification of different geomechanics behavior has been a constant focus of interest for the rock mechanics engineering applied to planning and projects at El Teniente Mine, considering that most commonly used rock mass classification systems (i.e.: RMR, RQD and Q), represent similar values where most important exploitation sectors are taken place up to now. From that point of view, geologists have reached a good advance for establishing differences in terms of geotechnical aspects, mainly associated to vein infilling and in situ fragmentation. Then, for the planning process, it is very important to be able to have a geomechanics model which interprets geotechnical characterization against mining exploitation on different scales, such as local scale (excavations) or global behavior (cavities). In this study, a methodology for the analysis and evaluation of geomechanics behavior is presented, considering a case study with panel caving exploitation, emphasizing aspects like induced stresses, seismicity induced by mining, hydraulic fracturing and damages ahead the undercutting front. Finally, discussion of results is focused on its relationships with geotechnical characterization and geomechanics guidelines for mine planning.

1 Introduction El Teniente Mine includes different production sectors (see Figure 1), all of them located around a chimney of sub-volcanic breccias with an inverted cone shape, known as ‘Braden Pipe’. Reservas Norte – also known as Sub6 Sector at the beginning – is located on the north-eastern side, and its exploitation started in 1989 using conventional panel caving. Several rockbursts occurred during the 1990s and different exploitation sequences were tested to ensure continuous operation. Up to date, Reservas Norte is mined by advance panel caving, considering a production plan close to 40,000 tons/day. Up to date, the cave at Reservas Norte mine is in a steady-state condition, and its geometry consists of an active 700 meters wide undercutting front. One of the main constrain to the explotaition at El Teniente is related to the geomechanical hazards. This hazards also mean planning constrains (rate of draw, ….etc) that depends on the sector to be analised. In this article we present the methodology used at El Teniente to define the geomechanical areas for planning purposes. These are based on an understanding of the geotechnical environment, understanding of the induced stresses on mine infrastructure, seismic potential and blasting induced seismicity. In the following sections, each criteria is described finalizing with the definition of zones for Reservas Norte´s sector.

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Figure 1 Productive sectors and projects, El Teniente Mine (Business Plan 2013)

2

Geological and geotechnical data

Predominant lithology corresponds to Andesite intruded by three major ore bodies: Dacite Porphyry and Anhydrite Breccia on the western side and Diorite Porphyry, on the central and eastern sides. In terms of intact rock properties, all of them are very stiff with average Young’s Modulus approximately 60 GPa. On the other hand, in terms of rock mass quality indexes, most of these ore bodies are very similar and competent with GSI in the range of 75 to 90 and IRMR in the range of 55 to 62. Most important geological faults are classified as “master faults” (faults G, C, N1 and N2) and “major faults” (faults F, AND-1, AND2, AND-3, AND-4 and AND-5), see Figure 2.

3

Induced stress state

2.1

Numerical modelling

The geometry of the broken material cavity controls major differences in terms of induced stress conditions. For the evaluation of this impact into the mining plan, comparative analysis are developed based on a tri-dimensional numerical analysis with a linear elastic boundary element software, calibrated with field information such as stress measurements, damage ahead the undercutting front and propagation pressures of hydraulic fracturing (Cuello et al 2010). Reservas Norte’s numerical model is part of a bigger mine scale model, considering cavities of different productive sectors and lithological aspects such as ‘Braden Pipe’. This model is calibrated to get a good approach of pre mining stress states in different zones of the mine. Then, to get a local stress condition at Reservas Norte mine, improved geometries are incorporated such as specific caveback and undercutting surfaces, as shown in Figure 3.

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Figure 2 Plan view of major geological aspects, Reservas Norte mine, undercutting level (modified from Gonzalez 2013 and Gallardo 2013)

Figure 3 Mine scale numerical model (left) and local conditions for Reservas Norte sector (right), map3d code

Input parameters are defined, mainly considering three predominant materials: Braden Pipe, Andesites and broken material. Numerical modelling strategy considers the utilisation of field information to calibrate and validate the model. Calibration process is described by Cuello et al 2010, and it considers historical record of damage ahead the undercutting front and propagation pressures obtained during the hydraulic fracturing process. Differences between model estimation and field information are shown in Figure 4. 2.2

Propagation pressures of Hydraulic Fracturing (HF)

Average magnitude of the propagation pressure obtained from the pre-conditioning process by hydraulic fracturing allows obtaining a very good approximation of confinement magnitudes inside the rock mass. This valuable information is also used for calibrating numerical modelling. Geostatistical analysis have been developed using this information and a block modelling by kriging techniques (Cornejo and Landeros 2013). Results for the zone of interest are shown in Figure 5.

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Figure 4 Severe damage criterion applied to undercutting layout, Reservas Norte (modified from Cuello et al 2010)

Figure 5 Propagation pressures of HF (left) and kriging results (right), from Cornejo and Landeros 2013

4

Analysis of seismicity induced by mining

3.1

Evaluation of seismic hazard

For the evaluation of the seismic hazard, the selection of the volume of analysis covered 375*10^6 m3, including both Reservas Norte and Pilar Norte sectors. All the events recorded by the seismic monitoring system from December 2011 to April 2013 were considered.

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Geotechnical Characterization Initial filtering is related to spatial location of each event and the general uncertainty of its estimation. Then, a distance is estimated with the 95% of the data lower error. By the other hand, modal magnitude is used for estimating minimal sensibility of the seismic system for the zone in analysis, in this case, this local magnitude corresponds to Mw=-0.7. The objective of the application of a spatial-time filtering is to eliminate all the events that do not interact with principal clusters, because their location is further according to the data base characteristics or because their occurrence in time is not representative of the real latency of the zone of analysis. Then, only events with an effective interaction are included on the analysis, as shown in Figure 6.

Figure 6 Seismic events considered for the analysis (left) and the spatial-time filtering applied (right)

All the selected events are clustered in a later stage of analysis, using agglomerative hierarchical techniques, maximizing distance between clusters. At first, a dendogram is built to calculate the distance matrix between events and then, an optimal number of groups are determined with at least 250 events each. The whole process is described in Figure 7.

Figure 7 Identified groups according to agglomerative hierarchical techniques

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Caving 2014, Santiago, Chile Rockburst database was used as base information to categorize the seismic hazard, where 95% of rockbursts are related to local magnitudes higher than Mw=1.5 and energy release higher than 10^6 J. There is also a high correlation between magnitudes, energy releases and location of damage. Then, a dispersion graph was used to examine tendencies, estimating 50 meters of linear damage. Finally, different hazard levels were defined, considering the occurrence of those conditions. Higher hazard level are related to all defined conditions; by the other hand, lower hazard levels are related to clusters where there is no occurrence of magnitudes and energy releases higher than previously estimated, as shown in Figure 8.

Figure 8 Zones identified with different seismic hazard

5

Seismic events after blasting

This stage of the analysis considered all seismic events with magnitude higher than Mw>0 recorded in on each single blasting. Several assumptions were defined as it follows:

• The radius of influence of each blasting corresponds to 100 meters spherically measured. • Time of influence of each blasting corresponds to 24 hours after. • Spatial coordinates are referred to the centre for each blasting. The analysis was developed per year, and considering geotechnical zones as shown in Figure 9. The geotechbical domains are based on different kind of vein infilling, establishing limits between rock mass characteristics. In this Figure also thje magnitude and location of events are plotted. This shows that the

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Geotechnical Characterization blasting related seismic events are located to the West of the sector. As the front moves to the west so did the events. In general terms, it is possible to identify that seismic events related to blasting events with higher magnitudes have a very similar spatial distribution during 2008 and 2009, when the undercutting front were positioned in geotechnical zones with similar characteristics. Once the undercutting front was moving ahead to the west (years 2010 and 2011), the frequency of these kinds of events increased. As mining conditions could be considered constant similarly, the increase could be related to a different rock mass.

Figure 9 Seismicity recorded after blastings, including undercutting front at the end of each year (modified from Riquelme 2012 and Benado 2008)

6

Delimitation of different geomechanics zones

The process of identifying areas with different geomechanical behavior includes all the parameters described in the preceding paragraphs, adding some operational aspects such as orientation of undercutting front, the extension of the transition zone and orientation of the drifts. Results are focus on the definition of polygons, used for the daily operational activities and the short term analysis developed by geomechanics engineers, supporting the operational process. A plan view with the zones is shown in Figure 10, where x polygons were defined for geotechnical control.

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Figure 10 Plan view with the current operational polygons for geomechanics ground control (modified from Gallardo 2013)

7

Conclusions

The analysis and evaluation of geomechanics aspects is a relevant focus for mine design and planning. The identification of different geomechanics behaviour is based on a complementary analysis between induced stresses, seismicity induced by mining, hydraulic fracturing, damages ahead the undercutting front and some operational aspects. This process is iterative permits to define polygons which represent the base for daily evaluation of seismic activity and for further analysis. These polygons are updated periodically by geomechanics staff of El Teniente.

Acknowledgement The authors wish to thank Codelco Chile, El Teniente Division for allowing the publication of this paper and the Geomechanics Staff that supplied data and information.

References Benado D 2008, ‘Geotechnical zones based on vein infilling stockwork’, Internal Report, Codelco Chile El Teniente. Cornejo, J, Landeros, P 2013, ‘Estimation of propagation pressures associated to hydraulic fracturing process, using geostatistics techniques, El Teniente Mine’, XVIII Symposium of mining engineering, Simin 2013, Universidad de Santiago, Chile.

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Geotechnical Characterization Cornejo, J 2013, ‘Identification of hazard zones, using seismic events clustering’, Msc thesis, mining engineering department, Universidad de Chile. Cuello, D, Landeros, P & Cavieres, P 2010, ‘The use of a 3D elastic model to identify rock mass damaged areas in the undercut level at Reservas Norte sector’, Proceedings of 5th international conference on deep and high stress mining, Santiago, Chile. Gallardo, M 2013, ‘Polygons for seismic control and frequency events criterion’, Internal Report, Codelco Chile El Teniente. Riquelme, O 2012, ‘Analysis and evaluation of the geomechanics behavior of high drawbell’, Mining Engineer Thesis, Mining Engineering Department, Universidad de Santiago de Chile.

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Geostatistical evaluation of fracture frequency and crushing SA Séguret MINES ParisTech, France C Guajardo Codelco, Chile R Freire Rivera Codelco, Chile

Abstract This work details how to estimate the Fracture Frequency (FF) ratio of a number of fractures divided by a sample length. The difficulty is that, often, a part of the sample cannot be analysed by the geologist because it is crushed, a characteristic of the rock strength that must also be considered for the Rock Mass Rating. After analysing the usual practices, the paper describes the (geo)-statistical link between fracturing and crushing and the resulting method to obtain an unbiased estimate of FF at a block or point support scale. Some concepts are introduced: “True” FF, “Crushed” FF, crushing probability and crushing proportion. The study is based on a real data set containing more than 13,000 samples. An appendix gives a very general formal demonstration on how to obtain unbiased ratio estimation.

1 Introduction One of the most important attributes used in the Rock Mass Rating (RMR) is the Fracture Frequency (FF); a ratio of a number of fractures counted by the geologist divided by the sample length. However, the calculation is not that simple because it often happens that a significant part of the sample is crushed, making the fractures counting impossible, and FF becomes the ratio of two quantities that both change from one location to another one in the deposit, making the evaluation difficult, whether at sample or block scales - in other words, this ratio is not additive (Carrasco et al. 2008). To get around this difficulty, the usual practice consists of using an additive formula that combines fractures number and crush length. The aim of this paper is:

• Analyzing the geostatistical link between fracturing and crushing. • Proposing an unbiased way to estimate FF. • Introducing the concept of crushing probability.

2 Formalization Figure 1 shows a sample with fractures and defines the vocabulary. In the following, all the samples are supposed to have the same length (1.5 m). For simplification, one will consider just one location “x” (center of gravity of the sample) for LNC, LC and Nfract. The quantities LNC, LC and Nfract, counted by 1.5 m length, are additive and can be estimated by the basic geostatistical method called “kriging” (Matheron 1963). Nfract plays the role of a “fractures accumulation”, the equivalent of the “metal accumulation” in conventional mining, i.e. the product of the grade by the thickness of the vein.

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Figure 1 Scheme presenting the useful variables, Crush Length and Fractures Number

The quantity:

FFtrue ( x ) =

N fract ( x ) L NC ( x )

(1)

is the key frequency as it represents the true fractures frequency in the non-crushed part of the material.

N

However, it is not additive: when x moves in the space, fract frequency between two measurements located at x1 and x2 is:

FFtrue ( x1 U x2 ) =

This latter ratio is equal to the average of Therefore, a direct “kriging” of not possible.

( x) and L NC ( x) change and the average

N fract ( x1 ) + N fract ( x2 ) L NC ( x1 ) + L NC ( x2 )

FFtrue ( x1 ) and FFtrue ( x2 ) only if L NC ( x1 ) = L NC ( x2 ) .

FFtrue ( x0 ) for any x0, using surrounding measurements FFtrue ( xi ) , is

This is the reason why practices use the formula:

FFcorrected ( x ) =

N fract ( x ) + aLC ( x ) 1.5

(2)

In Equation (2), the coefficient “a” represents an arbitrary quantity supposed to give more or less importance to crushing in comparison with fracturing (a=40 in our case). By this way, the geotechnician incorporates the information given by crushing. Equation (2) has also the advantage of combining additive quantities that can be estimated separately and then combined:

ˆ FF corrected ( x ) =

N *fract ( x ) + a.L*C ( x ) 1.5

(3)

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Caving 2014, Santiago, Chile In Equation (3), the exponent “*” denotes various estimates. To understand what the coefficient “a” represents, let us develop Equation (2):

FFcorrected ( x ) =

L NC ( x )FFtrue ( x ) + LC ( x )a L NC ( x )FFtrue ( x ) + LC ( x )FFcrushed ( x ) = (2’) L NC ( x ) + LC ( x ) L NC ( x ) + LC ( x )

Presented in this way, Equation (2’) appears as an additive formula combining two frequencies: “a” being the one associated with crushing (now written any observable

FFcrushed ). This latter quantity must be at least greater than

FFtrue and we will detail this point in the following.

First, let us analyse the link between fracturing and crushing.

2

Observation of a natural phenomenon

We start by the examination of two samples:

Figure 2 Two samples: Few crushing and fractures (a) and important crushing, numerous fractures (b)

Figure 2a presents a drill core where the crush length is only 11 cm with just one fracture in the non-crushed part; Figure 2b presents the contrary: crush length is important (74 cm over 1.5 m) and 16 fractures in the remaining part. Is it a particular example or is there a statistical link between Nfract and Lc? We have analysed 13,000 samples (1.5 m length) coming from an underground mine in a 1000 x 2300 x 1000 m3 box along x, y, z. (Figure 3).

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Figure 3 Planes presenting projections of the data

The scatter diagram between Nfract and LC (Figure 4a) leads to mixed conclusions:

• The correlation coefficient is important (0.75). • 70% of the population lies inside the confidence interval defined by the conditional expectation curve, the remaining part does not present significant correlation.

4

True frequency estimation

Figures 4b, 4c and 4d present, respectively, the direct Nfract variogram (Matheron 1962, or a possible alternative calculation given by Emery 2007), Lc variogram and their cross variogram. All these variograms can be modelled by a unique model, up to a multiplicative factor, in other words, Nfract and Lc are in intrinsic correlation (Wackernagel 1995). Two important consequences result from this experimental property:

• It is not useful to use cokriging (Wackernagel 1995) for estimating Nfract or Lc. • The ratio of both estimates obtained by kriging is non biased (see Appendix). This latter property leads immediately to the method for estimating the non additive quantity FFtrue at a block scale V located at coordinates x: * FFtrue (Vx ) =

N Kfract (Vx ) LKNC (Vx )

(4)

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Figure 4 Scatter diagram between crush length (Lc, horizontal axis) (a) and Fractures number (Nfract). Line represents the linear regression of Nfract against LC, and the conditional expectation curve. Red dotted lines represent the standard deviation around the conditional curve. Resp. Nfract, Lc, and Nfract cross Lc variograms. Points are experimental, continuous curves the intrinsic model (all the variograms are proportional) (b-c-d)

In Equation (4), exponent K denotes the estimate of the variable by kriging, using a set of around 50 surrounding samples that change when the location x changes (”moving neighbourhood”, Chilès & Delfiner 1999). The samples used for numerator and denominator must be the same to preserve the non bias of the ratio.

1 FF (Vx ) , when Vx is sized 10 x 10 x 9 m3. Geotechnicians prefer the Figure 5a presents a map of * true

reverse of the frequency because it represents the average size of non-fractured core. When this quantity is small, the strength of the rock is bad and a low RMR is associated with the block. Another consequence of intrinsic correlation between both terms of the ratio is that estimating the ratio or its reverse is the same problem. Generally, this is not the case. For example, the reverse of an additive grade is not additive.

5

Crushing percentage or probability

Equation (4) is a ratio of two estimations that can be used separately. When we divide the denominator by the sample length, we can obtain an unbiased and optimal estimate of the crushing proportion:

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Geotechnical Characterization (5)

Figure 5 Map of inverse True Fracture Frequency using block kriging (a). Map of inverse Usual Fracture Frequency that incorporates crushing estimate and arbitrary frequency for crushing equal to 40 (b). Same as (b) but with crushing frequency inferred from statistics and set to 80 (c). Crushing proportions at block scale estimated by kriging (d)

Figure 5d shows a cross section of the result with important crushing proportions at the West of the domain, that correspond to a well known damage zone due to a major fault.

6

Usual formula improvement

The intrinsic correlation between crushing and fracturing leads to the optimal and unbiased estimate of formula (2) at block scale, for example:

(6)

Figure 5b shows a cross section of , a combination of Figure 5a and Figure 5d, with the result that the West damaged zone is reinforced by accounting for crushing proportions.

285


Crushing frequency inference

Equation (2’) shows that the coefficient “a” used in Equations (2) and (6) plays the role of a fracture frequency associated with crushing and named FFcrushed. In our case, for some reasons unknown when writing this paper, this quantity was set to 40 and the question is: could this parameter be obtained experimentally? Let us consider the scatter diagram between Lc and FFtrue calculated using the 13,000 samples at our disposal (Figure 6).

Figure 6 Scatter diagram between crush length (Lc, horizontal axis) and FFtrue as defined by (1). Solid line represents the conditional expectation curve; dotted segment represents a conservative extrapolation

When Lc increases, FFtrue increases, this is a consequence of the correlation between crushing and fracturing (the number of fractures are in average more numerous when crushing length is important). The increasing rate is not linear but hyperbolic because we divide Nfract by a quantity that tends to zero when Lc increase. If we suppose that: The crushing phenomenon appears when FFTrue is high, FFcrushed > FFTrue. On average, FFcrushed is independent from LC, then FFcrushed can be characterised by its average (reference to the conditional expectation curve) and must be at least equal to the limit of FFTrue when LC tends to 1.5 m. Figure 6 shows that FFTrue = 40 for LC around 1 m. There is still a part of the sample that is not crushed, in contrary to the previous hypothesis and FFcrushed must be at least greater than the maximum of E[FFtrue | LC] we can calculate, here 50 at Lc = 1.14 m. If we make a crude linear extrapolation of the curve we obtain, for Lc = 1.5 m: FFcrushed > FFTrue = 85 (7) As every extrapolation, this result is extremely sensitive to the hypothesis on the non linear regression modeling. The mapping of the Fracture Frequency obtained when we replace 45 by 85 in Equation (2) is presented in Figure 5c. Compared to the map using the traditional formula (Figure 5b), the West damage zone is reinforced because the influence of crushing is multiplied by more than two.

8 Conclusions Analysis of usual practices and properties of the two variables involved in the Fracture Frequency: the Crush Length and the Fracture Number, does not require inclusion of both quantities in a single arbitrary

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Geotechnical Characterization formula. Analysis of a data set showed that both variables are statistically highly correlated as well as spatially and they share the same variogram. This circumstance makes possible to estimate directly the real interesting quantity that is the ratio of fractures number divided by the sample length to shortcut the lack of additivity of this ratio. The resulting estimate is unbiased, a basic requirement when evaluating a quantity. On the other hand, the crushing phenomena must be estimated separately, giving a crushing proportion (at block scale) or a crushing probability (at point support scale) that must be incorporated in RMR in the same way as FF and other geotechnical attributes. All these possibilities depend directly on the mutual behaviour of Fractures Number and Crush Length and any study on the subject should start by the geostatistical analysis of these two variables. A more detailed analysis of their link, and another case study that will be published in the next future, showed that the present observed correlation is not due to hazard: fracturing sometime contributes to crushing, sometime not, depending on the mutual organization of the fractures. Finally, with such studies, we evaluate the mechanical properties of the rock.

Acknowledgement The authors would like to acknowledge Sergio Fuentes Sepulveda, Vice President of the Projects Division of CODELCO, Chile, and his company, for their strong support in the implementation of good geostatistical practices along the copper business value chain, as well as anonymous reviewers who greatly contributed to improving the quality of the manuscript.

Appendix: Unbiased ratio estimation Consider Z1(x) and Z2(x), two unknown values to be estimated using a set of 2n measurements {Z1(xi), Z2(xi), i:1,n}. Let “*” denote any estimate and wi any scalars. If: (8)

then the ratio is unbiased on average if we assume its order one stationarity at the neighbourhood scale. Proof:

(9)

If “*” is Kriging (whether Ordinary or Simple, Rivoirard 1984), with the same variogram for Z1 and Z2 and same sample locations for both variables (isotopy), then the ratio is unbiased. Proof: As the kriging weights λi are identical for both terms of the ratio, we have (10)

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Caving 2014, Santiago, Chile with (11) Equation (8) is verified, the ratio is unbiased.

References The authors did not find any reference concerning Fracture Frequency estimation in Geostatistics, which essentially focus on fracture network characterization and simulation (Chiles 1999). They notice some papers mentioning the use of Artificial Neural Network (Fitgerald &Al 1999) and crushing phenomena is never studied as a Regionalized Variable. Carrasco, P, Chilès, JP & Séguret, SA 2008, ‘Additivity, Metallurgical Recovery, and Grade’, in Proceedings of Geostatistics-the Eighth International Geostatistics Congress, ed. Emery, X., vol. 1, pp. 237246. Chilès, JP & Delfiner, P 1999, ‘Geostatistics. Modeling Spatial Uncertainty’, Wiley, 703 p. Chilès, JP & de Marsily, G 1999, ‘Stochastic models of fracture systems and their use in flow and transport modeling’, in Flow and Contaminant Transport in Fractured Rock, eds. Academic Press, San Diego, Ca, Chapter 4, pp. 169-236. Emery, X 2007, ‘Reducing fluctuations in the sample variogram, Stochastic Environmental Research and Risk Assessment, vol. 21(4), pp.391-403. Fitzgerald, E. M., Bean, C. J., Reilly, R., ‘Fracture-frequency prediction from borehole wireline logs using artificial neural networks’, Geophysical Prospecting Journal, vol. 47, Nº 6, pp. 1031-1044. Matheron, G 1962, ‘Traité de Géostatistique Appliquée’, Tome I, Mémoire du Bureau de Recherche Géologique et Minières, No 14, Editions Technip, Paris, France. Matheron, G 1963, ’Principles of Geostatistics’, Economic Geology, vol. 58, pp. 1246-1266. Rivoirard, J 1984, ‘Le comportement des poids de krigeage’, Doctoral Thesis, E.N.S. des Mines de Paris. Wackernagel, H 1995, ‘Multivariate Geostatistics’, Springer, Berlin.

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Geomechanical ground control in block/panel caving J Díaz DERK Ltda., Chile Y Sepúlveda DERK Ltda., Chile P Lledó DERK Ltda., Chile

Abstract Numerous underground mining projects that use caving methods will be in operation in the next 10 years. For example, we have the case of Chuquicamata Underground Mine (2018) and El Teniente New Mine Level (2017) in Chile, Oyu Tolgoi in Mongolia (2015), and Grasberg Block Cave (2017) in Indonesia, among other smaller projects. Basis for their success is both the constructive capacity of their mining and major infrastructure works in the initial-phase and in the projects’ operation. On the other hand, currently, there are many mining sites operating that apply caving methods, which have faced different geotechnical contexts and various operational challenges, where the ground geomechanical control has been fundamental in the mine development and a complement to operational decisions. This geomechanical control directly receives the acquired experience, both bad and good, of what we are doing as a mining activity. A field geomechanical engineer has the main goal of controlling the potential deviations from the various operational activities that directly influence the geotechnical design and planning parameters defined in the previous project study stages and, in turn, facing the short-term geomechanical problems that typically arise from the daily field activities. From the geomechanical viewpoint, three general lines associated with the field control work can be established; namely, (1) Control line associated with Mine Preparation (developments – construction); (2) Control line associated with Mine Production activities (short-term planning and mining), both in a shortterm horizon, and finally, (3) Control line associated with the Principal and Permanent Infrastructure in a short and medium term horizon. This work consolidates and summarizes the major activities to be executed by a field geomechanical professional during a mine shift, the types of outputs or deliverables of his technical work to be normally used during the caving of a sector and the resources that must be available for its adequate field geotechnical or geomechanical execution. Finally, this work includes a general description of the most adequate technical profile for the professional that will be in charge of the field geotechnical or geomechanical control in a short and medium term horizon.

1 Introduction As shown in Figure 1, Geomechanics identifies three major focuses or “clients”: Planning, Design and Mine Operations, the latter one being the context of interest for this work.

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Figure 1 General diagram with main focus on the Geomechanics discipline in caving mining

In general, the successful preparation and exploitation of a mining project according to quality standards and in geomechanical terms is based on the adequate and permanent control, recording and analysis made on the mine’s short term activities. The objective is to know and control the rock mass and cave behaviour since their beginning and therefore, proactively detecting, evaluating and correcting any sign of deviation that could cause any type of potential geotechnical hazard that affects the normal operation, safety and continuity of the method’s productive process and its associated major infrastructure. Hence, the routine visual inspection is vital in the work of a field geomechanical engineer, which must be carried out on a daily basis, supported by instruments and geological – geotechnical characterization for an adequate recording of the information. This visual inspection is developed in all the mine levels and on the surface with the frequency and periodicity associated to the importance and timing of the facilities. That is why the inspections at the strategic levels and at the highly geotechnically exposed levels (i.e., ventilation levels, size reduction, main accesses, permanent transportation and infrastructure, among others) are usually scheduled in more extended periods, according to the geomechanical context of each site and that usually can be: once a week, once a month, once every semester and even once a year.

2

Operational geomechanical control lines

Based on the schematic in Figure 1, we can establish three general lines related to field control, in particular: 1. Control line associated to Mine Preparation (developments – construction). 2. Control line associated to Mine Production activities (Short term planning and draw rate), both in a short term horizon, and finally 3. Control line associated to Principal and Permanent Infrastructure in a short and medium term horizon.

290

Geotechnical Characterization The first geomechanical control line considers activities related to the field of construction quality of a design, such as:

• Marking, drilling, blasting and number of draw bell commissioning stages to control damage and achieve designs (i.e., rock brow width at draw point, draw point support damage, etc.)

• Marking, drilling, blasting and amount of caving runs to control damage and achieve designs

(i.e., prevent remnant pillars and support damage at the undercutting and production level, prevent geometrical singularities in the undercutting level, etc.).

• Review of the topographical control of the advance in the production and undercutting level infrastructure (mining pattern), specifically, pillar overbreak.

• Systematic and definite advance support installed: pre-mining zone, and transition zone established in an as-built ground support drawing.

The second geomechanical control line considers activities related to the field of planning, such as:

• Advance sequence management, mainly to allow the evaluation of the undercutting front geometry and orientation.

• Strategy to mine the mineralized and in situ column to assess the caveback geometry and caving propagation .

• Subsidence propagation and behaviour; crater and influence zones. The third control line specifically refers to the periodical control of the status of the service and major infrastructure work that is operational and/or in a preparation stage in a short and medium term horizon. A well documented and timely field geomechanical control will favour continuous improvement in the medium– long term mining, especially giving a feedback to the design and mine planning areas (Figure 2).

Figure 2 Feedback from operational planning to the short, medium and long term planning and mine design

3

Deliverables and activities

The field geomechanical control considers the observation and recording of various pieces of information. If the information is timely analysed and evaluated, it allows improving mining and foresee important geotechnical hazards. In the following, we describe the main activities and deliverables associated to the operational geomechanical work:

291

Caving 2014, Santiago, Chile 1. Geomechanical Behaviour As-Built Drawing: This drawing corresponds to the recording of the excavation behaviour in terms of associated damage and presence of abnormal conditions, such as draw point hang-ups, brow damage, notorious overbreak, loaded pillars, etc. Damage can be observed both in the installed ground support and in the rock mass. The objective is to qualify and evaluate the type of damage or condition and their evolution through time to determine their potential causes and to adopt remedial, control or restriction measures in the operational activities. It is important to verify if there is any relation between the documented behaviour, the mining in the sector and the presence of any geotechnical hazard that is typical of the caving method. Monthly delivery. 2. Geomechanical Hazards As-Built Drawing: This drawing maintains the records of all the geomechanical events or hazards occurred during the mine’s life. This allows the traceability of the information to make evaluations at different periods (retrospective, present and future) that can contribute to the identification of any characteristic phenomenon, their conditions and therefore their potential solutions. These drawings must include the potential geomechanical hazards (i.e., collapses, airblasts, rockbursts, etc.) identified by the Project and that need to be validated period by period with updated geological – geotechnical information. Monthly delivery. 3. Ground Support As-Built Drawing: This drawing corresponds to a detailed record of all the ground support installed both in the advance and definite excavations and pillars. This drawing specifies the type of support system and element, amount, distribution and their relevant characteristics. Based on these records, an evaluation is made to know if what has been installed actually corresponds to what is established in the design guidelines or not. If there is any deviation, it must be reported and corrected. As the former one, this record or deliverable can also be used for later studies and retrospective geomechanical hazard analyses. Monthly delivery. 4. Draw bell Commissioning Control: This control consists of recording the draw bell development status, its characteristics (commissioning in 1, 2 or 3 stages), location and amount. This information allows controlling the commissioned draw points, the achievement of the defined design (i.e., brow width) and the draw bell commissioning rate in agreement with the established mining plans. Weekly or as required delivery. 5. Undercutting Control: This control consists of recording the undercutting front advance, the characteristics and conditions of the commissioned area. This implies controlling the amount of blasted rounds, amount of associated explosives, round condition (charged and cut blastholes), type of undercutting (low or high), undercutting design, and blastholes firing diagram. In addition, this information allows controlling the undercutting front geometrical characteristics, presence of remnant pillars and association of potential damage due to the execution, and the area commissioning rate, characteristics and amount, in agreement with the established mining plans. Daily delivery. 6. Review of Development Advances: This implies having available a development progress drawing with the condition of the daily advance fronts’ condition to detect any instability condition or failure mechanism that recommends applying preventative or control measures. In addition, this review includes the topographical activity records in order to characterize the excavations developed, which allows detecting potential overbreaks, underbreaks and/or their deviations. This could influence the infrastructure construction quality in the levels, namely, the condition of the draw rate module (excavations and pillars) in the production level. Monthly delivery.

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Geotechnical Characterization 7. Draw Control: This control consists of recording the draw at the draw points to create draw isocurves, to verify the low and high draw, executed rates in the front perimeter and in the caving regime zones. The draw rate is a parameter that is related to seismic activity and the geotechnical hazard potential in caving. Weekly delivery and monthly summary. 8. Fragmentation Control: This control consists of recording the particle size by means of digital photography at the draw points in a systematic manner, correlating it with the draw point life stage, associated column height and draw rate. Occasionally, it’s necessary to create control sheets for each draw point and monthly control. Weekly delivery and in accordance to the draw rate. 9. Subsidence Advance Control: This control consists of recording the different types of damage associated to subsidence zones. Ideally, this recording must be made in old workings located in the sub-surface intermediate zone and on the ground surface. This information allows controlling the crater geometry, its behaviour, influence zone and correlating its growth with the existing mining activity. If possible, this work must be supported by semi-annual or annual orthoreferenced and/or satellite aerial photography. Semestral and annual delivery. 10. Seismicity Review: This review implies updating and reviewing the daily, weekly and monthly seismic records statistics and trends. This type of instrumentation and information allows analysing the behaviour in zones that can’t be observed by the “human eye”, namely, the behaviour of the caveback zone in the areas and their relationship with draw rates and caving propagation. Daily, weekly and monthly delivery. 11. Blasting Damage Characterization and Evaluation: In specific situations, mainly when there is no dedicated service to control the blasting-induced vibrations (development and production), it is necessary to have the field Geomechanical Engineer carrying out some measurements and near and/or far field vibration analysis, as required, to evaluate the effects of the development and production blasting in the rock mass and surrounding excavations, in addition to the construction and/or calibration of the damage models available for the geotechnical units. This commonly happens with the draw bell commissioning blasting (phases) or caving (undercutting) blasting that creates potential damages in the production and undercutting level pillars and excavations. Delivery frequency according to studies. 12. Review of Geotechnical Instrumentation: This implies being updated and reviewing the statistics and trends of the installed instruments’ monitoring on a daily, weekly and monthly basis (production level pillars, undercutting, pre-mining zone, transition, de-stressing), based on the monitored parameters: deformation, displacement, and stress state, among others. Figure 3 shows the documents’ scheme for the deliverables developed by the operational geomechanical control and their delivery frequency. This example underlines the importance of including the operational geomechanical works in the mine integral management system through each one of the updated and available working procedures (DERK 2013).

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Figure 3 Document management examples for the operational geomechanical control in caving (DERK 2009; Díaz 2010; DERK 2011)

3

Resources required

This section provides a summary of equipment, instruments, tools and accessories that must be included in the basic resources that must be available for the field geomechanical control personnel:

• Personal computer with Windows platform, with Office, Grapher and Autocad software applications.

• Software for fragmentation estimation from digital photography processing. • Tablet for damage digital mapping and field geotechnical information. • State-of-the-art personal protection equipment and/or elements. • Compass and 50 meter measuring tape. • Digital distance meter. • Photo camera and high-resolution digital film camera. • Long range flashlights. • Tripod halogen floodlights. • Geology hammer. • Triaxial geophones and/or accelerographs and cables. • Seismograph (for example, Minimate Plus Equipment). • Borehole Camera equipment.

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Geotechnical Characterization • Adhesive agent as required by humidity and pollution (for example, Poxipol). • Laser Scanner equipment (for example, Mine Scan Geosite). • Logbook in three copies. • 4-wheel drive diesel light truck equipped with radio broadcasting. • 1 field technical assistant. • Drillhole lifting equipment (drillhole deviation).

4

Technical profile

There are several technical aspects that must be known by the professional that will work on the field geomechanical control or in the operational Geomechanics. In particular, he/she must know and manage concepts related to the following:

• Operational processes in a mine that uses caving methods. • Mine Caving planning and design. • Geology and geotechnical aspects associated to caving. • Induced seismicity. • Civil works construction. • Management of support computer tools for geomechanical analysis and evaluation; i.e., Rocscience, Autocad, Grapher, etc.

• Geotechnical instrumentation; i.e., convergence stations, extensometers, stress measurement, TDRs, among other typical instruments applied.

• Vibration control and modelling of blasting-induced damage. The professional shall preferably be a Mining Civil Engineer, with minimum general experience in underground mining from 2 to 5 years, and with specific expertise in Geomechanics for caving mining of at least 1 to 3 years.

5 Conclusions The short-term geomechanical knowledge management and documentation in a mine site is the responsibility of the operational Geomechanics and it is of vital importance for its medium to long-term sustainability. An efficient and constant communication among the mine operations areas, planning, geology and Geomechanics is fundamental to successfully mine a productive sector and to control its losses. The on-going training of the mine personnel on operational Geomechanics increasingly reduces the deviations that can take to geotechnical hazards that affect safety and the continuity of the productive process. The operational geomechanical management must be included in the mine’s integral management systems through its updated and available working procedures.

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Caving 2014, Santiago, Chile Acknowledgement DERK Ltda. gives thanks to its geomechanical team who worked with a high quality in El Salvador Division’ contracts (Codelco Chile) since 2009 to 2013.

References DERK 2013, Sistema de Gestión Integral DERK Ingeniería y Geología Ltda. DERK 2011, Servicio de asesoría integral en geología, geotecnia, geomecánica y planificación minera – División Salvador, Gerencia de Operaciones Minas-Plantas, Superintendencia de Geología y Planificación Minera-Metalúrgica, División Salvador – Codelco Chile. Díaz, J 2010, Productos técnicos servicio geomecánica operacional mina subterránea (SGOMS), DERK Ltda. PR-DSAL_SAEG_MS-P02-01-2011, Servicio de Asesoría Integral en Geología, Geotecnia, Geomecánica y Planificación Minera – División Salvador de CODELCO CHILE. DERK 2009, Servicio de asesoría integral en geotécnia y geomecánica – División Salvador, Gerencia de Operaciones Minas-Plantas, Superintendencia de Geología y Planificación MineraMetalúrgica, División Salvador – Codelco Chile.

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Gravity Flow

297

Gravity Flow

Use of experiments to quantify the flow-ability of caved rock for block caving RE Gómez, University of Chile, Chile R Castro, University of Chile D Olivares, University of Chile, Chile

Abstract Block/panel caving mining is a massive underground method, in which an ore column of broken rock is generate above the production level, as the cave propagates upwards through the ore body. ,As reserves deplete from near surface, the next generation of block caves will be carried out in deeper condition than currently known, with large column heights and therefore higher vertical stresses. There are unknowns related to the flow characteristics that deeper caves would face. The aim of this study is to quantify the impact of large vertical pressure on the flow-ability of fragmented rock. For this reason, experiments representing the stress and geometry conditions of deep caves were conducted under a range of vertical pressures, materials and humidity conditions. The results indicate that the flow-ability of caved rock depends on the vertical stresses, fines content and humidity conditions. Keyword: caving mining, flow-ability, hang up, vertical stresses, fines and humidity content.

1 Introduction In block/panel caving, ore production is affected by interferences associated with the caving process, especially, those related to the gravity flow, such as, hang-ups and oversize rocks. The mine design capability to provide a given production rate is affected, among other factors, by the ore flow. Flow-ability is defined as the flow condition or ability of a granular material to flow under a given set of material properties, infrastructure geometry and stress conditions. The flow-ability can be classified into free flow, intermittent flow, assisted flow and no-flow (Castro 2014). Kvapil (2008) indicates that flow-ability depends on many parameters including particle size, extraction rate, particles shape, surface roughness between particles, friction between particles, moisture content, compressibility, compaction, particle resistance, and magnitude, distribution and direction of external loads and forces. However, despite being listed, the flow-ability under all those sets of parameters has not been quantified. Flow-ability could be characterised both qualitatively and quantitatively. In terms of qualitative characterization, the flow could be qualified as free flow, intermittent flow and no flow, depending on the ratio between particle size and opening (Laubscher 2006). Studies on gravel have shown also that the flowability of granular material is influenced also by the vertical load (Fuenzalida 2012). Castro et al. (2014) have proposed a flow-ability chart for coarse and dry rock, which is presented in terms of vertical stress and drawpoint width/d50.

299


Figure 1 Influence of vertical stress in flow-ability

In quantitative terms, flow-ability can be characterised in terms of the number of hang-ups every 1,000 tons or broken rock drawn. Hang ups are one of the flow interferences that affect productivity. Moreover, hang ups can be used to measure the flow-ability of material because when a hang up occurs it means the flow of material has been interrupted and the broken rock cannot be extracted (Troncoso 2006). Two kinds of hang-ups can be formed in coarse material: cohesive and mechanical (Kvapil, 2008. Beus et al., 2001; Hadjigeorgiou & Lessard 2007). The formation of arches on a rough wall is generated by the rotation of the principal stresses on the wall and by induced wall pressures (Handy 1985). The dimensions of the arch depends on the friction angle of the material, depth or vertical stress, inclination of the walls in a drawpoint, draw rate, shape and strength of the particles, and humidity (Kvapil 2008). At the mine it has been observed that as more material is extracted from a drawpoint the frequency of the hang ups decreases (Maass 2013). This phenomenon is probably related to the decrease of the particle size during the extraction of an ore column (Montecino 2001). There are many unknowns related to the flow of materials especially under confined conditions. For example, what is the role of the fines, water and stresses on the flow-ability of the broken rock. In this article, we present the experiments conducted to evaluate the flow-ability of caved rock under high vertical load for different fines and humidity conditions. Extraction is carried out by a scaled LHD system to represent current caving characteristics.

2

Laboratory scaled model and material characterization

2.1

Experimental set up and materials

The experiments were conducted in a set up to study confined flow. This consist of a steel cylinder, which is filled with broken rock (70-80 kg of crushed ore) under a hydraulic press machine with a capacity of 1,800 kN. The steel cylinder has diameter of 340 mm, as shown in Figure 2. A cylindrical shape was chosen to avoid the concentration of stresses at singularities. The height of the designed cylinder is 700 mm in order to hold the desired volume of gravel and to suit the emulated Andina mine drawbell with a scale of 1:75 (Figure 2b), with a rectangular opening of 53 x 96 mm2. Since the drawbell is located in the center of the cylinder, flow zones will not intersect the walls of the model. A steel extraction system was built to replicate the extraction the same as LHD does from an extraction point (Figure 2a).

300

Gravity Flow

Figure 2 Experimental equipment: (a) cylindrical model in a press machine which changes the vertical load, σV, and (b) extraction system, located in the bottom, center of the model

The material used in the experimental tests was crushed sulphide ore with a high aspect ratio to represent the geometry of caved rock (sphericity of 0.58 and a roundness of 0.25). Two different particle size distributions of this material were prepared and tested: one with a passing size d80 of 11.8 mm and the other one with a d80 of 15.6 mm. Both samples have the same uniformity coefficient (Cu=d60/d10) of 2. Those particle size distributions were scaled from the size distribution of the primary fragmentation curve of the underground´s Chuquicamata project (Figure 3). Table 1 summarizes the characteristics of the samples.

Figure 3 Particle size distribution of samples used in the experiments

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Caving 2014, Santiago, Chile Table 1 Samples characteristics

*Drawpoint width for non-square geometry can be represented by hydraulic diameter (Jennings & Parslow 1988). 2.2

Experiments

A total of 18 experiments have been carried out to date described in Table 2. Ten experiments were performed without fines or humidity in order to define a base case considering different vertical loads. Then humidity and fines were added to the samples to measure their impact on the flow-ability. Fine material used in this study had a uniform distribution with d100 equals 1 mm. Humidity used in this study was 1.5 liters per 10 kg of fine material that is 15% of water. Table 2 Summary of experimental conditions

Test

Material

1 2 3 4

Humidity [%]

Fines [%]

1.5

0

0

0

A1

3 6

5

10

7

1.5

6 8 9

0

A2

10 11

12 13 14 15 16 17 18

302

Vertical load σv [MPa]

3 6

10 B C D E

0 6 0 6

0

0

0

0

0 0 0 0 0 0 0

0

15

15 15

11.8 11.8 11.8

0

15.6

0 0

40

0

11.8

15.6

0

0

0

11.8

0

20

15

6

0

0

0 6

0

Size d80 [mm]

20 40 20 20 40 40

15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6 15.6

Gravity Flow 3 Results The experiments were performed twice for coarse ore and once for fine material (the latter only for one particle size distribution). During the tests, the flow-ability, the hang up frequency and the hang up height were recorded. 3.1

Flow-ability

Flow-ability is classified into free flow, intermittent flow, assisted flow and no-flow (Figure 1). In terms of flow-ability, the results (Table 3) indicate that for materials A1 and A2 flow-ability decreased from free flow to no flow when vertical load increased from 0 to 10 MPa. When fines were added without humidity, the flow condition was intermittent or assisted. When water was added, the flow was assisted flow and, when there were a 40% of fines, the flow condition was no flow at all. Table 3 Summary of experimental results

Test

Material

1 2 3

Vertical load σv [MPa]

Flow condition

1.5

Assisted Flow

0

928

371

1246

Intermittent flow

1068

5

10

No Flow

0

7

1.5

Intermittent flow

1036

6

Assisted Flow

599

6 8 9

11

12 13 14 15 16 17 18

6 0

A2

10

3.2

Standard dev. [g/hang up]

3

4

A1

Free Flow

Interferences [g/hang up]

3

10 B C D E

Assisted Flow Free Flow

Intermittent flow No Flow

368

640 256 246 -

1177

471

761

356

0

471 276 -

0

Intermittent flow

1014

248

0

Assisted Flow

501

153

0

Assisted Flow

0

No flow

6 6 6 6

Assisted Flow Assisted Flow Assisted Flow No flow

586 475 352

312 180 97

378

112

0

-

0

-

Hang up frequency

During the experiments, it was possible to detect flow interruptions or hang-ups, which were recorded in terms of mass and height. The hang up frequency (Hg) is defined as the amount of material that can be drawn before an interruption happens. The experimental results of Hg as a function of the vertical stress for each laboratory test are presented in Figure 4.

303


Figure 4 Hang up result in laboratory test. A: coarse material test (duplicates included), B: 20% of fine material, C: 40% of fine material and D: 20% of fine material with humidity

The experimental results in coarse material (A) show that increasing the vertical stress decreases the flowability of material. For the media B, that is the one with 20% of dried fines, the hang-up frequency number decreased. For materials C and D, the vertical load had no significant influence on the frequency of hangups as they were not able to flow. Field measured hang-ups are quantified by their hang-ups index (number of hang-ups per 1,000 ton of ore). The measured hang-ups index in the experiments is similar to the observed index of primary sulphides in mines. The index varies from 1.6 to 3.6 in mines and, as can be seen in Table 4, the scaled experimental index varies from 0.75 to 3.95. Table 4 Scaled hang ups index

Vertical load [MPa]

Hang ups index [# hang up/ 1000 ton] A1

A2

0

1.05

2.05

3

1.09

3.11

1.5 6

10

1.26 3.17 0.75

2.29 3.95 3.32

For the experiments with vertical load of 10 MPa the material got strongly compacted over the drawbell generating a great hang-up above the drawbell and almost no hang-ups of lower height.

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Gravity Flow 3.3

Height of hang-ups

A classification was made according to the height of the hang-up:

• Low: in extraction point. • Medium: in drawbell (0-13.5m from the roof of the production level) • High: Above drawbell (over 13.5 m). The geometry of the drawbell from which the dimensions were scaled is represented in Figure 5.

Figure 5 Drawbell geometry in real size

Average hang-up height of each experiment based on the vertical stress is shown in Figure 6. It can be seen that as the vertical stress increases, the height of the hang-ups increases simultaneously. The dimensions of each observed hang-up were scaled in order to quantify their height in the mine.

Figure 6 Hang-up heights in laboratory test. On coarse material A1: d80=15.6 [mm], A2: d80=11.8 [mm], B: 20% of fine material, C: 40% of fine material and D: 20% of fine material with humidity

In general, the height of the hang-ups increases with the vertical pressure for the coarse material (A1 and A2). In the case of the addition of fine (materials B and C) the results indicate that vertical load has a small impact in the increase of the height of the hang-ups. When fines and water were added (material D) there is no effect of the vertical load on the height of the hang-ups.

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Conclusions and discussion

Based on experimental tests, this paper shows that particle size as well as the moisture content and vertical stress have a noticeable impact on the flow-ability of caved rock. The number and height of hang-ups increases as the vertical load increases. Fines and humidity increase the number of hang ups. Also, high hang-ups occurs when a vertical pressure was applied or when humidity and fines were acting together. The scaled model was successful in understanding how confinement, particle size distribution, humidity and fines’ presence affect the flow-ability of material. It is expected that as block caves get deeper the number of hang ups would increase if fragmentation keeps constant. The results of the above experiments shows that the hang-up number for mine design applications could be obtained from this kind of experiments. This would require further research and analysis. It is expected that this kind of experiments would be will become standard to the caving industry, especially for future and unknown conditions.

Acknowledgement We want to thank Patricio Ávila and Alonso Vives for their help in these experiments, the staff of Block Caving Laboratory for their support and encouragement and Asieh Hekmat for her advices. This project was conducted under the partial funding of the basal project for Basal Excellences, which is an initiative of the Chilean government.

References Beus, M, Pariseau, W, Stewart, B & Iverson, S 2001, ‘Design of Ore Passes’, in Underground Mining Method, pp. 627-634. Castro, R, Fuenzalida, M & Lund, F 2014, ‘Experimental study of gravity flow under confined condition’, International Journal of Rock Mechanics and Mining Sciences, vol 67, pp. 164-169. Fuenzalida, MA 2012, Study of the confined gravity flow and its application to caving, Master’s Thesis, Santiago, Chile, Universidad de Chile. (in Spanish). Jennings, BR & Parslow, K 1988, Particle size measurement: the equivalent spherical diameter, in Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 419(1856), pp. 137-149. Hadjigeorgiou, J & Lessard, JF 2007, ‘Numerical investigations of ore pass hang-up phenomena’, International Journal of Rock Mechanics and Mining Sciences, vol. 44 Nº6, pp. 820-834. Handy, RL 1985, ‘The arch in soil arching’, Journal of Geotechnical Engineering, vol. 111 Nº3, pp. 302318. Kvapil, DR 2008, Gravity flow in sublevel and panel caving – A common sense approach. Laubscher 2006, Cave Mining Handbook. Maass, S 2013, Technological alternatives for hang ups removal, Master’s Thesis, Santiago, Chile, Universidad de Chile. (in Spanish). Montecino, N 2011, Secondary fragmentation mix model in block/panel caving mining. Master’s Thesis. Santiago, Universidad de Chile. (in Spanish). Troncoso, S 2006, Simulation of operational interferences impact on production planning, Thesis, Santiago, Chile, Universidad de Chile. (in Spanish).

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Gravity Flow

An analysis of the lateral dilution entry mechanisms in Panel Caving PS Paredes University of Chile, Chile MF Pineda University of Chile, Chile

Abstract Dilution is an integral part of a cave mining operation and its behaviour affects different dimensions of a caving project from the economic results to safety inside the mine. One of the objectives of mine planning and draw control in panel caving is to maximize ore recovery by minimizing the overall dilution content extracted. The existing gravity flow models that aim to predict dilution entry and its behaviour do not consider the possibility of dilution particles to move laterally and affect the content of drawpoints that are contiguous to a previously exhausted area. This paper contributes to the understanding of this important variable in cave mining methods by presenting an analysis of the lateral dilution entry mechanism hypothesis presented by Castro & Paredes (2014). The objectives of this analysis were to determine the processes through which this mechanism enables dilution particles to move laterally from a previously exhausted panel to a new panel in production and to set the basis for further development of guidelines for mine design and planning of new panels contiguous to previously exhausted panels. The analysis was performed with two approaches: a mechanical analysis supported with mine data and experimental observations at a laboratory scale. The authors postulate that there are two processes through which dilution can migrate lateral distances: lateral displacement over the broken ore pile and lateral preferential flow from the dilution source. A mechanical analysis supported with mine data shows that lateral displacement of dilution over the broken ore pile is feasible under cave mining conditions. Consequently, experimental observations suggest that lateral preferential flow of dilution is possible under certain conditions. Additionally, vertical fines migration through shear zones was observed. Finally, for mine design and planning of a new panel contiguous to a previously exhausted panel, an expression for the maximum caving face width as a function of several mining parameters is proposed. Keywords: panel caving, dilution entry; gravity flow mechanics; draw control.

1

Introduction

Dilution control is crucial in managing a block/panel caving operation. This variable affects different dimensions of a caving project from the economics to safety in the mine. Several authors have stated different parameters affecting dilution entry (Table 1) and two main dilution behavior models have been developed in order to provide tools for dilution entry prediction and control: Laubscher´s (1994) and Susaeta´s (2004). Laubscher´s formulae for dilution entry prediction are based on the estimation of the height of interaction, the swell factor of caved material and a measure of the uniformity of extraction. This last quantity is based on the standard deviation of the tonnages extracted from the working drawpoints. He postulates that as the draw uniformity increases, dilution entry will be delayed. Susaeta used mine data to expand Laubscher’s model and postulated that dilution entry depends on the way draw is conducted, defining three behavioral models: Isolated Flow, Isolated-Interactive Flow and Interactive Flow. Flow behavior can pass from one model to another by increasing or decreasing uniformity of extraction. As the draw is more uniform, the flow will be more interactive and dilution will be lower. Both approaches assume that the dilution source is

307

Caving 2014, Santiago, Chile located above the area under analysis. Finally, they assume that the dilution particles will flow downward to the drawpoints without being affected by the cave back propagation profile or rilling potential. Table 1 Parameters affecting dilution (Castro & Paredes 2014)

Parameter

Ore/waste contact surface inclination Ore volume to ore/waste interface area Attitude of ore/ waste interface area Fragmentation range from ore and waste

Height of the interaction zone

Differences in density of ore and waste

Effect on dilution

Author

To minimize dilution, ore/waste contact interface must be kept at 45 to 50° moving away from the uncaved ore through draw control.

DeWolfe (1981), Julin (1992)

Irregular ore/waste interface areas will cause severe dilution entry.

Laubscher (2000)

The higher the ratio of ore volume to the surface area of the ore/ waste interface, the lower the overall percentage of dilution.

Finely fragmented waste and coarse ore means early and extensive dilution, whilst coarse waste and fine ore means a low overall extracted dilution percentage. Good draw zone interaction and parallel flow will represent the optimum conditions. Poor drawpoint interaction and draw zones angled according to local variations will lead to high dilution.

Laubscher (2000)

Laubscher (2000) Laubscher (2000)

High-density ore and low-density waste lead to low dilution and vice versa.

Laubscher (2000)

Block or panel caving strategy

A block cave strategy will lead to more lateral dilution mixing than panel caving.

Laubscher (2000)

Uniformity of draw

High uniformity of the tonnage drawn from the neighbour drawpoints will lead to high interaction and late dilution entry.

Julin (1992), Susaeta (2004)

Despite the fact that the latest field studies using markers have shown how chaotic ore flow behavior is in the near field (Brunton et. al. 2012; Castro & Armijo 2012), there is still significant potential to conduct far field experiments to better understand dilution behaviour in the far-field. Empirical observations of the historical dilution behavior at CODELCO’s El Salvador and Andina mines conducted by Castro & Paredes (2014) showed that 51% of the drawpoints with dilution (over a total of 1674 drawpoints with dilution analyzed) had a pulsed-shaped behaviour in which the dilution goes back to zero after its first appearance. This non-continuous dilution entry behavior led to the definition of a dilution entry criterion based on the cumulative dilution curves of the drawpoints. Thus, when the cumulative dilution curve overcame a certain threshold (3% for Castro & Paredes’ analysis) significant dilution entry was declared for the drawpoint at its corresponding extraction percentage. Using this criterion, Castro & Paredes analyzed the dilution entry behavior at a drawpoint scale for 6 different sectors considering the initial in-situ column heights, extraction sequence and uniformity of extraction using Susaetas’s Uniformity Index. This analysis led to the development of a hypothesis for the mechanisms by which dilution entered the drawpoints: (1) vertical entry by descending gravitationally from the source located above the sector, (2) dilution entry after an air blast event due to sudden propagation of the cave back when new area is incorporated, and (3) lateral dilution due to horizontal displacement of dilution particles from a source located next to the panel under analysis.

308

Gravity Flow The following sections correspond to an analysis of the lateral dilution entry mechanism hypothesized by Castro & Paredes, incorporating experimental results derived by Pineda (2012), in order to contribute to the understanding of dilution behavior in Panel Caving methods.

2

Lateral dilution entry mechanism hypothesis

According to Castro & Paredes (2014), when the dilution source is located next to the panel under analysis, the cave back could preferentially propagate, in terms of rate, to the lateral interface of the in-situ and caved waste rock. This preferential propagation of the cave back could enable dilution particles to enter the draw columns and flow along the cave boundary (as observed in sand flow experiments by Kvapil (2004)), travelling horizontal distances depending on the pile’s slope and producing early dilution entry in drawpoints located far from the dilution source. This phenomena was observed at the Inca Central East (ICE) and the Inca North (IN) sectors in El Salvador mine and at the Grizzly Cluster of Andina mine’s Panel III sector.

Figure 1 Schematic sequential drawbell sections, showing lateral dilution mechanism (Castro & Paredes 2014)

Figure 2 illustrates ICE’s drawpoints dilution entry behavior: as the sequence progresses going from the dilution source (IC previously exhausted panel), allocated to the north of ICE, towards the south; dilution particles enter the drawpoints earlier (DEP decreases as drawpoints are more distant from the dilution source). According to Castro & Paredes, this behaviour suggests that dilution particles coming from the lateral dilution source travelled large lateral distances rilling over the broken ore pile, following the process described in the previous paragraphs. The authors identified a second mechanism governing lateral dilution entry, that is, lateral percolation preferential flow of particles from the dilution source into the drawpoints of a recently incorporated area. Under certain conditions, when the new broken ore is in contact with waste, there is a potential for the movement zone to develop towards the higher porosity zone, since it represents lower strength path for the movement to develop. This, in the case of a new panel contiguous to a previously exhausted panel, could

309

Caving 2014, Santiago, Chile promote lateral preferential flow of dilution particles into the new drawpoints. There is a laboratory scale evidence of this phenomenon as observed in the experimental results derived from the use of a physical model by Pineda (2012).

Figure 2 Plan view and Schematic cross-section of an ICE sector´s drift showing decrease in DEP as the sequence progresses from the lateral dilution source

3

Lateral displacement mechanical feasibility analysis

In order to probe the mechanical feasibility of this hypothesis, a limiting equilibrium analysis of the broken material wedge, formed by the existence of an air gap, was performed. This consisted of analysing the stability of the parameterized problem under the conditions in a panel cave operation (Figure 3). α: Failure surface angle. β: Granular material pile slope.

W: Vertical force due to the wedge’s weight. σz: Force applied by the overload. , φ, c: Bulk density, friction angle and cohesion of the broken material. h: Air gap height. b: Length of the horizontal projection of the failure surface. z: Overload height. L: Failure surface length. Figure 3 Limiting equilibrium analysis theoretical approach

The equilibrium analysis can be reduced to the determination of the safety factor (SF) defined as:

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Gravity Flow SF = FFE

D

[1]

where FE and FD are the summation of stabilizing and destabilizing forces acting over the failure surface, respectively. If the SF value is less than 1, the broken material wedge is unstable and the material will slide over the failure surface to enter the granular material pile. Considering g as the acceleration of gravity, and the Janssen (1985) approach for the force due to the overload, the destabilizing forces can be expressed as follows:

[2]

Where:

[3]

For the broken material, the stabilizing force corresponds to the shear strength of the failure surface. Using the Mohr-Coulomb criterion, the stabilizing forces can be expressed as follows:

[4]

Thus, an expression for the safety factor as a function of the problem’s parameters is: [5] From the evaluation of the safety factor using the geotechnical characterization of the broken material from the Panel III sector, it is possible to observe that for overload heights over 8 m, the formation of a failure plane with an incline of over 35° will cause instability in the wedge even for a 10 cm air gap height. The previously described condition is sufficient to allow the detachment of a group of dilution particles. Nevertheless, to allow a significant dilution entry on the granular material pile, the air gap height must be enough to allow dilution particles to overcome the arching effect described by Hoek (2004). Hoek (2004) states that the opening through which a group of particles can flow must be greater than 3 times the average particle size in order to overcome the arching effect. Thus, considering Panel III’s conditions, an air gap height over 60 cm could allow a significant number of dilution particles to enter the granular material pile (Paredes 2012). Once dilution particles have been detached from the lateral dilution source and entered the granular material pile, lateral displacement over the pile will be controlled by the pile’s slope (β) and the angle of repose of these dilution particles (Ød). If β is greater than Ød, dilution particles will be able to rill over the granular material pile. In a Panel Caving operation, β will take different values depending on the extraction rates, and exceeding Ød in some cases. If we consider the long term case, where the granular material pile finds its angle of repose (Øp), lateral migration of dilution particles will occur if this angle (Øp) is greater than Ød. Considering Panel III’s geotechnical characterization of dilution and ore, the angle of repose (32° and 53°, respectively) would allow the occurrence of lateral migration of dilution over the granular material pile.

4

Lateral preferential flow observations at lab scale

At the request of Agnico-Eagle, a physical model was built and tested by the Block Caving Laboratory at the University of Chile in order to study the gravity flow mechanisms of a large stope (320 m height and 45.000 m2 of production area) extracted using a block cave layout for blasted material. This free flow material condition emulates a panel caving operation once the whole ore column has been caved.

311


Physical model experimental set-up and plan

For experimental purposes a 1:200 scale model was built. During the design stage of the experiments, all the laws of kinematic similitude for granular materials (full geometrical similitude and extraction rate) were taken into consideration as detailed by Castro et al. (2012). Table 2 describes the experimental set-up and Figure 4 presents the experimental design.

Figure 4 Experimental design: Physical Model (right) and drawpoints and apex (left) Table 2 Experimental set-up

Main Assembly Loading System Extraction Material System

Model Media

4 dismountable plexiglass walls delineating the final geometry of the stope Plexiglass assembly supported by iron vertical columns and rows

Dimensions of the model 1.6 m height x 1 m length x 0.25 m width at lab scale Material and labelled markers loaded manually 11 drawpoints, each one includes shovel linked to a servomechanism device that simulates the LHD extraction Servomechanism allows varying the extraction rate

Coarse gravel with a d100 of 4.75 mm and a d50 of 2.2 mm allowing free flow according to Hustrulid and Sun (2004)

Material dried to eliminate the humidity effect and capillarity between grains For simulating dilution entry the model media is dyed using red dust

The experimental plan and its main objectives are outlined in Table 3. Table 3 Experimental Plan

Experiment

Draw strategy

1

Isolated draw

2

Uniform draw

312

Objective To determine isolated flow zone diameter geometry and to test the proper operation of the model.

This experiment simulates continuous dilution entry at the top of the stope. The aim is to quantify potential dilution entry mechanism

Gravity Flow 4.2

Experimental Results

4.4.1

Experiment 1: Isolated draw

In a first stage, an Isolated Draw Zone experiment was performed to calibrate the correct operation of the extraction system for the physical model. For this experience, the extraction was conducted only in one drawpoint. Once the Isolated Movement Zone (IMZ) reached the surface of the stope the extraction was completed and the IMZ was measured. The main results of this experiment can be observed in Figure 5.

Figure 5 Progress of the Isolated Draw Zone

4.4.2

Experiment 2: Uniform draw with continuous dilution entry from the top

An experiment simulating dilution entry at the top of the stope was performed. Dilution was simulated by adding a granular material on top of the stope dyed in red colour. This experiment was performed by extracting from two extraction levels; the main extraction level located at the base of the model and a secondary extraction level located in the stope footwall, 30 m (15 cm at model scale) above and 60 m (30 cm at model scale) away from the main extraction level; these distances are provided at prototype scale. This horizontal distance was determined based on the IMZ experiment conducted previously in order to avoid an early connection between flow zones. The extraction for each drawpoint, in the main level, was continued until dilution was reported, at which point draw from that individual drawpoint was stopped immediately. Afterwards, extraction was continued for the rest of the drawpoints until all of them exhibited a significant content of dilution. Once all the drawpoints of the main extraction level were closed, because of the dilution content, the drawpoints from the secondary level were started into production following the same considerations stated above. As a result from this experiment, Pineda (2012) observed how the flow zone developed faster at the lower column height, generating an extraction profile; this profile is key in order to define the ore/waste interface during the whole extraction process. Dilution moves downwards according to the flow velocity profile, being faster in the proximity of the hangingwall as observed in sand experiments by Kvapil (2004). As extraction progressed, it was noted that the fines contained in the red material (tinting red dust) migrated to the drawpoints (Figure 6d). Once the area relative to the extraction from the main level was exhausted (Figure 6g), the extraction from the secondary level began. This generated a different extraction profile and promoted lateral movement for the dilution source. Due to the lateral movement of the material, dilution particles move towards the

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Caving 2014, Santiago, Chile recently opened drawpoints diluting them in an early stage as hypothesized formerly (Figure 6h). Therefore, in terms of mine design, a larger spacing would be recommended to avoid the flow zones to connect to the diluting lower density zones.

Figure 6 Progress of the extraction under uniform draw

5

Avoiding lateral displacement of dilution particles over the ore pile

Considering the lateral dilution entry mechanisms described previously, in this section, an analysis of the main variables in mine design and planning of a new panel is performed. The analysis proposes an expression of the maximum caving face width that would prevent lateral displacement (rilling) of dilution particles over the ore pile. Block Caving methods need to maintain equilibrium between the material extracted and the material caved to propagate caving during the early stages of a block or panel production. Therefore, it is not possible to avoid the formation of short air gaps. Thus, one possible strategy to avoid lateral dilution migration over the ore pile is to control the granular material pile’s slope during caving propagation. Once the air gap is formed, if the local slope between the pile heights of two contiguous drawbells in the sequence direction β (the local pile slope) is greater than the angle of repose of dilution particles Ød, dilution will be able to rill over the pile (Figure 7a). Using a simple approach, the time delay in days, defined as the difference between the beginning of production for two contiguous drawbells, can be expressed as a function of the new area incorporation rate (VD, in m2/day), the width of the caving face (W, in m), the distance between the contiguous drawbells (d, in m) and the strike angle of the footprint δ (see Figure 7b): [6] Thus, for a given period during the caving propagation phase, the pile height difference between two contiguous drawbells in the direction of the sequence, that have the same caving vertical propagation rate, will be:

314

Gravity Flow [7] Where: VEX, in t/m2/day, is the extraction rate of the drawpoints; VCP, in m/day, is the caving vertical propagation rate; ρS, in t/m3, is the ore solid density; and e is the void ratio inside the pile. Then, the local pile slope (β) will be given by: [8] And dilution will be able to migrate laterally over the pile if: [9] From the parameters shown above, there are two main mining variables that can be controlled for design and planning purposes: (1) the new area incorporation rate, and (2) the caving face width. The new area incorporation rate is generally limited by several technical conditions, so the most flexible variable is the caving face width. Thus, an expression for the caving face width that would ensure the prevention of early dilution entry caused by rilling particles coming from a lateral dilution source is presented: [10]

Figure 7 a) Schematic section view showing pile heights difference (Δh) and spacing (d) for two contiguous drawbells; b) Schematic plan view of drawpoints showing caving width (W), sequence direction, contiguous drawbell spacing (d) and footprint strike (δ)

The maximum caving face width for different vertical caving propagation rates Vcp is calculated, considering the values in Table 4, as an academic example (Figure 8). From the figure, it is worth noting that the maximum resulting caving face width varies significantly with small changes in Vcp.

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Caving 2014, Santiago, Chile Table 4 Variable values for maximum caving face width example

Variable e Vex S

δ φd VD

Unit % t/m2/d t/m3 ° ° m2/d

Value 40 0.3 3 30 32 30*

* equivalent to a rate of 2 drawbells per month in a 30 m x 15 m drawpattern

Figure 8 Example of maximum caving face with as a function of the vertical caving propagation rate considering Table 4 values

6 Conclusions Two hypotheses for the lateral dilution entry mechanism in panel caving were presented and described: (1) displacement of dilution particles from a lateral dilution source over the broken ore pile (via rilling) and (2) preferential flow of dilution particles from a lateral dilution source (via inclination of the IMZs towards more free-flowing material). Additionally, the observation of fines vertical migration suggests that the relation between ore and dilution particle’s size affects the vertical entry of dilution, as the tinting red dust’s particle size was significantly smaller than the model media used to represent ore. Through a limiting equilibrium analysis, the first mechanism was proven to be feasible under panel caving operation conditions. On the other hand, experimental evidence suggests that, under free flow conditions, the second mechanism occurrence is possible. Finally, an expression for the maximum caving face width that avoids dilution entry through the first mechanism described is proposed for mine design and planning purposes.

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Gravity Flow 7 Acknowledgments The authors would like to acknowledge CODELCO-Chile, Agnico-Eagle and the Block Caving Laboratory of the University of Chile for providing funding and data for the research. We would also like to thank Mr Raul Hott for his helpful support during the writing of this article.

8 References Brunton, I, Sharrock, G & Lett, J 2012, ‘Full Scale Near Field Flow Behaviour at the Ridgeway Deeps Block Cave Mine’, Proceedings of MassMin 2012, Sudbury, Canada. Castro, R, Pineda, M 2012, ‘Draw control at Goldex mine. Internal report to Agnico-Eagle’, Laboratorio de Block Caving, Universidad de Chile. Castro, R, Armijo, F 2012, ‘Experimental design for the full scale flow test marker at El Teniente’, Internal Report to Codelco Chile, Laboratorio de Block Caving, University of Chile. Castro, R, Pablo, P 2014, ‘Empirical observations of dilution in panel caving’, Accepted for publication. Paper 12/110. Journal of South African Institution of Mining and Metallurgy. DeWolfe, V 1981, ‘Draw control in principle and practice at Henderson Mine’, Desing and Operation of Caving and Sublevel Stopping Mines, (Ed: D. R. Stewart), Society of Mining Engineers, USA. Hoek, E 2004, Model to demonstrate how rockbolts work. [online] Hoek’s corner < http://www.rocscience. com/hoek/corner/15_Model_to_demonstrate_how_rockbolts_work.pdf> Hustrulid, W & Sun, C 2004, ‘Some remarks on ore pass design guidelines’, Proceedings of MassMin 2004, Santiago, Chile. (Ed(s): A.Karzulovic and M.Alfaro), Chilean Engineers Institute, pp. 301-308. Janssen, H 2004, ‘Experiments regarding grain pressure in silos written in 1895’, Proceedings of MassMin 2004, Instituto de Ingenieros de Chile, Chile, pp. 293-300. Julin, D 1992, ‘Block Caving’, SME Mining Engineering Handbook, 2nd edition. Society for Mining, Metallurgy and Exploration. Kvapil, R 2004, Gravity flow in sublevel and panel caving - a common sense approach, Luleå, Sweden: Luleå University of Technology. Laubscher, D 1994, ‘Cave Mining – the state of the art’, Journal of South African Institute of Mining and Metallurgy. Laubscher, D 2000, Block Cave Manual. Prepare for the International Caving Study 1997-2000. Julius Kruttschnitt Mineral Research Centre, The University of Queensland. pp. 111-118. Paredes, P 2012, Dilution Entry Mechanisms in Block and Panel Caving Operations, Master Thesis. University of Chile, Chile, (in Spanish). Pineda, M 2012, Study of the gravity flow mechanisms at Goldex by means of a physical model, Master Thesis, University of Chile, Chile. Susaeta, A 2004, ‘Theory of gravity flow (Part 1)’, Proceedings MassMin 2004, Santiago, Instituto de Ingenieros de Chile, Chile. Susaeta, A 2004, ‘Theory of gravity flow (Part 2)’, Proceedings MassMin 2004, Santiago. Instituto de Ingenieros de Chile, Chile.

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Application of a methodology for secondary fragmentation prediction in cave mines MA Fuenzalida Itasca Consulting Group, Inc., USA T Garza-Cruz Itasca Consulting Group, Inc., USA M Pierce Itasca Consulting Group, Inc., USA P Andrieux Itasca Consulting Group, Inc., USA

Abstract This paper describes how primary fragmentation estimates can be combined with REBOP simulations to predict the secondary fragmentation of caved rock. Primary fragmentation estimates are obtained from separate numerical models that link caving induced stresses at failure (estimated with FLAC3D cave-scale models) with associated fragmentation of rock masses (estimated through systematic Synthetic Rock Mass testing with 3DEC). REBOP takes the predicted spatial distribution of primary fragmentation (along with intact rock strength, drawpoint layout and draw schedule) as input and provides estimates of the evolving secondary fragmentation in the column and at the drawpoints. The logic is based on laboratory studies that link attrition (via block splitting and rounding) to the product of shear strain and normal stress (essentially the work done on the fragments).

1 Introduction Fragmentation of an orebody plays an important role in cave mines, dictating whether the operation will be successful. According to Laubscher (1994, 2000), fragmentation will influence drawpoint size and spacing, equipment selection, draw control procedures, production rates, dilution entry onto the draw column, hangups and the need for secondary breakage. As caved rock is extracted, the cave progresses upwards forming rock fragments in the cave back vicinity due to the effect of gravity or induced stress acting on the inherent discontinuities in the rock mass, which is a process known as primary fragmentation. Correspondingly, zones of moving material known as Isolated Movement Zones (IMZ) develop in the ore column above the drawpoints. Within the IMZs, fragments of rock are subjected to relatively low normal stresses (due to arching) but high shear strains (due to the non-uniform nature of the velocity profile, which is characterized by a central plug flow region surrounded by a shear annulus roughly 10-15 fragment diameters wide). In the surrounding stagnant zone outside the IMZs, normal stresses are high but shear strains are very low (Lorig and Cundall 2000; Pierce et al. 2010). Fragment attrition is believed to occur in both of these zones, a process known as secondary fragmentation (Brown 2007). Efforts have been made to provide means to predict secondary fragmentation based on rules established through experience and engineering judgement (Esterhuizen 1998), and also through experimental work (Castro et al. 2014). In the following sections, a methodology for secondary fragmentation estimation is presented that is based on the results of experimental studies of attrition under shear combined with REBOP flow simulation. The approach considers shearing-dominated attrition within the IMZs, but not compaction-dominated attrition within the stagnant zones. Application to a case study is also presented.

318

Gravity Flow 2

Methodology to assess fragmentation in cave mines

A new methodology for drawpoint fragment size distribution prediction has been formulated (Figure 1). The first step is to calculate the primary fragmentation through the use of a combination of numerical models taking into account the discontinuities in the rock mass and the induced stresses that are likely to develop in the ore column as the caving progresses. Using the resulting primary fragmentation estimates as input, REBOP (Pierce 2010) is able to estimate the secondary fragmentation through the use of an attrition model.

Figure 1 Diagram illustrating the methodology to predict secondary fragmentation

2.1

Assessment of primary fragmentation

Synthetic rock mass (SRM) samples were constructed by assembling a collection of tetrahedral blocks and populating their contacts with strength values randomly selected from a cumulative strength distribution (Garza-Cruz & Pierce 2014). A variety of SRM samples were tested under a suite of triaxial tests as well as uniaxial compression and tensile tests to characterize their strength. The model allows the blocks forming the sample to break at their subcontacts as a result of stress concentrations, mimicking the initiation of cracks that can coalesce and/or propagate to fracture the rock mass. The rock mass strength derived from testing the SRM samples was used to inform a cave-scale FLAC3D (Itasca 2013) model. As the cave is propagated, the model records the induced stresses at failure. The stresses at failure then are combined with the SRM-derived associated fragmentation distribution to predict spatial primary fragmentation through the column (Garza-Cruz et al. 2014). Because REBOP considers primary fragmentation as a Gaussian distribution, a least squares technique was used to best fit the different primary fragmentation curves for the different geological domains. 2.2

REBOP as a tool to estimate secondary fragmentation

REBOP was developed by Itasca for the industry-funded International Caving Study (ICSI and ICSII) and Mass Mining Technology (MMT) projects as a tool for rapid simulation of material flow within block, panel and sublevel caves. REBOP models flow by tracking the growth of IMZs associated with draw. An

319

Caving 2014, Santiago, Chile IMZ in REBOP is comprised of a number of discrete disk-shaped layers stacked above the drawpoint. The volume of an IMZ is tracked in REBOP by balancing the incremental mass drawn from the drawpoint with the mass produced by a bulking of material at the periphery of the IMZ. The amount of bulking is controlled at the local level by the average initial and maximum porosities of material at the IMZ periphery as specified in the block model. The relative rate of upward and lateral growth (which controls IMZ shape) is controlled by the fragmentation and friction angle of material at the IMZ periphery as specified in the block model. Experiments (e.g., Castro 2006) and simulations (e.g., Lorig & Cundall 2000) reveal that an IMZ tends to develop an approximately ellipsoidal or cylindrical shape with an evolving width that is most strongly controlled by mean fragment size. As a result, IMZs tend to be narrow in finely fragmented rock and wide in coarsely fragmented rock. This behavior, which can be simulated within REBOP, directly affects the drawpoint spacing that is required to maximize mobilization of material throughout the volume of interest (Pierce 2010). Material movements within the IMZs are tracked via a field of markers, the positions of which are updated daily according to the local velocity profile within the IMZ. Markers are assigned a tensile strength and fragment size randomly from the input distributions of their parent lithology when they are first created. As markers move down the IMZs and mix, their diameters are averaged within each disk-shaped layer to control the local IMZ expansion rate and internal velocity profile. Secondary fragmentation in REBOP is handled by systematically reducing the fragment size associated with markers based on their tensile strength and the stress and strain experienced as they transit through the IMZ toward the drawpoint. More specifically, the degree of breakage experienced by a rock fragment moving inside the IMZ is a function of the average stress inside the IMZ (estimated via bin theory) relative to the strength of the fragments and the incremental shear strain. At present, REBOP makes use of the shearing attrition model developed by Bridgwater et al. (2003):

(1)

Where: = the mass fraction attrited from the mono-sized initial assemblies (percent); = the normal stress applied to the assembly (MPa); = the total shear strain applied to the assembly (dimensionless); = the fracture or tensile strength of the constituent particles in the assembly (MPa); and = empirical constants (dimensionless). 2.2.1

Strength Scale Effect

REBOP’s secondary fragmentation methodology takes into account the rock block strength scale effect. It has been established that the intact strength of rock decreases with increasing scale due to a higher probability of the presence of discontinuities. Hoek & Brown (1980) developed an empirical scale effect relation for intact strength on the basis of laboratory testing conducted by a number of different researchers on homogenous hard rock samples (i.e., samples lacking significant microfracturing or alteration). (2)

320

Gravity Flow Where: σc

= uniaxial compressive strength;

σc50

= uniaxial compressive strength of a cylindrical specimen with a 2:1 height-to-width ratio and a base fragment diameter of 50 mm;

de

= arbitrary diameter in mm; and

k

= constant, scale effect exponent.

While Hoek and Brown (1980) estimated k being equal to 0.18 for relatively homogenous hard rock, work by Yoshinaka et al. (2008) demonstrated that the range in scale effect can be much wider when more flawed intact rocks are considered, with k ranging from 0.1 to 0.9 (Figure 2). Adjustments to the strength scale effect exponent, k, allow REBOP models to account for potential effects of defects that may exist in the intact rock blocks, and therefore reduce rock block strength.

Figure 2 Scale effect relations for intact rock UCS proposed by Yoshinaka et al. (2008). The relation of Hoek & Brown (1980) is shown for comparison (Pierce et al. 2009)

2.2.2

Modes of secondary fragmentation

REBOP accounts for the following three main effects that link attrition to the product of shear strain and normal stress with respect to secondary fragmentation:

1. Block splitting occurs when the average normal stresses inside the IMZ are high relative to the

block strength. Splitting results from tension induced in a fragment via compression. If the induced tensile stress exceeds the tensile strength of the grain, it will split.

2. Rounding of block corners and generation of fines refer to the removal of asperities from the surface of the block as the fragments move down in the draw column.

3. Fines cushioning takes place when large blocks are surrounded by a large number of smaller fragments, providing confinement and increasing strength and survivability as a result.

321

Caving 2014, Santiago, Chile All three modes are responsible for the reduction of block size. The resulting characteristic evolution in fragment size distribution is illustrated in Figure 3.

Figure 3 Main effects of secondary fragmentation results observed using REBOP: (1) corner rounding leading to fines production, (2) block splitting leading to a breakdown in blocks of intermediate dimension and (3) fines cushioning leading to survivability of large blocks

3

Application of the methodology in cave mines

3.1

Case study

The case of study consisted of an ore deposit with three main geological domains: “weak,” “moderate” and “strong” representing different rock mass strengths. The results of SRM primary fragmentation predictions were combined with the distribution of strength domains and the predictions of cave back stresses at failure to derive a distribution of primary fragmentation for input to REBOP. As illustrated in Figure 4, a relationship between induced stress and mean fragment volume could be established assuming a linear behavior. Based on the assumption that fragments are likely shaped as cubes, the following equations can be fitted to the mean and standard deviation of the fragment diameters. Table 1 shows the fitted parameters for each geological domain contained in the block model.

322

(3)

Gravity Flow

Figure 4 Mean fragment volume with respect to the induced stress at failure Table 1 Parameters for the Mean and Standard Deviation Fragment Size

Type of Rock Strong Moderate Weak

Parameter a = -0.015, b = 1.9, c = -0.014, d = 1.7 a = -0.011, b = 1.3, c = -0.011, d = 1.2 a = -0.006 , b = 0.8, c = -0.007, d = 0.8

Taking into account the induced stresses at failure from the FLAC3D simulations and using the relationships in Equation 2 with the parameters established in Table 1, a block model can be populated with a primary fragmentation curve fitted as Gaussian distributions. As a result, Figure 4 illustrates the populated block model showing the mean fragment size for different geological rock domains. 3.2

Results

The secondary fragmentation logic in REBOP is sensitive to several inputs. Since IMZ shape is controlled by the primary fragmentation and friction angle, a change in these inputs will have an impact on how the size of fragments evolve as they go down through the column. Furthermore, the strength scale effect also plays a major role in secondary fragmentation, as a weaker intact rock (low tensile strength) will produced a finer fragmentation. This effect can be achieved by increasing the input exponent factor k. Hence, having a coarser initial fragmentation, blocks will break more easily.

323


Figure 5 Cross-section of the block model mean fragment size for different geological rock domains, blue being fine fragmentation and purple coarse fragmentation

Additionally, drawpoint spacing and draw schedule are also essential inputs for secondary fragmentation. REBOP will predict overlap between IMZs and associated uniform drawdown if closely spaced drawpoints and coarse fragmentation are considered. This will achieve a more uniform drawdown, lower shear strains in the column and correspondingly lower secondary fragmentation. On the other hand, more breakage is likely to occur with widely spaced drawpoints (isolated draw scenario). It is anticipated that as the production progresses, the size of rock fragments will tend to decrease. Having a differential draw will increase the effect of secondary fragmentation. On the contrary, having a uniform extraction rate among drawpoints will result in more uniform drawdown, resulting in less secondary fragmentation. Employing a user-defined drawpoint layout and draw schedule, and using the parameters detailed in Table 2 as inputs for REBOP, a drawpoint fragment size distribution can be estimated. In order to capture the effect of having different lithologies with different fragment sizes in the draw column, a first simulation for each zone was run with the mechanism of secondary fragmentation being inactive (primary fragmentation curve). The effect of the secondary fragmentation with respect to the initial fragmentation over the years of production is illustrated in Figure 6. In general, the size of rock fragments tends to decrease over time due to the effects of block splitting, fines cushioning and rounding of block corners and generation of fines as the blocks move down through the column as described in Section 2.2. However, some of the changes in fragmentation can also be attributed to the spatial distribution of primary fragmentation, which tends to be coarse at the top and bottom of the column and finer at the column midheight, and also varies laterally with lithology.

324

Gravity Flow Table 2 Inputs parameters for REBOP model

Input Parameters Initial/Maximum Porosity [%] Solid Density [tonnes/m3] Angle of Repose [°] Friction angle[°]

Tensile Strength [MPa] Fragment Size [m]

Block Model Cell Size [m]

Base fragment Size for Scale effect relation [m] Exponent in Scale Effect relation

Clay: 45/65, Rock: 0/45 2.8 50 Rock: 42, Clay: 20 Mean: 8.23 Stdv: 2.18 Clay: 0.1, Rock: Mean [0.6-1] 12 0.05

k = 0.18

Figure 6 Evolution of the global fragmentation over years of production

4 Conclusions A new methodology for prediction of secondary fragmentation in cave mines has been presented. The methodology relies on the use of flow simulation as well as experimental studies on shearing-induced attrition. The primary fragmentation input to REBOP has been derived from separate numerical models that link caving induced stresses at failure in the cave back vicinity with associated fragmentation of rock masses. REBOP simulations of flow (employing a user-defined drawpoint layout and draw schedule) estimate secondary fragmentation in the column and at the drawpoints as a function of shear strain, normal stress and tensile strength.

325

Caving 2014, Santiago, Chile It has been confirmed that the methodology is reliable; using the mechanisms found in the literature at the same time serve the purpose of being practical and applicable to perform forward analyses in cave mines. A direction to improve this methodology would be to include a secondary fragmentation mechanism via impact loading, which can occur when rock fragments fall through an air gap. By including this approach and performing further enhancements, this methodology could be used routinely as a practical tool to estimate caved rock secondary fragmentation for the design of caving operations.

References Bridgwater, J, Utsumi, R, Zhang, Z & Tuladhar, T 2003, ‘Particle attrition due to shearing – the effect of stress, strain and particle shape’, Chemical Engineering Science, vol. 58, pp. 4649-4665. Brown, ET 2007, Block Caving Geomechanics, The International Caving Study 1997-2004, 2nd ed., JKRMC: Brisbane. Castro, R, Fuenzalida, M & Lund, F 2014, ‘Experimental study of gravity flow under confined conditions’, Int J Rock Mech and Min Sc, vol. 67, pp. 164-169. Castro, R 2006, Study of the mechanisms of granular flow for block caving, PhD Thesis, University of Queensland, Australia. Esterhuizen, GS 1998, ICS Meeting Minutes, BCF Review, Brisbane, Australia. Garza-Cruz, TV & Pierce, M 2014, ‘A 3DEC Model for Heavily Veined Massive Rock Masses’, Proceedings 48th US Rock Mechanics / Geomechanics Symposium, Minneapolis, USA. Garza-Cruz, TV 2014, ‘A 3DEC-FLAC3D Model to Predict Primary Fragmentation Distribution in Cave Mines’, Proceedings of the 3rd International Symposium on Block and Sublevel Caving, (ed. R. Castro), Santiago, Chile. Hoek, E & Brown, ET 1980, Underground Excavations in Rock, London, Instn. Min. Metall. Itasca Consulting Group, Inc. 2013, 3DEC – Three-Dimensional Distinct Element Code, Ver. 5.0. Minneapolis: Itasca Itasca Consulting Group, Inc. 2013, FLAC3D – Fast Lagrangian Analysis of Continua in 3Dimensions, Ver. 5.0. Minneapolis: Itasca Lausbcher, D 2000, Block Caving Manual. Prepared for International Caving Study. JKMRC and Itasca Consulting Group, Inc.: Brisbane. Laubscher, D 1994, ‘Cave Mining-The State of the Art’, J. South African Inst. Min. Metall., vol. 94, pp. 279-293. Lorig, LJ & Cundall PA 2000, ‘A rapid gravity flow simulator, Final Report’, International Caving Study, E.T. Brown (Ed.), JKMRC and Itasca Consulting Group Inc., Brisbane, Australia. Pierce, M 2010, A Model for Gravity Flow of Fragmented Rock in Block Caving Mines, Ph.D. Thesis, University of Queensland.

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Gravity Flow Pierce, M, Weatherley, EK & Kojovic, T 2010, ‘A hybrid methodology for secondary fragmentation prediction in cave mines’, in Proceedings of the 2nd International Symposium on Block and Sublevel Caving, (ed. Y. Potvin), pp. 567-581. Pierce M, Gaida, M & DeGagne, D 2009, ‘Estimation of rock block strength’, Proceedings of the 3rd CANUS Rock Mechanics Symposium, ed. M. Diederichs and G. Graselli, Toronto, Canada. Yoshinaka, R, Osada M, Park H, Sasaki T & Sasaki, K 2008, ‘Practical determination of mechanical design parameters of intact rock considering scale effect’, Engineering Geology, vol.96, pp. 173-186.

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Case Study: Improving SLC recovery by measuring ore flow with electronic markers S Steffen Elexon Mining, Australia P Kuiper Elexon Mining, Australia

Abstract Many industries have embraced the concept of improving an operation’s performance by measuring a process, analysing the results, implementing an improvement, and then measuring the effect of the improvement. Sublevel caving lends itself well to such an approach, because the mining method involves a process that is continually repeated. Changes can be implemented commencing from any blast ring, which means each blast ring is an opportunity to improve the process, and that improvement can be extended to all subsequent rings. Sublevel caving (SLC) is prone to dilution, which has a significant effect on a mine’s profitability, so keeping dilution low or reducing dilution by process improvement is attractive. However, most of the factors relating to dilution ultimately relate to ore flow and until recently, there were limited means to monitor ore flow. Before Smart Markers, steel markers were used to measure ore flow; however, their use was time consuming and interfered with production and they were therefore not systematically used. Thus, SLC mining remained a “black box” mining method with limited control over production. An SLC gold mine in Australia, managed by a dynamic and innovative team, has succeeded in closing the process improvement loop: The mine used Smart Markers to measure ore flow and analysed the data. Based on the data, it then reassessed its drill and blast processes and implemented changes. It then measured the effect of the changes. The new data indicates a 4% improvement in primary recovery. An analysis of the effect of this improvement indicates that, if the improvement in primary recovery is sustained, the mine’s overall profit should increase by between 4% and 14%.

1 Introduction With open stoping, mines assess the performance of their recovery by monitoring the mined-out cavity. Under- and over-break are easily identifiable and can be targeted for improvement. However, with Sublevel Caving, voids are automatically filled by gravity with broken rock. This mechanism does not usually allow a visible assessment of ore recovery and flow, in particular where the desired ore and waste material (dilution) is coming from, and which material has been left behind (ore loss). Understanding ore flow is crucial to be able to systematically improve mining practices. For example, Smart Marker data has shown that parts of targeted SLC rings are consistently not recovered and result in underperforming mineral recovery. Knowing which parts of the ring are not recovered can enable mines to systematically target the causes of the ore loss. Since the 1960’s, SLC research has concentrated on understanding and modelling material flow. The main thrust has been to identify a generally valid ore flow mechanism that could then be used to optimise recovery in sublevel cave mines. Investigations involved small-scale physical models, full scale flow monitoring marker trials and computer modelling. To date, a number of theories have been proposed to explain the ore flow mechanism in sublevel caving. These investigations have primarily been scientific in nature and the results have not usually been integrated into mining production or used to improve mining procedures. This has been partly due to the impracticality of using steel markers in production environments. The availability of electronic Smart Markers has now made it possible to monitor flow and ore recovery with minimal interference to production.

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Gravity Flow Over recent decades, the understanding of modern sublevel caving has advanced significantly mainly due to academic research in conjunction with some larger mining operations. Newcrest’s Ridgeway SLC mine is regularly cited as an example of what can be achieved by a well organised production team focused on optimising mining. Their achievements included:

• Mining production above 6 million tons per year which was well above the planned 4 million ton per year name plate production (Popa et al. 2012),

• Mined tonnes were 10% more than reserve tonnes (Manca 2012), • Au grade was down by 6% but Au quantity was 3% higher than reserve (Manca 2012), and • Cu grade was down by 2.5% but Cu quantity was 7% higher than reserve (Manca 2012). Ridgeway’s SLC mining success can be attributed to a combination of disciplined mining and the implementation of feedback loops to systematically improve mining procedures. The Ridgeway team used steel markers to investigate dilution entry points and understand multilevel recovery (Power, 2004). Initially, an improvement in primary recovery was not achieved (Power, 2004), although a 20% cost reduction was achieved. Later work by Luca Popa showed that improved drill and blast techniques did increase primary recovery (Popa et al, 2012).

2

The importance of controlling dilution

Uncontrolled ore flow is likely to happen if mining practices are not optimised for the local mine. The consequence of uncontrolled ore flow from a recovery perspective is excessive grade dilution and ore losses. (Uncontrolled ore flow can have other consequences, including safety issues, but that is not covered here). Dilution and ore loss both have a detrimental effect on the economic performance of SLC mines; however dilution has a greater economic impact than ore loss. Suppose a tonne of ore is lost. Another tonne will be mined instead of it, and eventually, the mine close a little bit earlier. That means the profit on that tonne is lost, and that loss is realised at the end of the mine’s life. However, if a tonne of waste material is mined instead of ore, that tonne will still be hauled and processed. The costs involved in processing a tonne of waste material are the same as processing high grade ore; however that tonne generates zero revenue. That means mine profits are reduced by the revenue from the mineral in a tonne of ore, not the profit made on a tonne of ore. That causes an amplified effect on profit. For example, with a mine like Ridgeway SLC, an improvement of 0.1 g/t in mined grade through reduced dilution would have generated additional revenue of USD 25,200,000 per year (6 Mt/y * 0.1g/t * 42 USD/g). Conversely, an increase in dilution of 0.1g/t grade would reduce revenue by US 25,200,000 per year. Higher than planned dilution rates quickly erode mine profits. Projects with marginal economic rates of return are particularly sensitive to changes in dilution rates: a small change can mean the difference between a profitable mine and a loss-making one. In sublevel caving, dilution and ore loss go usually hand in hand because a certain number of tonnes are extracted from each drawpoint. If, for whatever reason, a certain number of tonnes of ore are left behind, material will flow from somewhere else to make up the tonnes extracted. This material’s grade is more likely to be diluted.

3

The importance of drill and blast to control ore flow

A mentioned above, exerting optimised control over ore flow is critical for achieving good mine performance. Drill and blast procedures are the main instrument to influence ore flow and engineer ore recovery. The purpose of drill and blast is to fragment the targeted rock and to mobilise and induce the flow of the blasted ore to drawpoints. Appropriate drill and blast procedures are critical to the success of sublevel caving (Popa 2012). Production blasting in sublevel caving is confined blasting, meaning that the material lying flush

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Caving 2014, Santiago, Chile with the rock to be blasted is either previously blasted material, caved material or fill (Brunton, 2009). Confined blasting is different to unconfined blasting as is generally used in open stoping. The effects of suboptimal drill and blast in sublevel caving can include: Sub-optimal ore flow, Oversized rocks, hangups, frozen rings; hole losses, brow damage and backbreak. The consequences of this are safety issues (e.g. working near unstable brows), increased production costs due to production interruption and reworks, and grade and production variability. Assessing the performance of a confined blast is challenging due to the fact that the physical impact of drill and blast on the in-situ rocks is not easily assessable. Confined blast performance is highly dependent on an adequate distribution of explosive energy optimised for the local mine conditions, such as the mine’s design, rock properties, stresses, mining sequence etc. The delivery of explosive energy depends on a multistep process. Blast rings need to be designed with the optimal explosive energy distribution in mind. The blast rings need to be drilled according to the plan. The blast holes need to be prepared and charged properly. The holes have to be drilled accurately, as any deviation may change the geometry of the explosive energy distribution. Holes found to be blocked during prepping may require cleaning or redrilling in order to charge them with explosive. Boosters must be positioned appropriately and the detonator sequencing timing must be properly implemented. Each step prior to the initiation of the blast is important to success. The key to improving drill and blast results lies in measuring the performance of each step, including ore recovery and then implementing changes to drill and blast design and practice to achieve improved results. Optimised drill and blast can improve primary recovery, which has been achieved at Ridgeway SLC (Popa, 2012). Primary recovery is the best opportunity to recover undiluted ore grade (Brunton 2009). The potential consequence of non-primary recovery is that high grade ore may mix with low grade or waste material while flowing over several sublevels to the extraction point. The result can be significant dilution of mined ore grade.

4

Deviating flow

There are three main recovery target zones in an SLC ring: the core, shoulder and apex zone (Figure 1).

Figure 1 SLC ring recovery zones (Steffen & Clark 2013)

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Gravity Flow Marker data has shown that:

• Most of the primary recovery tends to be extracted from the core zone, which is vertically above the extraction drive. However, only a certain fraction of this material is recovered during primary recovery.

• Less material tends to be recovered from the shoulder zone, but as it is lies above the apex of the level below it, it should be accessible as secondary recovery from the drive below.

• Recovering the apex zone is important for overall recovery as it is a significant component of

the ring’s tonnage and it is also the key to recovering the shoulder zone material in the level above it.

In cases where there is a 12.5 m spacing between drive centres, the core zone represents approximately 50% of the ring’s tonnage, while the apex and shoulder zone each represent around 25% of the ring’s material. Gavin Power (Power 2004), described the effect of deviating flow, which occurs when the apex is not sufficiently fragmented and mobilised. This prevents ore flow from propagating through the apex and into the shoulder zones targeted by secondary recovery. Instead, the ore flow deviates into the depleted drives of the level above (Figure 2). This will introduce material of potentially diluted grade. The apex and shoulder zone, which account for around 50% of the ring tonnage, are not being well recovered due the deviating flow effect described. With tonnage-based draw control, a predetermined amount of ore is being recovered from the production ring. If the amount of drawn material is larger than the material recovered from the targeted ring or from the shoulder zone of the level above, it has to be coming from other places which are of unknown and potentially diluted grade. If deviating flow occurs systematically, possibly to due to inadequate drill and blast processes, a significant amount of high grade ore can be left behind in the apexes and the shoulder zone.

Figure 2 Deviating flow illustration (Steffen & Clark 2013)

5

Case study

This case study shows an example of a mine that has actively used Smart Markers to understand their recovery and made changes to mining procedures to improve their performance. This case study is of an Australian gold mine employing sublevel caving production principles with zero grade waste backfill from

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Caving 2014, Santiago, Chile the top to reduce the likelihood of caving of the crown pillar. The mine is anonymous in this paper, because there was insufficient time to have the mine management review this paper before the submission deadline. The mine agreed to have their data used without their review, if their details were kept anonymous. The mine has an annual production rate of around 1.5 Mt. The mine is in full production and to-date has met its economic performance expectations. This can be attributed mainly to the efforts of the dynamic technical team at the mine site. The mine has gone through several stages of optimising their mining procedures, including drill and blast, using a Smart Marker system. This paper shows the actual ore flow patterns recorded by the Smart Marker System in this mine. To date, Smart Markers have been installed on six sublevels. On the first two levels, Smart Markers were installed in upholes. From the third level down, Smart Markers were installed in down-holes from the level above the targeted sublevel. This approach was chosen to simplify installation by lowering chains of Markers down the holes instead of having to push them up individually. After mining had progressed through the production rings on levels two and three, the Smart Marker data was analysed to evaluate areas of poor recovery and adverse flow effect. The data collected indicated that deviating ore flow had occurred. Second quartile primary marker recovery had dropped with every level and reached 30.3%. The primary recovery figure quoted here is somewhat low. The actual figure is probably higher, because highly fragmented material close to the blast holes is more likely to flow to the drawpoint; Markers can only be placed 65 cm from the blast ring. The Second quartile marker recovery from the apex zones was 20.6%. Figures 3 and 4 show Smart Marker data signatures; these show deviating flow and partial recovery of the apex and the shoulder zones on the level above.

Figure 3 Deviating Flow Pattern measured with Smart Markers (L) and Example of partial apex and shoulder zone recovery (R)

An outcome of the marker data analysis was a recommendation to reassess the drill and blast processes, which was subsequently done. The reassessment indicated potential areas for improvements in drill and blast design and procedures. A number of changes were implemented and their results assessed. More Smart Markers were installed to analyse the effect of the changes on primary recovery. The changes involved:

• Increasing the distance between the toes of blast holes and any preconditioned ground (such as

drives and blasted ore at higher levels), in order to prevent explosive energy from venting into preconditioned ground, to reduce hole blockage due to loose material and to decrease damage to ring holes in neighbouring production drives.

• reducing the powder factor. • changing the uncharged collar lengths.

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Gravity Flow • improving drilling accuracy. • trialling different ring designs including an innovative asymmetrical ring design. Reducing the number of blast holes would reduce production costs accordingly. The results from the first round of process changes, which included a ring design with a lower number of holes, resulted in a notable reduction of flow deviating through the depleted drive of the level above and improved recovery of the apex zones. Second quartile primary marker recovery increased to 31.6% and apex recovery of 45%. The effect on secondary recovery was unfortunately not observable because Smart Markers were not installed in the rings on the sublevel above.

Figure 4 Improved Apex and primary recovery

Figure 5 Further improvements to apex and primary recovery

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Caving 2014, Santiago, Chile In a second set of trials, an innovative asymmetrical blast-ring design was trialled. The aim of this design was to reduce the damage to shoulder blast holes in the neighbouring production drive and to increase primary ore recovery. The results are shown in the graphic below. A further increase in the apex recovery and overall primary recovery was observed. Second quartile primary recovery increased to 34.4% and apex recovery of 50%. Again, secondary recovery was unfortunately not assessed because Smart Markers were not installed directly above the relevant Smart Marker installation. These trials have shown some promising initial results. The author believes that even more drill and blast improvements are achievable through refining the current approach.

7

Financial effect of improving primary recovery

This section attempts to estimate the financial benefit of improving primary recovery, assuming the improvement is implemented in all future rings. SLC mines have an overall rate of dilution. For the subject mine, this is assumed to be 20%. Primary recovery extracts virgin ore, with zero dilution. That means the dilution is always in the non-primary recovery. The subject mine draws approximately 1450 tonnes of ore from each ring. The early data indicates that the implemented changes improved primary recovery by 4%. This means that 4% x 1450 = 58 tonnes of ore came from the target ring instead of from somewhere else. (As mentioned previously, the overall primary recovery figure is somewhat low; however a lower-thanactual primary recovery figure understates, rather than overstates the benefit calculations). Here are figures from the subject mine’s 2013 financial reports: 141846 ounces produced (4,411 kg at 31.1 grams per troy ounce) AUD$1122 per ounce all in costs ($36 per gram) AUD$1562 per ounce ($50 per gram) Ore grade: 2.8 grams per tonne High estimate: Let us assume that the improvement in primary recovery also results in an equivalent improvement to secondary, tertiary, etc recovery. In that case, revenue will increase by 4%. Using the above figures, revenue was 4,411x103 g x 50 dollars/g = $220 Million. 4% x revenue = $8.8 Million The 2013 profit was 4,411x103 g x (50-36 dollars/g) = $62 Million Therefore, the high estimate of improvement to profit, expressed as a percentage is $8.8 m / $62 m = 14%. Low estimate: Let us assume that the changes only improve primary recovery, but do not improve secondary, tertiary etc recovery at all. If that was the case, the 58 tonnes per ring of improvement in primary recovery would be replacing 58 tonnes of diluted ore. To work out the improved ore grade, we need to estimate the dilution in the non-primary recovery. If overall dilution (including primary recovery) is 20%, and primary recovery is 30%, then all of the dilution is in the 70% non-primary recovery. Therefore, the non-primary recovery has 20% / 70% = 29 % dilution. If primary recovery improves from 30% to 34%, then primary recovery would have increased annually by (0.34 – 0.3) x 4,411,000 g / 2.8 g/t = 64 kt This tonnage would have had an improvement in grade of 29% (the difference between undiluted and diluted ore). That works out to 0.29 x 64x103 x 2.8 grams = 52,000 g of additional gold.

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Gravity Flow This equates to 52000 x $50 = $2.6 million in additional profit. From above, the mine’s overall profit was $62 Million. Therefore the low estimate of percentage improved profit is 4%. Overall estimate: The actual improvement will lie somewhere between the low and high estimate. Secondary, tertiary etc recovery should improve, but we do not know by how much. For now, let’s assume halfway in between. See Table 1. Table 1 Estimate of improved profit from implemented changes

Estimate

Dollars

As % of profit

High

$8.8 Million

14%

Low

$2.6 Million

4%

Probable

$5.7 Million

9%

6 Conclusion Now that there are suitable tools to measure ore flow, SLC mines are ideal to implement process improvement by measuring performance, analysing the data, implementing an improvement and measuring the effect of the improvement. This paper shows early results from a successful implementation of this approach. Preliminary analysis shows that the improvements, see Table 1, are very promising, but the data was quite “fresh” at time of writing. As more data is gathered, the picture will become clearer. Also, this is only the beginning - further gains are very likely to be found by continuing this process of improvement.

Acknowledgments The author would like to thank the following people for sharing their knowledge and for taking the time to engage in many debates on how to improve cave mines (in no particular order): Stuart Long, Luca Popa, Nigel Clark, Ian Brunton, Gideon Chitombo, Alan Guest and Otto Richter. This paper does not necessarily represent their opinions.

References Brunton, I 2009, The impact of blasting on sublevel caving material flow behaviour and recovery, PhD thesis, W H Bryan Mining and Geology Research Centre, University of Queensland, St Lucia, Australia Jamieson, M 2012, ‘ Development of Sub Level Cave Draw Optimisation at Newcrest Mining’, MassMIN 2012 Conference Proceedings. Manca, L & Malone, E 2012, ‘Cadia Valley Mines past, present and future’, AUSIMM, Available from: http://www.ausimm.com.au/content/docs/branch/sydney_2012_11_02_presentation.pdf [28 April 2014]. Power, GR 2014, Modelling granular flow in caving mines: large scale physical modelling and full scale experiments, PhD Thesis, The University of Queensland, Brisbane.

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Caving 2014, Santiago, Chile Power, G 2004, ‘Full scale SLC draw trials at Ridgeway Gold Mine’, Proceedings of MassMin 2004, (Karzulovic A. and Alfaro M. eds), 22–25 August, Santiago, Chile: Instituto de Ingenieros de Chile, pp. 225–230. Popa, L, Trout, P & Jones, C 2012, ‘The evolution and optimization of sublevel cave drill and blast practice at Ridgeway Gold Mine - production rings’, in Proceedings Massmin 2012. Vila, D 2012, Calibration of a mixing model for sublevel caving, PhD Thesis, the Faculty of Graduate Studies, The University of British Columbia, Vancouver, Canada. Steffen, S & Clark, N 2013, ‘Sub-level caving: engineered to perform’, The Mining Magazine, September Edition.

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Gravity Flow

Stochastic models for gravity flow: numerical considerations WH Gibson SRK Consulting (Australasia) Pty Ltd, Australia

Abstract Material flow analysis is commonly required in mining methods where the rock has to move from its initial position to the extraction point, commonly known as gravity flow. This happens in mining methods, such as, sublevel caving, panel caving, block caving. During this process, the ore is diluted with waste from the walls of the cave column or waste sitting on top of the ore. It is essential for the mining design to assess the degree of dilution for different draw strategies to minimise the waste extraction and optimise the ore recovery. Stochastic methods have proved to be good options for this type of assessment.

1 Introduction Rules and probabilities are the bases of stochastic methods for describing phenomena, they are not founded on a physical principle that “forces” the math to produce the right results (for example, the Finite Element Method minimising potential energy to derive the equations used to solve the problem). Stochastic models for gravity flow use just conservation of mass. Despite the weak formulation, stochastic methods can be very powerful tools to solve material flow problems, and some of the merits, limitations and pitfalls of these methods are explored in this paper.

2

Description of the program MFlow

Stochastic methods are modelling tools for estimating outcomes by allowing for random variations in one or more inputs over time. When material is removed from a drawpoint, a void is created that is filled with material from above the void. The exact source of that material is unknown; therefore, a random location is assigned. Figure 1 shows a plan view of a grid describing this concept. When part of the material is removed from a cell below the centre of the grid, the void is filled from any of the nine cells above. This process is random, making stochastic models ideal for this type of problem solving. In this particular case, a probability is assigned to each cell indicating the chances that the void will be filled with material from the cell immediately above (60%) or any of the surrounding cells (8% chance cells with adjacent sides and 2% for cells with adjacent corners; percentages given as an example only). The grid shown in Figure 1 does not match the axisymmetric nature of the problem; a better grid is shown in Figure 2. This type of grid is better for analysing material flow because the grid can model naturally an axisymmetric problem. The disadvantage of this type of grid is the shape of the cells, which complicates the calculation.

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Figure 1 Plan view of basic cube grid

Figure 2 Plan view of hexagonal prisms grid

Figure 3 shows a grid that combines the simple geometry of cubes and the circular location of hexagonal prisms. MFlow uses this configuration of cells; p represents the probability that material will be transferred from the cell above while (1-p)/6 is the probability that one of the cells around the central cell can transfer material to the void below.

Figure 3 Plan view of cube shifted grid

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Gravity Flow 3

Pascal “cone” and correlation between probabilities and ellipsoid width

The Pascal triangle can be used to understand how probabilities can be used to assess material flow in 2D. The concept is extended to a 3D (Pascal Cone) to model the gravity flow in three dimensions. 3.1

Model in 2 Dimensions

The Pascal triangle can be used to assess the probability that a drunken man can reach home (depicted by the shaded box in Figure 4), starting in a particular street and walking randomly (Harr 1987). At every corner, there is a 50% chance that the man will turn either left or right. From Figure 5, it is possible to see the probability of reaching home is 5/16. In general, the probability of reaching corner r at street n can be calculated as follows: (1)

Figure 4 2D random walk (after Harr 1987)

The probabilistic analysis indicates the chances of the man ending some blocks away from home. The actual distance between the man and his home will depend on the size of the city blocks. The same problem occurs in material flow as is explained in the following paragraphs. Figure 5 presents the probabilities associated with this 2D random walk. If we imagine Figure 5 upside down, we can see that the value indicated in any cell represents the probability of a cell being affected by extraction of the cell with probability 1 (now at the bottom). This is a very simplistic 2D model, where each cell has only two cells above with equal probability to fill the void generated in the cell below, in this case, the chance that the cell highlighted in Figure 5 is affected by the extraction is 5/16. We can say, after this analysis, that the cell affected is one (1) cell to the left of the cell of extraction but the actual distance of this cell to the extraction point will be a function of the cell size. A 2D model can be built with only three (3) cells that can transfer material to cells below. With this simple model, it is possible to study the impact of the parameter p on the ellipsoid of extraction. Figure 6 presents a vertical section showing probabilities that each cell will transfer material to the cell below.

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Figure 5 Probabilities associated with 2D random walk

Figure 6 Probabilities within a simple 2D model

When this concept is applied in a 2D problem, it is possible to assess the chances that a particular cell will be affected by the extraction. Figure 7 presents a cross-section with a single draw located at x=7 m. It shows the probability that material located above the drawpoint is mobilised during extraction. The results are presented for two values of p = 0.40 and 0.70. In both cases, the same amount of material is removed from each drawpoint. It is possible to observe that the parameter p controls the width and height of the ellipsoid of extraction. A p-value closer to 1 produces a narrower ellipsoid of extraction than a smaller p-value. A correlation between p and the actual width of the ellipsoid of extraction is discussed later. The real challenge is in 3D, where the calculations of the probabilities are more complicated. If Figure 3 is used as a reference, then there are seven (7) cells that can fill the void in the cell below and the probabilities for each of the cells are not the same. 3.2

Model in 3 dimensions

The 3D problem can be viewed not as a Pascal triangle, but more as an inverted “Pascal cone”. It starts with a cell with probability of 1 (certainly we will remove material from there) and the next layer above represent the probability that one cell will fill the void created below. As we move up a layer and more cells are involved in the calculation, the width of interaction between cells is controlled by the value of the probability p indicated in Figure 3.

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Gravity Flow

Figure 7 Probability of material being mobilised for parameter p=0.7 and p=0.4

A similar calculation can be carried out for the 3D case. With this calculation, it is possible to evaluate the diameter of the ellipsoid of extraction for different values of p, as shown in Figure 8. Note that the width is expressed in number of cells affected by the extraction, and not in actual distance measured in metres.

Figure 8 Width of ellipsoid of material mobilised as a function of parameter p

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Caving 2014, Santiago, Chile This effect can be visualised on a 3D model of two independent drawpoints in uniform material. Figure 9 shows the mobilised ellipsoids, clearly illustrating the effect of parameter p on the width of the ellipsoid of extraction. This is an observed behaviour that, depending on physical properties of the broken rock, is controlled by the width of the ellipsoid of extraction.

Figure 9 Extraction column width for different p parameters

3.3

Calibration of parameter p with observed material behaviour

It was shown that in these types of stochastic models, the cell size plays a role in the results; therefore, the selection of the cell size has to be made considering other parameters in the model to be able to reproduce the phenomena that have actually been observed. The parameter p controls the width or diameter of the draw column in the model (not the actual length in metres, but the number of cells that will be affected by the draw). If the width of the ellipsoid of extraction is known, it is possible to select a cell size and a probability p to be used in the model. The question that remains is how to relate the rock mass condition or other observed behaviour to parameter p in order to build models that represent the actual material flow. There have been some attempts to relate rock mass condition to ellipsoid width. Laubscher relates the width of the ellipsoid with rock mass classification: the better the rock quality, the wider the ellipsoid (Laubscher 1994). Kvapil recognised the same fact – that an increase in the rock mass quality increases the ellipsoid width (Kvapil 2008). Sharrock (2008) made a review of several aspects of isolated draw and discusses the results of scaled models. Unfortunately, the scaled models did not capture changes in ellipsoid width with material properties or particle size. Susaeta (2004) presented a correlation between friction angle, particle size and ellipsoid width. For mines in operation, it is possible to use information collected from the previous extraction to calibrate p. Figure 10 shows the ounces extracted in a period of time compared with the values predicted using an

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Gravity Flow MFlow model. The parameter p was modified to minimise the difference between the curves and then calibrated values were used in the future forecasting models.

Figure 10 Ore extracted used to calibrate parameter p

There are some guidelines to correlate observed material behaviour with ellipsoid width and this can be used to define parameter p for a given cell size (using Figure 8). The process of assessing ellipsoid width is based heavily on experience and observations, and less on a strong formulation including the characteristics of the material. Nevertheless, there are some guidelines that can be used to build the stochastic model.

4

Stochastic modelling capabilities

Despite the simplicity of the formulation of stochastic models, they can address complex behaviour of materials. Some of these are shown and discussed in the following paragraphs. 4.1

Variable flow of different materials (w Factor)

Several factors allow for some materials to travel faster than others in the draw column (Hashim 2009). This type of phenomenon can be included in the model by introducing a weighting factor, w, that modifies the probability of material moving from one cell to another. A value of w=0 renders the material unaffected by draw and it does not flow. This can be used to define the limits of the draw rings in SLC (Sub Level Caving) if it is assumed that the outside of the blasted ring will not move. A value of w=1 allows the material to move freely. Values between 0 and 1 can, therefore, be used to control the speed of material flow in the model. To show the effect of the parameter w on the results, the model shown in Figure 11 was built. Above the extraction

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Caving 2014, Santiago, Chile point A there is fine ore under coarse waste, above the extraction point B the fine material is on top. The results are shown in Figure 12. Due to fine waste material, there is a reduction in ore extraction in drawpoint B due to a higher dilution of fine material travelling faster than the ore.

Figure 11 Section of simple model with different materials

Figure 12 Effect of fine material on extraction

4.2 Markers The formulation of stochastic models is easy and that simplicity allows us to incorporate additional calculation into the analysis without adding complexity to the overall analysis. In this case, markers are added in the model to track the movement of the material.

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Gravity Flow Figure 13 shows the trajectory of markers located above drawpoint A. The sequence of extraction is A then B then C. It is possible to see the change in trajectory of some of the markers and to compare the nature of material that is extracted at the drawpoints.

Figure 13 Effect of fine material on extraction

4.3

Finger path (void diffusion)

It has been observed that an ellipsoid shaped extraction column is not always generated, and that material can flow following a “finger path” - reaching surface earlier than expected (Brown 2003). This can be modelled by modifying the weighting factor w and giving a higher probability of movement to material already mobilised. This is shown in Figure 14 for a single drawpoint. 4.4

Modelling Surface Flow

Surface flow or unconfined flow is controlled by the repose angle; the material will flow until the repose angle is reached. This introduces another constraint for the geometry, driven by material properties. Castro (2009) mentioned that in order to model the surface flow, the height (h) and the side length of the cells (L) should follow the relationship h/L=tanf, where f is the repose angle. Figure 15 illustrates a failure on a bench and flow of the loose material.

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Figure 14 Finger path for a single draw point

Figure 15 Surface Flow

5 Conclusions Stochastic models have a much easier formulation than other methods, such as, the Finite Element Method. However, the lack of formulation based on a physical principle makes them more difficult to set up unless information about the rock mass to be modelled is available, thus enabling the modeller to calibrate the model. The results are cell size dependent; therefore, cell size has to be selected along with other parameters (probabilities) to ensure the model best represents the material behaviour observed.

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Gravity Flow It is suggested to calibrate a model using the ellipsoid of extraction width estimated from rock mass characterisation or assessment of eccentricity of the ellipsoid, and a correlation presented between probability p and number cells. Past draw performance at the mine can be used for calibration when information about the type of material extracted, such as ore, waste and grades, is available. Despite the limitations and the weak formulation, this paper shows that stochastic models can be used to describe complex behaviour, such as, accelerated flow of fine material, finger path flow and dilution.

References Brown, ET 2003, Block Caving Geomechanics, The International Caving Study Stage I, 1997-2000, University of Quuensland, Julius Kruttschnitt Mineral Research Centre, Brisbane. Castro, R, Gonzalez & Arancibia 2009, ‘Development of a Gravity Flow Numerical Model for the Evaluation of Drawpoint Spacing for Block/Panel Caving’, J South African Inst Min and Metall. vol 119. Harr, ME 2005, Reliability-based design in Geotechnical Engineering, Dover Publications Inc., Mineola, New York. Hashim, MHM & Sharrock, GB 2009, ‘Numerical investigation of the effect of particle shape on percolation’, Proceedings 43rd U.S. Rock Mechanics Symposium & 4th U.S. - Canada Rock Mechanics Symposium, American Rock Mechanics Association, 28 June-1 July, Asheville, North Carolina, 8 pages. Kvapil, R 2004, Gravity Flow in Sublevel and Panel Caving – A Common Sense Approach, Lulea University of Technology Press, Lulea, Sweden. Laubscher, DH 1994, ‘Cave mining – the state of art’, The Journal of The South African Institute of Mining and Metallurgy, October 1994, pp. 278-293. Sharrock, GB 2008, ‘The Isolated Extraction Zone in Block Caving – A Review’, in Proceedings SHIRMS 2008, Perth, pp. 255-272. The International Caving Study Stage I, 1997-2000, University of Quensland, Julius Kruttschnitt Mineral Research Centre, Brisbane.

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First steps in monitoring gravity flow at El Teniente Mine: installation stage in Block-2, Esmeralda Mine E Viera Codelco, Chile M Montecino Codelco, Chile M Meléndez Codelco, Chile

Abstract The future scenarios under which panel caving is developing will be even more adverse, mainly because of the complex geomechanical conditions and greater demands for production plans. The quantity and quality of the required information for monitoring and controlling these variables fulfill a main role, thus technology incorporation constitutes a key step in this topic. From the variables that rule the mining method, cavity control and gravitational flow mechanisms are of vital importance. Technology development has allowed smart electronic devices (Smart Markers) usage, which are installed on higher levels with respect to the production level (undercut level, haulage levels, special drillholes, etc.) Once undercutting and the later caving process are started, the smart markers are part of fragmented material which, once the mining process starts are subject to induced movements, dependent on the draw strategies to be performed. The emergence of these markers in drawpoints and their later transportation through LHD equipment, allow the installed readers at production level (or main haulage level) to record the markers passing, under a certain detection radius. The aim of the following work is to detail the Smart Markers installation stage, and to show that preliminary results obtained at Esmeralda Mine. In this, near field flow tests (31 m maximum length drillholes between ring blast hole) and far field test (vertical drillholes with a maximum length of 100 m), are being performed on 3 undercut level drift (55, 57 and 59). The installation of 305 markers in the near field test is highlighted (in 16 ring drillholes) from which 96 markers have been registered so far. In the far field tests, 92 markers were installed in 3 vertical drillholes, from which 4 markers have been recorded.

1 Introduction In the coming years, El Teniente will be facing even more challenging scenarios, characterized by complex geomechanical views along with greater productive demands. Understanding the fragmented material flow inside the ore column has been a very debated and investigated topic for several years. Different flow mechanisms have been proposed, most of them based on scale size models. However, up to date trials to monitor the material flow inside the cave at full scale have been few (Brunton 2012). Most of the design and planning tools that have been developed throughout the years for the material flow predicting are based on empirical relationships mainly. In order to improve this understanding, Smart Markers were installed at Esmeralda Mine’s Bloque 2 aiming to understand and know, in a better way, the fragmented material flow inside a through Block Caving method.

2 Objectives The main goal of this work is to detail the installation process and markers registry performed at Bloque 2 Esmeralda Mine, by distinguishing between near and far field tests.

348

Gravity Flow 3 Methodology For gravitational flow markers system deployment, stages for markers and readers installation are planned. As for the markers installation, stages are defined for far and near field tests. Both of them detailed below. 3.1

Markers installation

3.1.1

Near field test

The near field are those measurement of flow under 30 from the undercutting level as shown in Figure 1. The near field test were carried out between undercuts drifts C- 57 and C-59 which are spaced at 30 m apart, with a maximum installation height of 31 m. The main goal of the tests is to study the interactions of flow at the drawbell and through the minor and major apex. The markers are installed on radial drillholes ring (3” diameter) interspaced with blasthole rings used during the undercutting process.

Figure 1 Near field test design (isometric view and HW-FW section)

The installation in near field test considered the use of a spider with markers, which provides adherence inside the drillhole. Once the first marker is installed, a 5 m long PVC bar is introduced, which allows to keep the distance between each marker (avoiding vertical drifts from surrounding blasting). Once all of the estimated markers are installed, each one of the drillholes is covered with wooden cones, which avoids the whole markers complete column vertical descent.

Figure 2 Installation process in near field

349

Caving 2014, Santiago, Chile The parameters used for markers installation in this test are listed in Table 1. Table 1 Design parameters in short reach test

3.1.2

Near field design

Number /Quantity

Number of stands of pipe

10

Number of drillholes per stand of pipe

9

Markers per stand of pipe

39

Drilled meters per stand of pipe (m)

217

Vertical spacing between markers (m)

5

Far field test

Far field flow measurements are carried out by installing markers above the 30 m, up to 70 – 100 m (according to available drillhole length). The markers are installed in vertical drillholes with a maximum length of 100 m, which are used in the pre-conditioning process. The goal is to obtain relevant information (markers drifts, mainly), which allows vertical and lateral movement of the markers to be quantified for medium/long term production planning. The used parameters for markers installation in this test are are shown in Table 2. Table 2 Design parameters in long reach test

Far field design

Number /Quantity

Number of drillholes

6

Number of markers per drillhole

35

Drillholes length (m)

70-100

Spacing between markers (m)

2

Total number of markers

210

The design for markers installation in long drill holes is shown in Figure 3. The steps for markers installations in vertical drillholes are the following:

1. Anchor installation: an anchor is installed in the drillhole bottom (6” diameter) with the rope linked to

the pulley. This process uses Wassara or Cubex drilling equipment to raise the anchor. One end is fixed and the other one remains free. A slow advance must be considered, since any anchor tilting might result in its loss.

2. Free end fixation to hoisting winch: once the anchor is installed, the fixed end must be adhered to a

hoisting winch strongly enough to rollback more than 100 m rope and rising the planned load (40 kg approximately of rope).

3. On rope markers installation: markers are installed on rope with high resistance adhesive tape. They are not installed with spiders, since it is possible they get stuck with the other rope’s end.

350

Gravity Flow

Figure 3 Far field design

4. Rope lifting with markers: lifting is performed until the first installed marker gets to the drillhole bottom or, until the rope’s added load overcomes the winch hoisting capacity.

5. Free end fixation and security plug: the hanged load is secured and the vertical drillhole is blocked on its base.

6. Grouted stage: at this stage a concrete bomb was used (Putzmeister equipment TK – 40) given that the grouted length is considerable. To do this a 2” shut-off valve was used at drillhole top, under which the pipeline is connected to the equipment exit. A 1/2” diameter hose into the drillhole bottom allows to know whenever it is completely grouted. This is shown in Figure 4.

Figure 4 Markers installation stages in vertical drillholes

351


Installation of markers from higher levels

Above Esmeralda’s Bloque 2 is located the main haulage level; the railroad Teniente 5. The Superintendence of Geology drilled a series of descending holes, aiming to perform auscultation of the rock mass, in order to determine the caveback growing. Once these labors are performed, the drillholes are ready for markers installation. The drillholes location and the markers installation can be seen in Figure 5.

Figure 5 Markers installation in XC-40 FFCC Teniente 5

The total installation in drillhole P-3 (see Figure 5) were10 markers. In this area markers will continue to be installed as auscultation labors are carried out. Note that 10 markers we installed in the XC-40 box in a row of 10 short drillholes of 60 cm each. Considering the proposed design for the installation and the operational restrictions under which the process was performed, there have been 397 markers as installation result (92 installed for far field test and 305 for near field test):

Figure 6 Markers installation in near and far field tests

352

Gravity Flow The near field installation covers a 3.192 m2 surface, which corresponds to 5 drawbells between production drifts C-57 and C-59 (from drawbells Z-30 to Z-34). The first vertical drillhole (P1T-C59) was installed in the area where Bloque 2 (drawbells Z-30 between production drifts C-57 and C-59) mining started. The apparition of markers installed at heights of 30 m was important for production control purposes, since it allows to identify through its extraction that the caving was progressing as the extraction and area of the block increased. 3.2

Readers installation

In order to record most of the installed markers, it is necessary to place the readers, which capture a series of information whenever the markers pass under the de antenna-reader set. The data that is collected includes the date and time registration, ID number and marker type. Figure 7 shows the readers’ location in the production level of Bloque 2.

Figure 7 Readers installation (1, 2 and 3) in Bloque 2 production level

These location of the readers near available in ore passes, ensures that all of the LHD equipment working at the area had to pass through the antenna-reader set. Note that out of service readers were not detected during the estimated time. In March 2014, a reader was installed at Teniente’s 8 main railroad haulage level, which is located at the main haulage drift, on which Teniente’s 8 main railway passes by. This reader will allow every marker installed in project Bloque-2 (and also in the future ones) to be recorded, which turns into a better markers control.

4

Results

4.1

Markers registry

Up to March 2014 a total of 335 markers have been recovered. This equal to a 30% of recovery. For the near field test case, 96 markers from a total of 305 have been recorded, which can be seen in Figure 8. Another four markers have been recovered from the far field. The recovery of the markers does not allow to perform too many conclusions and this would require more information to be collected and analyzed.

353


Figure 8 Markers recovery in near field test markers installed under drawbells

In the near field test, 2 installation areas have been identified, in which the recovery percentage is different:

a. Markers recovery installed in drawbells: as for the markers installed under a drawbell mining direct

effect case, a greater markers recover can be seen, which gets to 58 markers (60% of the total markers recovered in the near field test).

b. Markers recovery installed in minor apex: the markers recovery installed in the minor apex has been

lower in comparison to the markers installed in the drawbell (40% of the total recovered markers in the near field test).

c. Markers recovery in far field test: as for the markers installed in vertical drillholes case, 4 markers have

been recorded up to date. These markers recovery and the heights evolution mined from the nearing drawbells cluster can be observed below:

Figure 9 Markers recovery in far field test

In Figure 9, red color underlines the extraction height from the drawpoint, by which the markers were recorded in the different indicated dates. Note that all of the entries match the area that shows greater heights mined in the indicated date. In the near future further analysis will be carried out to describe the flow from these results.

354

Gravity Flow 5

Discussion and conclusions

The smart markers test performed in Bloque 2 Esmeralda Mine validates the usage of this technology for the future projects of the El Teniente´s Division. This is based on the successful installation of 397 markers installed, with no no major incidents and reaching a high level of reliability. Up-to-date, 100 markers has been recovered at the mine. Regarding the installation process, the main work in the future will be to improve the process of grouting vertical drillholes above 70 m. This is key for this project as markers needs to be fixed to represent the flow of the rock. One of the learnings of the process was the installation of a reader in the main haulage levels, which allows increase possibilities record possibilities (and so getting a greater amount of information). The markers’ record installed in vertical drillholes, that is above heights of 50 m, it is important, since it helped to correlate to the information by the Operational Geomechanical area, which indicated the connection of the caveback of Bloque 2 with Teniente 4-Sur, which is located in an upper level. From a extraction point of view, this meant that the rate of draw was increased to 1 tpd/m2 on an area of 4.700 m2.

References Brunton, I, Sharrock, G & Lett, J 2012, ‘Full Scale Near Field Flow Behaviour at the Ridgeway Deeps Block Cave Mine’, in Proceedings of MassMin2012, Sudbury, Canada.

355


Experimental study of fines migration for caving mines F Armijo BCTEC Engineering and Technology, Chile S Irribarra Universidad de Chile, Chile R Castro Universidad de Chile, Chile

Abstract Fine material has the potential to flow and migrate through the caved zone during its gravity flow. This may have a large impact on the dilution content depending on the grades that fine rock could have. To date there have been some theory and experiments which have tried to quantify the fines migration potential. The objective of this paper is to present results of fine migration using experiments to quantify for a given particle size and draw strategy to the fines migration. For this purpose, a pilot test was constructed to study fines migration on block caving mine using a production level that is geometrical similar to a block cave. In this case, fines and coarse particles were dried while the size of the coarse particles was thirty times the size of the fine particles. The results show that no shear strain occurred when the draw was uniform from drawpoints and then no migration occurs. On the other hand, shear strain occurred under isolated draw and, therefore, fines migration was observed. The results shows that more research needs to be done in terms of fine migration quantification.

1 Introduction Gravity flow in block caving is one of the key mechanisms of ore drawing. Under this concept, gravity is one of the most important parameters to allow the percolation of fines through coarse particles. If fines fragments of caved rock can migrate more rapidly than coarse fragments, it may have significant impact on ore recovery and dilution, particularly if the fines does not have mineral with economic interest (Pierce 2009). There is evidence to suggest that fines can accumulate in stagnant portions of the cave (Pierce 2009), in this case, with the presence of water, highly unstable mud may be happened (Guest 2008, in Pierce 2009). Then, fines migration is an important issue in mining business, therefore, understand and mitigate fines migration is fundamental to generate value for the mining business. According to (Laubscher 1994) the fines migration is related to the difference that exists between the rock mass rating RMR of the rocks. Thus, an in-situ column with a large difference of RMR between the ore and dilution has a higher height of interaction than an in-situ column with a small difference of RMR. A higher height of interaction means also a smaller point of entry dilution (Figure 1).

Figure 1 Fines migration quantification through the HIZ (Laubscher 1994)

356

Gravity Flow As noted by Pierce (2009) experimental studies of fines migration were performed by (Bridgwater et al. 1978, 9) who used a simple shear cell to deform a rectangular bed of spheres. The fines particles with the diameter of dp were placed on the top of the bed of coarse particles with the diameter of db, then the rate of percolation is given by: (1) Where: - γ is shear strain; - L (m) is defined as the mean percolation distance; - k1 and k2 are two arbitrary constants equal to 20 and 8 respectively (Bridgwater et al. 1978, in Pierce 2009). Note that it is necessary to apply a shear strain () to measure percolation (), shear strain and percolation are directly proportional. Recently, the Equation 1 has been tested in PFC3D (Pierce 2009), (Hashim & Sharrock 2009; Hashim 2011; Hashim & Sharrock 2012).This formulation has been included in the software REBOP to calculate the percolation of fine particles (Pierce 2009). From an experimental point of view, experiments large 3D physical model have been also included the quantification of fines migration (Power 2004 and Castro 2007). In large 3D experiments, flow cannot be observed, so researchers have considered the use of 2D experiments (Pineda 2012 & Orellana 2012). In this article, it shows the results of an experimental program aim to quantify fines migration. The results of the experiments are presented, so the audience could make their own interpretation of one of the paradigms of block caving practice.

3 Methodology 3.1

Physical model design

In order to perform experiments some rules needs first to be complied, so the results of the model does something to do with reality. There are three similarity types between a prototype (mine full-scale situation under study and which include all the features of interest) and a model (simplified physical representation of the prototype, in which the essential features are included), which are geometric, kinematic and dynamic similarity.

• Two systems satisfy the geometric similarity when the distance between two homologous points depends on a scale factor λL.

• Two systems satisfy the kinematic similarity when two events homologous occur at a time scale factor λT and

• Two systems satisfy the dynamic similarity when the relation between the inertia and any external force in two homologous points depends on the force scale factor λF.

In this test, only geometry and kinematics similarity will be considered. Compaction and breakage could only be observed if vertical load could be applied. In Table 1, the geometry scale factors are considered to what have termed unconfined flow condition.

357

Caving 2014, Santiago, Chile Table 1 Scale factor used in pilot model

From them and the geometry, experiments were conducted using a pilot model of 250 [cm] height, 70 [cm] width and 23.3 [cm] length, which in a scale 1:200 represents 500 [m] of in-situ column and 140 [m] of gallery. There are thirty-six drawpoints, which indicates three galleries; Figure 3 shows the pilot model. This pilot model has been constructed considering geometry similitude conditions with a real mining operation.

(A)

(B)

Figure 3 Front view of the physical model (A) and isometric view of draw system (B)

3.2

Physical model experiments

Two experiments were realized with different draw, uniform and isolated draw. Physical model is loaded with coarse particle up to 240 [cm] and fine particles are loaded over coarse particle. The size distribution of fine and coarse particles is showed in Figure 4.

358

Gravity Flow

Figure 4 Particle size distributions of fine and coarse materials

The average size of fine and coarse materials are 0.14 [mm] and 4.45 [mm] respectively, which correspond to a ratio of 30. Each drawpoint in the model has a separate bin, so it is possible to determine the extracted weight of each one. There is an electronic draw system which simulates the extraction at drawpoints. This system is controlled by a computer and enables to move the particles in each drawpoint independently. Each drawpoint has a sensor that identifies when material flows or produces a hang-up. During the experiments, the point of dilution entry (PDE) was obtained, which is the drawn mass in a drawpoint until the first fine particle is drawn, divided by the assigned mass for the point. For isolated draw experiment the assigned mass corresponds to the total mass into the pilot model.

4 Results 4.1

Results of uniform draw

Figure 5 shows the flow pattern as a function of the draw stage which observed in experiments. In this case, the dilution is represented by red color. The sequence is shown in terms of mass drawn. As illustrated in Figure 5, fine migration is not happened in any percentage of drawn column. This is to the fact that there is no shear strain when drawpoints are draw concurrently. At the approximate height of 20 centimeters the irregular blue and green layers of material show no uniform movement so that we could infer that shear strain is occurring (Figure 5 at 20% of mass drawn) . This is what Laubscher (1994) have termed height of interaction which is a height at which fines migrates when 95% of the mass drawn have been drawn.

359


Figure 5 Images of uniform draw experiment.

4.2

Results of isolated draw

Because for this experiment fines migration not was observed, an experiment was set when only one drawpoint was drawn. Figure 6 shows the flow pattern as a function of the stage of draw. In this case, the dilution is represented by red color. The sequence is shown in terms of mass drawn. In this case the flow zone was not uniform and deviated to the left side of the set up. Clearly also the isolated draw created condition for shear strain. After drawing 60% of the total mass, fine migration occurred due to the shear strain. Difference in movement speed in fine and coarse material were clearly identified.

Stagnant Zone

Migration

Figure 6 Images of isolated draw experiment

360

Gravity Flow Table 2 shows the mean PDE for thirty-six draw points for uniform and isolated draw experiment (considering dilution as fine red particles). Table 2 Point of dilution of uniform and isolated draw experiment.

PDE [% of material]

PDE [kg of material]

Uniform draw

Isolated draw

507

323

95%

60%

5 Conclusions In this article we present some experiments aimed to quantify fines migration potential for gravity flow. For uniform draw experiment there was no migration because there was no shear strain, which keeps straight the most part of the time until they are close to the drawpoint so the migration occurs in the end. The isolated draw experiments allow fines migration due to shear strain because the movement speed depends on the height. It is expected that an isolated draw in a block caving mines precipitate the entry dilution when it is located at the back; however, a uniform draw retards the entry dilution. These results should not be taking as conclusive and many others experiments should be proposed and carried out to further quantify fines migration. For example experiments should be conducted to find at which ratio of sizes (fines and course) fines migration occurs. In addition, there are some complex phenomena that could affect migration and are not modeled at the physical model specially the influence of water which could increase fines migration for caving mines.

Acknowledgment We would like to thank the Chuquicamata Underground Project for discussion of the above results and the Block Caving Laboratory at University of Chile for the experiments being conducted.

References Castro, R 2001, Escalamiento para modelo físico de flujo gravitacional, Memoria para optar al títulos de Ingeniero Civil de Minas, Universidad de Chile, Santiago, Chile. Castro, R 2007, Study of the mechanics of granular flow for block caving, PhD Thesis, University of Queensland, Brisbane, Australia. Laubscher, D 1994, ‘Cave mining – State of the art’, Journal of the South African Institute of Mining and Metallurgy, vol. 94 Nº10, pp. 279–293. Hashim, M & Sharrock, G 2012, ‘Dimensionless percolation rate of particles in block caving mines’, MassMin 2012, 6th International Conference and Exhibition on Mass Mining, Canadian Institute of Mining, Metallurgy and Petroleum. Hashim, M 2011, Particle Percolation in block caving mines, PhD Thesis, University of New South Wales Australia. Hashim, M & Sharrock, G 2009, ‘Numerical Investigation of the Effect of Particle Shape on Percolation’, 43rd US Rock Mechanics Symposium & 4th US-Canada Rock Mechanics Symposium, American Rock Mechanics Association.

361

Caving 2014, Santiago, Chile Orellana, L 2012, Estudio de variables de diseño del sistema de minería continua a partir de experimentación en laboratorio, Tesis de Magister en Minería, Universidad de Chile, Santiago. Pierce, M 2009, A model for gravity flow of fragmented rock in block caving mines, PhD Thesis, University of Queensland, Australia. Pineda, M 2012, Study of the gravity flow mechanisms at Goldex by means of a physical model, Tesis de Magister en Minería, Universidad de Chile, Santiago. Power, G 2004, Modelling granular flow in caving mines: large scale physical modelling and full-scale experiments, PhD Thesis, University of Queensland. Brisbane, Australia.

362

Gravity Flow

Towards an understanding of mud rush behaviour in block-panel caving mines ME Valencia University of Chile, Chile K Basaure University of Chile, Chile R Castro University of Chile, Chile J Vallejos University of Chile, Chile

Abstract One of the most serious caving mine geotechnical risks are mud rushes. Mud rushes in block caving could be defined as sudden inflows of saturated material from drawpoints. In the last years literature has been written that tries to propose the causes for mud rushes. In general is accepted that mud rushes are due to the presence of water, fines and extraction through drawpoints. This paper describes a framework to develop a model to predict mud rush potential and to gain fundamental understanding from a geotechnical point of view. In order to do that, the authors establish a limit equilibrium model and also carried out a geotechnical characterization of mud obtained at El Teniente´s mine at the lab. From this the main geotechnical indexes are establish and a model to establish the shear strength of the mud as a function of density and water content.

1 Introduction Block/panel caving operations can involve numerous hazards, one of those are mud rushes. Mud rushes are sudden inflows of saturated fines from drawpoints or other underground openings (Butcher et al 2000). The quick response of this phenomenon has terrible consequences for safety. Mud rushes are responsible of numerous fatalities and severe damage to infrastructure. Caving operations are inherently susceptible to mud rushes (Jakubec 2012). Due to the nature of caving it has the potential of accumulating water from subsidence field as well as generating fines (comminution process) during the extraction process. Persistence of both water and fine material could cause a mud rush. El Teniente mine (CODELCO Chile) is not immune to the problem of mud rushes. According to Becerra (2011), El Teniente history has plenty of examples. One of the last mud large rushes occurred in October of 2007. The incident inflicted an extensive restructure of the control and extraction in saturated drawpoints. After this event, the operations policies have been set to limit the extraction rate and close areas when drawpoints has presence of mud (Ferrada 2011). The strategy of restrict extraction has not only had a severe impact on ore reserves but it is also unable to resolve the progressive appearance of mud in drawpoints. There are four conditions necessary for a mud rush; water, mud forming material, a disturbance and a discharge point. Operational experience shows these four are mandatory elements for the occurrence of a mudrush (Butcher et al. 2000). According to published literature, there are several triggering mechanisms of mud rushes which are classified based on the source of mud forming material and water. Table 1 resumes the mud rushes classification proposed by Butcher et al. (2005).

363

Caving 2014, Santiago, Chile Table 1 Mudrushes classification and proposed mechanism (Butcher et al. 2005)

Classification

Mechanism

External

Inflow of tailings. The material flows through a shaft, adit or open bench. Failure of backfill in stopes. Material can flow through a barricade due to the poor quality backfill. Open pit slope failures. Mud flows due to the failure of a open cut slope.

Internal/Mix

Formation of mud pockets in ore column with comminuted shale and rainwater. Rapid muckpile compactation. Responsible of mud pocket discharge.

Today mine operations with mud rush risk deals with the problem of mitigation practices through:

• Drawpoint categorization according to the percentage of fines and moist (Call and Nicholas et al., 1998).

• Draw control to ensure uniform draw (Butcher et al. 2000). • Limitation of ore reserves by height for specific drawpoints (Butcher et al. 2000). • Drainage to reduce the potential for mud rushes (Call and Nicholas et al. 1998). • Limitation of extraction rate and closure of areas with drawpoints containing mud (Ferrada 2011). • Mud rush score system (Holder 2013). Geotechnical characterization of the mud was carried out by Call & Nicholas (1998) for IOZ mine (Freeport McMoran Indonesia). On the other hand, Jakubec (2012) fulfil experiments about mudflow behaviour. Both were conducted to know the material properties and size the flow potential. Likewise, they have established failure mechanism for fine granular materials: mudflow and liquefaction. Nevertheless, they have not suggested a model that explains a mechanism for mud rushes. The main objective of this article is to provide a geotechnical model framework for mud rush potential for a drawpoint. Then, results from the a geotechnical characterization of mud to El Teniente´s mine are described and tests to define the shear strength of the mud.

2

Framework for a geotechnical model of mud rush

Mud rushes are essentially a stability problem. Variations in water content and stress conditions can increase the pore pressure and therefore the potential for sudden failure. In this case the granular material losses its strength and behave as a fluid. Besides, they specify three kinds of mechanisms that make the granular material fluid:

• Static mechanisms: Related with the extraction of mud. • Dynamic mechanisms: Related with perturbations like blasting vibrations. These cause induced liquefaction by seismic movement.

• Water as a movement force: Related with the increase of water content. This can change the mud properties, making the material fluid or drag along due the excess of pressure.

364

Gravity Flow The mudrush issue in block caving could be considered similar to the case of mining with backfill, particularly stopes with hydraulic and paste backfill. Hydraulic backfill are characterized by include fine granular material and high water content from tails. An analogy can be drawn between the geotechnical analysis of filled stopes and an isolated drawpoint. Both are made in rock (drawbell and stope) filled with saturated fine material. Stope stability analyses allow calculating the resistance of a bulkhead to seal off the extraction point. For block caving application stress analysis is alike, except for the bulkhead. In block caving there is not a permanent bulkhead and only relies on part of the broken ore at the drawpoint (see Figure 1). As noted in Figure 1, the acting forces are from the weight of the material that fill the drawpoint (Fm + Fc) and water (Fw). Filling material has two parts: broken ore and mud (Heights of this material are Hb and Hm respectively). The horizontal component of this forces (FH) is trying to get the ore out of the drawpoint. The shear resistance of this material (FB) is acting against the horizontal force.

Figure 1 Analytical model of a single extraction point

To be in equilibrium:

FB=Kh (Fm+Fc)+Fw

(1)

A limit equilibrium analysis can be developed according methods to design barricades for backfilled stopes under submerged conditions (Smith and Roettger, 1984). To understand how the mud pressure acts it is necessary to acknowledge two components of total pressure: effective fill pressure and water pressure. According to Smith and Mitchell (1982) the total bulkhead pressure for a fully saturated fill can be estimated as equation (2). The first component represents the horizontal effective fill pressure on the pile of material in drawpoint. On the other hand, the second component represents the water pressure on the drawpoint.

(2)

R is referred as a drainage ratio and is calculated according equation (3).

(3)

365

Caving 2014, Santiago, Chile Where:

H

= Fill height of mud in column (m)

γm

= Unit weight of the mud (kN/m3)

l

= Pile of material length (m)

w

= Pile of material width (m)

γ w

= Unit weight of water (kN/m3)

P

= Percolation rate in the drawpoint (cm/s)

P1

= Percolation rate in ore column (cm/s)

A

= Total area of drawpoint (m2)

A1

= Ore column cross-sectional area (m2)

This model is useful for the first part of ore column, filled with mud. According to the incidents reported to date, this height (H) should not exceed the height of the drawbell. The overload is considered. using the Janssen (2004):

(4)

Where:

γcr

= Unit weight of caved rock (kN/m3)

φ

= Internal friction angle (º)

Rh

= Hydraulic radius

μ

= Friction coefficient ( tan (φ))

k

= 1 – sen2(φ) / 1 + sen2 (φ)

z

= Depth (m)

Then, the total stress on the pile of material in drawpoint it can be estimated as: 3

(5)

Case study

The use of the stress equation is illustrated with a sample application. The parameters used for the example are showed in Table 2.

366

Gravity Flow Table 2 Parameters for illustrative case

Parameter

Unit

Value

Depth (z)

m

100

Unit weight of caved rock (γcr )

kN/m3

19

Pile of material length (l)

m

2

Pile of material width (w)

m

4

Hydraulic radious (Rh)

-

2.25

Internal friction angle (ϕ)

º

45

Unit weight of the mud (γm)

kN/m3

27

Figure 2 shows the variation of the total stress on the pile of material as a function of drainage ratio through the ore column. It is calculated for three heights of mud in a drawbell. It can be seen that stresses increases when there is no drainage through the ore column. The stress of water only matters when it is accumulating in the drawbell. When there are not drainage conditions (R = 0.1) more than a half of the total horizontal stress acting at the drawpoint is due to the height of water .

Figure 2 Total stress on the pile of material as a function of drainage ratio (R) and mud height (H)

Once, stresses on drawpoint have been estimated, it is necessary to know the resistance of the pile of material. This ore could be under different conditions of moisture and granulometry. In order to get the correct parameters to estimate this resistance, a geotechnical characterization needs to be performed. This was achieved through an understanding of the strength of the mud which is discussed in the next sections.

4

Geotechnical characterization

Samples of saturated fine material were collected from drawpoints classified as “Critical” in El Teniente mine . The risk classification was implemented in the mine according to the fine material and water content

367

Caving 2014, Santiago, Chile (Becerra, 2011).Following the standard procedure for representative sampling in El Teniente, three samples were collected. This samples represent three different muds differentiated through the observed colour: Grey (Sample 1), Yellow (Sample 2) and Mixture (Sample 3). Two different test are implemented in this study:

1. Tests to define the index properties of material: Grain Size Distribution, Atterberg Limits, Specific Gravity, Maximum and Minimum Void Ratio.

2. Tests to evaluate the relations between water content and compaction: Set of unconfined compression and set of slump tests.

The outcomes of the first test are used define the conditions for the second tests. Full size distribution curves were used to perform slump test and unconfined compression test. These tests are both carried out with different saturation and relative density values. Saturation, Void Ratio and Relative Density are defined using (6), (7) and (8) equations respectively.

(6)

(7)

(8)

Where: Vw =

Volume of water

Vv =

Volume of voids

Vs =

Volume of solids

Water content is a relation between mass of water and mass of solids. These measure are used to determine the degree of saturation and mobility of the material. The Atterberg limits are a measure of critical water content of fine soil. Those limits classify behavior of fine grain soil from solid to plastic (LP) and plastic to liquid (LL). Density of the mud material is important in determining how much water and air can fill the voids between particles, which effects the flow potential of the material. The void ratio is an important material property in assessing strength permeability, and collapse potential. Tests are accomplished accord ASTM standard and are described in Table 3. Table 3 Performed tests for geotechnical characterization

Parameter

Method

Grain size distribution

Sieve Analysis (ASTM)

Water Content

Atterberg limits

Specific Gravity

Void Ratio Maximum Void Ratio Minimum

368

Oven Dry (ASTM)

Method of Casagrande (ASTM)

Picnometer, Submerged Mass (ASTM) Minimum Density (ASTM) Modified Proctor (ASTM)

Gravity Flow 5

Geotechnical Characterization results

Geotechnical indexes are used to understand the behaviour of saturated fine material. Table 4 synthetise the main results. According to the results of laboratory test, the material is a well graded granular aggregate. It contains gravel, sand, lime and clay in different proportions. Sample 2 seems to be the one with the most portion of fines (22% limes and clays) and less gravel. While, sample 1 has the fewer portions of fines (11% lime and clay) and most gravel (60%). Moisture contents range from 9.5 to 13.4 percent. Atterberg limits produced a liquid limit range between 21.7 and 26.1 percent. The plastic limits are between 16.9 and 21 percent. These values indicate that the fines are classified as low plasticity silt and clay (ASTM D2487-00). Table 4 Outcomes from geotechnical characterization

Water Content

Grain size distribution

Atterberg limits Specific Gravity (Gs)

Maximum Void Ratio (emax) Minimum Void Ratio (emin)

Sample 1

Sample 2

Sample 3

D60=11.35 mm

D60=3.907 mm

D60=12.288 mm

D10=0.043 mm

D10=0.006 mm

D10=0.018 mm

LP=16.9%

LP=21%

9.50%

D30=2.385 mm LL=21.7% 2.76 0.9

0.26

13.40%

D30=2.43 mm LL=25.7% 2.68 1.00 0.28

12.50%

D30=1.045 mm LL=26.1% LP=19.1% 2.72 0.92 0.22

Up to date tests to determine the strength for the mud have considered unconfined compression test. Figure 3 shows the unconfined strength for the three samples at different saturations and relative densities. Relative densities are related to the maximum density achieved by the mud using a Proctor test. The results indicate that resistance increases with a decrease on the relative density. At low relative density and high saturation the mud shows no shear resistance. The water content for this case is over the plastic limit so the mud behaves like a liquid. Slump test indicate that in conditions of high relative density, there is no settling (no deformation). For a relative density lower than 65%, the possibility of flow or plastic behaviors is depending on saturation. In particular, when fluid behavior appears, water content is over or near the plastic limit. Sample 1, including the most cases on fluid state, has the lowest limit. The outcomes of the geotechnical characterization suggest that the resistance of the pile of material could be overcome depending on the relative density. Relative density depends on the extraction of the mud at drawpoints and it is not usually measured.

6 Conclusions This paper outlines a geotechnical model framework for mud rush prediction. The new model represents an instant before a mud rush. This model takes account three components: the weight of the mud in drawbell, the pressure of water that is accumulated in the column and the overload of the broken material. The simplified model is consistent with some mitigation practices like drainage. Drainage ratio is one of the most important variable as well as relative density. When there are hardly drainage conditions (R = 0.1) more than a half of the stress belongs to water pressure.

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Figure 3 Unconfined compression test results (average between three samples)

Once the amount of load was estimated, a geotechnical characterization was performed regarding the resistance of the pile of material. Saturated fine material was tested to know the behaviour of mud under different conditions. Results indicate that plastic deformations could explain mud rushes for abrupt changes of load or moist. Furthermore, relative density has an important role in the resistance of material. Incorporate the extraction of material is the next step to understand the behaviour of mud under block caving mining. This will be developing a numerical model and experiments at lab scale.

Acknowledgement This paper has been prepared as an output of the Innova CORFO Project 12IDL2 - 15145. The authors wish to thank Mauricio Melendez (CODELCO Chile) for provide the material of the experimental study, as well as to CORFO that allowed the development of this research.

References Becerra, C, 2011, ‘Controlling drawpoints prone to pumping, El Teniente Mine’, Proceedings of Second international Seminar on Geology for the Mining Industry, Antofagasta. Butcher, R, Joughin, W & Stacey, TR, 2000, ‘A Booklet on methods of combating mudrushes in diamond and base metal mines’. Simrac. Butcher, R, Stacey, T & Joughin, W, 2005, ‘Mud rushes and methods of combating them’, The Journal of The South African Institute of Mining and Metallurgy, vol 105, no 11, pp. 807-824. Call & Nicholas, 1998, ‘IOZ Wet Muck Study’, Freeport McMoRan Copper and Gold, C. & Hydrologic Consultants.

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Gravity Flow Ferrada, M, 2011, ‘Gravity flow under moisture conditions – Control and management of drawpoint mudflow’, In 35th APCOM Symposium, Wollongong. Jakubec, J, Clayton, R & Guest, A, 2012, ‘Mudush Risk Evaluation’, Proceedings of Massmin 2012, Subdury. Jansen, HA, 2004, ‘Experiments regarding grain pressure in silos (Translated from german by W. Hustrulid and Norbert Krauland)’, Proceedings of Massmin 2004, Santiago. Holder, A, Rogers, AJ, Bartlett, PJ & Keyter, GJ, 2013, ‘Review of mud rush mitigation on Kimberley’s old scraper drift block caves’, The Journal of The South African Institute of Mining and Metallurgy, vol 113, no 7, pp. 529-537. Smith, JD & Mitchell, RJ, 1982, ‘Design and control of large hydraulic backfill pours’, CIM Bulletin, vol. 75, no 838, pp. 102-111.

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Statistical analyses of mud entry at Diablo Regimiento sector - El Teniente’s Mine IM Navia Universidad de Chile, Chile RL Castro Universidad de Chile, Chile MA Valencia, Universidad de Chile, Chile

Abstract Mudrushes have plagued block and panel caving operators with many fatalities and can have posed a major hazard to safety in block and panel caving mining. Closing drawpoints with a high mudrush potential can be introduced as an effective way of preventing mudrush hazards. Since draw point closure is due to mudrush potential, not dilution, different amount of remnant saturated ore (RSO) would be remaining in the block column. Tonnage and grade of RSO in this group were calculated based on the actual situation of closed drawpoints. The second group contains drawpoints located in a zone with a high potential mud entrance. In this group, the RSO that could potentially be removed once mud enters in drawpoints was predicted based on the historical extraction data. The results indicated that RSO is itself an interesting quantity in terms of tonnage and average grade. Respecting to occurrence of mud, the initial inflow of mud was associated to drawn heights and draw uniformity that are similar to in situ height of the initial entrance area. It is proposed that the subsequent entry of mud resulted not only in relation to the connection with higher mined levels, but other mechanisms, such as the entry of water directly from surface.

1 Introduction A flow of mud in block/panel caving, called “mudflow”, “mudpush” o “mudrush”, is defined as a sudden and violent inflow of a mixture of water and fines to mine openings, with a high injury, death and damage potential. A mudflow can damage equipment, cause operating losses and even, can cause fatalities (Butcher et al. 2005). The existence of mud at broken columns can cause two effects: mudflows, either violent mudflows or less violent spills of mud; and the redefinition of reserves due to the cutting of the drawable heights. The last with the purpose of not including mud drawing in mine planning due to safety actions and technical capability (Barahona 2014, pers. comm., 03 February). This meant that there is ore that cannot be extracted which has been termed remnant saturated ore (RSO). There is a need to quantify the economic potential of RSO in drawpoints which have been closed to prevent the hazards of mud-water. Nowadays, three statuses have been defined to face the mudrush hazard in El Teniente’s sectors: 1. Mud-water status, or critical zone, has the most probability of mudrush occurrence and, thus have been closed forever to prevent the entrance of mud. A wet muck classification matrix has been developed, to define the mudrush risk considering fine material and moisture percentage (Becerra 2011). 2. Limited status, which is happened in the drawpoints surrounding the mud-water status. These drawpoints have the hazard of lateral immigration of mud from the critical zone. Therefore, the extraction rate from these drawpoints is limited. The result of extracting the limited status drawpoints is mud immigration to operative drawpoints.

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Gravity Flow 3. Barrier status, in which some drawpoints’s ore columns are used as a barrier to control the entry of mud. The content of moisture and fine material in these drawpoints are not necessarily critical but they are estimated as high risk points based on the flow direction of mud. Due to a null extraction in this statue, the lateral advance of mud is paused or delayed (Vargas 2013, pers. comm., 30 September). In order to minimize the problem of dealing with mudrush in production areas, a number of researchers have suggested to draw uniformly (Widijanto et al. 2012; Laubscher 2000). This strategy would allow the extraction of mud in many drawpoints, as a result, would prevent mud concentration in just a few drawpoints. An extensive dewatering program can also reduce water which leads to the wet muck runs (Barber et al. 2000). Drainage strategy can be implemented in both surface water, which enters the cave through rain falling onto the subsidence, or underground water. (Samosir et al. 2008; Barber et al. 2000). In this article the economic potential of RSO in the closed drawpoints as well as those that could be closed in the future due to the ingress of water-mud was calculated. Furthermore, the historical databases of resources, reserves and extraction conditions at a mine of El Teniente known as Diablo Regimiento was used to define the relationship between the appearance of mud and the drawn strategy based on the back analysis statistical method. It should be noted that this database includes all resource and production history of Diablo Regimiento from the initial date of extraction to November 2013.

2

Economic potential of RSO

The economic potential of the RSO at Diablo Regimiento was calculated based on the column model and production data history of each drawpoint. Through this database it is possible to identify grade, tonnage and density of each bench in every draw column. Due to mudrush hazards, some draw points have been closed before reaching the economic drawable heights; therefore, two groups of drawpoints are introduced to determine the accurate RSO as:

• Drawpoints affected by mud-water: This group includes drawpoints with mud rush hazards

which are categorized in three statuses (Mud/Water, Limited and Barrier). To evaluate RSO, both economical and marginal drawable heights are considered, under which the minimum and maximum RSO defined respectively. Table 1 shows the results in each drawable height. As it is indicated in table 1, in the case of marginal drawable height, two different cutoff grade was taken into consider.

• Drawpoints not affected by mud-water: This group includes drawpoints located in the zone under upper mined levels which are already drawn (from East to West, they are Regimiento, Puente and Fortuna). The drawpoints are considered with a high mud entrance potential. It should be noted that the drawpoints considered in the previous section were excluded.

Table 1 shows the results of RSO calculation based on marginal drawable height for two various cut off grades. As illustrated in table 1 the minimum RSO is 11.8 Mt ore material with the average grade of 0.63%.

3

General analysis of mud occurrence at Diablo Regimiento

According to critical matrix used in El Teniente copper mine (Becerra 2011), if the percentage of fine material and moisture content in a drawpoint reaches the critical value the status of drawpoint will changes to Mud/Water status. In this situation the drawpoint will be closed to prevent the hazard of mudrush. In this paper the historical database of closed drawpoints is considered as a situation that mud occurrence.

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Caving 2014, Santiago, Chile Closure grades Closure grade is defined as the final extracted grade of a drawpoint which is closed due to the mudrush hazards. In figure 1 the closure grade of different drawpoints at Diablo Regimiento is illustrated. It can be concluded from this figure that most drawpoints are closed with high copper grades. Table 1 RSO at Diablo Regimiento

Considerations

RSO (Mt)

Initial reserves at Diablo Regimiento

Average grade (%CuT)

4.4

0.72%

Considering marginal heights; cutoff grade equal to 0.4%CuT

14.9

0.62%

11.7

0.51%

Considering marginal heights; cutoff grade equal to 0.5%CuT

11.4

0.67%

7.4

0.57%

Drawpoints considered Affected by mudwater Affected by mudwater Not affected by mud-water Affected by mudwater Not affected by mud-water

Total of Average grade RSO (Mt) (%CuT) 4.4

0.72%

26.6

0.57%

18.8

0.63%

Figure 1 The frequency of closure grades at Diablo Regimiento

3.2

Drawn heights

The drawn heights were calculated using daily drawn databases as well as resources model per bench and drawpoint. The historical database of Diablo Regimiento is used to analyses closed drawpoints in the case of closure sequence and drawn heights.

374

Gravity Flow 3.2.1

Closure Sequence

A plan view of closed drawpoints at Diablo Regimiento is illustrated in figure 2. It is observed in figure 2(a) that the initial mud entry drawpoint is at the center of the Diablo Regimiento sector, and it coincides with the drawpoints where extraction began in order to generate the dome. Subsequently, the mud was always appearing in neighboring points, and then appeared in the east sector. Based on the drawn height in figure 2(b) it is possible to compare the extraction height at different part of Diablo Regimiento sector.

Figure 2 (a) Closure sequence and (b) drawn height of closed drawpoints

3.2.2

Drawn heights at closed drawpoints

Figure 3 shows the frequency of drawn heights in closed drawpoints at Diablo Regimiento sector. The high variability is illustrated for different drawpoints. Based on figure 3 and 2(b), it appears that the lower elevations correspond to the drawpoints that begin connecting with mined and caved upper levels; that is the center of the sector and the east side. Subsequently, the neighboring points to the aforementioned are associated to a greater drawn height before the apparition of mud.

Figure 3 Drawn height frequency of closed drawpoints

It is observed that 30% of the drawpoints in Mud/Water status, were at a lower or equal to 170 m height drawn, corresponding to the approximate distance between Diablo Regimiento and mined upper levels. In addition, at that height is where the highest frequency of closure drawpoints occurred. Occurrence of mud in these drawpoints may be through to the accumulation of water and mud in the overlying Regimiento sector (Diablo Regimiento is below Regimiento). However, the entry of mud at a greater drawn height may

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Caving 2014, Santiago, Chile be attributable to the draw maintaining an irregular drawing profile combined with the accumulation of mud-water on the surface. Mud entrance below 170 m may be due to vertical or lateral migration of mud already within broken columns. Analyzing the drawn height of closed drawpoints (figure 4) observed that as mining production progresses, the range of possible heights closed drawpoints is increased. It can be concluded from figure 4 that the first mud entrance is due to connection with upper sectors; but in other drawpoints that were extracted latter in the sequence, it may have been other reasons for mud entrance (water from other sources).

Figure 4 Evolution of accumulated drawn height at Mud/Water status declaration

According to the analysis of the data the following could be concluded:

• Rock caving commencement in virgin areas has a great influence on the potential of mud entrance

to the sector. For example, the dome shape of cave back in the center of a mine sector causes an early interaction to the surface or to mined levels located above. This creates channels through which fine material and water would entrance to production level.

• In general an effective way of avoiding mud ingress is through uniform draw so as to bring the ore/ mud interface as horizontal as possible.

• It is important to detect the sources of water and mud and to define a strategy for dewatering and for a draw strategy to face high potential areas for mud ingress.

4

Determining the probability of mud entrance

As it is illustrated, the RSO could have an important role on the reserves evaluation; therefore the mid and long term production planning would be changed based on RSO. Since RSO is the result of mud occurrence in draw columns, it is essential to determine in advance the entrance of mud in drawpoints. As a result, a model is proposed to predict the probability of mud occurrence employing a logistic regression. This model can be used as a mine planning tool. The main data which are considered in this model are temporal evolution of draw rate, fine material content, drawn height and season of the year. The last is due to a correlation that could exist between water seasons and drawpoints closed due to mud. Some aspects related to mudflows are described below.

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Gravity Flow A logistic regression model is proposed in this study to predict the Mud/Water status based on historical data gather at Diablo Regimiento sector. Logistic regression is a technique for making predictions when the dependent variable is a dichotomy, and the independent variables are continuous and/or discrete. In order to predict the risk of mudrush hazard, a logistic regression used with dependant variable (p) as the probability of persistence of mud. For each drawpoint the obtained value of p shows if there is mud in the column (p=1) or not (p=0). Formally, logistic regression model is defined as equation (1). (1) Solving for p, this gives equation (2). (2) Where βi (i=0 ,…, n) are the estimators and Xi (i=1 ,…, n) the independent variables including: X1: Draw rate X2: Fine material content X3: Drawn heigth X4: Season The variable season added to this model because in the probability of mud occurrence in spring is more than other seasons of the year. The estimators obtained for equation (1) are shown on equation (3).

(3)

Based on logistic regression model, the persistence of mud in various drawpoints in Diablo Regimiento sector evaluated. Figure 5 shows the results. It is illustrated in figure 5 that this method enables to predict mud occurrence in different part of the sectors. Moreover, the precision of model is 74%.

5 Conclusions In this paper, statistical analyses of database at Diablo Regimiento sector was used to study the effect of different parameters on mud occurrence in drawpoints. Based on this study, it is concluded that the accumulated drawn height could be the most influence parameter in controlling mud entrance. According to data analysis in this research, it is concluded that in the case of irregular profile of drawn heights, uniformity and continuously strategy cannot solve the mudrush problem. In this situation, first objective of short term production should be obtaining a uniform drawn height profile. After reaching this objective, uniformity and continuously strategy seems to reduce the above mentioned problems. Moreover, in this study the economic potential of RSO is evaluated. Even though closing drawpoints is the best way to ensure safety in production level, the results of economic evaluation shows that RSO are potential to provide at least one half year production of this sector.

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Figure 5 Status of drawpoints on Diablo Regimiento sector; (a) real status of drawpoints and (b) predicted status of drawpoints (W: wet drawpoints; D: dry drawpoints)

Finally, it is illustrated that logical regression method can be used to predict the mudrush hazards in different part of sector. Further research needs to be conducted in order to evaluate the mud potential ingress for mine planning purposes.

Acknowledgement The authors acknowledge the assistance of Rodrigo Barrera, Max Barahona, Ricardo Vargas and Antonio Pinochet from Codelco´s El Teniente Mine, and Asieh Hekmat from BCLAB, for their helpful support. This research has been funded through Corfo and by the Conicyt funds for the Advance Technology Center (AMTC) of the University of Chile.

References Barber, J, Thomas, L, Casten, T 2000, ‘Freeport Indonesia’s Deep Ore Zone Mine’, in Proceeding of Massmin 2000, Brisbane, Queensland, 29-October-2-November 2000, ed. G.C., The Australasian Institute of Mining and Metallurgy, pp 289-294. Becerra, C 2011, ‘Controlling Drawpoints Prone to Pumping - El Teniente Mine’, in International Seminar on Geology for the Mining Industry, 8-10 June 2011, Antofagasta, Chile. Butcher, R, Joughin, W & Stacey, T 2000, Methods of Combating Mudrushes in Diamond and Base Metal Mines, SRK Consulting, The Safety in Mines Research Advisory Committee (SIMRAC), Braamfontein. Butcher, R, Stacey, TR & Joughin, WC 2005, ‘Mudrushes and methods of combating them’, The Journal of The South African Institute of Mining and Metallurgy, SAIMM, Volume 105, pp. 817-824. Laubscher, D 2000, A Practical Manual on Block Caving, International Caving Study. Samosir, E, Basuni, J, Widijanto, E & Syaifullah, T 2008, ‘The Management of Wet Muck at PT Freeport Indonesia’s Deep Ore Zone Mine’, in Proceeding of Massmin 2008, Luleå, Sweden, 9-11 June 2008, eds. H. S. & E. N., Luleå University of Technology, Luleå, pp. 323-332. Widijanto, E, Sunyoto, WS, Wilson, AD, Yudanto, W & Soebari, L, ‘Lessons Learned in Wet Muck Management in Ertsberg East Skarn System of PT Freeport Indonesia’, Proceedings of Mass 2012, Sudbury, Canada.

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Innovation

379

Innovation

Hybrid composite, a way to enhance the mechanical properties of breakable ground support V Barrera Mining and Metallurgy Innovation Institute IM2 – Codelco, Chile P Lara Mining and Metallurgy Innovation Institute IM2 – Codelco, Chile G Pinilla Codelco, Chile F Báez Codelco, Chile

Abstract When old mining workings levels are caved, the traditional ground support that has offered a safe place for mining could become a problem for the continuity of the production process. This problem is related to the flow of unbreakable traditional support elements (tramp iron) in the ore conveyance lines and comminution circuits in underground mining. For this reason, the need to find breakable systems for ground reinforcement is an open problem. This issue is partially addressed by market alternatives of fibre-reinforced polymers. However, these products that warrant a good tensile strength show poor deformability due to their brittle nature. IM2’s Geological Mining Area and GT&I Codelco Chile are carrying out a research study to develop breakable ground support elements based in the hybridization effect applied to fibre-reinforced polymer to obtain systems with enhanced mechanical properties.

1 Introduction Cave Mining, used to mine large ore bodies such as the tasks of Codelco, uses gravity and considers the existence of different levels in their design. These levels, once mined, are successively abandoned and the mining is deepened. Each of these levels requires the use of support systems in order to ensure a safe working environment for both operators and machinery. The basic function of the support and containment systems in the rock mass is to help self-supporting because every time an underground excavation is made, the natural tendency is to occupy the empty volume and return to its undisturbed condition. This return to balance is done by stress redistribution around the excavation, resulting in a gradual deformation of the excavated cavity. However, when these processes exceed the mechanical strength of the rock surrounding the excavation, this may cause the breakage and shedding of blocks and, in extreme cases, cause violent rock bursts that occur when the existence of brittle rock is combined with a high stress concentration. It is under these conditions that the support systems contribute to create a safe working environment during the mining works. The traditional support systems are made of ferrous elements due to their high plasticity and they become unbreakable tramp iron when they are installed into new production caving levels. Table 1 provides a description of the mechanical properties of the steel used in rock bolt making (Carvajal 2008). Table 1 Minimum mechanical properties of steel used for rock bolts

Tensile Strength 440 MPa Elongation 16.0% Shear Strength 251 MPa Density 7850 kg/m3

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Caving 2014, Santiago, Chile For this reason, the passage of these unbreakable materials in conveyance and crushing lines in mining operations introduces disturbances in the continuity of the productive process. Sometimes, this scenario will create unscheduled shutdowns, increasing the risk of not fulfilling the commitments stipulated in the production planning. A way to solve the problem of unbreakable underground support, chosen by IM2 Geomining - Codelco, is to design new elements using materials that have the capability to withstand stresses inherent to the mining works and that become breakable once they are absorbed into the caving. Modified fibreglass composite has been chosen for this purpose because it meets both requirements. To increase its ability to deform (trying to reach the properties of ferrous elements), the modified fibreglass composite has been provided with a core of ductile material (minimum volumes of steel), using the hybridization effect (Marom et al. 1978) applied to fibre-reinforced polymer. Tensile and shear tests in a universal testing machine have been made to obtain measurable parameters under static loads.

2 Methodology 2.1 Fabrication Two types of Hybrid Spun Bars (HSB-X1, with SAE 1045 steel core, and HSB-X2, with A630-420H steel core) were manufactured by pultrusion. This production technique is a low-cost, high-volume manufacturing process in which resin-impregnated fibres are pulled through a die to make the part. The process is similar to metal extrusion, with the difference being that instead of material being pushed through the die initially, it is pulled through the die in a second process. Pultrusion creates parts of constant cross-section and continuous length (Mazumdar 2002). Figure 1 shows this forming technique.

Figure 1 Schematic pultrusion process

2.2 Test A survey conducted in the Chilean supplier market, carried out by IM2-CODELCO, determined the strength of Glass Fibre Reinforced Polymer (GFRP) bars (Barrera et al. 2013). This information is summarized in Table 2.

382

Innovation Table 2 Properties for GFRP bars supplied in the Chilean market

Tensile Strength 506 MPa Elongation 7.31% Shear Strength 158 MPa Density 709 kg/m3 To determine the properties of these elements, two types of static tests were considered: tensile and shear tests. The breakability property will be research in further step of this investigation. 2.2.1

Tensile Test

The standard tensile test is the uniaxial tensile test. In this case, as the test sample was made of polymericnature materials, the ASTM D7205 standard (ASTM 2006) was applied.

Figure 2 Tensile test and HSB specimen

2.2.2

Shear Test

The shear strength of the hybrid prototype bars was determined using a double shear test based on ASTM D7617 (ASTM 2011). To execute this test, a piece specially designed for this purpose, which was assembled in the universal testing machine, was used.

Figure 3 Parts and assembly of HSB specimen for double shear test

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Caving 2014, Santiago, Chile 3 Results 3.1

Fabrication Results

Figure 4 shows the raw materials that were used and the result of the pultrusion process, which are the hybrid spun bars, HSB.

Figure 4 Raw materials and HSB (right bottom corner picture)

3.2

Test Results

The tests performed was made to evaluate the mechanical strength characteristics of this elements. As it is indicated, Figure 5 shows the fragile fracture mode in both, tensile and shear tests.

Figure 5 HSB specimens after tensile (left) and shear (right) tests

Table 3 summarizes the results of tensile and shear tests. Table 3 Properties for HSB prototypes

HSB-X1

Tensile Strength 445 MPa; Elongation 10.8%; Shear Strength 191 MPa; Density 1,473 kg/m3

HSB-X2

Tensile Strength 392 MPa; Elongation 14.4%; Shear Strength 190 MPa; Density 1,592 kg/m3

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Innovation In the light of these results, a comparison can be made with both ferrous elements as well as GFRP bars provided in the market. This comparison is provided in Table 4, where the loss (gain) is shown in percentage with respect to the base state for this research (rock bolt steel and GFRP bar). Table 4 Comparison among prototypes and existing technology (rock bolt steel and GFRP bar)

Prototype HSB-X1 HSB-X2 HSB-X1 HSB-X2

With Respect to Rock bolt Steel GFRP Bar

Tensile Strength

Elongation

Shear Strength

Density

-1.10%

32.5%

23.9%

81.2%

10.9%

10.0%

24.3%

79.7%

12.1%

-47.7%

-20.9%

-108%

22.5%

-97.0%

-20.3%

-125%

-: Percentage gain with respect to; +: percentage lost with respect to

4 Conclusions The tensile strength of the HSB-X1 prototype shows the best behaviour with respect to both base states: rock bolt steel and GFRP bar. Furthermore, it also shows an increase of about 50% elongation and 21% in the shear strength of the GFRP bar. The loss of about 32% in the shear strength with respect to the steel rock bolt leaves a margin for further research to improve this property with new material cores. As for the properties shown by the HSB-X2 prototype with respect to the GFRP bar, they are lower than the previous prototype, except for elongation, where it shows a response close to the minimum accepted for steel. Both prototypes show a lower density than rock bolt steel, however, this property is enhanced with respect to the GFRP bar.

Acknowledgement The authors acknowledge the sponsorship of Codelco in the context of the completion of Project API M11DE12 “Conceptualization and Experimentation of Breakable Ground Support Elements”. In addition, Patricio Lara is grateful for the valuable assistance of Cristian Welsch, who made it possible to fabricate the breakable prototypes at PERNOMIN Ltda.

References ASTM International 2006, ‘ASTM D7205 Standard Test Method for Tensile Properties of Fibre Reinforced Polymer Matrix Composite Bars’, ASTM International, Pennsylvania. ASTM International 2011, ‘ASTM D7617 Standard Test Method for Transverse Shear Strength of Fibre reinforced Polymer Matrix Composite Bars’, ASTM International, Pennsylvania. Barrera, V, Lara, P, Pinilla, G, Arancibia, E 2013, ‘Breakable Ground Support a verification of mechanical properties to diminish ferrous Solid Waste in Underground Mining’, Proceedings of Copper 2013, ed. IIMCH, Santiago. Carvajal, A 2008, Manual Sistema de Refuerzo de Rocas con Pernos Saferock, Gerdau Aza, Santiago. Marom, G, Fischer, S, Tuler, FR, Wagner, HD 1978, ‘Hybrid effects in composites’, Journal of Material Science, vol. 13, pp. 1419 – 1426. Mazumdar, S 2002, Composite Manufacturing Materials: product, and process engineering, CRC Press, Boca Raton.

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Pilot tests as a tool for the design of autonomous mining systems J Riquelme University of Chile, Chile R Castro University of Chile, Chile S Valerio University of Chile, Chile J Baraqui Codelco Chile, Chile

Abstract For many years mining innovation has relied on full scale tests to verify the implementation of new technologies. At the University of Chile, a methodology has been developed for using pilot tests for mining engineering with the aim of speeding the process of design and implementation of novel technologies. In this article, we present the potential of this approach for the conceptual design of an autonomous and continuum mining system using scaled models. Autonomous means that the system could operate without people taking decisions on when each dozer would draw. This is achieved using sensors (lasers and cameras) and a control system for the dozers. This research also assists in the understanding of the behaviour of interactions between the components of the Continuous Mining System. The results indicate the extraction sequence and the use or not of controlling systems could increase the draw rate for this type of material handling systems.

1 Introduction Continuous Mining is a new material handling system designed to increase the rate of draw for block caving mines (Encina et al. 2008). This technology has been tested at Codelco´s El Salvador Division between 2006 and 2007 and is currently under construction at Andina´s Division. At the same time a number of experiments using scaled and numerical modelling have been carried out at the University of Chile (Alvarez 2010; Orellana 2011; Orellana 2012). The Continuous Mining system consist on the use of novel-to-block caving´s material handling equipment: (1) stationary feeders located in draw points (dozers); (2) a continuous conveyor (panzer) which receives the material from dozers; and (3) a crusher in order to reduce the size of material (Encina et al. 2008) (Figure 1-Left). The system which is currently being tested at Andina Mine could operate 8 dozers per panzer as shown in Figure 1-Right.

Figure 1 (Left) General scheme of extraction level in a Continuous Mining module (Encina et al. 2008); (Right) Plan view scheme of extraction level used in the current industrial test. The arrows indicate the direction of movement of the material

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Innovation One of the key factors of successful continuous mining systems is achieving higher extraction rates compared to LHDs. Therefore, it is necessary to simulate the results of interaction between equipment and materials. In this article the results of a pilot test program are presented to illustrate the interactions between the components of the Continuous Mining System. The results also include the implementation of Autonomous and Continuous Mining System for future applications in mining.

2

Pilot tests

2.1 Objectives The aim of the pilot testing was to simulate the extraction of a production drift in a Continuous Mining module (Figure 1). This research focused on the production rate and the potential interaction of the dozer - panzer system. The steps performed in this research were as follows:

1. Conducting a scaling study and construction of a 1:50 scale pilot test with eight dozers and one panzer as considered on the detailed engineering (JRI Ingeniería 2010). In this case both the geometry and model media were scaled accordingly. The material with which the tests were performed corresponds to approximately 1.2 tonnes of gravel, with a size range between 6.35 mm and 40 mm with a mean diameter of 16 mm (Figure 2).

2. Commissioning: detection and resolution of operating system problems. 3. Continuous Mining Experimentation: testing stage without sensing or control systems. 4. Autonomous Continuous Mining Experimentation: development of tests with sensing and control systems for autonomous operation of the dozers.

5. Analysis of experimental results: comparison and analysis of results obtained after the completion of the tests, generating conclusions and recommendations for future studies.

Figure 2 (Left) Material’s sample used in experiments; (Right) Particle size distribution of the sample

3

Laboratory equipment

The laboratory equipment consisted of a model frame, extraction equipment, sensing system and control system (Figure 3). The model framework consisted of a plexiglass framework of 1.58 m width x 0.57 m

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Caving 2014, Santiago, Chile depth x 0.82 m height. In this model, eight drawbells were built also in plexiglass to observe the hang ups and gravity flow during the tests.

Figure 3 Physical model used for experiments: (Left) Physical model with the electrical and actuation panel, and a computer desk; (Right) Perspective of filled physical model

Figure 4 shows different views of the scaled dozer which is located under the drawbell with a width of 40 mm (Figure 4-Center). The extraction system operates by compressed air, pushes the upper part and mobile part through a cylinder. In this scaled model it is possible to measure the pressure for each movement of the dozer.

Figure 4 Dozer system in the scaled model: (Left) Frontal view of dozer; (Center) Side view of dozer; (Right) Plan view of dozer gallery

The second equipment of the Continuous Mining System is a Panzer. Based on experiments, this part of the system was represented by a belt conveyor with gaps as in the chain system (Figure 5). The belt operates by two electrical engines located at the end of it.

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Innovation

Figure 5 Panzer construction in the scaled model: (Left) Perspective view of panzer gallery; (Right) Plan view of panzer

The sensing system consists of four volume sensor prototypes designed and implemented for the studied physical model. The system is based on the principle of structured-light photogrammetry, which is a combination of image processing and structured light. A video camera, which obtains a static image of the material on the panzer, is installed on the roof of the panzer gallery and is oriented at a specific inclination angle to the horizontal axis of the gallery (Figure 6). The image processing algorithm outlines the edges or the contours of the transported material based on the obtain image.

Figure 6 Sensing system in the scaled model: (Left) Side view of the sensing system; (Right) Perspective view of panzer gallery with sensing system

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Caving 2014, Santiago, Chile The last component is the control draw system (hardware and software). The aim of the control system is to maximize the amount of material as well as the uniform load in the panzer. To do that the system starts with an assumption of the amount of material draw per dozer which is then compared to the measurements at the panzer by the sensing system. The system then corrects the amount of material in the panzer and actives if required other dozers in the panzer. The implementation of the control system is multi-platform: it has been tested in Windows, Linux and OS-X systems. It has a control interface in which it is possible to set extraction rates, dozer’s state (active or inactive), the speed of the panzer, and cycle times of the dozer. It also shows a model representation of the load distribution in panzer.

4

Experiments and results

4.1 Experiments Until now five experiments have been run at the pilot tests infrastructure. The experiments were run to set up the pilot tests, to quantify the extraction of the dozer and panzer system, to test different draw sequences and to test the autonomous system. The aim of each experiment is presented in Table 1. The main variables are as follow:

• Dozer dump length (Dl) (mm): is the panzer length which is used by the drawn rock from a dozer. • Dozer productivity (Dp) (g/cycle): is the amount of rock per cycle of the dozer. • Panzer utilization (Pu) (%): is the percentage of the total length of the panzer used by rocks. The

panzer utilization is calculated as Pu = Lg / Lt; In which Lg is the portion of panzer with visible material (discounting panzer gaps) and Lt is theoretical length estimated considering the first and last particle out of the panzer, independent of the vertical extension of material. Utilization was estimated using ten consecutive cycles of the system (case without automation) and full test duration (case with automation).

• System production rate (Sp) (g/min; t/h): is the total broken rock extracted by the continuous system (panzer).

• Dozer sequence: experiments considered three operational dozer’s configurations (Figure 7).

Type I: Two alternating dozers operating from the same panzer gallery side; Type II: Two extreme and two central dozers operate; Type III: Only the four center dozers operate.

The main parameters used in tests are the following:

• Panzer speed (Ps) (cm/s): fixed parameter scaled from the full scale speed of the panzer. • Dozer cycle time (Dt) (s/cycle): is the time takes to make a dozer’s dump. • Distance between operational dozers (Dd) (mm): is the distance between the centers of the operational dozer’s galleries.

• Maximum number of simultaneously running dozers (Nd): is the maximum number of permitted operational dozers due to the full scale constraints.

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Innovation Table 1 Experiments conducted in this research

Exp.

Objectives

0

Check system function

1

Characterize the interaction of dozerpanzer system

2

Determine main operational configurations and generate dozer’s sequences Determine number of cycles required to reach a steady state Estimate panzer utilization

3

Determine productivity of the Continuous Mining for a given sequence (base case)

4

Determine productivity and panzer utilization of the Autonomous Continuous Mining system with sensing and control components

Figure 7 Operational dozer’s configurations: (Left) Type I configuration; (Right) Type II configuration; (Bottom) Type III configuration. Active dozers are the marked by grey colour

4.2 Results The results of the dozer and panzer systems are shown in Table 2. Values were scaled using the 1:50 scale factor. The results indicate that when a dozer is activated an average of 19 cm (9.6 m scaled) is used by the drawn rock. This has a variability of 9 cm (4.8 m scaled) with each discharge from the dozers. Thus the results indicate that when a dozer discharges the used length is variable as well as the amount of drawn rock. A second series of experiments consisted in testing the continuous mining (experimental base case) and the autonomous system for different draw strategies. The time to activate a dozer in the sequence was calculated using the average dozer’s dump length, the distance between the dozers and the panzer speed (Barriga

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Caving 2014, Santiago, Chile 2012). For both studied cases type III configuration is the one that produces more. This is possibly due to the greater proximity of the active extraction points, resulting in the interaction of the respective movement ellipsoids. Table 3 indicates the percentage increase of productivity and panzer utilization associated by using an autonomous system, compared to the base case. Table 2 Statistic results of dozer’s dump length and dozer’s productivity

Laboratory scale

Average

Scaled results

Dump length [cm]

Productivity [g/cycle]

Dump length [m]

Productivity [t/cycle]

9.76

74.38

4.88

9.30

Standard deviation Minimum

Maximum

Cycles number

19.18 0.50

76.35 0.25

50.50

0.25

423.72

400

9.59

25.25

400

400

9.54 0.03

52.97 400

Comparing the same draw sequence, the autonomous continuous mining productivity is, on average, 58% higher than the uncontrolled system. Regarding panzer utilization, the average for automation case is 7% higher than nonautomation case. Thus, it can be concluded that Autonomous Continuous Mining has also a higher panzer utilization. Table 3 Percentage increase of productivity and panzer utilization for studied cases

Configuration

Type I

Type II

Type III

Average

Panzer utilization

1.0

21.4

-2.1

6.8

Productivity

50.2

60.6

62.0

57.6

In general, productivities and utilization of the system increase through the implementation of the sensing and controlling systems.

5 Conclusions This research work confirmed the usefulness of pilot testing tool towards the understanding of behavior of innovative mining systems. It was verified that, under experiments and particle size distribution conditions presented, the dozer-panzer system is a high production system and that the draw per dozer is high and variable. The results of testing the Continuous Mining System subjected to experiments conditions showed that configurations using central extraction points allow reaching higher extraction rates. The results from the Autonomous Continuous Mining testing subjected to experiments conditions show that the implementation of an autonomous control system allows the achievement of higher productivity, extraction rates and panzer utilization. Based on the results obtained, it is recommended to implement the autonomous system in the mine in order to verify the increase of productivity of the system. It would be beneficial to perform new experiments under a more demanding scenario. For example, incrementing the particle size distribution with the aim of quantify the reduction in system productivity. It will be relevant to identify the potential problems, which

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Innovation this technology could face in the future in case of not achieving the expected particle size distribution. Finally, it is recommended to integrate uniformity indicators towards maximizing the production rates and the extraction of reserves in long term production planning.

Acknowledgement The authors would like to acknowledge the financial and technical support from Codelco Chile. The authors would also like to acknowledge the contribution of Ernesto Arancibia from Codelco Chile and to IM2 (Institute for Innovation in Mining and Metallurgy) researchers during this project.

References Alvarez, P 2010, Modelamiento físico de la Minería Continua, Memoria de Ingeniería, Universidad de Chile, Santiago, Chile. (in spanish) Barriga, J 2012, Secuencia Accionamiento Dozer, Nota Técnica Nº IA-004, IM2, Santiago, Chile. (in spanish) Encina, V, Baez, F, Geister, F, & Steinberg, J 2008, Mechanized continuous drawing system: A technical answer to increase production capacity for large block caving mines, Proceedings of Mass Mining 2008 Conference, Lulea, Suecia, pp. 553-562. JRI Ingeniería 2010, Informe de la Ingeniería Conceptual y Básica de la Validación Industrial Tecnológica de la Minería Continua, Santiago, Chile. (in spanish) Orellana, L 2012, Estudio de variables de diseño del sistema de Minería Continua a partir de experimentación en laboratorio, Tesis de Magister, Universidad de Chile, Santiago, Chile. (in spanish) Orellana, M 2011, Modelamiento Numérico de la Minería Continua, Tesis de Magister, Universidad de Chile, Santiago, Chile. (in spanish)

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Implementation of LiDAR technology to evaluate deformation field induced by panel caving exploitation, Codelco Chile El Teniente Division AE Espinosa Codelco, Chile P Landeros Codelco, Chile

Abstract Empirical experience and theoretical developments in rock mechanics have demonstrated that cavities generated by extraction in cave mining induce changes in deformation fields, reaching different extensions over time while their location varies according to the mine growth. Considering changes in deformation field as a result of induced stress field on a rock material by caving, extensive recording of this information is an important data that permits to evaluate the performance of mine design and allowing short term engineers to have the chance to take appropriate actions, if deviations are identified. LiDAR technology is based on the principal of calculating laser pulse time of flight (TOF). Therefore, if the information, such as, laser pulse velocity, angular reference used to measure and the difference of time between emitted and reflected ray is known, it is possible to determinate the relative distance of an obstacle or object. This work explains in detail the implementation of these concepts to geomechanical monitoring at Dacita Project at El Teniente mine, showing results obtained as a the baseline measurement and during a comparative analysis while considering mining activity and ground control information.

1 Introduction 1.1

El Teniente Mine overview

El Teniente Mine is a Codelco Chile underground copper mine. It is located in the Andes range in the central zone of Chile, approximately 70 km SSE from the capital city, Santiago. El Teniente is the largest known copper–molybdenum deposit in the world. It is hosted in a copper porphyry system. The main rock types include Andesites, Diorites and Hydrothermal Breccias of the Miocene era. Since 1906, more than 1,100 million tons of ore have been mined. The mine is currently extracting approximately 140,000 tons/ day using mechanized caving methods. Panel and post-undercut caving methods, variations of the standard block caving, were introduced in 1982 and 1994, respectively to exploit primary copper ore. 1.2

Dacita Project overview

El Teniente Mine includes different productive sectors, all of them located around a chimney of subvolcanic breccias with an inverted cone shape, known as “Braden Pipe”. Dacita Project is located on the western side of Reservas Norte Mine and it corresponds geometrically to an extension of that productive sector (Figure 1). Its exploitation started in November 2013 and it considers a production plan close to 17,000 tons/day for the year 2019, using a conventional panel caving (post-cut undercutting) and an integrated mining sequence of both mines.

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Innovation Average primary columns’ heights are 180 meters, varying from 90 to 250 meters, finding lower values beneath Sur Andes Pipa Mine and Pipa Norte Mine. Those values could be associated with low stress state regime, however, the size of all the cavities around Dacita Project induces a higher level of pre-mining stress very similar to Reservas Norte Mine.

Figure 1 Schematic view from the north of Dacita Project footprint. It is possible to identify its location between cavities (Espinosa et al. 2012)

1.3

Geological and geotechnical data

According to Brzovic (2012), predominant lithology corresponds to Dacite Porphyry, which in terms of intact rock properties is very stiff with average Young’s Modulus of approximately 60 GPa. In terms of rock mass quality indexes, Dacite Porphyry is very competent with GSI in the range of 75 to 90. Most important geological faults are classified as “master faults” (faults G, C, N1 and N2) and “major faults” (faults F, K, L).

2

Geomechanics monitoring plan concepts

Considering changes in deformation field as a result of induced stress field on a rock material by caving, extensive recording of this information provides important data that permits to evaluate the performance of mine design and allowing the short term engineers to take appropriate actions, if deviations are identified. Therefore, some of the main objectives of the geomechanics monitoring plan are based on the following considerations:

• To be able to identify changes, in terms of induced stresses and deformations, affecting excavations in the productive levels.

• To provide new field information for numerical modelling calibration. • To estimate zones affected by the mining advance.

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Caving 2014, Santiago, Chile Rockburst is one of the most relevant geomechanics risks identified in the development of the geomechanics engineering of Dacita Project. To complement its evaluation, vulnerable zones are identified, using an integrated methodology, considering geological aspects, size of the excavations and stress conditions, selecting the places to be measured and then, comparing results with field information obtained by ground control engineers. In specific cases, identification is complemented by numerical modelling analysis (Cuello et al. 2010). Major geomechanical hazards, such as, rockburst and collapses that affect large panel caving operations, are highly dependent on a caveback geometry (Landeros et al. 2012). Based on the assumption that any geometrical change is related to a change of the stress field, the geomechanics plan considered the concept shown in Figure 2.

Figure 2 Interpretation process of monitoring results (Espinosa 2012)

According to all the aspects mentioned previously, one of the areas of geomechanics monitoring plan was focused on the implementation of Light Detection and Ranging technology (LiDAR) as a tool for measuring the geometries of excavations and their changes in time. Development of LiDAR technology started in the 1970’s in USA and Canada, used with satellites for topography scanner with high cost and many limitations. With higher development of informatic technology, it is currently used in many different fields. The device basically works emitting laser light pulses to determinate the distance between surfaces and its position, generating clouds with millions of points. This technology is highly accurate and precise and it is widely used for surveying measurements both open pit mining and underground mining. There are different types of LiDAR but it is not a matter of this study to describe all types. The one used in this case corresponds to a laser-based ranging and imaging system, terrestrial and static (mounted on a tripod), capable to capture data in medium and long distance inside the galleries.

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Innovation Principal advantages of this technology, as compared with traditional methods, such as, convergence stations or extensometers, are related to less operational interference and the capability to capture new information in a wider extension and not only at one selected point.

3

Preliminary results and data processing

3.1

Preliminary results

First stage of measurements started in July 2012, in both the undercut and the production levels; it represented the base line for future comparisons. During June 2013, new measurements were completed at specific locations, based on an “excavation vulnerability criterion”; approximately 45% of the surface was compared in each level. Figure 3 shows an example of captured data in a production drift.

Figure 3 Example of production level drifts LiDAR measurements at Dacita Project (Espinosa & Landeros 2012)

The decision to include LiDAR technology in the development of Dacita Project involved a new challenge. There are at least four different documented algorithms for the calculations of the measurement results, each one of those based on certain assumptions and limitations. 3.2

Existing distance measurement methods

The approaches described by Girardeau (2006) and Lague et al. (2013), used to measure the distance between two point clouds in the context of geomorphologic applications, are shown in Figure 4 and are described in the following paragraphs:

• Digital elevation model (DEM) of difference: ○ DEM of difference is the most common method of point cloud comparison in earth sciences when the large scale geometry of the scene is planar.

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Caving 2014, Santiago, Chile ○ The two point clouds are gridded to generate DEMs either directly if the large scale

surface is near horizontal. The two DEMs are then differentiated on a pixel-by-pixel basis which amounts at measuring a vertical distance.

○ This technique is very fast but it suffers from a major drawback: it cannot operate properly on 3D environments or rough surfaces.

Figure 4 Existing 3D comparison methods between two point clouds PC1 and PC2 (modified from Lague et al. 2013)

• Direct cloud-to-cloud comparison with closest point technique (C2C): ○ This method is the simplest and fastest direct 3D comparison method of point clouds as it does not require gridding or meshing of the data, nor calculation of surface normal.

○ For each point of the second point cloud, a closest point can be defined in the first point

cloud. In its simplest version, the surface change is estimated as the distance between the two points.

○ Improvements can be obtained by a local model of the reference surface either by a height function or by a least square fit of the closest point neighbours.

○ The measured distance is sensitive to the clouds roughness, outliers and point spacing. • Cloud-to-mesh distance or cloud-to-model distance (C2M): ○ This approach is the most common technique in inspection software. Surface change is

calculated by the distance between a point cloud and a reference 3D mesh or theoretical model.

○ This approach works well on flat surfaces as a mesh corresponding to the average

reference point cloud position can be constructed. However, creating a surface mesh is complex for point clouds with significant roughness at all scales or missing data due to occlusion.

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Innovation ○ Interpolation over missing data introduces uncertainties that are difficult to quantify. Mesh construction also smooth out some details that may be important to assess local roughness properties.

• Multiscale Model to Model Cloud Comparison (M3C2): ○ This approach operates directly on point clouds without meshing or gridding. ○ It computes the local distance between two point clouds along the normal surface direction which tracks 3D variations in surface orientation.

○ It estimates for each distance measurement a confidence interval depending on point cloud roughness and registration error.

3.3

Comparing preliminary results and field information

As mentioned previously, there are different numerical approaches available to calculate differences between point clouds. The analysis of results was divided into several stages, as follows: 3.3.1

Stage 1

All available algorithms described in Section 3.2 were used to calculate differences between point clouds. These results represent the baseline for further comparative analysis. 3.3.2

Stage 2

Considering reinforcement and support installed at production level (Figure 5), several zones were chosen for comparative analysis.

Figure 5 Description of typical reinforcement systems used for the analysis.

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Caving 2014, Santiago, Chile Reinforcement systems used in the production level are composed by systematic installation of bolt-meshshotcrete system during the development of the galleries. In a later stage, a cable-wire mesh-concrete confinement wall is built, this kind of structure is more rigid and it has a different expected behaviour against loadings. The evaluation of induced damaged is done by short term geomechanics engineers and the information is hosted in an internal database available for further analysis (Cifuentes et al. 2012). In this case of study, observed changes on roofs and shoulders are considered for the analysis. 3.3.3

Stage 3

Places with no observed damage were used to compare different approaches. Differences should tend to minimal values. An example is shown in Figure 6; it is possible to observe that M3C2 algorithm estimate lower valued in this case, compared to C2C and C2C_HF.

Figure 6 Comparison of results between different approaches and field information at C21/Z9N, production level of Dacita Project (modified from Cortes 2014)

In the same example, if 90% of reliability is considered for filtering the data, minimum and maximum differential values are between -0.01 and 0.03 meters. A plan view with the filtered data and its respective histogram is shown in Figure 7.

Figure 7 Differential analyses at C21/Z9N with M3C2 algorithm, production level of Dacita Project

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Innovation On the other hand, the same procedure was applied to zones with observed damage as shown in Figure 8. It is possible to observe that results show two different populations of data, considering deformation of the gallery and the looseness of some shotcrete layer.

Figure 8 Differential analyses at C21/Z5N with M3C2 algorithm, production level of Dacita Project

4 Conclusions Implementation of LiDAR technology for geomechanics monitoring is based on the concept that any change in deformation field is directly related to changes of induced stress field on a rock material by caving and extensive recorded data will assist short term engineers to identify the deviations to the mining plan. Comparative analysis between field information and available algorithms for calculation indicates that is possible to process large amount of data and build differential maps. Multiscale model to model cloud comparison (M3C2) fits well for complex topographies. The results are promising in order to evaluate the performance of the mine design during mine exploitation life time.

Acknowledgement The authors wish to thank Codelco Chile, El Teniente Division for allowing the publication of this paper and the Geomechanics staff that supplied data and information.

References Brzovic, A 2012, ‘Geology and mineral resources, Dacita Project’, Internal Report for feasibility study. Cifuentes, C, Zepeda, R, Parraguez, R & Gaete, S 2012, ‘Implementation of systematic damage mapping for geotechnical evaluation, El Teniente Mine’, Proceedings of 6th International Conference on Mass Mining, Massmin 2012, Sudbury, Canada. Cortes, O 2014, ‘Geomechanics monitoring using 3D scanner, Dacita Project Codelco El Teniente’, Thesis work develop at the Geomechanics Superintendent of Codelco El Teniente. Cuello, D, Landeros, P & Cavieres, P 2010, ‘The use of a 3D elastic model to identify rock mass damaged areas in the undercut level at Reservas Norte sector’, Proceedings of 5th International Conference on Deep and High Stress Mining, Santiago, Chile.

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Caving 2014, Santiago, Chile Espinosa, A, Cornejo, J, Fuentes, R & Rojas, E 2012, Geomechanics guidelines for Dacita Project, detailed engineering stage, Internal Report. Espinosa, A & Landeros, P 2012, ‘Geomechanics aspects for detailed engineering stage’, Internal Presentation for Teniente Geotechnical Advisory Board. Girardeau, D 2006, Detection de Changement sur des Données Géométriques 3D, PhD Thesis, Signal and Image processing, Telecom Paris. (in french) Girardeau, D 2013, ‘Cloud compare: 3D point cloud and mesh processing software, Open Source Project’, Available at http://www.cloudcompare.org. Lague, D, Brodu, N & Leroux, J 2013, ‘Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 82, pp. 10-26. Landeros, P, Cuello, D & Rojas, E 2012, ‘Caveback management at Reservas Norte Mine, Codelco Chile El Teniente Division’, Proceedings of 6th International Conference on Mass Mining, Massmin 2012, Sudbury, Canada.

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Innovation

Semi-autonomous mining model M Fishwick Codelco, Chile M Telias IM2-Codelco, Chile

Abstract A conceptualization of semi-autonomous nining has been prepared by CODELCO in a “Semi-autonomous Mining Program”. In this conceptualization each of the constituent features of the model were defined, generating a version that is ready for industrial validation. A diagnosis and a comprehensive assessment of the applications and trials of SA LHD technology in CODELCO were performed, taking into account, as main parameters, the following: • The industrial application in Pipa Norte Mine (El Teniente Division) with Sandvik as provider (current). • The trials in Andina Division with Caterpillar (2011) and Atlas Copco (2012) as providers. This paper presents the SA Mining Model developed in order to satisfy the requirements of structural projects.

1 Introduction The Semi-autonomous Underground Mining is a technological breakthrough for underground mining using caving methodology, based on semi-autonomous LHD technology. Its objective is to provide higher levels of security and sustainability aligned with the productivity required for CODELCO’s structural projects. The experience acquired in industrial applications indicates that in order to obtain the same results of manual operations, it is not enough replacing manned LHD units by semi-autonomous units (as was used in Kirunavaara (IM2 2013; Fredrik Kangas et. al. 2004)). A complete redesign of the extraction process is required, in particular, scheduling and redefining the so-called interferences, which are an inherent part of the extraction process Hence, a SA Mining Model must be built in order to fully leverage this technology. The new model has features which drastically changes the present operation model and its parts. These parts are: mine layout, operational strategy, technology, safety and health, a new business model (technological development model between clients and providers), human resources and management, and maintainability. It is clear that each of these aspects requires a specific treatment, based on the new technology. It is necessary to modify the mine layout so as to be compatible with the capabilities and constraints the SA LHD technology, e.g., wearing course quality, size and layout of draw points, number of operating units per module, etc. Furthermore, operation strategy is completely different depending on how these machines operate in relation to manned units, especially if control is centralized. It is necessary to define requirements on the technology depending on layouts and strategies. For this, a different model of technological development is needed - since, due to the reduced amount of clients, companies do not spontaneously develop the required improvements. Besides all, operator profile for this kind of machines (and technology) differs completely from manual operations, as well as more specialized maintainers are required. In addition to the above, the cultural change associated with this technological breakthrough needs to be addressed. Regarding the aspects of safety and health, it is important to note that there are necessary legal changes which must be supported.

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Caving 2014, Santiago, Chile A conceptualization of Semi-autonomous Mining has been prepared by CODELCO in a Semi-autonomous Mining Program. In this conceptualization, each of the constituent features of the model were defined, generating a version which is ready for industrial validation. In order to model these components, a diagnosis and a comprehensive assessment of the applications and trials of SA LHD technology in CODELCO were performed, taking into account the following data (Schweikart, V. & soikkeli, T. 2004): • The industrial application in Pipa Norte Mine (El Teniente Division) with Sandvik as provider (current). • The trials in Andina Division with Caterpillar (2011) and Atlas Copco (2012) as providers. This paper presents the SA Mining Model developed to satisfy the requirements of structural projects.

2

Semiautonomous Mining (Semi-autonomous mineral extraction solution)

This solution is based on the conjunction of two main action scopes: 2.1

Operational model for semi-autonomous underground mining

This model is specially developed for introducing this technology, it generates the requirements and allows establishing the parameters that ensure expected KPIs from the application (in this case, CODELCO’s structural projects), if industrially validated (The expected KPIs are at less the same obtained using manual technology). 2.2

Technological development model

Automation technology development program oriented to develop the technology that allows the system to work, in successive stages. On the one hand, it enables a continuous and sustainable development; on the other hand, it allows establishing a common baseline for future development in the different stages with providers which a commercial agreement for technological development is established with, following a pre-established model.

3

Operational model for semi-autonomous underground mining

3.1

Key components

As part of the Automation Program for Underground mining, Technology and Innovation Management (TIM) developed an OPERATIONAL MODEL FOR AUTONOMOUS/SEMI-AUTONOMOUS UNDERGROUND MINING, focused on SA LHD that will operate in underground structural projects. Autonomous mining with SA LHD is performed with pieces of equipment that do not require an operator on board; they are controlled from an operation and management center, usually off site, with remote operation limited to a few of their functions. This implies a deep change in the way mining is conceived and requires an operational model ensuring the productive capabilities and productivity, controlling costs and variability of processes. Introducing this technology without a tailor made validated operational model leads to the risk of having a loss of value with automation technology, as observed in recent applications. Experiences of application of automation technologies in extraction processes — e.g. autonomous trucks for CODELCO’s Gabriela Mistral mine — indicate that in order to successfully introduce autonomous

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Innovation equipment, a huge amount of the scopes which are part of the process are required to be modified, such us: mine layout, operation and maintenance strategies, planning and control, safety, human resources, etc. On top of all, a deep cultural change regarding the conception and operation of the mine is required — particularly in people, and involving the top management. In this context, an Autonomous Operation Model for Underground Mining was designed, that was completely introduced by CODELCO’s Chuquicamata Underground Project (PMCHS) and partially introduced by CODELCO’s New Mine Level Project (PNNM). Bases of the model are characterized as:

• SA Module: Productive unit that consists on a semi-autonomous LHD operating on a segment of the production drift confined in both ends, with a ore pass and several draw points.

• Several SA Modules per street. Two adjacent modules could be simultaneously involved on the extraction process with SA LHD, allowing the increase of the extraction speed per street, counteracting the lower performance of the SA LHD compared to the Manual LHD units.

• The SA modules are always performing a task: production, secondary reduction, maintenance, sampling, etc. If this is ended, the LHD unit shifts Module. There will be no SA Modules on hold, without activity.

• Centralized Control from a room off site with strategic, tactical and control levels; with expert systems for control and management of production, control of vital signs for predictive maintenance, interaction with other mine control systems, etc.

• 24/7 Continuous Operations from control desk with ‘call center’ system: Operator shift change

with minimum relief time (manilla a manilla). The equipment are stopped only for needs inherent to them.

• Rigorous maintainability, since the autonomous system requires lower tolerance thresholds and variance of the electrical-hydraulic systems of the equipment.

• Ongoing technological development model that requires mid-term agreements with manufacturers. 3.2

Expected impact on the production process

• Higher use of the active area This is feasible because it is a simultaneous process in more than one module per drift, increasing the utilization of productive area.

• Simultaneity and operational continuity Operational continuity is increased, eliminating the concept of production shift. There is an operational strategy making modules to be under production until they are stopped because of process considerations — moving, then, onto an optimized support activity, so as not to have downtimes in modules. A maintenance and reliability system is also considered, supporting the availability of the semi-autonomous operation in modules, in order to have as much as available productive time as possible. In addition, Semi-autonomous LHD and centralized operation and control features from an offsite operation room are leveraged, in order to have a minimum relief time operation.

• Increase in productivity Tailor made mining design and planning so as to systematically achieve a better productivity of the open area. Along with that, new automation functionalities generating better operational performances oriented

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Caving 2014, Santiago, Chile to: fleet management and control, decrease in cycle times in order to increase performance, and an increase in utilization and availability. Productivity parameters and learning curves for operators in short tramming distances (below 40 meters) with a 1:1 and 1:2 equipment operator ratio will be determined along the shift. Distribution and time range for equipment queuing will be determined in order to assess productivity and required rosters for the scalability of the operation model in Structural Projects and Divisions.

• Operational Variability Having a lower operational variability, deviations between what is planned versus what is produced decrease, having a more reliable production plan. This is feasible since technology in automated tramming and dumping activities has proven to have low variability (around 5% to 10%). The technology is also required to be highly reliable, with high availability and operational continuity, having an extraction processes with productive modules with low interferences and operational losses. Noteworthy that the fragmentation handled with SA LHD is the same as the manual LHD. During the range of 0% to 30% of the ore column extracted a manual technology is recommended over the semi-automation. 3.3

Construction of the operational model for semi-autonomous underground mining

The model is built from:

1. Actions for the development of a specific operation model Results from trials and applications performed within the Corporation: Operation of three Sandvik SA LHD in Pipa Norte mine — El Teniente Division 2004 - 2013 — and pilot trials of 1 SA LHD unit from Caterpillar and Atlas Copco (III Panel, Andina Division, 2011-2012) showed the need of establishing a different operation model adapted to the conditions of the SA LHD system, so as to obtain the highest return from this technology. It was evidenced the need of a strategy and operation tactics; and autonomous equipment oriented maintenance; a suitable mine layout; different skills and abilities from the staff; a critical involvement of the top managerial level; hardware, software and technological platform with redundancy needs for automation, giving room to a technology development plan carried out together with providers, so as to fully satisfy the mining operation demands.

2. Structural Project’s requirements In addition to performance requirements (tons per hour, effective hours per shift, availability percentage), structural projects need a number of management tools and functionalities that have to be included in automation systems in order to increase efficiency in production management and maintenance, huge fleet management and other features oriented to increase automation levels of the process, such as online data collection related to infrastructure status and active items of the productive process. (E.g.: Draw point status, shaft).

3. State of the art technology Through a specialized survey, a detailed technical analysis on the state of the art regarding LHD automation and an assessment of strengths and weaknesses of automation technologies from the four available LHD providers; their functioning principles; components and key mechanisms; as well as a projection of the technology were performed, confirming the existence of room for improvement. This way, developments to be carry out by providers were stated.

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Innovation 3.4

Justification for introducing this technology

3.4.1

Strategic Alignment

• Taking into account the first VALUE OF CODELCO: RESPECT FOR LIFE AND DIGNITY OF PEOPLE, taking personnel out of high risky working environments.

• It takes operators out of the Line of fire, being aligned with the goal of CODELCO of eradicating silicosis. In addition, there is no compliance with the regulation of exposure to vibrations.

• Fulfilling corporate strategic goals of having all highly risky processes automated by 2015. • Taking into account compliance with FATALITY CONTROL STANDARDS: • ECF 1 and 3: Having a blockage system allowing equipment isolation • ECF 3: Anti-collide system, heavy equipment handling • It significantly improves conditions towards the compliance of few of the standards. E.g.: Mud rushes

(ECF-15), Rockburst(ECF-16), Oxygen and gas control in underground mining (ECF-17). In addition to

• EST #4: Ergonomics. Trials and future operations will take care of ergonomic design of the LHD Operation Room (User Interface Design)

• EST #5: Compatible health. Pre-labor and labor examinations οο EST #6: Fatigue and somnolence 3.4.2

Contributing to the results of PMCHS and PNNM Structural Projects.

3.4.2.1 Result improvement Performance, usage and availability results obtained during trials and applications performed in CODELCO show through simulations that the operation model developed is able to contribute in improving the most relevant KPIs (Ton/hr, Hr/Shift, Avail.) 3.4.2.2 Enabling technology and requirements The state of the art technology does not account for all the requirements of structural projects, that is why the development of new proven and validated functionalities and technological improvements is required (huge fleet management, connectivity with other systems and equipment in the mine, enhancing security and safety systems, better support for remote operation, environment visualization, dynamic allocation of draw point schedule, etc.). 3.4.2.3 Terms and opportunity The schedules of structural projects require the implementation of the OPERATIONAL MODEL FOR SEMI-AUTONOMOUS MINING validated for implementation and commissioning in 2017 and 2019 in the PNNM and PMCHS projects, accordingly. Taking into account that technological developments of this size take long time, new functionalities and validation will start immediately, not later than 2014.

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Caving 2014, Santiago, Chile 3.4.2.4 Knowledge management

• A technological leadership from CODELCO is required, in order to boost the market development

so as to satisfy CODELCO needs, which are unique in the industry. In addition, bases for a mid-term development plan oriented to a multi-brand automation system are required.

• Creating a critical mass of professionals, technicians and operators, both within CODELCO as

well as within service and support provider companies, in order to ensure an efficient and effective commissioning of structural projects.

• Keeping technical experience of the autonomous operation. In 2014, Pipa Norte operation reaches its end, the only one with SA LHD.

3.4.2.5 Validation trial for SA LHD operation model Industrial Validation trial for the Semi-autonomous mining operation model will be taking place in Block 1 of Esmeralda, during 2015. In this block, the full implementation after the SA LHD trial will be assessed during the trial. This sector have 26 Mton ore reserves, with a mid-grade copper ore body of 1.07%. The area encompasses 45,200 m2, with nine 220-meter-long production streets (approximate length), with ore pass every 100 m, with semi-steady remote controlled hammers and 40”*40” mesh. It has a Teniente-style 30 * 19.6 m extraction layout with Panel Caving extraction method with traditional undercut.

Figure 1 SA LHD industrial validation trial layout

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Innovation The trial will take place in two drift, with four modules for the operation of 2 SA LHD units, as shown in Figure 1.

• It considers mining layouts as similar as possible to the ones developed for PNMM and PMCHS. • It takes into account pieces of equipment with specifications for automation systems and scalable performances, taking as a baseline the performances considered for the Structural Projects.

• Use of technology from SA system providers, under specifications of the established operation model and technological requirements (alpha level).

• Technical parameters and costs of the operation per piece of equipment, module, street and trial sector are determined, with their corresponding variability analysis.

3.4.2.6 SA LHD industrial validation trial objectives and deliverables Industrial validation trial aims to develop SA LHD industrial trials in CODELCO’s Esmeralda Division, El Teniente, in order to industrially validate the technology, and at the same time validating the technological development model in its first stage. As a result of the trial, the following deliverables will be provided:

• Scalability model — i.e. design bases, calculation methodologies, and backup parameters for engineering of semi-autonomous mining for Esmeralda Division, El Teniente.

• Scalability model for semi-autonomous mining for PNNM and PMCHS structural projects. • Necessary technical parameters for bidding (October 2015 for PNNM, 2017 for PCHMS) • Baseline for the generation of a mid-term development plan for the technology. • Establishment of optimization items for the operation model for the New Mine Level Project. • Development of a market of providers, pointing to a multi-brand system • Generation and maintenance of corporate technical expertise. 3.5

Key variables of the SA mining model

3.5.1

Mining layout and operational strategy:

• Blocks are divided in semi-autonomous modules. • Each module consists on one drift segment (i.e. one street contains several modules) with independent access per drift.

• Each semi-autonomous module has the following features: • Isolated and confined by physical and/or virtual barriers. • One module should have a suitable amount of draw points and ore pass so as to ensure the

maximum productivity (ton/day) of the mining system. Based on the analyses performed up to date, each module considers 16 to 22 draw points and one ore pass.

• Operation of 1 SA LHD per module. • Several modules per block operating simultaneously, even in the same street.

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Caving 2014, Santiago, Chile • The continuous extraction of the SA LHD is subject to: full stop due to hang ups, human decisions,

oversize muck in draw points, full shaft or any other event impeding the extraction in the module. If required, the piece of equipment should be able to shift module in order to continue operating in other available module.

• Each module has always an activity allocated. This way, if it is not in production, is performing an activity supporting production: extraction, secondary reduction, solving hang ups, sampling, repairing, etc.

• Operational losses and interferences between unitary operations are minimized. The working cycle of the autonomous module is independent from the shift schedule of the roster.

• Operation from a centralized control room with 1 operator available to operate 1, 2 or more SA LHD units for tramming distances below 40 meters.

Figure 2 Design of a Semi-autonomous mining operation model

3.5.2

Planning and production control

Focused on a higher utilization of the open area, in order to maximize the mining system performance with semi-autonomous technology. This involves the Integration of the Mining Plan and Production Control, with on-line feedback with the production information from origin to destination (bucket tonnage from draw points – ore pass).

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Innovation • Short term management: Assistance from the CIOG Room for compliance of the production program, with production tools.

• Long term planning: Historic information for re-defining criteria and parameters. It requires significant changes in:

• Modular production, associated to the confined functioning of semi-autonomous LHD units:

This is translated into the creation of a new extraction unit, the module that has to be managed taking into account that maintenance of these modules has to be coordinated so as to keep an availability rate supporting the production all the time.

• Systemic management of productive and support resources: Always in a modularity condition,

the use of different items and resources of the system (LHD, crushing hammers, auxiliary equipment, crews, infrastructure, spaces, etc.) must be effectively coordinated so as to reduce bottle necks and minimize interferences. This coordination have to account for both, planned availability (e.g. maintenance of a piece of equipment) as well as sudden availability (e.g. hang up in a shaft).

• Operation and planning alignment: Rule or strategy definition will be oriented to the compliance in the long run with the production and budgetary goals defined in the planning process.

• Production management system with central control: Allowing integrated data collection

and decision making in order to support the bullets above. This implies a fleet management with centralized allocation, dispatch, draw point schedule control, supervision, monitoring and fleet control. A predictive maintenance system based on a vital health management system.

• Extraction equipment: Semi-autonomous LHD with incremental innovation of new functionalities, aiming to improve performance for production standards, performance and availability of huge flees for future CODELCO projects.

4

Technological development model

It is understood as a SA LHD technological development model the strategy that CODELCO will put in place to establish its relationship with LHD providers, in order to obtain a SA LHD system with desired functionalities and performances, aligned with the interest of strengthening the technology and making a contribution to the Structural Projects. The model aims for:

• Creating value for CODELCO, generating stable innovations in SA LHD, suitable to the needs of mining operations CODELCO is developing.

• Changing the relationship between CODELCO and its providers, making it an active purchaser,

guiding SA LHD developments in order to fulfill production commitments of these equipment, expressed in engineering parameters.

The technological development model has a series of items that allow fluid interaction of CODELCO with its providers. First of all, CODELCO is the one in charge of designing the extraction system it needs, with desired productivity and reliability parameters. From this standpoint, a road map is designed, containing successive technological developments for LHD units, along with their corresponding ore extraction and tramming systems, taking the technology to the desired level in the shortest term possible.

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Caving 2014, Santiago, Chile The plan encompasses at least three stages: α considers the short term, that is, developments able to be achieved within one year, β considers the mid term, two to three years, γ and δ long term developments, that is, five years. Each stage has a set of features (performance and functionalities) that CODELCO will demand as a minimum for SA LHDs and other optional features that are desirable, not compulsory. With this, it is expected to mobilize R&D&E capabilities of factories, i.e. mobilizing development engineers from providers rather than marketing engineers who do not have the necessary capabilities nor the vision. CODELCO will be committed not to procure SA LHD equipment that not comply with the compulsory features in the corresponding stage. This will be done through the inclusion of these features in the technical bases of ALL LHD bids of the corporation. CODELCO will keep, at least, two SA LHD providers who are up to date in the corresponding technological stage. In time, for specific features considered to be critical, co-financing structures or CODELCO funding will be implemented. In this latter case, CODELCO will be the owner of the intellectual property generated and will be entitled to transfer such knowledge to other providers. All the aforementioned requires a realignment within CODELCO — since up to date, each Division and Structural project acts independently, diluting the strengths of their corresponding points of view. From this point and on, the way CODELCO will be operating will be briefly described. Technology and Innovation Management will act as a representative of clients (Divisions, Structural Projects) against providers. For this to happen, it is necessary to agree with the Divisions and Structural Projects the minimum technological thresholds to be stated on each of the described stages (α,β,γ,δ). In addition, specific needs each of them may have due to the own characteristics of a Division will be stated as desirable. TIM will review vendor capabilities pertaining the achievement of the minimum technological standards stated on each stage (α,β,γ,δ), agreeing with them a development path for the corresponding functionalities. TIM will participate, at least, as an observer and inspector in factory prototype trials (FAT), and will participate both, in designing trial protocols as well as in measuring pilot trials on mine site (SAT). TIM will provide technical support for trials and industrial applications Divisions and/or structural projects perform. It will supervise trial realization and will determine whether they comply with protocols and procedures for them to be valid. From this trials, TIM will commit to its clients the statistical certitude of relevant parameters and will develop with them mine layouts and operation strategies suitable to these parameters. α development is immediate work (< 2 years), and solves short run problem in trials and applications, and generates the α solution for the extraction system, alpha version (TIM-CODELCO design). β develpment is a mid term work (> 2 years) that address more complex technological and operational needs. It contains a tailor-made development for an operation model.Finally, γ development is a long run work (full automation) with the development of relevant innovations. This plan has been designed by TIM together with structural projects, acting TIM as the coordinator entity. CODELCO will develop trials of the different versions (α,β) with the different providers, generating certifications for bidding processes of structural projects.

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Innovation For the development of each of the stages, a trial plan enabling CODELCO to keep a suitable control on results will be foreseen, minimizing costs. These are: Factory trials — trials made in factory, there might or might not be a CODELCO observer present, but the results are delivered to CODELCO, who issues a judgment. On site trials — trials within CODELCO facilities with vendor support, but the ultimate responsible of data is CODELCO. Trials could measure global indicators (e.g. performance per hour) or more specific variables (e.g. tons per cycle). In the first case, CODELCO will establish the conditions the measurements should take place at.

5

Future development

In Figure 3, the future development plan being performed is shown. In this, operational losses and interferences between unitary operations are minimized. The working cycle of the autonomous module is independent from the shift schedule of the roster. This plan is being developed in alignment with the future and current requirements of CODELCO, in cooperation with providers and technological development centers, in order to achieve the introduction of significant improvements in technology, based on a CODELCO’s proprietary automation road map, developed by the Technology and Innovation Management. During the term of validity of structural projects, after the ramp up, it is expected to have multi-brand, fully-automated technology, enabling the best expression of this technology.

Figure 3 Over time technological solutions evolution

6

Main conclusions

In order to introduce automation, it is necessary to redesign the entire process in all of its parts; to assess the impact of automation; and then to determine strategic and operational requirements and goals of the process. Establishing an operation model in order to achieve the goals generates a requirement for enabler technology, driving technological development that finishes with the development of the technology, its validation, and implementation of the model. All these stages could lead to upstream reviews, if results are not consistent, but the most important is to consider the process in all its aspects and not try to automate a single machine.

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Caving 2014, Santiago, Chile The aforementioned shows that the contribution of the operation model makes this technology competitive, even though a few parameters are far below manned systems. The alignment of providers as well as the mobilization of their capabilities towards the real requirements will lead to a significant acceleration of a success.

7 References Gustafson, A 2011, Automation of Load Haul Dump Machines, Research Report, Luleå University of Technology. Schweikart, V, Soikkeli, T 2004, ‘Codelco El Teniente–Loading automation in panel caving using AutoMine™’, Proceedings of the 4th Int. Conference and Exhibition on Mass Mining, MassMin 2004. pp. 686-689. Kangas, F, Emmoth CE, & Lindahl, P 2004, ‘Codelco PCRB-Surface control centre for mine automation’, Proceedings of the 4th Int. Conference and Exhibition on Mass Mining, MassMin. 2004, pp. 690-691. IM2 2013, Informe Técnico 1 Proyecto IM2-65-12, Prueba Atlas Copco, Marzo, 2013. Superintendencia Mina Sur, 2007, Post evaluación proceso de extracción con LHD semiautomático, sectores Pipa Norte y Diablo Regimiento, Internal Report. (in spanish)

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Innovation

Future automated mine operation: Synergistic collaboration between humans and automated systems J Ruiz-del-Solar EE Department, University of Chile-AMTC, Chile E Widzyk-Capehart University of Chile-AMTC, Chile P Vallejos University of Chile-AMTC, Chile R Asenjo University of Chile-AMTC, Chile

Abstract The world resource industry is being transformed by its increasing use of automation technologies. At one end of the scale, this revolution is occurring through leveraging off-the-shelf technologies to incrementally improve the control of various mining processing lines with best industrial practice. At the other end are bold initiatives to implement fully autonomous mines, which, currently, are based on the use of automated systems that work in confined spaces; they are not allowed to interact with other systems or humans (isolated). However, between these two extremes, is a spectrum of innovations that stands to profoundly change the industry over the next 30 years by enabling future mining systems to be flexible with simultaneous operations of automated, tele- and manually-operated machines as well as humans. In this paper, we present a new automation paradigm that considers the synergistic collaboration between humans and automated systems, underpinned by mine planning and design required for such operation within mining environment. In this paradigm, humans and machines would interact in a flexible, collaborative and synergistic way, without the need for isolation. The implementation of this concept within mining operations will have to be underpinned by science and technological developments or adaptation from many fields: robotics, automation, distributed systems, communication, human factors, pattern recognition, mine design, and others.

1 Introduction In 2011, at the 2nd International Future Mining Conference (Bednarz et al. 2011; Gipps et al. 2001; Giurco et al. 2011; Klein et al. 2011), visions of the “future mine” were presented from various perspectives: mining companies, service industry, equipment manufacturers (OEMs) and research organisations. In general, though along different paths, they were all heading towards mines operated using autonomous robots and computers with people removed to the safety of remote operating centres. Simultaneously, several research centers and think tanks have been focusing on imagining the future of mining, with the Swedish Rock Tech Center (Dozolme 2014) releasing in 2011 its vision of the mining industry in 2030. Amongst the top 3 characteristics of the industry after 2030, RTC believes in “fully automated mining operations without human interface” with “no human exposure at production faces, no accidents and employees satisfaction”. Hedlin (2013). However, despite the tremendous technological advances over the past several years, this vision of the future mine might still be several years away. In the meantime, autonomous units, tele- and manuallyoperated equipment and humans are continuously interacting within the mine environment. At present, these interactions, however, are achieved under strict constraints: physical barriers are placed between the areas where autonomous equipment operates and the rest of the mining operation, with stringent regulations and control when humans must enter the area of autonomous operation. Such restrictions, while enabling autonomous operations and thus higher production and lower costs, naturally lead to the discontinuity within the mining operation as strict safety protocols are followed to avoid fatalities and/or damage to

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Caving 2014, Santiago, Chile the equipment and the environment. The question arises whether an intermediate step, one between the current autonomous-human arrangement and the fully autonomous mine, is possible to further increase the continuity of operation with higher equipment availability but without compromising safety, before the automated mine is achieved. In this paper, the current strives towards fully autonomous mines are presented followed by the introduction of the hybrid collaborative systems for mining in view of planned technological and operational developments.

2

The Mine of the Future, without people?

Currently, for the mining enterprises, mining automation projects appear to be one of the available cost-cutting strategies by driving human resources cost down, improving global safety and increasing productivity. The main emphasis is on the development of unmanned haul and transportation technologies, both in surface and underground mining. 2.1

Surface mining

Working with Komatsu, Codelco was the first to trial the autonomous trucks at its Radimiro Tomic copper mine in 2007 (Walker 2014) followed by the commissioning of the 11 Komatsu driverless truck fleet in 2008 at the Gabriela Mistral “Gaby” copper mine (Jordan 2008). In the same year, 2008, Rio Tinto launched its Mine of the Future™ programme, with the mission to“…finding advanced ways to extract minerals deep within the Earth while reducing environmental impacts and further improving safety”. Rio Tinto (2014). The R&D efforts within this programme, have been focusing on the development and implementation of driverless trucks and trains. For the former, Rio Tinto relies on Komatsu autonomous trucks, with 53 units in operation at Rio Tinto´s Pilbara sites in early 2013, which, according to Rio Tinto, were moving high grade ore and were controlled from Rio Tinto´s Operations Centre in Perth more than 1,500 kilometres away (Rio Tinto 2014). By early 2014, Rio Tinto was planning to further increase the number of driverless units through additions of Komatsu trucks at Lilleyman mine (Duffy 2013) and Hope Downs 4 (Validakis 2013) with up to 150 trucks to be commissioned by 2015 in their iron ore operations in Pilbara, Western Australia (Walker 2014). Other companies are, likewise, becoming part of the autonomous world with Fortescue Metals Group (FMG) commencing its introduction of automated truck in 2013 at its Solomon hub (King´s Mine) with eight of Caterpillar´s 793F and planning to have 37 more delivered over the next few years to run 45 autonomous trucks when the King´s mine reaches its full operational capacity (Probert 2013). In addition, BHPB is commencing its operational experience with autonomous trucks at its Jimblebar mine while Hitachi is conducting trials of autonomous trucks at the Meandu coal mine in Queensland, Australia (Walker 2014). Beyond the transportation sector, automation R&D is being applied to other equipment, from drill rigs to loaders (Dozolme 2014). 2.2

Underground mining

The use of autonomous LHD (Load – Haul – Dump) vehicles and trucks is very relevant for current and future massive underground mine operations. The development of the technology behind these autonomous machines has been driven by the need of increasing the safety of the operations and reducing the costs. An example of the application of these autonomous machines to large underground mines is El Teniente mine, CODELCO, where semi-autonomous LHDs have been operating for about 10 years. 2.3

Automation implementation: where are the people?

As mentioned previously, the autonomous and semi-autonomous machines are part of many surface and underground mine operations throughout the world (Chile, Australia, Scandinavia), however, their operation is achieved through the implementation of physical or logical/virtual (in case of open pits) barriers between

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Innovation the workplaces of those machines and other manually operated equipment and/or humans (Figure 1). The maintenance or other actions requiring the present of people is possible within the “automated zones” only with the full shutdown of the equipment. As Jorge Nillson pointed out in his presentation during the Expomin 2014 (Nillson 2014), there is still need for people within the mines even though the planned mine operations are heading towards the tele-remotely or autonomously operated equipment; there will be need for humans to install and maintain the sensors that provide the situation awareness for the tele- or autonomous operations and/or monitor in-ground or surface slope displacement in open pit mines. For underground mines, the maintenance of equipment needs to be addressed either in-situ or in special maintenance bays while the hang-ups in drawpoints in caving operations are still, in many cases, handled by mine personnel.

Figure 1 Current Mine Automation paradigm

3

Hybrid Collaborative Mining Systems

3.1

General concept

Whilst technological advances are continually being made, the mine of the future will require the interactions between humans and equipment. As the level of autonomy of the equipment increases, it is clear that the role of humans will change: the humans will act as co-pilots to an automated machine or remotely supervise machinery. However, there will continue to be a need for humans to physically inspect the mine environment or mine equipment, and for machinery that has not been automated (either because it is too hard or not cost effective) humans will need to physically operate the machinery. Thus, mining equipment might still range from manual (requiring a physical operator) to fully autonomous (requiring no human intervention at all). In the future mine, all of these various roles and modes of operation will need to operate in the same workspace and, more importantly, some of the roles may change dynamically as the environment changes. For example, a vehicle that is being manually driven, could be switched to an unmanned autonomous mode whilst the person may change their role to supervisory. For this workspace to operate safely and productively, we need to develop systems that can cope with the “mixed traffic” and “changing roles”. In this dynamic environment, there is a need for significant improvements in communications and situational awareness.

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Caving 2014, Santiago, Chile Consider the scenario shown in Figure 2, where we have a mine with a number of machines. In this workspace, there are a number of humans as well: some may act as observers, some as operators and some may be maintaining equipment or infrastructure. There may also be a number of humans working back at the mine office or the head office – potentially thousands of km away. The workspace itself is monitored by a number of sensors (i.e. cameras) in a mixture of wired and wireless communication networks. If, in this workspace, we envisioned a remotely-located human that wishes to take control of a piece of equipment, it is essential for him/her that they are made aware of the local environment. Likewise, humans and machines that are in proximity of the machine that is about to be taken over also need to be made aware of the expected behaviour of that machine. In the same scenario, the local human may act as a “nanny” to the local machine; having control of the E-stop whilst the remote human controls the machine. The joint and harmonious operation of people and equipment within the mining environment might be possible with the introduction of the so-called Hybrid Collaborative Mining Systems. Under this paradigm, it will not be necessary to confine the autonomous teams to certain areas of the mine nor stop the operation of a section of the mine when tasks require the presence of humans, as in the case of clean-up or emergency maintenance. The joint operation of men and machines could be achieved if all mobile devices, regardless if they are autonomous, tele- or manually operated as well as all the people working inside the mine, are equipped with sensors that enable safe interaction among all production elements in a joint operational space. The centralized control system would consider all teams and individuals as mobile users with performance features and movement patterns shared among the individual users. From the point of view of each user, having the relevant information from the environment in real time, this working environment provides them with the ability to make decisions locally and to have assistance during unexpected situations (i.e., emergency alarms, automated braking) when such conditions arise (Figure 2).

Figure 2 Future Mine Automation Paradigm

3.2

Requirements and challenges

For humans and machines to interact safely and productively in the future mine, they need to be fully aware of their local environment. Whilst there are a number of technical challenges to implement such operation (communication and automation), there are three areas that need to be developed for the synergistic collaboration between humans and machines:

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Innovation 1. Data framework that facilitates communication and task planning between humans and machines with different levels of sensing and autonomy.

2. Robust sensor-based surveillance system to support future mine operations in which increased interaction between humans and machines is envisioned.

3. User interfaces that are capable of supervising a number of remote machines and integrating complex geological data with real-time data streams from multiple remote sensors.

3.2.1

Data framework

For the synergistic collaboration between humans and machines, a distributed data framework in which machines and humans (agents) can interrogate their local and global environment needs to be created. This framework will need to (Duff 2007):

• Maintain a consistent view of the world (maintain a shared reality) • Be dynamic: have the ability to be modified with additional data as it is being collected. In mining, where the terrain of the environment is being modified due to excavation, this requirement is vital

• Have the ability to share and synchronize regions of the world with both humans and machines • Handle the movement of humans and machines • Handle uncertainties or even disagreements about the state of the world • Have low communication bandwidth and latency between agents • Be robust to loss of agents 3.2.2

Sensor-based surveillance system

Within the future interactive mine paradigm, in which human and machine occupied spaces overlap, mine security and safety will require robust monitoring systems for the coordination of activities, avoidance of accidents and elimination of hazards/risks to personnel, equipment and the operation. To aid future mine operations in which increased interaction between humans and machines is envisioned, robust sensor-based surveillance systems will be necessary, which are capable of detecting, tracking and classifying moving objects within both underground and open pit mining environments. Robust solutions to this problem would require these techniques to function under a variety of atmospheric conditions including dust and humidity. Effectively, a variety of sensors and sensing techniques have to be combined to provide a reliable monitoring system with adequate redundancy in the system. Therefore, the feasibility of commercially available RFID technologies in terms of its multi-tracking abilities and time overhead for the scanning of RFID tags will need to be investigated together with automated systems requiring no person/equipment identification tags, based on cameras, laser range finders, radar and thermal imaging. The differences and complementary nature of the two approaches will need to be analyzed with the aim of combining their advantages for maximum efficiency and cost benefit. The final selection of sensors would have to offer improved detection, tracking and identification of objects in various atmospheric conditions including high dust levels. The system would have to be designed to be deployed in both stationary mode, at multiple permanent locations within a mine, and agile mode, on-board a tele-operated or autonomous vehicle, with the objectives of identifying people and vehicles

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Caving 2014, Santiago, Chile and their types within the mines, analyse the information required for the safety and efficient operation of interactive mining environments, with accompanying software for providing this information to the overall mine monitoring system. 3.2.3

User interface

The user interface needs to be created for both the local and remote operators. The nature of the interface is dictated by the level of control over each machine. This relationship is related to the level of autonomy of the machine (Figure 3), which can be divided into a number of areas:

• The machine • The intelligence of the machine • The extent of knowledge about the environment • The communication • The user interface The user interface can range from real to virtual with mixed realities in between these two extremes, which further ranged from:

• Augmented reality, where data is overlayed onto reality • Augmented virtuality, where real data is used to drive a virtual reality. For augmented reality to work it is important to be able to:

• Put disparate and numerous data sets into a coherent whole • Base the displayed information on the actual state of the machine/environment in real-time In addition, the augmented reality should provide an immersive and interactive environment to the human, including:

• Experience of being fully present • Appropriate presentation of data to the user • Engagement of the operator in a natural and intuitive manner Drury et al. (2003), provide further guidelines regarding the human-machine interfaces, including but not limited to:

• Provision of user interface that supports multiple robots (machines) in a single window, if possible. In general, minimize the number and use of multiple windows

• Lower information overload: automatically present contextually appropriate information. The information display should include: a frame of reference and indicator of robot health/state

• Consistency within the user interfaces for each robot if the operator is operating multiple robots and in the functionality assigned to manipulators (joysticks) for modes of operation

• Grouping in controls and display

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Innovation • Fusion of sensors information to decrease cognitive load • Increase in user efficiency through additional sensors or streamlined design tailored to users´ needs.

Figure 3 Autonomy and virtuality spectrum (after Duff 2007)

3.3

Next Steps

For this new paradigm to be fully functional in mining environment in the future as a stepping stone to the fully automated mine, different and/or additional technology development steps need to be considered to those already advocated within the existing technology maps. The required technology must be underpinned by science and development from many fields: robotics, automation, distributed systems, communications, human factors, pattern recognition, mine design, and others, and will need to be either developed or adapted. Therefore, it is proposed that a technology roadmap is developed as a practical plan that matches short-term and long-term goals with specific technological solutions. In the past, technology roadmaps were prepared to identify the gaps within the existing mining operations that prevented them from reaching the fully autonomous operation with humans removed from mine site to a remote location. These roadmaps did not consider the paradigm of synergistic collaboration between humans and automated equipment without the need of isolations. Thus, the roadmap development will provide full understanding of the new, and perhaps yet unknown or not fully understood, challenges facing the mining industry in the future and identify the technology gaps, which will have to be filled through the implementation/adaptation of existing technologies, development of new products and/or processes and the assessment of emerging technologies. The development of the roadmap for future mine operation will have three major uses: it will guide the mining companies in specifying technological requirements to satisfy future operational needs for highlevel collaborative human-machine systems, it will provide a mechanism to identify the existing technology gaps, and it will provide a framework to plan and coordinate technology developments. It is envisioned that the initial phase of this activity will define the vision of the future mine from the industry perspective and conduct a survey of the existing technologies. This phase will be guided by the

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Caving 2014, Santiago, Chile mining industry to ensure full alignment with the future operational directions within the mining industry and identification of future challenges the mining operations might face to accomplish an automated mine operation where humans and machines can interact safely and efficiently. On this basis, the 2nd phase will develop a framework to plan and coordinate technology developments, implementation and/or adaptation.

4

Impact on the mining industry

We believe that the development of the Future Automated Mine Operation: Synergistic collaboration between humans and automated systems will have a profound impact on the mining industry value drivers: economic, environmental and social with the benefits manifested through:

• Reduction in capital and operating costs through: elimination of the operational constraints of isolation to enable access to equipment and operational site on a continuous basis, precise and reliable equipment operation, high utilization of equipment (fewer number of machines for the required productivity), reduction in energy consumption and process optimization.

• Increased production through high utilization of equipment and process optimization. • Improved security and quality of working life through: reduction of accidents involving heavy

machinery, safe interactions between humans and equipment without the need for isolation, teleoperation and tele-presence, predictive maintenance and training for automation, and precise equipment operation.

• Development of new products that will strengthen the mining providers including communication infrastructure, sensing technologies and new software integration packages.

5 Conclusions The paradigm of synergistic collaboration between humans and automated equipment without the need for isolation was presented in this paper. The feasibility of its implementation within the existing and new mines will depend on the technological developments that will fill the current gaps in sensing, communication, data processing and user interfaces with the benefits in the economic, environmental and social areas for the mining industry.

References Bednarz, T, James, C, Alem, L & Widzyk-Capehart, E 2011, ‘Distributed Collaborative Immersive Virtual Reality Framework for Future Mine Scenarios’, Proceedings of the 2nd International Future Mine Conference, pp. 145 – 151. Dozolme P 2014, ‘The Unmanned Mine Haul and Transport’, About.com Mining-Maintenance-Equipment. Available from: http://mining.about.com/od/MaintenanceEquipment/a/The-Unmanned-MineHaul-And-Transport.htm. <5 May 2014>. Drury, JL, Scholtz, J & Yanco, HA 2003, ‘Awareness in human-robot interactions’, Proceedings IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 912 – 918. Duff, E 2007, Telerobotic requirements, Draft Internal Report, CSIRO. Duffy, A 2013, ‘Rio expanding driverless truck fleet’, Mining Australia. Available from: http://www. miningaustralia.com.au/news/rio-expanding-autonomous-truck-fleet. <4 May 2014>.

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Innovation Gipps, I, Cunningham, J, Fraser , S & Widzyk-Capehart, E 2011, ‘Now to the Future – a Path Towards the Future Mine’, in Proceedings of the 2nd International Future Mine Conference, pp. 157 – 163 Giurco. D, Prior, T & Mason, L 2011, ‘Vision 2040 – Mining Technology, Policy and Market Innovation’, in Proceedings of the 2nd International Future Mine Conference, pp. 163 – 171. Hedlin, J 2011, ‘Smart Mine of the Future, Rock Tech Centre’. Available from: http://mining.about.com/ gi/o.htm?zi=1/XJ&zTi=1&sdn=mining&cdn=b2b&tm=7&f=11&tt=14&bt=0&bts=1&zu=htt p%3A//bergforsk.se/wp-content/uploads/2011/05/hedlin.pdf. <5 May 2014>. Jordan, P 2008, ‘Chile’s new Gaby copper mine steps into the future’, Mining About.com. Available from: http://mining.about.com/gi/o.htm?zi=1/XJ&zTi=1&sdn=mining&cdn=b2b&tm= 123&gps=362_12_1242_585&f=11&tt=2&bt=7&bts=7&zu=http%3A//uk.reuters.com/ article/2008/05/21/chile-codelco-gaby-idUKN2133325020080521. <3 May 2014>. Klein, B, Bamber, A, Altun NE & Scoble, M 2011, ‘Towards Tomorrow’s ‘Smart Mine’ – Embedded Sensor Telemetry and Sensor-Based Sorting’, in Proceedings of the 2nd International Future Mine Conference, pp. 59-69. Nillson, J 2014, ‘Tecnología & Innovación Minería a Rajo Abierto Codelco’, Presentation at the 3rd International Workshop Codelco: Innovation in Underground and Open Pit Mines, Expomin 2014, Santiago, Chile. Probert, O 2013, ‘Cat delivers 8 driverless trucks to FMG’, Australian Journal of Mining. Available from: http://www.theajmonline.com.au/mining_news/news/2013/october/october-10-2013/suppliernews/cat-delivers-8-driverless-trucks-to-fmg. <3 May 2014>. Rio Tinto 2014, Mine of the Future. Available from: http://www.riotinto.com/ironore/mine-of-thefuture-9603.aspx. <4 May 2014>. Validakis, V 2013, ‘Rio’s driverless trucks move 100 million tonnes’, Mining Australia. Available from: http://www.miningaustralia.com.au/news/rio-s-driverless-trucks-move-100-million-tonnes. <3 May 2014>. Walker, S 2014, ‘Autonomy gradually gains momentum’, Engineering & Mining Journal, January 2014, pp. 32-37.

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Mine sequence optimization for Block Caving using concept of ‘best and worst case’ D Villa, Dassault Systems Geovia, Canada

Abstract The generation of a mine sequence for a block caving mine is always a challenge since it represents the direction for opening draw points, but it is controlled by several factors as caveability, orebody geometry, induced stress, primary fragmentation, grade distribution, production requirements, etc. and in most the cases the main objective is to combine all these factor to maximize the economic value of the project and this is more challenging. Several complex theories and mathematical optimizations have been presented in the last decade, but most of them are too complex to provide a real solution for real Block Caving mines where the dimension exceeds the capacity of these models or it needs to work with super computers and the processing time is an issue. This paper presents a new option to get an optimum mine sequence using the concept of ‘best and worst case’ adopted from open pit mines. The value of the sequence for the project can quickly be evaluated using a reasonable number of iterations to provide an optimum and realistic solution. Examples of this optimization are presented in this paper to demonstrate the concept and their implementation in practical cases.

1 Introduction The choice of initiation point for the sequence and the preferred direction can be influenced by several factors including shape of the orebody, access infrastructure, grade distribution, in situ stress directions and magnitudes, etc. but one of the main factors is to optimize the value of a project creating a production schedule maximizing the Net Present value. There are many studies and theories developed trying to solve this problem with very complex mathematical approach where the amount of variables, constraint and formulation make of the solution a difficult implementation and not flexible enough to add new constraint, for example mixed integer linear programming (Y. Pourrahimian et al. 2012) or integer programming (T. Elkington et al. 2012). This paper will discuss the option to generate an optimum mine sequence using the approach of ‘Best and Worst Case’ intensively used in Open Pit optimization (Smith 2001) applied in Footprint Finder.

2

Footprint Finder

Footprint Finder is a module of GEOVIA PCBC™. It was developed primarily to do quick study working with a block model trying to find the best elevation and orientation for locating an extraction level for block cave mining, but now it is able to create a simple production schedule where the sequence is an input and then it offers a perfect opportunity to do several runs in a short time to evaluate different options for direction and shape of the cave front to get an optimum mine sequence. Typical steps to run Footprint Finder are illustrated in Figure 1, where all blocks inside the clipping polygons are evaluated for each level selected. Each block represents one draw point and then the system creates a

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Mine Planning draw column where Laubscher’s mixing method (Laubscher, 1994) is applied. After that the best height of draw is calculated based on the economic model.

Figure 1 Typical Footprint Finder’s evaluation steps

The graph at the left in Figure 2 shows the typical results for an evaluation of the entire block model. It is possible to see the value for each elevation and the tonnage reported. The best elevation is located at 1200 level. Also the economic value per column is displayed at the right in Figure 2, where higher values are shown in warmer colors. It is very interesting to see the irregular shape of the economic value per elevation (red line), it suggests the possibility to have more than one lift as optimum solution.

Figure 2 Results from Footprint Finder

The creation of a production schedule requires a sequence which is defined based on a shape of the cave front. This is simply created using an X-Y curve and applied with certain direction (azimuth). An example a production schedule using a sequence going East in V-shape is shown in Figure 3. Having the option to create a mine sequence defining a shape and direction provides the opportunity to run many schedules evaluating different scenarios. Finally the option to start in a specific point and moving in a circle to emulate a diamond shape is also available and then all the sequence options can be rapidly tested using Footprint Finder.

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Figure 3 Production schedule in Footprint Finder

3

Best and worst case concept

The concept of ‘Best and Worst Case’ has been intensively used in Open Pit optimization for more than 15 years (Smith, 2001) by GEOVIA Whittle™. Whittle is able to generate a series of nested pits based on the economic values (grades, revenue factor and costs) providing limits on where to mine and when. The best case schedule is associated with completely mining a pushback before proceeding to the first bench in the subsequent pit. In this manner the highest valued ore is mined as early as possible maximising NPV. In the worst case schedule the entire bench across all pushbacks is mined prior to proceeding to the second bench. This amounts to prestripping the entire deposit, defers ore production and thereby minimises cash flow by placing stripping cost up front while delaying positive revenue. The same concept was adapted and implemented in a block caving mining environment to identify the best and worst sequence, based on the results obtained from Footprint Finder. Where each block column can be treated individually as a drawpoint to calculate its economic value based on the metal price, cost and grade and dilution profile, etc. The sequence definition is basically an order to open each block column using certain shape and direction and obviously this needs to be suitable for block caving purposes where geotechnical, design and operational constraints need to be satisfied. Now the Best and Worst case concept can be easily implemented in block cave mine trying to get the maximum and minimum NPV. In this case the Best sequence will be the extraction of the column sorting from the highest to the lowest economic value and the Worst sequence will be the opposite. Both cases are non-operational but provide a valid reference for planning purposes as maximum and minimum value of any operational sequence. Figure 4 describes an example of the application of this concept showing a plan view of the block model in Excel format, where it is possible to see the Best and Worst sequence generated based on the economic value of each block.

Figure 4 Application of Best and Worst sequence

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Mine Planning Figure 5 shows an example of the application of the Best and Worst sequence and how any other sequence will be located inside of this range. If a sequence value is close to the best case, then there is little further opportunity to improve the sequence. If the difference between best and worst cases is small, then the overall project itself is not sensitive to sequencing. So a simple plot such as shown in Figure 5 is very useful in assessing the effectiveness of various sequences.

Range between Best and Worst sequence

Figure 5 Example of sequence value for several options including Best and Worst case

4

Application of this concept in real footprint

The Regal deposit (Bui 2014) is a fictitious ore body but it is modelled as a massive porphyry copper deposit similar to many of the large block cave mines currently in operation. Overall view of the copper distribution in 3D and plan view at 1,200 level is shown in Figure 6.

Figure 6 Ore body displaying copper grade

An example of the application of mine sequence optimization using concept of best and worst case was done in Regal deposit using the input for Footprint Finder shown in Table 1.

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Caving 2014, Santiago, Chile Table 1 Inputs for Footprint Finder (Regal deposit) Footprint Finder parameters HIZ FIRST_DIL DEV_COST DISCOUNT HMAX TONS_MAX YEARS_BUILD NEW_PER_PERIOD RATE_MAX

4.1

Value Description 100 Height of Interaction zone (Laubscher) 60% First dilution entry (Laubscher) 1,000 Development cost per unit area 10% Discount rate (Eg 0.1 or 10%) 600 Maximum allowable HOD 4,000,000 Max tons/year in schedule 3 Years to build to maximum 96 New blocks per period 50 Max vertial mining rate (m/yr)

Mine sequence applied for the entire footprint

For this example only one level will be evaluated and it is 1,200 where the optimum elevation was found based on run done with the entire block model. Figure 7 shows the result of the Footprint Finder evaluation for each block, where the height of draw is displayed at the left and the economic value at the right. A black line was digitized to limit the footprint to have a more realistic shape for a block cave footprint. It is interesting to see the size of the footprint (1,500 m long and 400 m wide on average) also the distribution of the economic value since there are three potential areas with high grade where the sequence can start.

Figure 7 Results from Footprint Finder, height of draw (left) and economic value (right)

The best and worst sequence was created for the footprint defined by the black line and the graphic results are shown in Figure 8, where the start of the sequence is shown in warmer colors. It is clear that three points are highlighted as high economic values and potential initial point for the sequence. Once we have the best and worst sequence done we can evaluate any other possible sequences and typically the main options evaluated are:

• Start in the border of the footprint and advanced with a flat shape or V-shape for cave front. Both

options are very common since there are geotechnical constraints to be considered (e.g. lead/lag, abutment stress, etc.)

• Starting in the center of the footprint and moving in a diamond shape. This option is very attractive from the NPV perspective, since the sequence can start where the high grade is located, but it also generates many challenges from the operational side, since it concentrates lot of activities in the same area and then in one production drift we can have production, construction and development at the same time.

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Figure 8 Sequence created using best (left) and worst (right) option

Using Footprint Finder, it is possible to evaluate several scenarios in one step and then be able to compare and assess which of these options is closer to the best case. Table 2 shows the list of the 55 runs done for the footprint described above. Table 2 Inputs for Footprint Finder (Regal deposit) Run Azimuth Shape Name 1 BEST BEST 2 0 VSHAPE VSHAPE-0 3 20 VSHAPE VSHAPE-20 4 40 VSHAPE VSHAPE-40 5 60 VSHAPE VSHAPE-60 6 80 VSHAPE VSHAPE-80 7 100 VSHAPE VSHAPE-100 8 120 VSHAPE VSHAPE-120 9 140 VSHAPE VSHAPE-140 10 160 VSHAPE VSHAPE-160 11 180 VSHAPE VSHAPE-180 12 200 VSHAPE VSHAPE-200 13 220 VSHAPE VSHAPE-220 14 240 VSHAPE VSHAPE-240 15 260 VSHAPE VSHAPE-260 16 280 VSHAPE VSHAPE-280 17 300 VSHAPE VSHAPE-300 18 320 VSHAPE VSHAPE-320 19 340 VSHAPE VSHAPE-340 20 WORST WORST

Run Azimuth Shape Name 20 0 FLAT FLAT-0 21 20 FLAT FLAT-20 22 40 FLAT FLAT-40 23 60 FLAT FLAT-60 24 80 FLAT FLAT-80 25 100 FLAT FLAT-100 26 120 FLAT FLAT-120 27 140 FLAT FLAT-140 28 160 FLAT FLAT-160 29 180 FLAT FLAT-180 30 200 FLAT FLAT-200 31 220 FLAT FLAT-220 32 240 FLAT FLAT-240 33 260 FLAT FLAT-260 34 280 FLAT FLAT-280 35 300 FLAT FLAT-300 36 320 FLAT FLAT-320 37 340 FLAT FLAT-340

Run Loaction Shape Name 38 1 CENTRE CENTRE-0 39 2 CENTRE CENTRE-20 40 3 CENTRE CENTRE-40 41 4 CENTRE CENTRE-60 42 5 CENTRE CENTRE-80 43 6 CENTRE CENTRE-100 44 7 CENTRE CENTRE-120 45 8 CENTRE CENTRE-140 46 9 CENTRE CENTRE-160 47 10 CENTRE CENTRE-180 48 11 CENTRE CENTRE-200 49 12 CENTRE CENTRE-220 50 13 CENTRE CENTRE-240 51 14 CENTRE CENTRE-260 52 15 CENTRE CENTRE-280 53 16 CENTRE CENTRE-300 54 17 CENTRE CENTRE-320 55 18 CENTRE CENTRE-340

For each run a production schedule was created and the discounted cash flow was calculated to create a comparison between all of them. An example of the schedule is shown in Figure 9.

Figure 9 Production schedule result from Footprint Finder

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Caving 2014, Santiago, Chile The summary results are shown in Figure 10, where it is possible to see the location of the best and worst sequence, two lines of runs for sequences created using a flat and V-shape (45 deg) cave front shape and one additional line starting at the centre of the footprint from 18 different initial points located in the highest economic value. The values of the operational sequences are in a range of 75% to 85% comparing with the best option. In general the centre options seem to be better when compared with flat and V-shape options.

Figure 10 Schedule value for all 49 runs done

Three sequences are shown in Figure 11, where the start of the sequence is shown in warmer colors. Due to the shape of the footprint all these option create a very long faces (more than 500 m) and this is very difficult to maintain in practice. In addition, comparing values for each option the differences between them suggests initiation in the centre of the footprint where this alternative generates a more complex scenario for operation, construction and development. As a result of the consideration a new analysis was done dividing the footprint in two zones (East and West) where each zone was evaluated independently to identify new options and a possible better overall sequence.

Flat sequence (Az=60 deg)

V-shape sequence (Az=100 deg)

Sequence starting at the Centre

Figure 11 Four sequences used for the entire footprint

4.2

Mine sequence applied in two zones (East and West)

Because of orebody shape and grade distribution the footprint was divided into two zones as shown in Figure 12.

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Figure 12 Footprint divisions into two zones

4.2.1

Sequence evaluation for West zone

Using the same procedure described before a new set of sequence was modelled for West zone. Due to the size of this zone only one centre sequence was created starting at the highest economic value. The result of 38 sequences evaluated for this portion of the footprint is shown in Figure 13.

Figure 13 Schedule value for West Zone

Figure 14 shows three of the best alternatives sequence for this zone. In this zone, it is clear that the best alternative is starting in the centre or using V-shape with 280 deg of azimuth.

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Mine Planning

Flat seq. (Az=280 deg)

V-Shape seq. (Az=280 deg)

Centre sequence

Figure 14 Sequences shapes for West Zone

4.2.2

Sequence evaluation for East zone

The same process was repeated for the East zone, but in this case more sequences were done starting at the centre of the footprint. The result of 45 sequences for this zone is shown in Figure 15.

Figure 15 Schedule value for East Zone

Figure 16 shows three of the best alternatives sequence for this zone. In this zone, the results using flat or V-shape are very similar. Surprisingly the option to start in the centre was not better than the others. This is an indication that any of the best shape options could be used. Now taking into consideration the complexity of the operation for centre or the large of the front on flat shape, the best sequence is V-shape with an azimuth of 80 deg.

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Flat sequence (Az=80 deg)

V-Shape sequence (Az=80 deg)

Centre sequence

Figure 16 Sequences shapes for East Zone

4.2.3

Final results for sequence selection

Comparing all the options evaluated for Regal deposit, the best option was dividing the footprint into two zones. The sequence starts in the middle of both zones moving with a V-shape mining first East then West. The comparison between sequences for two zone vs entire footprint is shown in Figure 17.

Figure 17 Comparison between sequences for two zone vs entire footprint

It is very clear that more work needs to be done to model the final sequence in detail, since this is just a quick analysis of different options, but this tool provides strong evidence for which alternatives could be modeled in more detail to get an optimum result. The next step is to take these results and create a model in GEOVIA PCBC™, where the footprint can be modeled explicitly by drawpoint adding more resolution and detail to the analysis including opening sequence details by month and by zone and more sophisticated dilution / mixing model as a 3D Cellular Automaton.

5 Conclusion The results presented in this paper demonstrate that the mine sequence optimization for Block Caving using the concept of ‘best and worst case’ is a very useful tool for a mine planner to get a maximum and minimum value limit for a sequence, creating a valid reference to compare any sequence created using all the typical constraints for a block cave mine. Also, it is very helpful to have the opportunity to generate and evaluate several runs at once and to be able to see the impact of cave front shape, orientation, starting point, etc. upon the production schedule and final NPV. This is not a complex mathematical optimization solution for this problem since it is a simple iterative process, but the main advantage is that it is a very easy and straightforward method to evaluate many scenarios providing enough information to take the right decision in short time.

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Caving 2014, Santiago, Chile References Bui, T 2014, ‘Tactical Shut-off Value Strategies for Panel Cave Mines’, SME 2014, Salt Lake City. Laubscher, D 1994, ‘Cave Mining: State of the Art’, SAIMM, pp. 279 - 293. Smith, M 2001, ‘Using Milawa/4X as a starting solution for mixed integer programming optimization of large open cut production schedule’, Strategic Mine Planning Conference, Perth. Elkington, T, Bates, L, Richter, O 2012, ‘Block Caving Outline Optimisation’, Massmin 2012, Sudbury, Canada. Pourrahimian, Y, Askari-Nasab, H, Tannant, D 2012, ‘Block Cave Production Scheduling Optimization Using Mathematical Programming’, Massmin 2012, Sudbury, Canada.

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Fast-track detailed engineering for Panel Caving JC Vienne AMEC Internacional, Chile

Abstract All mining projects, including those in underground mining, require meeting a series of sequential steps to be materialized. The completion of the engineering enables the start of works associated with the project construction activities. Generally, detailed engineering is the longest stage in project design and defines the constructive aspects. This paper shows how it is possible to implement “fast-track” detailed engineering for a typical panel caving project, with the use of LHD equipment, trucks or conveyors and in-mine crushing. It highlights the staffing requirements, the need for coordination between the client and the consulting firm, and the advantages and risks of performing “fast-track” detailed engineering.

1 Introduction A typical underground mining project using block/panel caving considers the design of undercut, production, ventilation and intermediate transport levels, in order to feed centralized crushers. Mine designs also need to consider subsequent ore handling to surface, which is mainly done through shafts (S) and conveyor belts (C), or even trains (T). These types of design are used for example in Resolution (S), Argyle (C), Oyu Tolgoi (S), and Grasberg (C). Some designs eliminate the intermediate transport levels by locating the crushers in the perimeter of the footprint, with direct transport of ore to crushers by LHD. Examples of this type of operation include: Northparkes (S), Palabora (S), Ridgeway Deeps (C), Cadia East (C), Diablo Regimiento (T) and Pipa Norte (T). Depending on the rock mass characteristics, some of these projects add a special level for hydraulic or explosive preconditioning. Every project includes a sequence of several engineering phases which are required to be completed before the commencement of detailed engineering. Detailed engineering needs timely completion of project milestones, in order to generate the required designs, comply with bidding times, construction and project start up that complies with the project production plan. This paper focuses on the development of detailed engineering for underground facilities for medium sized projects. This could correspond to, for example, the initial phase of a large project or the full design of a medium size underground mine, with production capacity of between 10 to 20 ktpd. Therefore, the engineering scope could include:

• Mine level design of undercut, extraction, haulage and ventilation levels, including the ore passes and chutes to truck loading.

• Design of the crusher chamber with participation from all disciplines, including excavation, construction and installation of all associated equipment.

• Infrastructure design including full multidisciplinary design for excavation, construction and assembly of the LHD workshop, truck workshop (if required), offices, sewage treatment plant and management plant of industrial residual wastewater.

• Design of ancillary facilities such as ventilation, drainage, surveying, accesses, compressed air

system, industrial water, fire water, electrical power supply and electrical power distribution, refuge chambers, automation, communications and control systems.

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Caving 2014, Santiago, Chile • Economical studies, including capital and operational expenditure estimates. • Procurement support including equipment definition and the preparation of the data-sheet and technical specifications for purchasing.

• Other studies like constructability, maintainability, energy efficiency analysis, risk analysis and closure plan.

• Preparation of master program, engineering program and technical basis for bidding construction, assembly and start-up of crusher and other facilities with all associated equipment.

Detailed engineering with this scope should have a nominal estimated duration of around 12 months and should generate between 2,300 to 2,800 deliverables, including drawings and documents. This paper analyzes the conditions that must be satisfied and actions to be developed by both the company owning the project (the owner) and the consulting firm (the consultant) to reduce the time used in the study from the estimate 12 months to only 8 months.

2

Factors affecting the timing of a successful engineering

This section shows some factors, which are influential to develop successful fast track detailed engineering, in terms of time and quality. 2.1

Quality of previous information

A key requirement for reducing project duration at the detailed engineering level is the quality of the prefeasibility study. In this respect, it is the project owner´s responsibility to ensure that each of the aspects, in all disciplines, have been adequately covered in the previous engineering stages. For fast-track detailed engineering to be performed, the owner must consolidate the engineering designs either at the feasibility stage or at liaison engineering stages, performed during the bidding process of the detailed engineering. Failure of just one discipline to properly develop the designs on previous engineering phases can produce a delay to the whole project, due to the necessary adjustments related to alternative reviews of designs, modifications or even relocation of facilities. 2.2

Client and consultant project and engineering managers

The approach and attitude of each project stakeholder to fast track detailed engineering is critical to the success of the project. Collaborative attitude of every stakeholder is mandatory, especially, from both the owner´s and consultant´s administration team. In this regard, it is important that the owner´s engineering manager has a close liaison with the consultant in order to provide his global vision to each discipline leader as well as effectively interacting with the consultant´s counterpart. To facilitate this it is recommended that the owner´s engineering manager is based permanently in the offices of the consultant. The owner´s project manager should meet with the consultant´s project manager at least once a week in order to solve any technical or administrative issues, minimizing project delays due to unsolved or pending issues. Either the consultant´s project manager or engineering manager need to fulfill the role of giving coherence to the project. This means close coordination between disciplines and strict quality control.

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Owner and consultant discipline leaders

Experienced discipline leaders should form an essential part of the consultant team who should have significant underground mining experience. Ideally, these leaders should have performed other detailed engineering underground projects previously. The same requirements apply to the owner´s discipline leaders. An inexperienced counterpart can potentially generate delays to design approvals due to the need to consult with more experienced team members. This situation is exacerbated when underground mine operational staff are consulted, as these persons are generally not involved in the project and are focused on different tasks with operational priorities. The owner´s counterpart should have daily contact with the consultant´s offices to effectively participate in the consultant´s designs. This reduces the review and approval process. A characteristic of fast-track detailed engineering projects is the amount of deliverables produced each week by the consultant team that need be reviewed by the technical specialists from the owner. During peak times, usually at months 3 to 6 from commencement, there is a possibility that the volume of the consultant´s monthly deliverables exceeds the owner´s review team capacity, usually one person per discipline. To complete the reviews in a timely fashion, an adequately staffed counterpart team is required, if not significant project delays can be expected. The owner’s review teams should be carefully staffed and managed to avoid duplicated reviews, and more important, applying different acceptability criteria. As a rule of thumb, the review of a deliverable should be performed by the same person on each stage of the review. A change of reviewer may delay the consultant due to additional potential checks and comments that were previously solved with the former reviewer. 2.4

Quality and quantity of technical personnel

Suitable technical staff for both owner and consultant is critical for team performance and to allow weekly progress according to project milestones. In this regard, some consultant companies may be challenged, as it is not always easy to have experienced technical staff in all engineering disciplines involved in underground mining. The approach that AMEC uses to tackle this issue is to assemble a technical team that includes one or two senior designers per discipline who have the required experience, and who also act as mentors for the less experienced designers or drafters. 2.5

Project control

A key role in order to achieve project milestones is performed by the project control person, designated by the consulting company. This person must generate global and by discipline progress reports, alerting each discipline leader two weeks in advance regarding deliverables associated to forthcoming project milestones. The project control role is also required to support the project manager with productivity estimates of each discipline, and to monitor and control costs. Therefore, a key requirement is ensuring that experienced project control personnel are assigned to the project. 2.6

Change orders management

In any engineering project, change orders are generated mainly associated to additional work, design changes or additional studies. The manner in which these change orders are approached from both the owner and consulting team is critical for successful project progression. If the owner´s attitude is to refuse design changes, or budget modifications, or if the consultant presents change notes that are not technically supported, a mutual distrust will be generated.

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Caving 2014, Santiago, Chile A collaborative and professional approach is required, such that change orders are approved or otherwise within approval timelines, in order to minimize or avoid project delays. 2.7

Document control

Fast-track detailed engineering generates a substantial amount of deliverables in a very short period of time. A process of internal interdisciplinary review must be followed by the consultant for each deliverable. After that process, the consultant´s discipline lead incorporates comments and issues (in revision B) and the deliverable is sent, through consultant’s document control, to the owner’s document control area, which distributes and coordinates the internal movement of the deliverables received, until it is returned to the consultant with comments. The process is repeated with successive revisions and approvals. This means that for a project with 2,500 deliverables, at least 10,000 submissions are generated between the owner and the consultant, in addition to all internal coordination of both parties. All this flow of information both internally on each side, as between the parties, is managed by the document control area, which should know exactly, at all times, where each deliverable is located. Therefore, it is required that both the owner and the consultant assign personnel with adequate experience in document control procedures, otherwise delays may be generated in undertaking reviews, risk of low productivity of deliverables or even misplaced or lost documentation on deliverables. An important tool to improve coordination and transfer of information between both parties, is the concurrent use of document control and delivery software, usually provided by the consultant. In this way the client can use the same methodology to transfer information, facilitating tracking of deliverables between both the consultant and the client until its final delivery. 2.8

Quality control

In fast-track detailed engineering there generally is no time to redo deliverables that may have not been properly completed. Therefore, the consultant must implement, from the beginning of the project, strict quality control with clear allocation of responsibilities of each of the professionals involved in the development of deliverables. A common error, for example, is the different drafting standards used by each discipline, potentially creating difficulties in the use by other participants. To avoid this, at the beginning of the project, the Systems Engineering consulting firm area, must generate the formats and standards to be used on the project, which have been previously agreed with the customer, and ensure that all participants utilize them. The consultant´s Engineering Manager must request to conduct at least two technical audits to randomly selected project deliverables, the first one no later than the second month, in order to detect deviations and generate corrective actions before the number of deliverables with problems will be greater. Additionally, each discipline on the consultant’s team must implement a strict quality control system, generally based on a standard check list, and applied to each deliverable prior to deliver the document to the document control area. 2.9

Site visits

Detailed engineering must acquire knowledge of existing site conditions of the project. This usually requires conducting site visits in order to collect information and inspect site conditions. The consultant generally should define, with at least two weeks notice in advance, what they want to see and at what level of detail. This allows the owners to perform the necessary site visit coordination. For

440

Mine Planning example; obtain requested information, authorization and access to specific areas, as well as commitment of operations staff to host the visit. A poorly coordinated site visit by both parties, is a waste of time and resources, and may result in the need to undertake additional site visits. 2.10

Risk assumptions by the owner

To perform fast track detailed engineering, the owner must be willing to take some risks associated with the final selection of equipment to be installed. The designs made by the consultant are based on dimensions and specifications of the “most probable equipment” according to the definitions of basic engineering, and the experience of the consultant. These designs are generally valid for the bidding stage of the work, but should be validated for the construction phase and final assembly. Therefore, the owner must accept that some drawings are subject to change. These drawings must be clearly identified and generally will have a stamp with the phrase “Final Design Pending of Vendor Information”. These drawings must be reviewed again once the technical specifications of the selected equipment to be installed become available, be it by the consultant or by the owner´s team. 2.11

Customer satisfaction surveys

It is recommended that the consulting company perform at least two satisfaction surveys, to be answered by the Project Manager of the owner. The first is undertaken when the engineering has progressed between 30 to 40% in order to detect whether the client is in accordance with the consultant’s work and take corrective actions as appropriate. The first survey, in fast track detailed engineering, must include customer perception about the progress and compliance with the milestones criteria. The second survey should be conducted at the end of engineering, after the client’s final acceptance. These are done to generate lessons learned and generally improve consultant´s work flows for future projects.

3 Conclusions It is possible to perform fast-track detailed engineering that allows the owner to continue on to timely tender excavation, construction, installation and commissioning of all different project installations, according to the milestones defined in the master project plan. However, there are a number of critical issues that need to be considered when deciding on fast-track detailed engineering. Firstly, an experienced underground mining team, for both the consultant and the client, is a basic requirement for the timely achievement of milestones and project success. In addition some administrative tasks, such as project and document control, play an important role for both the consultant´s and owner’s teams in order to manage the progress information, and transfer of deliverables that support achieving project milestones.

Acknowledgement The author would like to thank to AMEC International, Ingenieria y Construccion for providing authorization to publish this paper.

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Optimizing Hill of Value for Block Caving A Ovalle AMEC International, Chile M Vera AMEC International, Chile

Abstract The intrinsic value of a mining project is realized with the selection of the optimal production capacity and cut-off grade. We describe how this can be achieved for a project planned to be mined using the block/panel caving methods. The “hill of value” tool is used as a starting point. After selecting the cut-off grade, the mineral resources and the corresponding throughput that offers the maximum NPV, these values are used to investigate how this optimum may vary by incorporating the influence of specific realistic characteristics for the selected mining method. The main characteristics influencing block caving NPV evaluation are: dilution, undercutting level elevation, height of the extraction columns, footprint of the production area, production capacity, production ramp-up and ramp-down profiles, sequence and undercutting rates, production program, development and construction schedule, capital investment schedule and operating costs. The methodology is illustrated with an example for optimizing a project, where the cut-off grade, the production capacity and a near-optimal NPV were determined.

1 Introduction The bulk of the economic return of a project is defined in the process of identifying the mineral reserves. It is a common practice in the mining industry to use the marginal cut-off grade (COG) to define reserves with the aim of maximizing tonnage and throughput. This however does not warrant the best economic return. This paper describes a procedure to determine the planning parameters (COG, mineral reserves and production rate) by incorporating real characteristics of the block/panel caving methods and also including an estimation of the capital expenditure for the whole project, which will provide a value that approaches the optimal Net present value or NPV.

2 Methodology 2.1

Definitions

a.

Marginal cut-off grade

The marginal COG is the metal grade in the ore that makes its profit of extraction equal to zero. For its application to block caving, diluted columns of a block model are assessed, calculating the profit associated to the exploitation of each block in the column. As long as the profit of the column is positive, the column is added to the resources to be mined (the column height is determined by maximizing the column profit). b.

Optimal cut-off grade

The previously defined COG does not necessarily define the best economic result for the company. A criterion of selecting greater than zero profit columns yields better economic results. This criterion is not unique in the industry and it varies from marginal determinations to very sophisticated optimizations. Here, we present a methodology that identifies the COG that very nearly maximizes the NPV of a project.

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Mine Planning c. NPV Companies use several methods to evaluate the maximum value of their projects. Some companies will be focused in the reduction of production costs by maximizing reserves and throughputs in their projects; others will search to optimize specific economic indicators such as NPV, the internal rate of return (IRR), the payback period (PB) or the profit investment ratio (PIR). This analysis assumes NPV as the indicator to maximize. A synthetic way to estimate NPV is using the “hill of value” tool, which allows a graphical identification of the full range of the project´s NPV value as a function of COG and production capacity. Normally, there is a single optimal point, defined by one COG and one production capacity combination. However, the hill of value is built on unrealistic assumptions as indicated in Table 1. The contribution of this paper is to define more realistic assumptions. The “hill of value” is used basically to select the COG range that provided an apparent optimal NPV. From this starting point, “more realistic NPV values” are calculated by incorporating the characteristic assumptions for block/panel caving shown in Table 1. Table 1 Comparison of Assumptions between NPV´s from “Hill of Value” and “More realistic NPV” values

NPV´s from “Hill of Value”

“More realistic NPV values”

Constant production capacity

Ramp-up, steady state and ramp-down production capacity

Constant average grade All CAPEX in year 0 Mathematical footprint 100% mining recovery

Variable grade from production schedules All CAPEX: scheduled in time Smoothed out footprint Mining recovery less than 100%

2.2 Steps The following steps were applied in a theoretical exercise for a massive copper, gold and silver deposit. a.

Block model visualization

This stage is fundamental to understand the depth and shape of the mineralized body, the spatial grade distribution, the location of higher grade zones, and whether the mineralized zone is composed by a single or multiple bodies. Multiple ore bodies may require different treatment, where assessment of individual zones may be more appropriate. b.

Mining method selection

The analysis is focused on the block/panel caving underground method. It is assumed that the selection of the mining method is appropriate for the characteristics of the ore body. c.

Mineral selection by Cut-off grade

The amount of mineralized material to be mined is determined for different COG’s. Firstly, the marginal COG which yields zero profit is selected. The formula is defined as follows (the example presented uses Cu equivalent grade, where Au and Ag grades are converted to CuEq):

Profit = Income - Cost = 0

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(1) Where: Lcm = Marginal COG (CuEq) M = Mining and processing costs per ton of ore PCu = Copper price SCu = Selling cost of copper RM = Metallurgical recovery of copper The marginal criterion to select reserves ignores the opportunity cost. This concept considers a profit greater than zero (COG greater than marginal COG), resulting in a reduction of total tonnage but will increase the project´s NPV. d.

Generating the “Hill of Value”

The “hill of value” is determined by calculating an NPV value for each combination of COG and production capacity. This is done by utilizing Revenue, the capital expenditure (Capex) and the operational expenditure (Opex) values as a function of production capacities. In the exercise which is presented, a total of 15 COG´s were used, starting with the marginal COG and incrementing the values. Each COG generated its own caving footprint, which is inversely related to the COG. In order to determine the NPV for each pair of points (COG, mine duration related to production capacity); the following formulations were used for Revenue, Capex and Opex:

• Revenue Each COG generates its own mineral resource value which is used to determine the associated revenues assuming that the total resources are mined out in 1, 2, … n years. An estimate of the yearly revenues for each productive capacity is:

(2)

Where: INCOMESi

=

Yearly income for duration “i” (USD/a)

TPYi

=

Yearly production capacity for duration “i” (t/a)

GCuEq

=

Average grade (% CuEq)

RCu

=

Metallurgical recovery (%)

PCu

=

Copper price (USD/lbCu)

SCu

=

Copper selling cost (USD/lbCu)

Conversion factor=

444

2,204 lb/t

Mine Planning • Opex Again the productive capacities previously determined are used to calculate yearly costs, using: (3) Where: OPEXi =

Yearly operating cost for duration “i” (USD/a)

TPYi

=

Yearly production capacity for duration “i” (t/a)

Cm

Cd

=

Mine extraction cost (USD/t)

=

Mine Preparation Cost (USD/m2)

At

=

Footprint area (m2)

TON

=

Total minable resouces (t)

Cp

=

Processing cost (USD/t)

• Capex The Capex estimate is composed of two figures, the Mine Capex and the Plant & Infrastructure Capex, and is basically a function of the productive capacity, but it is also a function of the total minable resources. The empirical relations that follow come from the experience in various projects:

(4) (5)

Where: CAPEXM

=

Mine CAPEX

CAPEXP&I

=

Processing plant, tailings and infrastructure CAPEX

TPDi

=

Daily production capacity for duration “i” (t/d)

TON

=

Total minable resources (t)

• NPV´s for “Hill of Value” The NPV was estimated for 15 COGs and for 40 durations of the project. The expression used to calculate the NPV for duration “i” of the project is the following:

(6)

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Caving 2014, Santiago, Chile Where: NPVi = NPV for duration “i”

f =

1 1 +r d

rd = discount rate e.

Selecting an apparent optimal NPV from “Hill of Value”

Each COG generates NPV values according to the durations of the project. The maximum value is selected from each curve, and then the maximum NPV value from the 15 COG curves is selected. This becomes the apparent optimal NPV from the “Hill of Value”, which defines the COG, the quantity of minable resources and the duration of the project with its corresponding production capacity. This point of the hill of value becomes the starting point to calculate more “realistic” NPV´s by incorporating the specific planning parameters influencing block/panel caving performance as described in table 1. The exercise is repeated one more time narrowing down the calculations to 4 COG´s (the optimal COG, the neighboring lower value and the two neighboring higher values). It has been our experience that the optimal COG value shifts to higher COGs and lower production capacities than the “apparent” optimum, when the realistic planning parameters are incorporated. f.

Calculating more realistic NPV values

Mineable resources are determined for the four COG´s selected from the “hill of value” exercise. The footprints of the resources are then inspected and smoothed. A production plan for each COG is calculated, establishing the undercutting sequence and the undercutting rate, resulting in a grade profile for each production capacity. Maximum production capacities for each COG are calculated using appropriate formula. A production ramp-up and production ramp-down is then incorporated. A total Capex schedule is also formulated. The end result is the determination of more realistic NPV values for the selected narrow range of COG´s and production capacities. This is performed for the cases that are close to the optimum using the simplified calculations.

3 Example A theoretical example was done using a block model and the methodology described previously, with the following planning parameters:

• Long range planning prices: 3 USD/lbCu, 1,300 USD/ozAu and 23 USD/ozAg. • Metallurgical recoveries: 85% Cu, 75% Au and 55% Ag. • Selling costs: 0.36 USD/lb Cu, 5.00 USD/oz Au and 2.00 USD/oz Ag. οο Block model: οο block dimensions are 20 m x 20 m x 10 m. οο 1,500,000 block units. οο block model dimension is 2,000 m wide, 3,000 m long and 2,000 m high. οο the model intersects the surface topography. • 50% dilution entry point using Meta-Laubscher volumetric model. • Minimum column height of 90 m and no maximum limit.

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Mine Planning 4 Results Figure 1 shows the “hill of value” with apparent NPV values on the vertical axis, the COG on the y axis and the mine durations on the x axis. Only the positive NPV values are shown and the negative values are not shown for clarity of representation. Five value ranges of NPV´s are shown with different colors. The yellow line represents the maximum NPV values for different COG’s. The marginal COG is 0.29 % CuEq, and it is clearly seen that there is no positive NPV value for this grade.

Figure 1 “Hill of Value” for NPV´s > 0

Figure 2 shows the optimum NPV values as a function of the COGs, both for the “hill of value” exercise and for the “more realistic NPV values”.

Figure 2 Maximum unrealistic NPV from Hill of Value and “more realistic NPV values”

As mentioned previously, the apparent maximum NPV of the “hill of value” (1,301 MUSD) and the three neighboring values (1,271; 1,228 & 953 MUSD) are selected as the starting points to calculate more realistic NPV values. All these values are highlighted in green. The corresponding “more realistic NPV values” are -480; 93; 241 & 248 MUSD. It is clearly shown that that the “more realistic NPV values” are much lower than the NPV values from the “hill of value” exercise, and that the NPV optimal value shifts from 0.42% CuEq to 0.48% CuEq.

447

Caving 2014, Santiago, Chile Table 2 shows a comparison between the “hill of value” NPV´s and the “more realistic NPV values” for the range of COGs selected as the starting points: Table 2 “Hill of Value” NPV´s and the “more realistic NPV values” CUT-OFF GRADE % CuEq 0.40 0.41 0.44 0.48

NPV “Hill of Value” NPV

MUSD 1,271 1,301 1,228 953

RESOURCES

Mt 1,600 1,200 1,000 700

% CuEq 0.49 0.52 0.54 0.58

“More realistic NPV values”

PROD.

FOOTPRINT

140

300,000

kt/d 270 220 190

m2 740,000 540,000 450,000

NPV

MUSD -480 93 241 248

RESOURCES

Mt 1,650 1,240 1,030 720

% CuEq 0.49 0.52 0.53 0.57

PROD.

FOOTPRINT

65

360,000

kt/d 100 90 80

m2 800,000 610,000 510,000

From Figure 2, it is not clear if the more realistic NPV reached the maximum value at 0.48% CuEq COG and an obvious question is whether the NPV for 0.50% CuEq COG is higher. An inspection of the footprint area for the latter COG shows that it is smaller and with an important discontinuity. This is the main reason for not considering the NPV at this COG, and to consider the NPV at 0.48% CuEq COG as very near the optimal solution.

5 Conclusions Traditional NPV evaluation based solely on “hill of value” tools make many unrealistic assumptions such as production profiles, grade, Capex expenditure and recoveries. The methodology outlined in this paper incorporates more realistic block caving characteristics and selects an optimum NPV value that results in higher COG; longer mine life and lower production capacities than traditional “hill of value” methods. In addition, the optimum NPV for a project is obtained at COG´s higher than the marginal COG, as the marginal COG does not consider capital cost expenditure.

Acknowledgements The authors thank AMEC International for the endorsement to publish this paper.

References Hall, BE 2003, ‘How mining companies improve share price by destroying shareholder value’, CIM Mining Conference and Exhibition, Montreal. Lane, KF 1988, ‘The economic definition of ore, cut-off grades in theory and practice’, Mining Journal Books: London. Lane, KF 1964, ‘Choosing the optimum cut-off grade’, Colorado School of Mines Quarterly, vol. 59, N°4.

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Mine Planning

Footprint and economic envelope calculation for Block/ Panel caving mines under geological uncertainty E Vargas University of Chile, Chile N Morales University of Chile, Chile X Emery University of Chile, Chile

Abstract Traditional long-term mine planning is based on deterministic ore body models, which ignore the uncertainty in the geological resources. Therefore, the mineable resources and mine plans are not robust and the actually obtained values may not meet the promised values at the beginning of the project. Geological uncertainty can result in important differences in the economic value of the plan and in the outline shape of the mine. This paper deals with developing a tool that incorporates geological uncertainty in early stages of the planning process: defining the economic envelope in a massive underground mine. The rationale is to compute an economic outline of the mine that aims to maximise the contained value while limiting the difference of the height of adjacent columns, all this for each level. As a result, this tool gives an approximation of the shape and value of the economic envelope of a block cave mine, which can be used as an input to a post scheduling process. The algorithm is tested on a real case study and validated against existing software alternatives. Afterwards, it is extended to work with geological uncertainty, which is modelled using a set of conditional simulations of the mineral grades. The results for this case study indicate that geological uncertainty can generate a gap greater than 100% in the economic value of the footprint and the total tonnage of the envelope, between the best and the worst grade scenarios. On the other hand, the shape of the envelope varies in each grade scenario, making it difficult to make an optimal decision on the placement of the developments for a posterior extraction sequence.

1 Introduction Traditional long-term mine planning is based on deterministic information, therefore, plans and decisions may not be robust against uncertainty and estimated value and production promises may not be achieved. One example of this is the uncertainty on the resource model: while techniques like conditional simulations are well developed to model the spatial variability of grades, existing mine planning tools do not allow incorporating them into the planning process. They only allow integrating uncertainty a posteriori, by means of sensitivity analyses, so that variability is estimated but not controlled. Many authors analyse the impact of geological uncertainty in open pit mines in terms of differences between promises and actual values (e.g., Dimitrakopoulos 2011), but there is a lack of references about uncertainty in underground mines. On the other hand, approaches are used to calculate mine reserves in block/panel caving mines. The draw point oriented methodology (Diering 2000) has been validated and improved along the years and seems to be the mainstream methodology; meanwhile another recent methodology based on the upside down pit algorithm (Elkington et al. 2012) generate mine outlines and footprints using different cut-offs, but none of these methodologies consider geological uncertainty in their algorithm. This work aims at developing a tool such can incorporate geological uncertainty in early stages of the planning process: defining the economic envelope in a massive underground mine. The results of the case study will be specific to the ore body and block/panel caving mining method.

449


Methodology

The main objective is to calculate the economic envelope of an ore body to be mined using the block/panel caving method. To this end, two methodologies are used to calculate the economic footprint and envelope, respectively. 2.1

Footprint calculation

Similar to the footprint finder methodology (Diering et al. 2008), we calculate the economic level where the undercut level should be placed, it means the economic boundary and layout of the underground mine. It is based on the profit of the blocks discounted by when they will be extracted given the position of the block in the block column (equation 1). v(x, y, z) (1) vi (x, y, z) = (1+α) Where: v and vi’ = block economic value and discounted value of the block assuming i as the undercut level [$/t]. dz = block height [m]. v mining = Vertical Mining Rate [m/yr]. α = discount rate. To simplify the decision where to put the undercut level, the value of the footprint will be the only decision variable. This implies finding the maximum footprint economic value. 2.2

Economic envelope calculation

Given the results of the economic level, the next step is to calculate the economic envelope. This will represent an approximation to the mining reserves in the ore body. The methodology behind this section is based on the ultimate pit algorithm, and is applied with some modifications in order to resemble the caving geometry, as follows:

• Cut the block model: οο Remove the block model data below the economic Z level. οο Set the maximum height of column. • Invert the Z coordinate in the block model. • Create a set of slope precedence constraints in order to control the maximum adjacent height of draw (HOD).

• Calculate the economic envelope using the constraints and modified block model, given equation 2:

Where:

450

B = total number of blocks

B max∑ =1v i · xi

(2)

Mine Planning

vi = economic value of block i

xi = binary decision variable to extract block i or not

• Post processing of the envelope: οο Set minimum column height οο Set minimum mining footprint dimensions. The steps described above are solved using the MineLink library which is part of the BOS2M open-pit scheduler and sequencer (Rubio et al 2011). In addition, the results of the economic level (footprint) are validated against PCBC (GEMS) software commonly used in caving mines. 2.3

Extension to consider geological uncertainty

Once we have developed a tool to optimise the economical envelope, the geological uncertainty is introduced by using conditional simulations to generate different resource models. The simulations are constructed with the TBSIM program (Emery & Lantuéjoul 2006). For each simulation (block model scenario), the optimal footprint and economic outline of the mine can be computed. Subsequently, a quantification of the uncertainty is done, applying the Value at Risk (VaR) evaluation which has been used in previous publications to assess the impact of geological uncertainty in open pit projects (Vielma et al. 2009).

3 Data The data consist of 100 simulations of a real ore body. Each one of these simulations has a total of 2.34 million blocks of 10x10x10 m3 and 149 levels (10 meters per level). The copper grade was the only simulated variable, and the tonnage and density for each block are assumed constant. It is also supposed that all the calculations are done over the same mineralised zone (same rock type). The economic data used in the block evaluation is shown in Table 1. Table 1 Economic parameters

Parameter

Value

Selling Cost [US$/t]

0.35

Price [US$/t]

Mine Cost [US$/t] Plant Cost [US$/t]

2.5 10

16.1

Recovery

87%

Maximum Column Height [m]

500

Productivity [tpd]

200

Density [ton/m3]

Minimum Column Height [m] Utilisation [days/year] Slope Angle

2.7

100 200

45°- 60°- 90°

Also, validation was done over block model obtained by kriging and 10 different simulations, using the same economic parameters in the two methodologies. No development costs where used in the economic evaluation.

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Caving 2014, Santiago, Chile 4 Results 4.1 Validation Between the methodology used in this paper and PCBC there are some differences in terms of accumulated value, tonnage and area of the footprint, thus there are differences in the economic level where the undercut level will be placed. Table 2 summarises these differences for the 11 block models evaluated (negative values mean PCBC values are greater than MineLink values). Table2 Differences between PCBC and MineLink methodologies

Differences

Block Model

Level

Economic Value

Tonnage

1

-5

9%

17%

Kriging 2 3 4 5 6 7

-2 -1 -1 -1

4% 8% 3%

-1

-2%

1

2%

-1

8

-1

10

0

9

-5%

-4

-4%

10%

-33%

14%

-6%

-14%

15%

-13%

11%

-18%

11%

-14%

13% 11%

1%

13%

6%

15%

0%

Area

11%

-7%

-17% -12% -8% -1%

To illustrate the previous table, the results for the accumulated economic value and tonnage for one simulated block model are shown in Figure 1.

Figure 1 Footprint validation results over one simulation

The difference in value between these two methodologies is up to 10% near the optimum economic level, and greater differences can be observed in the last and less deep levels.

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Mine Planning 4.2

Footprints results

Appling the methodology described in this paper, different curves of accumulated value and tonnage can be generated for each scenario. Given that all these curves were generated over 100 simulations of the ore body, the differences between the curves depict the geological variability or uncertainty (Figures 2 and 3).

Figure 2 Footprint Results: value over 100 simulations (dashed curve is the kriging scenario)

Figure 3 Footprint Results, tonnage over 100 simulations (dashed curve is the kriging scenario)

The accumulated value of the footprint varies for every simulation, thus the placement of the undercut level will have the same behaviour, resulting in a distribution of elevations (Figure 4). From Figure 4, class level 1 has the greatest average value and frequency while class level 36 (where the kriging scenario is placed) has one of the lowest average values.

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Mine Planning Footprint value statistics [MUSD]

Level

Minimum

Maximum

Average

6

5,404

5,691

5,548

1

11

16 21 26 31 36 41 46

4,846 5,096 5,529 4,389 4,802 4,322 4,187 4,534 3,853

6,325 5,704 6,461 6,347 7,516 5,938 6,478 5,038 4,628

5,613 5,325 5,995 5,349 5,745 5,183 5,091 4,799 4,190

Figure 4 Undercut Level Placement Distribution (Kr indicates the place of the kriging scenario)

4.3

Economic Envelope Results

Given the undercut elevation for each block model (economic footprint result), the envelope or outline of the mine is calculated over the 100 simulations in order to give an idea of the reserves on each block model. The distributions of the value and mean grade are shown in Figure 5.

Figure 5 Economic Envelope Value and Mean Grade Distribution

The shape of the envelope changes because of the geological uncertainty and the variability in the placement of the economic footprint. To illustrate this point, the kriging, best and worst economic values are displayed in Figure 6.

Figure 6 Economic Envelope for Kriging, Best and Worst Values Scenarios

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Caving 2014, Santiago, Chile Given the previous results, a measure of the risk is really useful to summarise the variability. In this case the value at risk (VaR) evaluation is done. To calculate the VaR of the economic value, the distribution can be estimated as a lognormal distribution, which allows calculating the value associated with some risks levels (Table 3). Similar fittings are considered for the tonnage, area and mean grade distributions. Table 3 Value at Risk for Economic Envelope results compared against average and kriging values

Value [MUSD]

Tonnage [Mton]

Area Footprint [m2] Mean Grade [%]

1%

Value at Risk 3%

5%

Average

Kriging

363

494

576

4,030

4,400

4,605

271,000

294,000

306,500

321

0.904

348

0.932

0.951

6,477

420,084 0.930

6,207

550,000 0.894

5 Conclusions Geological uncertainty is a subject that recently has been integrated in open pit mining to know the risks and opportunities present in mining projects, but this uncertainty has been less studied in underground mine projects, specifically in block/panel caving mines which represent massive operations and, once they start the cave, great modifications to the mining method are not easy to perform. With this motivation, a methodology able to calculate the footprint and economic envelope of an underground mine under geological uncertainty is proposed, in order to have a wide vision of the possibilities besides deterministic approaches or kriging estimates. The footprint tool was validated against commonly used PCBC software, resulting in differences around 10% near the maximum economic level, which is a good approximation considering that both tools are an approximation to reality. In terms of economic value, the kriging scenario is one of the worst along the levels in the ore body. Using the uncertainty approach, one generates possibilities to improve the profit, and in addition the placement of the economic footprint varies because of the variability in the accumulated value per column, noting differences in footprint value up to 8,000 MUSD. Given the 100 simulations shown here, there is a probability of about 36% to find the economic footprint in the deepest elevation (level 1) and only 14% probability to find it in the level 36 (where the kriged model says it should be). A good decision must consider the values and these probabilities so the maximum profit could be gained at the minimum risk. Once the placement of the footprint is done, the next step is to estimate the economic envelope or outline of the mine. In this aspect differences in the shape and value are noted. The envelope economic value obtained by the kriged block model is below the expected economic value obtained with the 100 simulations, which could be attributed to the grade smoothing made by the kriging method. The value at risk analysis in this case tell us that with a 5% of risk the value of the economic envelope could be 29% less than the expected value, which means approximately 1,800 MUSD. As a general thought, geostatistical simulations give us many possible scenarios, which can be assumed alike the real ore body, thus a risk analysis for the results of a large amount of simulations could help us to take the best decision for the project given the geological uncertainty.

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Caving 2014, Santiago, Chile Acknowledgements The authors thank the Advanced Mining Technology Centre (AMTC), Delphos Mine Planning Laboratory and the Mining Engineering Department at the University of Chile for supplying the resources needed to develop this research.

References Dimitrakopoulos, R 2011, ‘Stochastic Optimization For Strategic Mine Planning: A Decade of Developments’, Journal of Mining Science March 2011, vol.47, Nº 2, pp. 138-150. Diering, T 2000, ‘PC-BC: A Block Cave Design and Draw Control System’, MassMin 2000, Brisbane, Australia, pp. 469-484. Elkington ,T, Bates, L & Richter, O 2012, ‘Block Caving Outline Optimisation’, MassMin 2012, Sudbury, Ontario, Canada. Diering, T, Richter, O & Villa D 2008, ‘Block Cave Production Scheduling Using PCBC’, MassMin 2008, Luleå, Sweden. Vargas, M, Morales, N & Rubio, E 2009, ‘A short term mine planning model for open-pit mines with blending constraints’, MinePlanning 2009, Santiago, Chile. Emery, X, Lantuéjoul, C 2006, ‘TBSIM: A computer program for conditional simulation of three-dimensional Gaussian random fields via the turning bands method’, Computers & Geosciences, vol. 32, Nº 10, December 2006, Pages 1615–1628. Vielma, J, Espinoza, D & Moreno E 2009, ‘Risk control in ultimate pits using conditional simulations’, Proceeding of APCOM 2009, Vancouver, Canada.

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Determination of the best height of draw in block cave sequence optimization F Khodayari University of Alberta, Canada Y Pourrahimian University of Alberta, Canada

Abstract Production scheduling is one of the most important problems in mining operation which has a significant impact on the profitability of the mining project. Most of the common production-scheduling methods in the industry rely only on manual planning methods or computer software based on heuristic algorithms. These methods cannot guarantee the optimal solution. On the other hand, most of the software packages determine the height of draw (HOD) before production scheduling without considering the advancement direction. Improvements in computing power and scheduling algorithms over the past years have allowed planning engineers to develop models to schedule more complex mining systems. Applications of mathematical programming in mine planning have proven very effective in supporting decisions on sequencing the extraction of materials in mines. The objective of this paper is to develop a practical optimization framework to compute the best height of draw as result of the optimal production schedule for each advancement direction. This paper presents a model application of a production schedule for 102 drawpoints over 14 periods.

1 Introduction A production schedule must provide a mining sequence that takes into account the physical and technical constraints and, to the extent possible, meets the demanded quantities of each raw ore type at each time period throughout the mine life. In block cave mining, production scheduling determines the amount of material which should be mined from each drawpoint in each period of production, number of new drawpoints that need to be constructed, and their sequence during the life of mine (Pourrahimian 2013). Most of the common production-scheduling methods in the industry rely only on manual planning methods or computer software based on heuristic algorithms. These methods cannot guarantee the optimal solution. They lead to mine schedules that are not the optimal global solution. Improvements in computing power and scheduling algorithms over the past years have allowed planning engineers to develop models to schedule more complex mining systems (Alford et al. 2007). For optimization of block-caving scheduling, most researchers have used mathematical programming; Linear Programming (LP), Mixed-Integer Linear Programming (MILP) and Quadratic programming (QP). LP is the simplest one in modelling and solving. Since LP models cannot capture the discrete decisions required for scheduling, mixed-integer programming (MIP) is generally the appropriate mathematical programming approach to scheduling. Solving of a MILP problem can be difficult when the size of production system is large but it is a useful methodology for underground scheduling (Rahal 2008). In spite of the difficulties associated with applying mathematical programming to block-caving production scheduling in underground mines, the authors have attempted to develop methodologies to optimize blockcaving production schedules. They have used different objective functions and constraints. Table 1 shows some of the applied mathematical methodologies in block-caving production scheduling.

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Caving 2014, Santiago, Chile Table 1 Summary of applied mathematical methodologies in block-caving production scheduling Author Song (1989)

Methodology MILP

Chanda (1990)

Simulation and MIP

Guest et al. (2000) Rubio (2002)

LP MIP

Diering (2004)

NLP

Rubio and Diering (2004)

LP, IP, QP

Rahal et al. (2008)

MILGP

Weintraub et al. (2008) Smoljanovic et al. (2011)

MIP MILP

Parkinson (2012)

IP

Epstein et al. (2012) Diering (2012)

LP, IP QP

Pourrahimian et al. (2013) Alonso-Ayuso et al. (2014)

MILP MILP

Model’s objective(s) Minimization of total mining cost Minimization of the deviation in the average production grade between operating shifts Maximization of NPV Two models (a) maximization of NPV and (b) optimization of the mine life Maximizing NPV for M periods and minimization of the deviation between a current draw profile and a defined target Maximization of NPV, optimization of draw profile, and minimization of the gap between long and short term planning Minimizing deviation from the ideal draw profile while achieving a production target Maximization of profit Optimization of NPV and mining material handling system Finding an optimal opening sequence in an automated manner Maximization of NPV Objective tonnage (to optimize the shape of the cave) Maximization of NPV Maximization of NPV with considering uncertainty in copper price

Features LP: This method has been used most extensively and it can provide a mathematically provable optimum schedule. But straight LP lacks the flexibility to directly model complex underground operations which require integer decision variables. MILP: MILP could be used to provide a series of schedules which are marginally inferior to a provable optimum. Computational ease in solving an integer programming problem is dependent upon the formulation structure. It can provide a mathematically provable optimum schedule. The advantage that MILP has over simulation when used to generate sub-optimal schedules is that the gap between the MILP feasible solution and the relaxed LP solution provides a measure of solution quality. The drawback in using MILP is that it is often difficult to optimize large production systems by the branch-and-bound search method. QP: Block caving process is non-linear, so it would not be appropriate to use linear programming for production scheduling in block caving. But solving of this kind of problems could be a challenge because we must change them to LP and then solve them, so we have conversion errors.

The inherent difficulty in applying these models to the long-term production-planning problem is that they result in large-scale optimization problems containing many binary and continuous variables. These are difficult to solve with the current available computing software and hardware, and may require lengthy solution times. This paper will introduce a MILP mine-scheduling framework for block-caving in which solving a largescale problem in a reasonable CPU time and optimal mining reserve based on advancement direction will be addressed to generate a near-optimal production schedule with higher NPV.

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Mine Planning 2

Methodology, assumptions, and notation

The production schedule of a block-cave mine is subject to a variety of physical and economic constraints. In this paper, the objective of the theoretical framework is to maximize the net present value (NPV) of the mining operation and determine the best height of draw (BHOD), while the mine planner has control over the planning parameters. The planning parameters considered in this study are: (i) mining capacity, (ii) draw rate, (iii) mining precedence, (iv) maximum number of active drawpoints, (v) number of new drawpoints to be opened in each period, (vi) continuous mining and (vii) reserve. The production scheduler defines the opening and closing time of each drawpoint, the draw rate from each drawpoint, the number of new drawpoints that need to be constructed, the sequence of extraction from the drawpoints and the BHOD for each draw column. Several assumptions are used in the proposed MILP formulation. The ore-body is represented by a geological block model. The column of rock above each drawpoint, which is referred as a draw column, is vertical. Each draw column is divided into slices that match the vertical spacing of the geological block model. Numerical data are used to represent each slice’s ore-body attributes, such as tonnage, density, grade of elements, elevation, percentage of dilution, and economic data. The developed MILP model uses PCBC’s (GEOVIA-Dassault, 2012) slice file as input. In order to maximize the NPV, all the material in the draw column or some part of that can be extracted. In other words, the mining reserve will be computed as a result of the optimal production schedule. Extraction precedence for drawpoints and clusters is used to control the horizontal and vertical mining advancement direction. According to the advancement direction, the precedence between drawpoints is defined using the method presented by Pourrahimian et al. (2012; 2013). After creating the slice file using PCBC, the slices within each draw column are aggregated into selective units using a modified hierarchical clustering algorithm developed based on an algorithm presented by Tabesh and Askari-Nasab (2011). Then, the optimal life-of-mine multi-period schedule is generated for the clustered slices. The optimization formulation is implemented in the TOMLAB/CPLEX (Holmstrom, 2011) environment. An efficient way of overcoming the large number of decision variables and constraints is to apply a clustering technique. Various methods of aggregation have been used to reduce the number of integer variables that are required to formulate the mine-planning problem with mathematical programming (Epstein et al. 2003; Newman and Kuchta, 2007; Weintraub et al. 2008; Tabesh and Askari-Nasab 2011; Pourrahimian et al. 2012; Pourrahimian et al. 2013). In the modified algorithm, the similarity value (Sij) between slices i and j, is calculated by

(1)

Where: Lij = the normalized distance value between slices i and j, EVij = the normalized economic value difference between slices i and j, Dij = the normalized dilution difference between slices i and j. WL, WEV, and WD are weighting factors for distance, economic value, and dilution, respectively. The weights are defined by the mine planner. The notation used to formulate the problem is classified as indices, parameters, sets, and decision variables.

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Caving 2014, Santiago, Chile 2.1 Notation 2.1.1 Sets

Sd S dcl S dlcl S cl

For each drawpoint d, there is a set Sd defining the predecessor drawpoints that must be started prior to extracting drawpoint d. For each drawpoint d, there is a set Sdcl defining the clusters in the draw column associated with drawpoint d. For each drawpoint d, there is a set Sdlcl defining the lowest cluster within the draw column associated with drawpoint d. For each cluster cl, there is a set Scl defining the predecessor clusters that must be extracted prior to extracting cluster cl.

2.1.2 Indices

cl � {1,..., CL} e � {1,..., E} l n p q

Index for clusters. Index for elements of interest in each cluster. Index for a drawpoint belonging to set Sd. Index for a cluster belonging to set Sdcl. Index for a cluster belonging to set Sdlcl. Index for a cluster belonging to set Scl. Index for scheduling periods.

2.1.3 Parameters

CL CLSEVcl D DR d ,t

Maximum number of clusters in the model.

DR d ,t i Gecl

Maximum possible draw rate of drawpoint d in period t.

G e ,t G e ,t Mt

Economic value of cluster cl.

Maximum number of drawpoints in the model.

Minimum possible draw rate of drawpoint d in period t. Discount rate.

Average grade of element e in the ore portion of cluster cl.

Upper limit of the acceptable average head grade of element e in period t.

Lower limit of the acceptable average head grade of element e in period t. Lower limit of mining capacity in period t.

Mt N Ad ,t

Upper limit of mining capacity in period t.

Ncld

Number of clusters within the draw column associated with drawpoint d.

N Nd ,t

Maximum allowable number of active drawpoints in period t.

Lower limit for the number of new drawpoints, the extraction from which can start in period t.

N Nd ,t

Upper limit for the number of new drawpoints, the extraction from which can start in period t.

T

Maximum number of scheduling periods.

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Mine Planning Toncl

Total tonnage of material within cluster cl.

Tond

Total tonnage of material within the draw column associated with drawpoint d.

Tonhd

Tonnage of material related to the minimum height of draw h within the draw column associated with drawpoint d.

2.1.4

Decision variables Binary variable controlling the precedence of the extraction of clusters. It is equal to 1 if the extraction of cluster cl has started by or in period t; otherwise it is 0. Binary variable controlling the closing period of drawpoints. It is equal to 1 if the extraction of drawpoint d has finished by or in period t; otherwise it is 0. Binary variable controlling the starting period of drawpoints and precedence of extraction of drawpoints. It is equal to 1 if the extraction of drawpoint d has started by or in period t; otherwise it is 0. Continuous decision variable representing the portion of cluster cl to be extracted in period t.

3

Mathematical model

The objective function, equation (2), of the MILP formulation is to maximize the net present value of the mining operation which depends on the value of the clustered slices. The economic value of each cluster is equal to the summation of the economic value of the slices within the cluster and the costs incurred in mining. The CLSEV is a constant value for each cluster. The constraints are presented by equations (3) to (19).

(2)

(3)

(4)

(5)

(6) (7) (8)

(9)

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Caving 2014, Santiago, Chile (10) (11)

(12) (13)

(14)

(15) (16) (17) (18) (19) Equation (3) ensures that the total tonnage of material extracted from clusters in each period is within the acceptable range. Equations (4) and (5) force the mining system to achieve the desired grade. Equation (6) forces variable Ed,t to change to 1 when a portion of the lowest cluster of the draw column is extracted in period t, because The lowest cluster in each draw column controls the starting period of extraction from the associated drawpoint. Equation (7) ensures that when variable Ed,t changes to 1, it remains 1 until the end of the mine life. Equation (8) ensures that when drawpoint d is open, at least a portion of one of the clusters within the draw column associated with drawpoint d is extracted. If the extraction of a cluster is not started after finishing the extraction of the cluster below in period t or t+1, the relevant drawpoint must be closed. Equation (9) ensures that when variable Cd,t changes to 1, it remains 1 until the end of the mine life. The maximum number of active drawpoints in each period is controlled using equation (10). The precedence between drawpoints is controlled in a horizontal direction while the precedence between clusters is controlled in a vertical direction. Equations (11) to (14) control precedence between drawpoints and clustered slices. Equation (15) guarantees that cluster cl is extracted when the relevant drawpoint is active. The number of new drawpoints opened in or after period two is controlled by equation (16). At the beginning and in period one, the number of new drawpoints is equal to the maximum number of active drawpoints, equation (17). Equation (18) ensures that the draw rate from each drawpoint is within the desired range in each period. Equation (19) ensures that the amount of the extracted material from draw column associated with drawpoint d is not more than the total tonnage of the material within the related draw column. The lower bound of this constraint is the tonnage related to the minimum height of the draw in each draw column which is defined by the mine planner.

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Results and discussion

We have developed, implemented, and tested the proposed MILP model in the TOMLAB/CPLEX environment (Holmstrom 2011). The model is verified by numerical experiments on a data set containing 102 drawpoints and 3457 slices over 14 periods. One-thousand clusters were created based on the presented algorithm. The maximum number of slices in each cluster could not be more than five. The weight factors of the distance, economic value, and dilution were set to 5, 3, and 3, respectively. The height of draw is limited to not less than 50 m. This means at least 50 m of the drawpoints must be extracted. The problem was solved for two directions, west to east (WE) and south to north (SN). Table 2 presents the scheduling parameters. Results show that all assumed constraints are satisfied in the considered directions. Figure 1 illustrates the numerical results for the proposed formulation. The resulting NPVs at EPGAP of 3% are $135.13M and $132.91M in the WE and SN directions, respectively. Figure 2 illustrates the production tonnage and the average grade of production in each period. The total tonnage of material that must be extracted in the WE and SN directions is 11.9 Mt, which is less than the extractable material based on the slice file. Figure 3 illustrates the number of active drawpoints and the number of drawpoints that must be opened in each period. In the WE direction, the mine works with the maximum number of active drawpoints from period two to ten. In the SN direction, the mine works with the maximum number of active drawpoints from periods two to 13 except period nine. Table 2 Scheduling parameters

G e ,t / G e ,t

Mt /Mt (kt)

(kt/yr/per DP)

0.8 / 1.7

100 / 900

10 / 40

(%)

DR d ,t / DR d ,t

N Ad ,t

N Nd ,t / N Nd ,t

50

0 / 15

Figure 1 Obtained NPVs for different EPGAPs

Figure 2 Production tonnage and average grade of production in the WE and SN direction

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Figure 3 Number of active new drawpoints in the WE and SN direction

5 Conclusions Most of the common production scheduling software packages in the industry determine the height of draw before production scheduling without considering advancement direction. This method cannot guarantee the optimal solution and will lead to mine schedules that are not the optimal global solutions. This paper investigated the development of a MILP formulation for block-cave production scheduling optimization. The proposed formulation can be used in different advancement directions which are selected based on geotechnical considerations. Consequently, (i) the mining reserve, which is a result of optimization, also varies from one direction to another; (ii) planer is able to find the best single operation direction or combination thereof, and the best starting location to reach the maximum NPV.

References Alford, C, Brazil, M, & Lee, D 2007, ‘Optimisation in Underground Mining’, in Handbook Of Operations Research In Natural Resources, vol. 99, International Series In Operations Research, (A. Weintraub, C. Romero, T. Bjørndal, R. Epstein, and J. Miranda, Eds.), Springer US, pp. 561577. Alonso-Ayuso, A, Carvallo, F, Escudero, LF, Guignard, M, Pi, J, Puranmalka, R, & Weintraub, A 2014, ‘Medium range optimization of copper extraction planning under uncertainty in future copper prices’, European Journal of Operational Research, vol. 233, Nº3, pp.711-726. Chanda, ECK 1990, ‘An application of integer programming and simulation to production planning for a stratiform ore body’, Mining Science and Technology, vol. 11, Nº 2, pp. 165-172. Diering, T 2004, ‘Computational considerations for production scheduling of block cave mines’, Proceedings of MassMin 2004, Santiago, Chile, pp. 135-140. Diering, T 2012, ‘Quadratic Programming applications to block cave scheduling and cave management’, Massmin 2012, Sudbury, Canada, pp. 1-8. Epstein, R, Gaete, S, Caro, F, Weintraub, A, Santibanez, P, & Catalan, J 2003, ‘Optimizing long-term planning for underground copper mines’, Proceedings of Copper 2003, 5th International Conference, CIM and the Chilean Institute of Mining, Santiago, Chile, pp. 265-279. Epstein, R, Goic, M, Weintraub, A, Catalán, J, Santibáñez, P, Urrutia, R, Cancino, R, Gaete, S, Aguayo, A, & Caro, F 2012, ‘Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines’, Operations Research, vol. 60, Nº 1, pp. 4-17.

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Mine Planning GEOVIA-Dassault 2012, Ver. 6.2.4, Vancouver, BC, Canada. Guest, A, VanHout, GJ, Von, JA, & Scheepers, LF 2000, ‘An application of linear programming for block cave draw control’, Proceedings of MassMin 2000, The Australian Institute of Mining and Metallurgy: Melbourne, Brisbane, Australia. Holmstrom, K 2011, TOMLAB/CPLEX, ver. 11.2. Ver. Pullman, WA, USA: Tomlab Optimization. Newman, AM & Kuchta, M 2007, ‘Using aggregation to optimize long-term production planning at an underground mine, European Journal of Operational Research, vol. 176, Nº 2, pp. 1205-1218. Parkinson, A 2012, Essays on Sequence Optimization in Block Cave Mining and Inventory Policies with Two Delivery Sizes, Thesis, The University Of British Columbia, 199 p. Pourrahimian, Y 2013, Mathematical programing for sequence optimization in block cave mining. PhD Thesis, The University of Alberta, Edmonton, Alberta, Canada, Pages 238. Pourrahimian, Y, Askari-Nasab, H, and Dwayne, DT 2013, ‘A multi-step approach for block-cave production scheduling optimization’, International Journal of Mining Science and Technology, vol 23, Nº 5, pp. 739-750. Pourrahimian, Y, Askari-Nasab, H, and Tannant, D 2012, ‘Mixed-Integer Linear Programming formulation for block-cave sequence optimisation’, Int. J. Mining and Mineral Engineering, vol. 4, Nº 1 pp. 26-49. Rahal, D 2008, Draw Control in Block Caving Using Mixed Integer Linear Programming, PhD Thesis, The University of Queensland, 342 p. Rubio, E 2002, Long term planning of block caving operations using mathematical programming tools. Master Thesis, The University of British Columbia, 126 p. Rubio, E and Diering, T 2004, ‘Block cave production planning using operation research tool’, Massmin 2004, Santiago, Chile, pp. 141-149. Smoljanovic, M, Rubio, E & Morales, N 2011, ‘Panel Caving Scheduling Under Precedence Constraints Considering Mining System’, Proceedings of 35th APCOM Symposium, Wollongong, NSW, Australia, pp. 407-417. Song, X 1989, ‘Caving process simulation and optimal mining sequence at Tong Kuang Yu mine, China’, Proceedings of 21st Application of Computers and Operations Research in the Mineral Industry, Society of mining Engineering of the American Institute of Mining, Metallurgical, and Petroleum Engineers, Inc. Littleton, Colorado, Las Vegas, NV, USA, pp. 386-392. Tabesh, M & Askari-Nasab, H 2011, ‘Two-stage clustering algorithm for block aggregation in open pit mines’, Mining Technology, vol.120, Nº 3, pp. 158-169. Weintraub, A, Pereira, M, & Schultz, X 2008, ‘A Priori and A Posteriori Aggregation Procedures to Reduce Model Size in MIP Mine Planning Models’, Electronic Notes in Discrete Mathematics, vol. Nº 30, pp. 297–302.

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Block Caving strategic mine planning using RiskReturn Portfolio Optimization E Rubio REDCO Mining Consultants, Chile

Abstract Cave mining is a complex mining system that relies entirely in the constitutive behavior of rock mass caving that leads to ultimate fragmentation at the draw point, gravity flow behavior, stress performance among others. All these aspects of cave mining have shown the industry that block caving is a highly uncertain mining method. In the last years the industry has been pushing the concept of super caves that supports the concept of large underground block cave operations with production in the excess of hundred thousands tons per day of run of mine ore, promoting the concept of large footprints and high draw columns. This tendency has been supported by the traditional approach of net present value optimization throughout a constant throughput optimization. Nowadays, there is evidence in several operations around the world that the approach of ignoring the actual intrinsic caving variability and uncertainty of its constitutive behavior may lead to jeopardize the project value and eventually have a mine design or mine planning fatal flaw. This paper introduces the concept of portfolio optimization in which every decision related to mine design and mine planning could be a component of a set that defines a feasible portfolio. Thus, this set is optimized for different production targets to maximize return subject to a given level of reliability, as a result of this optimization process a frontier efficient is proposed as a boundary to display different strategic designs and planning options for the set of variables under study. The efficient frontier shows graphically the maximum return that a mining system could deliver throughout a coherent production schedule under a given level of risk. Thus, it is for the decision makers to define the point along the frontier efficient where they want to place a given project. This tool has been used in the industry at a prototype level to justify equipment technology and its mining system as well as to define production targets of large block cave operations that are efficient for the level of return and risk that shareholders what to place the mine set. In the paper there will be theoretical and applied examples of this technique that is under development and application to mine design and mine planning of large block cave operations. Key words: block caving, mine planning, strategic planning, sequence optimization, operational hedging, risk assessment, reliability production planning, portfolio optimization.

1 Introduction Block caving is a complex mining system since its functionality depends upon caving process that is induced at the base of a block and it propagates to surface as material is withdrawn from a set of regular draw points on the production level. Material taken from the draw points is dumped into ore passes that connect to the haulage level. From the haulage level the production is taken to crushers by trucks, trains or chain belts depending on the mining system. The process of undercutting a block and its sequence respect to the drawbell blasting has demonstrated to be quite critical to avoid early rock collapses or rockbursts. So the design of the drilling and blasting of drawbells and undercut in terms of geometry and sequence is extremely relevant for the success of a block cave operation. Nevertheless, despite the efforts on the design and operational discipline that can be applied in a Block Cave operation, there are still several uncertainties that triggered risk that perhaps could lead to jeopardize the expected return of one of these operations (Summer 2000). Some of the uncertainties that lead to risk are as follows:

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Mine Planning 1. Cave propagation. If the rock mass is high stress and competent the cave propagation could be

uncertain and triggered erratic dilution and non uniform grade extraction and/or air gaps that attend towards the safety of the method.

2. Fragmentation. Since rock mass is broken by caving the actual fragmentation expected at the draw points is uncertain. Thus, draw point productivity is uncertain and the amount of area that needs to be developed and undercutted also becomes uncertain.

3. Grade. Once the rock is fragmented the particles of rock flow towards the production level in different ways depending on the fragmentation profile and fragmentation distribution. Thus, forecast grade becomes quite difficult due to the number of underlying flowing mechanisms that could be interacting.

4. Stress distribution. Depending on the design of the Caving method one could obtain different

stress performances at the front cave. Typically, it is well know the effect of abutment stress that is produced by undercutting. Abutment stress implies that there are three times the pre mining vertical stress and sometimes there is rotation of the stress tensor. This effect leads to unexpected damage, drift collapses and sometimes rock burst.

All the above aspects lead to have a mining system that is unreliable in terms of production outcomes. In order to compute mining reserves once needs to simulate several excenarios integrating random variables that are connected constitutively to the sources of uncertainty mentioned above. Thus, expected production outcomes also become the result of a simulated stochastic process that is often presented as an histogram of potential production outcomes. Figure 1 depicture shows a production histogram of a block cave and its evolution a the caving propagates and the whole mining system matures.

Figure 1 Reliability evolution troughout an active production schedule due to draw point maturity and draw point opening sequence

As a result of the randomness of production outcomes a reliability assessment is needed in order to fix the amount of production that is desired to state under a given level of uncertainty. Reliability assessment is a tool to assess the robustness of a mining system. It helps to analyse different production scenarios and alternative mining systems. Also, it allows decision makers to evaluate different levels of hedging to achieve a given production outcome. In other words, flexibility needs to be added as a consequence of the characteristic of this complex mining system.

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Operational hedging

Operational hedging in this paper needs to be understood as the kind and the amount of undercutting area that needs to be developed and undercutted to sustain a certain production, the number of equipments that are going to be needed, the number of secondary blasting crews among others. Operational hedging is not free and often involves a fair amount of capital expenditure that must be engineered in a proper production schedules are quantified and rationalized in order to optimize the rent and the risk of the project. This papers describes a methodology called frontier efficient, commonly used in portfolio optimization in finance applications, that allows mining engineers and managers to be confident regarding the amount of drawing that is scheduled the number of secondary blasting crews, the opened area that is scheduled. Also, in some instances in which the real hedge can be feasible the optimality could be found at a lower production level to leverage the highest mining return. Figure 2 depicture shows both options either reducing the productivity of an active footprint due to its high production variability or the introduction of a real hedge such that will raise the reliability of the underlying production schedule.

Figure 2 Illustration of hedging options in a block cave scenario. For a given active layout production outcomes are reduced or for a fixed production target a larger amount of active area is undercutted

As a result of introducing operational hedging in planning and scheduling a block cave mine, the exercise of just optimizing the net present value becomes meaningless since there is CaPex as hedges that need to be optimized integrated as part of the mining system. Thus, the design and planning of these operations need to be systemic in a sence that caving, undercutting, drawbelling, draw performance, repairs, material handling system need to be integrated to value correctly the reliability of a given production scheduled that is planned with operational hedges.

3

Systemic approach to design and planning for Block Caving

Block Cave is the mining method that needs to be engineered as an integrated mining system in a sense that caving will influence the performance of the production area in terms of productivity and regularity of production. The material handling system could influence the ability to use different draw profiles that could influence the way how the rock mass caves. In order to introduce operational hedging in a block cave production schedule a model of the mining system needs to be built up in order to replicate and mimic the production performance in order to quantify as production means different options for the mining system in terms of hedging and production outcomes. Figure 3 depicture shows the interaction between modelling techniques in order to quantify the value of operational hedging.

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Mine Planning

Figure 3 Illustration of different numerical techniques used to quantify operational hedging in a Blok Cave operation

Planning and designing of a Block Cave following a mining system approach allows mining engineers to capture and to measure the effects of the interactions among the main production drivers and that could provide insides regarding the certainty or reliability of achieving a given production target. Several authors have provided with method to accomplish this such as (Rubio 2006; Troncoso 2006), however, it is very important to device an engineering tool that could be used at a strategic level to support planning decisions such as mining method, sequence, production targets, draw strategy among others. The team of REDCO mining consultants has device a method to mimic the market financial behaviour to the complexities of a block caving, using the frontier efficient method developed by Markowitz (1959) to optimize portfolio for uncertainty outcomes.

3

Block Caving Frontier Efficient Method

Efficient portfolio has been discussed extensively by Samis et al (2006), and Davis and Newman (2008) using real options and quantifying the risk of different mining strategies and also reviewing value at risk method. In this paper, the author wanted to give a fresh review at the Markowitz method (1959) and complemented by Haugen (1990) and Merton (1990) in which he defines a frontier efficient optimization method to allocate resources to a portfolio of assets with different return over investment and risk. The methodology consists of computing the cross covariance of all the possible combination of assets in a portfolio to compute the medium -. variance space upon which a given portfolio is efficient to be invested in. Thus for instance, in Figure 4 the highlighted dots represent a portfolio that is inefficient since there are combination of assets that could provide a higher return for the same computed average risk. Note that the risk in this context is seeing as the average volatility of the underlying asset portfolio, in block caving this could well be the volatitilty of metal production due to uncertainty or run of mine production due to uncertainty. Subsequently, the mining application will be to mimic several mining decisions such as mining methods, production rate, mining sequence and production schedule as if these decisions where component of a portfolio in the cave mining system model. Then the covariances of different decisions will define the variance of a given decision subject to the other status such as mine, production rate, sequence and others.

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Figure 4 Frontier efficient optimization

When the mining system model is deviced, a valuation model is integrated in order to quantify the reliability of different mine settings (design, sequence, production capacities, material handling, etc) and the return of the setting based on the amount of hedging that is involved in the scenario. For example, for a very large production capacity and minimum hedging the risk and the return will be high. For the same production scenario a large amount of hedging is added, for example a large production footprint is developed, then the risk of the scenario and the return will decrease. Figure 6 depicture shows the scenario valuation exercise. An optimization model can be used to find the frontier at the maximum level of return for a given amount of risk that shareholders and project stakeholders are willing to take. This tool allows engineering groups to make assessment of hedging and value of the project for different levels of risk and return.

Figure 5 Frontier efficient method used to value the risk and the return of different configurations of a mining system to support strategic decision making

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Mine Planning The methodology proposed to use the frontier efficient method to plan a Block Cave starts simulating different replications of the mining system for different levels of draw point maturity and material handling availability. Thus, the mining system is simulated using Arena software in which using volumetric transporters one could replicate scenarios of production modelling every cycle of the mucking process while introducing random variables such as fragmentation size, hang up occurrence, equipment availability, material handling availability, draw point structural failure. The result of this simulation provides production histograms for different stages of block maturity as shown below. Block maturity is understood as the block matures its caving as a function of drawing production.

Figure 6 Block production histogram for different levels of draw point maturity, cave 1, cave 2 steady production and closure

Clearly, the ability of the system to handle different levels of production changes accordingly for different stages of caving. Based on the above chart for a collection of blocks (production units composed of multiple draw points) one can set up the production distribution to take from every block to be a portfolio decision in which the production to be taken from every block would be the portfolio decision subject to the conditional probability distribution represented in the production histogram shown above. As a result of the portfolio optimization one could obtain the amount of tonnage to be taken from every block, the number of active blocks, the number of blocks that need to be under development and the drawing strategy that needs to be taken for different levels of risk. For a given portfolio, one could evaluate the production capacity of this scenario and its return. Given a block i of a set of n active blocks that contains qi draw points, the following optimization model can be formulated to model the block cave production schedule as a portfolio of multi assets subject to underlying production volatility.

471

Caving 2014, Santiago, Chile Tt; maximum production tonnage for period t, this is the optimization function Yi; Binary variable that is 1 if the block is opened or 0 otherwise Xi; Continues variable that is the proportion of tonnage to be taken from block i over the maximum tonnage to be drawn based on the overall drawing strategy. Tmi; Maximum tonnage that can be drawn from block i base don the overall drawing strategy Vmaxi; Maximum tonnage that can be taken from block i based on draw rate and draw points maturity The simple formulation allows to set up the production schedule of a block cave mine as a portfolio model. The above formulation is a simplification due to a lack of multi period setting, sequence constraint and exposure of material handling alternative designs that are a great portion of the operational hedging expected to be added in a block cave mining system. Nevertheless, this formulation allows to set up a comprehensive understanding of the method and leads to a more sophisticated models to be constructed as the research evolves. At the moment the REDCO´s team is setting up different experiments to better understand the constitutive of the covariance matrix and its relationship with different aspects of caving and flexibility. For examples it can be shown that the covariance matrix among active blocks would depend upon the flexibility of the mining system which translates into alternative material handling systems and perhaps equipment technology. For example automated LHD equipment would translate into a different covariance matrix compared to manual LHD because the technology adds different levels of intrinsic hedging that is not often quantified as more CapEx or more infrastructure. When solving the above model one could draw Figure 7.

Figure 7 Frontier efficient chart resulting from solving a portfolio optimization model to plan a Blockcave production schedule subject to multiple blocks with intrinsic dynamic uncertainty.

Figure 7 shows that once the optimization model is solved, there could be delineated a frontier at which for a given level of volatility or risk one could find the maximum reward, valuing the contained and the designed hedging. Thus, for a pre-planned scenario that is located below the frontier one could activate the over designed hedging i.e. accelerating production or re allocate the hedging by decreasing the level of risk or volatility of some blocks composing the production schedule that may be overstressed.

472

Mine Planning 4

Example of application

An industrial experiment was set up considering 20 alternative blocks which can be opened in any order to maximize production for a given level of risk.

Figure 8 Block setting and material handling to be included in the portfolio optimization model

Every block is composed out of:

• 4 production drifts. • 2 ore passes per production drift. • 2 crushers. •

4 ore passes dump into the 2 crushers.

•

2 belts one for each crusher.

•

1 belt to collect final production.

Every block was modelled using conditional probability distribution histogram, with production random variables, such as, draw point blockage as per oversize and hang-ups, ore pass interruptions, production drift repairs, crushers and LHDs availability, secondary breakage production performance, undercut area availability. Figure 9 shows the production histograms conditional to block maturity.

473


Figure 9 Production histogram derived from the simulation of the mining system integrating intrinsic uncertainty for different levels of draw point maturity

Using the portfolio optimization approach, a set of 11 scenarios were optimized, for every scenario the production contribution of each block to the overall production was computed. Figure 10 shows the result of applying the optimization model over the set of blocks subject to the above shown blocks volatility.

Figure 10 Production histogram derived from the simulation of the mining system integrating intrinsic uncertainty for different levels of draw point maturity

Figure 11 shows that, for different levels of risk acceptance, there is a different combination of blocks that need to drawn and the amount of drawing across the active blocks changes accordingly. This is an expected result since the internal balance between the level of volatility of each block and the amount of drawing

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Mine Planning need to be in balance to provide the maximum return to a given level of risk. Note that the operational hedging here is activated based on the number of blocks that are opened and the amount of drawing taken from each active block.

Figure 10 Production distribution per block as a function of the maximum tolerable production volatility

At the moment, there is a second model that is under construction that is multiperiod and integrates sequence and development constraints that should provide a set of efficient frontiers base on the maximum number of active blocks that are acceptable based on the development rates and general infrastructure availability.

5 Conclusions The main conclusion of the work to date is that the Frontier Efficient Method could provide insights regarding the block cave mine planning including mining method, production rate, sequence, drawing strategy, development rate and equipment/mining technology among others. Another observation is that the number of active resources involved, such as, general infrastructure or development rate capacity, would provide intrinsic hedging which is the ability to migrate from a frontier of risk return into a more aggressive return capture exposing the ability of the system to optimize its production capacity. Thus, a real option model can be set up on top of the frontier efficient model in order to optimize the intrinsic hedging that need to be devised in a given project. It is fully recommended that the mining industry adopts this way or a similar tool to set up strategic scenarios in which return and risk are both set up into the same map to leave the shareholders or directors to make decisions regarding the mining system production planning variables that positioned the project into the level of risk and return acceptable for the company. There are several examples in block caving and massive mining in which by just concentrating on net present value has led to unrealistic production targets and a complete unbalance between the contained operational hedging and production performance. This behaviour leads to inefficient scenarios that quite often when operating the mine it reflects into higher operating costs than in the scenario when optimizing the contained hedging. Another observed effect of operating a mine in the inefficient area of the risk return chart is the fact that development, production and overdrawing areas become unbalanced, i.e. there could be a large undercut area without constant and sustained production, there could be high overdrawn draw points, there could be a narrow area of steady production while large zone of overdrawn and little undercutting, in all these scenarios most likely there will be geotechnical damage at the drawbelling manifesting as rock collapses and strain bursts. Therefore, integrating operational hedging in an optimal way for a given production and mine setting would lead to a more controlled operating cost and less geotechnical hazards.

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Caving 2014, Santiago, Chile Acknowledgement The author would like to thank the whole REDCO Mining Consultants team for supporting the work and everyday contribute in a great deal to apply the risk return approach to the solutions recommended to our clients. Specially I would like to thank Joaquin Jimenez a graduate from the University of Chile who did most of the numerical applications. Also, I would like to thanks my closed colleagues Gabriel Pais and Sebastian Troncoso for contributing in many discussions, theoretical framework and computational analysis.

References Haugen, R & Nardin B 1990, ‘Dedicated Stock Portfolios’, Journal of Portfolio Management, Summer 1990, pp. 17-22. Markowitz, H 1959, Portfolio Selection:Efficient Diversification of Investments, John Wiley & Sons, Inc. Merton, RC 1990, Continuous-Time Finance, Blackwell. Norstad, J 1999, An introduction to portfolio theory. Available at: http://homepage.mac.com/j.norstad/ finance. Samis, M, Davis, GA, Laughton, D & Poulin, R 2006, ‘Valuing uncertain asset cash flows when there are no options: a real options approach’, Resources Policy, vol. 30, pp. 285-298. Kazakidis, V & Scoble, M 2002, ‘Accounting for ground-related Problems in planning mine production systems’, Mineral Resources Engineering, Imperial College Press, London, England, vol. 11, Nº 1. Rausand, M y Hoyland, A 2004, System reliability theory, models, statistical methods and applications, Second edition, Canada, Whiley-Interscience, 132p. Rubio, E 2006, Block cave mine infrastructure reliability applied to production planning, PhD Thesis, The Faculty of Graduate Studies (Mining Engineering), The University of British Columbia Vancouver, Canada. Summers, J 2000, ‘Analysis and management of mining risk’, MassMin 2000, Brisbane, The Australasian Institute of Mining and Metallurgy: Melbourne. Troncoso, S 2006, Simulación del impacto de interferencias operacionales para la planificación de producción, Memoria Ingeniero Civil de Minas, Universidad de Chile, Santiago, Chile. (in spanish) Troncoso, S 2009, Confiabilidad de Programas de Producción en Sistemas Mineros Subterráneos Complejos. Tesis de Magíster en Minería, Universidad de Chile, Santiago, Chile. (in spanish) Vesely, W 1991, ‘Incorporating aging effects into probabilistic risk analysis using a Taylor expansion approach’, Reliability Engineering and System Safety, pp. 315-337.

476

Mine Operations

Numerical Modelling

477

Numerical Modelling

Numerical modelling of Pilar Norte Mine development using Abaqus R Cabezas MVA Geoconsulta, Chile F García MVA Geoconsulta, Chile M Van Sint Jan MVA Geoconsulta, Chile R Zepeda CODELCO, Chile

Abstract Modelling mining processes and their future extraction is an important tool for planning and design, especially in deep stress mining, that can be affected by collapse or rock burst and creates several risk conditions for workers as well for expected economical revenue. The main purpose of this research is to explain and to comment our current computational modeling state of art, using as example some of main aspects in modeling geomechanics in the development of Pilar Norte Mine, El Teniente Division, CODELCO. Numerical modeling was performed using software ABAQUS. Finally, the advantages of numerical modeling and some future research requirements are defined. and expected short-terms improvements, making numerical modeling as an important but not ultimate decision tool, but takes advantage in relating the most important characteristics of mining process: geomechanics, design and operation. Keywords Geomechanics, ABAQUS, Pilar Norte, El Teniente, Numerical Modelling

1

Introduction

El Teniente is an underground mine located in the Andes Cordillera of central Chile, approximately at 100 km south of Santiago, under operation since the beginnings of the XIX century. Based in porphyry developed in the early Pliocene, it is formed by secondary and primary rock, were mineralization is formed principally by stockwork, reaching grade ore nearly to 0.6%. Geology have been extensivelly mapped. (Vry et al. 2010). Pilar Norte Mine is located at Northeast of Brecha Braden, the main geological formation without economical profit and center of administrative work. Pilar Norte is located between Esmeralda and Reservas Norte Mine. Since the early preparation for mining and first operations, Pilar Norte have presented rock burst problems. Also, Esmeralda mine was affected by the collapse of part of their tunnels, thus, collapse and squeezing has been an issue at El Teniente and cannot be underestimated. Due to recent history, it is necessary to evaluate future mining risky conditions. Thus, a numerical model was developed in order to identify hazardous zones, quantify possible problems and evaluate different excavations sequences that minimize risk or exposition time to hazardous conditions. This paper presents some of the major considerations of that model, as well as some conclusions about the validity of the results, their capacity to evaluate and design, some of their limitations and future work associated.

2

Methodology

Numerical model was developed using the Finite Element software ABAQUS, which can considers solid bodies or plates in a bidimensional environment. Nevertheless, interaction between 2D elements and 3D

479

Caving 2014, Santiago, Chile elements are not allowed. Interactions between different types of materials can be modeled as restrained or allowing relative displacement. Some of the main considerations are listed in the following sections. 2.1

Geology

The main unity is El Teniente Mafic Complex (CMET), placed around Brecha Braden unity, in a way that local structures decrease as well as the distance of Becha is increased. Local alteration includes veins with lower resistance that makes rock mass resistance decrease. Inside the CMET unit, several minor bodies are found: in Pilar Norte Mine, the three main bodies are Andesite, Brecha and Diorite. Interaction between this units are modeled with displacement restraint, therefore, only changes in stress due to elasticity are allowed. This simplification improves the calculation time. Regional faults and their stress implication were noticed by El Teniente (Karzulovic et al. 2006; Windsor et al. 2006). Local faults and main structures are obtained in internal reports, and ranked due to importance level, using 10 cases. Faults were considered as thin solid bodies that cannot yield and allow relative displacement, while intersections were treated by a relative importance criteria and most important faults containing the less important ones. Yielding hypothesis was dismissed because of convergence problems, however, due to this elements being almost stiffless, results tend to be similar to reported results by El Teniente. 2.2

Geometry

Boundaries are limited to a box of 6 km wide per 7 km length and 1.5 km in height, enough to ensure that scale effects won’t affect the results. Geometry of main bodies was obtained through software Vulcan, previous design of mining layout was an input. Some of the elements are presented in Figure 1.

(a)

(b)

Figure 1 Modelling of main bodies in FEM software. (a) Equivalency of Vulcan Model output to Abaqus Solid sketch. (b) Main faults and structures applied over the production layout of Pilar Norte Mine, colored by importance criteria, were red is more important than green and green more than blue

2.3

Geotechnical properties

Four types of rock lithology are considered, each one in a pre-mining condition and at broken condition, after mining have started. Failure model used for lithology was Mohr Coulomb instead of Hoek & Brown because of current limitations of the software. Parameters of some principal bodies are presented in Table 1. In order to optimize time calculations, an elastic domain and an elastoplastic domain were defined, were plastic properties are available in near mining volume.

480

Numerical Modelling Table 1 Parameters of main bodies in Pilar Norte Mine Model

Material Premining CMET

Young Modulus

Weight

Friction angle

Cohesive strength

E (GPa)

(ton/m3)

(º)

C (MPa)

2.0

2.0

42.0

1.0

Broken CMET Diorite

46.0

Brecha

36.0

Andesite Brecha Braden*

Faults * denotes elastic material

3

38.0

50.0 25.0 4.0

2.7

36.0

2.7

37.0

2.7 2.7 2.6 2.7

37.0 38.0 -

3.0

6.5 8.0 7.0 8.0 -

1.0

Field data and special considerations

Some specific considerations applied to panel Caving and Pilar Norte mine are explained in the following sections. 3.1

Preconditioning by hydraulic fracturing

Preconditioning causes a media exchange, expanding in a radial way all along the length of the boring. This radial propagation mobilizes cohesive strength, converting original rock in a big size blocks acting as frictional material. The propagation of fractures by hydraulic pressure was not directly modeled; instead a specific volume as a cylinder was defined. On the other hand, stiffness in axial axis is not widely altered, while stiffness at shear decreases, inducing modeling of an orthotropic media. Representative idea of orthotropic properties and calibration of the model using minor stress obtained by preconditioning field data are presented in Figure 2. Figure 2a shows a theoretical propagation of HF, which was calibrated considering isotropic models with explicit structures. Idealized media is presented in Figure 2b, without considering explicit structures in rock mass, in order to reach similar results. Finally, Figure 2c shows the comparison between HF assess of minor principal stress and model data at a single HF boring, obtained by calibrating model boundary stress condition.

(a)

(b)

(c)

Figure 2 Hydraulic fracturing characterization used in the model. (a) Elemental idea of radial propagation in HF (b) Equivalent orthotropic properties of preconditioned volume. (c) Calibration of minor principal stress with HF data, including their spatial location about the layouts

481

Caving 2014, Santiago, Chile The orthotropic media assumption improves calculation time, but needs to be carefully calibrated if additional information of preconditioned volume is required. Otherwise, if preconditioned volume is considered just as a overloading, hypothesis is recommended. 3.2

Progressive development of Cave

For the progression of the cave an external iteration is applied, related to Plastic Equivalent deformation (i.e. PEEQ variable at ABAQUS). This condition consist of changing material properties if a threshold value is reached, which is determined by typical values of yielding deformation like 0.1% to 0.4%. Physically, the hypothesis considers Mohr-Coulomb failure mode, that can be reached if media is unconfined and hanging blocks fall down by traction, if stress increases and ductile failure mode can occur. For this iteration, time is not directly a variable and is only controlled by the hypothesis of continuous mining process and extraction rate through the opening of the following extraction points. In Figure 3 results of the iteration technique are shown.

(a)

(b)

Figure 3 Progressive Development of Cave back. (a) Plastic equivalent yielding using different threshold limits (b) Shape of Cave using plastic equivalent strain

3.3

External loading and boundary conditions

Stresses in whole model are initially controlled by overloading and tectonics. Previous studies recommended coefficients at rest of 1.35 in E-W axis and 1.14 in N-S. Broken material due to mining process decrease their density to values similar to dense gravel, near to 2.2 ton/m3. This reduction is similar to all lithologies. Figure 4 presents results of stress condition in drawpoints. In Figure 4a principal stresses are presented. In Figure 4b a borehole camera record is shown, evidencing an acceptable level of correspondence between them. Interaction between geological bodies was previously explained. Relative displacement allowed between lithologies and structures can help to predict potential failure movements as well as strain energy able to be dissipated through seismic activity.

482

Numerical Modelling

(a)

(b)

Figure 4 Stress flow due to caving process and over excavations given by tectonics

4

Results

It was noted that a way to try to reduce risk is to open following extraction points trying to avoid stress over confining due to coupled effect by abutment stress (mining condition) and tectonic stress (pre-mining condition). In fact, it is not possible to dismiss both stresses, but the direction of the mining front can reduce the flowing of the stress. Changing orientation of mining front can be limited by the presence of main faults. The current numerical model can bring, in a basic form, an estimation of possible fault movement, going approximately from 0 cm to 2 cm in the most loaded zones, conducing to available relative displacement. Figure 5 shows the change in strain all over several main faults and structures considered in modeling. Available elastic energy to be dissipated can be computed with moment recommendation (Bath 1966):

(1)

In other case, energy can be approximated as force and displacement, making moment magnitude:

(2)

Figure 5 Faults and joints stored energy obtained by numerical simulation

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Caving 2014, Santiago, Chile As noted in Equation 1, the maximum seismic moment is calculated to be of 2.2, that is acceptable compared to geophysics estimation of 2.1. In other words, seismicity inferred by modeling cannot be easily separated from seismicity due to caving itself, which make it difficult to reduce or control future seismic events. It is important to say that elastic energy estimated by the software was corrected to empirical data available at El Teniente Division, where the energy value has to decreased to 5%. This correction is due to different causes. Firstly, faults are non persistent and therefore some portion of energy is dissipated as noise or heat. Secondly, stress measurements do not give reliable results. Thirdly, the frequency range in geophysical equipment is limited and do not cover all real ratio frequency. Changing in abutment stress can also be obtained as shown in Figure 6, shows increase of major principal stress due to mining advance as well stress reduce to zero at cave zone. Induced stress is near to 3 times the in-situ major principal stress.

Figure 6 Development of Cave back and change in abutment stress, connection to Reservas Norte Mine

Pillars condition can be estimated, for example, with principal stresses, Security Factor or Convergence rate. Estimations of changing in pillar loading are shown in Figure 7. It can be noticed that pillars and crown pillars are subjected to a reduction ofconfinement stress process at boundaries, which can be seen in practice before support is applied, despite central section of the pillar still remaining at high confinement stress. This estimation can be related to rock burst problems if high stress and low security factor are founded at the same time, nevertheless it is not possible to calculate that probability yet.

5

Conclusions

Pilar Norte Mine, part of El Teniente Mine in Chile, have experienced several problems, such as, rock burst during preparation to mining process, making necessary a method to locate and quantify risk or hazardous zones. Numerical model of Pilar Norte Mine was developed with some restrictions; limited geological entry and calculation time, which do not allow an exhaustive modeling process. However, current numerical model is an improvement in order to reach that goal. Some of the hypothesis were tested and calibrated with field data. Within those hypothesis modeling or hydraulic fractures as an orthotropic media and caving propagation as an plastic strain conditions are some of the most important. These are yet to be proven hypothesis, thus there is a need of further research.

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Numerical Modelling

Figure 7 Pillars condition at Pilar Norte mining process

Results are consistent with theory and are an advance to design and identify hazardous zones and conditions like rock burst or seismic activity. Nevertheless, is not possible yet to determine exactly how or when those situations will occur. Thus, it is necessary to improve numerical and relate results to field data in order to get better estimations of failure in pillars and correlations to practical problems, such as, seismicity. Future works can include modeling propagations of fracture hydraulics and their interactions over the entire network. A numerical model or correlation in order to determine where induced seismic events are located is suggested too. Finally, it is necessary to develop a tool or constitutive model that allows to recognize in an improved way where and how hazardous situations can occur, because it is not possible yet to quantify risk.

Acknowledgement The authors want to thanks El Teniente Division, CODELCO, for the permission to publish this study, as well for the entire assistance in the development of the research.

References Bath, M 1966, ‘Earthquake energy and Magnitude’, Contributions in Geophysics: In honor of Beno Gutenberg, (M. Benioff, & B. Howell eds), New York: Pergamon Press. Karzoluvic, A 2006, Modelo conceptual de campo de esfuerzos en Mina El Teniente. Reporte Interno, Santiago. (in spanish) Vry, V, Wilkinson, J, Seguel, J & Millán, J 2010, ‘Multistage Intrusion, Brecciation, and Veining at El Teniente, Chile: Evolution of a Nested Porphyry Systems’, Economic Geology, pp. 119-153. Windsor, C, Cavieres, P, Villaescusa, E & Pereira, J 2006, ‘Reconciliation of strain structure and stress in the Teniente Mine’, International Symposium on In-Situ Rock Stress, (L. Ming, L. Charlie, K. Halvor, & D. Halgeir eds.), Trondheim, Norway: Taylor and Francis, pp. 533-540.

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Geomechanical evaluation of large excavations at the New Level Mine - El Teniente E Hormazabal SRK Consulting, Chile J Pereira Codelco,Chile G Barindelli, Codelco, Chile R Alvarez SRK Consulting, Chile

Abstract The New Level Mine is a 130.000 tpd panel caving project set to start in 2017 at the El Teniente mine. VP-NNM CODELCO (Vice-President Office of the New Level Mine) is currently finishing a detailed engineering design of the underground mine. The evaluation considers, the design of the crusher cavern Nº1 located in the Braden Pipe, which is a waste rock chimney located in the central part of the ore body. A geo-mechanical study has been carried out to evaluate the stability of the planned infrastructure and to provide recommendations about the design of underground caverns and galleries, including support. As part of this study, empirical methods, two-dimensional and three-dimensional continuum models have been developed and applied to evaluate the influence of the high stresses and different geotechnical units, on the mechanical response of the excavation. This paper introduces general aspects of the New Mine Level underground project and discusses in particular geo-mechanical analyses and design carried out to evaluate stability and support of some of the large excavations involved in the project.

1 Introduction El Teniente copper mine is located in the central part of Chile, Cachapoal Province, VI Region, about 50 km NE from Rancagua City and about 70 km S-SE from Santiago City (Figure 1). At the El Teniente mine, the copper and molybdenum mineralization occurs in andesites, diorites and hydrothermal breccias surrounding a pipe of hydrothermal breccias called Braden Pipe and located in the central part of the ore body. The Braden Pipe has the shape of an inverted cone, with a diameter of 1,200 m at surface and a vertical extent of more than 3000 m. The Braden breccias are waste rock. Therefore, the different productive sectors of El Teniente mine are surrounds the Braden Pipe, and the main infrastructure and access shafts are located inside the pipe (Pereira et al. 2003). The New Mine Level is a 130,000 tpd panel caving project set to start in 2017 at the El Teniente mine. The mining project considers using the panel caving method to mine copper ore. The Vice-President Office of the New Level Mine (VP NNM) has finished a detailed engineering evaluation of the project, which considers the construction and operation of several mining units to be operated independently from each other. Among the most important elements of the permanent mining infrastructure to be designed and constructed first are large crusher caverns, designated as SCh Nº 1, SCh Nº 2 and SCh Nº 3 caverns. These caverns are required to reduce the ore size from the operation mining sectors that will guarantee the continued operation for a period of 50 years or more. The objective of this paper is to present general aspects of the design of one of the crusher chambers (SCh Nº1 cavern), including the interpretation of geotechnical site investigation data and use of empirical, analytical and numerical methods to determine the appropriate permanent support to be considered for this cavern.

486

Numerical Modelling

Figure 1 El Teniente mine location in relation to Santiago and Rancagua cities in the central part of Chile

2

Geotechnical characterization

Until the early 90’s the Braden Pipe was considered an almost homogeneous body, composed by a concrete-like rock called Braden breccia and, in its perimeter, by a breccia containing coarser rock blocks, called Marginal Breccia (Pereira et al. 2003). However, the behavior observed at different sectors of the Braden Pipe indicated differences that could only be explained by the presence of different breccia types. Therefore, a comprehensive geological characterization of the Braden Breccia was developed in the past, which allowed a much more detailed zonation of the Braden Pipe and the definition of several breccia types (Floody 2000 & Karzulovic 2000). The main breccia types are the following: a)

Sericite Breccia – this breccia constitutes a majority of the pipe.

b)

Chlorite Breccia – found primarily in the southern portion of the pipe.

c)

Tourmaline Breccia – characterized by large clasts and vein-like occurrence.

d)

Marginal Breccia – hard breccia at the boundary of the pipe.

For each of these breccias, there is variability in the size of the fragments or clasts and in the mineral constituents and alteration of the matrix cement. In the Braden Sericite Breccia, there appears to be an effect of the ratio of Sericite/Quartz content in the cement to the compressive strength of rock samples. Figure 2 represents a plan view containing the location of crusher cavern Nº1 and showing the different geotechnical units as interpreted from the available geological and geotechnical information from the site. The main geotechnical units are the Sericite Braden Breccia unit (BBS), Chlorite Braden Breccia unit (BBC), Tourmaline Braden Breccia unit (BBT) and the Dacitic Porphyry unit (PDAC).

487


Figure 2 Plan view at mine level 1790 of the Crusher Chamber SCh Nº1 location, indicating the main geotechnical units as interpreted from available geotechnical information (taken from SRK, 2014)

In general, the BBS, BBC and BBT units are rock masses of good quality with a Bieniawski’s RMR value larger than 70; for details about the Bieniaswki’s classification system see Bieniaswki (1989). For example, Figure 3 shows a photograph of some representative cores of the main geotechnical units at the site location of SCh Nº1; solid and intact cores, few joints, low fracturing, a common characteristic of the BBS, BBC and BBT units which translates into good quality rock mass, can be observed in the photograph. As part of the geotechnical characterization, a database with geotechnical information from site investigations (geotechnical boreholes) at El Teniente Mine was analyzed; this database was created and is maintained by VP-NNM (VCP 2010a and VCP 2010b). In particular, values of geotechnical parameters describing the quality of the rock mass, including Fracture Frequency (FF), Rock Quality Designation (RQD), Intact Rock Strength (IRS) and Bieniawski’s Rock Mass Rating (RMRB). Based on geotechnical window mapping of drifts and galleries close to the site location of the SCh Nº1, a characterization of the rock mass quality in terms of the Geological Strength Index (GSI) and Barton’s Q-system values were revised (for details about these systems see, Hoek, 1994, Hoek & Brown 1997, Hoek et al. 2002; Barton et al. 1974; Grimstan and Barton 1993; Barton, 2002). The resulting range of these values, expected to be encountered during excavation of the SCh Nº1, is shown in Table 1.

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Numerical Modelling

a)

b)

c)

d)

Figure 3 Cores of the main geotechnical units at the site location of the SCh Nº1.a) BBS. b) BBC. c) BBT and d) PDAC

From a structural geology point of view, the site where the crusher cavern will be emplaced has been referred to as ‘Brecha Braden Marginal’ (or ‘Braden Breccia Marginal Structural Domain’). Analysis of the available geological information has revealed the existence of three systems of minor faults and two joints sets. Table 2 summarizes the orientation of these structural systems. The in-situ stress state considered for the design of the crusher cavern SCh Nº 1 was obtained from overcoring tests performed at XC-01-AS site Nº 5 (undercutting level 1880). Table 3 summarizes the in-situ stress field at crusher cavern location. Values of strength and deformability for all the geotechnical units were computed according to the generalized Hoek-Brown failure criterion (Hoek et al. 2002; Hoek & Diederichs, 2006) and following some specific recommendations to the El Teniente mine by Diederichs (2013). The mechanical parameters were derived from laboratory unconfined, triaxial and tensile testing of rock samples and estimations of values of Geological Strength Index from geotechnical window mapping in the main access tunnel (TAP), drifts and galleries next to the SCh Nº1 location.

489

Caving 2014, Santiago, Chile Table 1 Classification systems values of the rock mass at the SCh Nº1 location

UGTB

RQD (%)

RMRB89

Q’

GSI

BBS

70 – 100 (80)

60 – 92 (72)

1.2 – 250 (14)

56 – 90 (69)

BBC

94 – 100 (98)

70 – 85 (77)

40 – 100 (70)

63 – 82 (72)

BBT PDAC

80 – 100 (90) 79 – 100 (89)

( ): Mean values.

72 – 82 (75) N/I

Q’: modified Barton’s Q-system (Jw/SRF = 1).

RMRB89: Rock Mass Classification system (Bieniawski ,1989).

5 – 71 (23) N/I

61 – 80 (73) 65 – 86 (72)

RQD: Rock Quality Designation (Deere, 1963).

GSI: Geological Strength Index (Hoek ,1994). N/I: No available information.

Table 2 Structures at the site location of the SCh Nº1 (VCP, 2010b) Minor Faults

SETS

Joints

Dip / DipDir

Nº data

Dip / DipDir

Nº data

S1

84° / 125°

12

75° / 324°

34

S2

83° / 035°

7

35° / 010°

21

S3

76° / 172°

6

Table 3 In situ stress field representative of the site location of the SCh Nº1

Principal Stresses

Magnitud (MPa)

Bearing (°)

Plunge (°)

σ1

50.73

344.0

-7.8

σ2 σ3

33.11 26.50

75.5 218.6

-10.7 -76.7

Table 3 summarizes the mechanical parameters for the rock mass, for the three geotechnical units analyzed with the Hoek-Brown method. [In Table 4, mi is the Hoek-Brown intact rock parameter; σci is unconfined compressive strength of the intact rock; γ is the specific gravity of the intact rock; Ei is the modulus of deformation of the intact rock; GSI is the Geological Strength Index; mb, s and a are Hoek-Brown rock mass parameters; and ERM and ν are the deformation modulus and Poisson’s ratio of the rock mass, respectively. To calibrate and validate the stress field and rock mass properties some back-analyses were done to check if the behavior predicted using these properties agrees with the observed behavior. Two-dimensional planestrain models were constructed for different sections with different geotechnical units and orientations, involving sections for which overbreak were measured. The models were developed using the finite element software Phase2 (Rocscience 2009), which allows analysis of excavations in plane-strain conditions. Figure 5 shows the results from a finite element back-analysis of one of the sectors considered for the TAP tunnel in Chlorite Braden Breccia unit. The light gray zone in the roof indicates failure by tension and/or yielding, and the black curve shows the measured overbreak each 5 m along the tunnel axis in this particular sector. Different tunnel orientations within the same geotechnical unit were considered for this analysis.

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Numerical Modelling These results indicate that the geomechanical properties of the different type of breccias presented in Table 3 are a good estimate of the rock mass properties for these types of massive rock. Table 4. Summary of rock mass strength and deformability parameters for the different geotechnical units according to the generalized Hoek-Brown method —see Hoek et al., 2002; Hoek & Diederichs, 2006.

UGTB

γ

GSI

σci

(KN/m3) Mean value (MPa)

mi

BBS

25.9

70

81.1

11.0

BBC

26.6

72

77.4

12.0

BBT

25.4

70

100.0

8.0

PDAC

25.8

73

144.5

28.5

σt

Em

(MPa)

(GPa)

0,768

0,384* 0,782

0,391* 1,302 0,651* 0,662

0,331*

v

29.31

0,20

25.60

0,20

23.01

0,20

34.55

0,20

c

φ

(kPa)

(°)

7,336

34

5,180*

33*

5,350*

34*

7,448

33

5,260

32*

12,078

48

7,578

8,500

35

43*

(*) Ubiquitous properties considers Jennings (1970) criterion with a k = 0.3.

3

Support requirements for the crusher cavern according to empirical methods

Figure 6 shows an isometric view for the crusher cavern that considers mainly the dumping chamber, apron feeder, crusher chamber, main silo, main feeder and lift. Based on the large experience of excavation of tunnels and caverns in different rock units at El Teniente mine, using the traditional method of full face blasting an appropriate (temporary) support consisting in rockbolts, steel wire mesh and shotcrete were proposed for the cavern ( SGM-I-011/2006, VCP, 2010c, among others). A preliminary estimation of the quantity of permanent support to use during excavation was done using empirical methods. The methods considered were those described by Barton (1974), Palmström & Nilsen (2000), Unal (1983), Hoek (2007) and Hönish (1985), among others. These methods give guidelines for permanent support requirement based on several of the geotechnical indexes discussed earlier on, such as values of RQD, Q and RMR. Table 5 summarizes the characteristics of the recommended support for SCh Nº1 according to the above mentioned methods. Due to the intrinsic limitations of the empirical methods (particularly in regard to the assumption of isotropy of stresses and rock mass continuity), these methods were used as a first step in selecting a support type for the SCH Nº1; the actual verification of the proposed support was carried out using tri-dimensional numerical models as described in the next sections, which among others, allowed incorporation of several geotechnical units existing in the rock mass and in situ stress field showed in Table 3. The acceptability criterion for permanent support was established based on factors of safety with respect to failure (in compression) of the support. Based on types of supports used and suggested length spans from empirical methods, factor of safety of 2.0 for permanent support (for static loading and dry ground) were judged appropriate. In this regard, a literature survey did not reveal the existence of established rules for factors of safety to consider for cavern of large dimensions (as the case of the SCh Nº1). For example, Hoek

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Caving 2014, Santiago, Chile (2007), suggest an acceptable design is achieved when numerical models indicate that the extent of failure has been controlled by installed support, that the support is not overstressed and that the displacements in the rock mass stabilize. Pariseau (2007) suggests that the load acting on the support for large excavation should not exceed half the value of the strength of the support material of (shotcrete or concrete) —i.e., this would mean considering a factor of safety of at least 2. For wedge and blocks failures in a large cavern design a factor of safety of 1.5 to 2.0 is commonly used as acceptability criteria (Hoek, 2007).

Figure 4 Results from a finite element back-analysis of one of the sectors considered for the TAP tunnel in BBT unit. The light gray zone surrounding the tunnel section indicates failure by tension and/or shear, and the blue curves show the measured overbreak each 5 m along the tunnel axis in this particular sector

Figure 5 Infrastructure considered for the geomechanical analysis in relation with the main geotechnical units

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Numerical Modelling Table 5. Summary of preliminary permanent support recommended for the SCh Nº1 as derived from application of empirical methods. Barton (1974) Excavation

B×H (m)

Pattern

Sector BBS

Dumping Chamber

24,3×8,8

Roof Walls

Storage Hooper

14,3×21,2 Walls

Apron Feeder

9,2×10,8

Crusher Chamber

16,8×43,6

Loading Hooper

17,0

Roof Walls Roof Walls Walls

BBC

1,3 x 1,3 to 1,7 x 1,7 to 1,7 x 1,7 m; 2,1 x 2,1 m; Shotcrete Shotcrete 120 - 150 mm 50 - 120 mm 1,3 x 1,3 to 1,7 x 1,7 m; Shotcrete 120 - 150 mm 1,3 x 1,3 to 1,7 x 1,7 m; Shotcrete 90 - 120 mm 1,3 x 1,3 to 1,7 x 1,7 m; Shotcrete 150 - 250 mm 1,3 x 1,3 to 1,7 x 1,7 m; Shotcrete 90 - 150 mm

1,7 x 1,7 to 2,1 x 2,1 m; Shotcrete 50 - 90 mm 1,7 x 1,7 to 2,1 x 2,1 m; Shotcrete 40 - 90 mm 1,7 x 1,7 to 2,1 x 2,1 m; Shotcrete 90 - 120 mm 1,7 x 1,7 to 2,1 x 2,1 m; Shotcrete 50 - 90 mm

Palmstrom & Nilsen (2000)

Hoek (2007)

Lb (m)

Lb / Lc (m)

7.5 – 8.1

5.8

5.6 / 9.7

2.4 – 2.6

4.4

N/A

5.7 – 6.2

4.0

5.2 / 7.4

2.8 – 3.1

3.2

N/A

2.9 – 3.2

3.0

3.6 / 3.8

5.2 – 5.6

4.4

4.5 / 6.7

11.7 – 12.7

5.3

8.5 / 15.3

5.2 – 5.7

4.6

4.6 / 6.8

Lc (m)

Section Height. Lb:

Unal (1983)

Hönisch (1985)

Lc (m)

Shotcrete Thickness (mm)

BBS

BBC

4.1 – 14.2 6.3 – 11.2

3.8 – 12.5 6.0 – 9.8

2.1 – 6.2

2.8 – 5.0

N/A

N/A

3.1 – 10.0 4.5 – 7.9

Bolt Length.

Lc:

BBS

BBC

100 - 150 100 a 150 50 (min)

50 (min)

50 - 150

50 - 100

50 (min)

50 (min)

50 - 100

50 (min)

50 - 150

50 - 100

150 - 200 150 - 200 50 - 150

50 - 100

B:

Section Length. H:

Cable Length.

4

Three-dimensional numerical analysis of the crusher cavern excavation

Three-dimensional models implemented in the finite difference software FLAC3D (Itasca 2007) were constructed for the main infrastructure of the SCh Nº1 (see Figure 6). The three-dimensional models incorporated only the permanent support (with characteristics described in the next section) and the proposed excavation advance, coinciding with the mining design excavation. The purpose of this model was to account for the actual three-dimensional nature of the excavation problem; the model allowed wall displacements on the large excavation, extent of the plastic-failure zone around the walls of the large excavations, and the performance of the permanent support to be quantified —i.e., the verification of the acceptability criteria in terms of factor of safety described in Section 3. In general, major principal stress (s1) reaches 60 to 80 MPa in the upper part of crusher chamber and apron feeder (see Figure 7a). Unconfined stress (s3 < 4.0 MPa) are observed below of the floor of the dumping chamber (see Figure 7b). Also, a maximum displacement of 4 cm is observed in the floor dumping chamber after the excavation of the crusher chamber (see Figure 7c). Maximum displacements of 5 cm are observed in the intersection of the crusher chamber walls and apron feeder and intersection of loading hooper and main feeder (see Figure 7d).

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Figure 6 Three-dimensional numerical model of the crusher cavern. The figure shows the 93 advance intervals considered for the excavation in different colors. The model, which incorporates only permanent support, was constructed using the finite difference code FLAC3D —see Itasca (2007)

Analysis of results from these three-dimensional models allowed to conclude that the support (with characteristics described in the next section) satisfies the acceptability criterion —i.e., a factor of safety of 2.0 for permanent support. Figure 8a and 8b shown the results for the double cables installed in the roof of the crusher chamber and the final excavation of the model. The values of loads resulting in permanent liners (i.e., the values of thrust, bending moment and shear force) were recorded for each of the large excavations analyzed. The values of support loading were plotted in capacity diagrams to verify that the factor of safety values were below admissible limits —for a discussion on the methodology involving verification of support using capacity diagrams, see Hoek et al. (2008); Carranza-Torres & Diederichs (2009). For example, Figure 8c represents capacity diagrams for a permanent support of thickness 0.3 m in the apron feeder roof for the final excavation of the model. In basically all the large excavations, loading in the proposed support analyzed with the capacity diagram approach was found to be within the admissible limits of factor of safety mentioned earlier on.

494

Numerical Modelling Finally, to verify the support recommended, a wedge/block analysis was performed based on the structural information provided in Table 2 using keyblock teory (Goodman & Shi, 1985) and the software Unwegde (Rocscience 2009). Figure 9 shows the application of key block theory to the dumping chamber roof. All the keyblocks in the roofs and walls for all the large excavations were verified.

a)

b)

c)

d)

Figure 7 Representation of the results in the model sliced by a cross section plane located at the midpoint of the apron feeder. Represented are: a) major principal stresses after crusher chamber excavation, b) minor principal stresses after crusher chamber excavation. c) displacements after crusher chamber excavation and d) displacements for the final excavation model

495


a)

b)

c) Figure 8 Support performance for some of the main large excavations. a) Axial force for cables in the crusher chamber roof at the end of excavation. b) Resulting axial force for cables installed in the crusher chamber at the end of excavation (yielding load, pre-stressing load and factors of safety of 1.5 and 2.0 also are shown). c) Capacity diagrams for shotcrete liner in apron feeder at the end of excavation

496

Numerical Modelling

Figure 9 Dumping chamber section showing maximum removable blocks for each JP superimposed on the stereographic projection of the JPs. To the upper left, the analysis for the roof with Unwedge program to verify the support recommendations for the JP 1011 block (shaded in red)

5

Proposed crusher cavern support

Based on experience in design of large excavations support and on the application of empirical, analytical and numerical models described in previous sections, for the large excavations crossing the good quality rock mass units (BBS, BBC and BBT units), permanent support with the characteristics summarized in Table 6 were proposed. The temporary support consists mainly of rock bolts (and wire mesh) with quite uniform characteristics for most of the large excavations. For the large excavations (dumping chamber, storage hooper, crusher chamber and apron feeder), in which high stress confinement in the rock mass could translate into ground instability, heavier permanent support proposed. Table 6 Summary of permanent support proposed for the Crusher Cavern SCh Nº1 Excavation

B (m)

H (m)

Dumping Chamber

24,3

8,8

Storage Hooper

14,3

21,2

Apron Feeder

9,2

10,8

Crusher Chamber

16,8

43,6

Sector

Pattern

Cables*

Length (m)

Shotcrete

Roof

1,0 x 1,0

10

Walls

2,0 x 2,0

8

Walls

1,5 x 1,5

14

Roof

1,0 x 1,0

14

H30 t = 200 mm

Roof

1,0 x 1,0

12

15 15

H30 t = 300 mm

Walls Walls

1,5 x 1,5

1,5 x 1,5

H30 t = 300 mm H30 t = 150 mm

Loading H30 17 Walls 1,5 x 1,5 12 Hooper t = 200 mm B: Section Length. H: Section Height. (*) All the cables are doubles single strand of f = 15.6 mm, additionally a steel wire mesh C443 was recommended.

497

Caving 2014, Santiago, Chile 6 Conclusions This paper has described several aspects of the process of determining the permanent support for the large crusher cavern SCh Nº1 at the New Mine Level project at El Teniente mine. The crusher cavern is to be excavated in a rock mass of generally good quality (BBS, BBC and BBT units), in a medium to high stress environment. The support recommended for crusher cavern, as described in this paper is not definitive and will have to be optimized once construction techniques are selected in a future phase of design of the underground infrastructure. The characteristics of the support recommended for the crusher cavern are based on the assumption of the rock mass is dry and that dynamic loading on permanent liner (e.g., due to blasting during future caving operations) is neglected. Also, a sensitivity analysis for Hoek-Browm parameters, ubiquitous model and an increment of the in situ stress was considered and the proposed support was found to be within the admissible limits of factor of safety mentioned earlier on. In terms of permanent support, considering the critical importance of continuous operation of the crusher cavern for at least 50 years, a permanent concrete liner of at least 0.3 meters thickness was judged appropriate. This permanent support thickness was established based on current practice used in civil engineering tunnel projects, and not based on the empirical methods described above.

Acknowledgements The authors would like to thank CODELCO and in particular, Mr. Pablo Vasquez Chief of the Engineering Department of VP-NNM Project, for granting permission to publish this paper.

References Barton, N, Lien, R & Lunde, J 1974, Engineering classification of rock masses for the design of tunnel support. 6(4), 189–236. Barton, N 2002, ‘Some new Q-value correlations to assist in site characterization and tunnel design’, Int. J. Rock Mech. & Min. Sci., vol. 39, Nº2, pp. 185-216. Bieniawski, ZT 1989, Engineering Rock Mass Classifications, JohnWiley & Sons. Bieniawski, ZT 1993, ‘Classification of Rock Masses for Engineering: The RMR System and Future Trends’, Comprehensive Rock Engineering, (J. A. Hudson Ed.), vol. 3, pp. 553–573. Pergamon Press, Oxford. Carranza-Torres, C & Diederichs, M 2009, ‘Mechanical analysis of a circular liner with particular reference to composite supports. For example, liners consisting of shotcrete and steel set’, Tunnelling and Underground Space Technology, vol. 24, Nº 4, pp. 506–532. Deere, DU 1963, ‘Technical description of rock cores for engineering purposes’, Rock Mech. Eng. Geol., vol. 1, pp. 18-22. Diederichs M 2013b, ‘Summary Report of Findings and Recommendations Based on NNM Technical Advisory Meetings El Teniente New Mine Tunnel Project’, 21- 25 October 2013. Floody, R 2000, ‘Estudio de Vulnerabilidad Geológico-Geotécnica de Chimenea de Brecha Braden. Fase I: Geología Complejo de Brechas Braden’, Report GL-044/00, Superintendence of Geology, Division El Teniente, Codelco.

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Numerical Modelling Goodman, R & Shi, GH 1985, ‘Block theory and its application to rock engineering’, Prentice Hall. USA. Grimstan, E & Barton 1993, ‘Updating the Q-system for NMT’, Proceedings Int. Symp. On sprayed concrete – Modern Use of Wet Mix Sprayed Concrete for Underground Support, Fagemes, (Kompen, Opsahl and Berg eds), Oslo: Norwegian Concrete Assn. Hoek, E 1994, ‘Strength of rock and rock masses’, ISRM News Journal, vol. 2, Nº 2, pp. 4-16. Hoek, E & Brown, ET 1997, ‘Practical estimates of rock mass strength’, International Journal of Rock Mechanics and Mining Sciences, vol. 34, Nº 8, pp.1165–1186. Hoek, E, Carranza-Torres, C & Corkum, B 2002, ‘Hoek-Brown failure criterion – 2002 edition’, NARMSTAC 2002, Mining Innovation and Technology, (H. R.,W. Bawden, J. Curran, & M. Telesnicki Eds.), Toronto – 10 July 2002, pp. 267–273. University of Toronto. (Available for downloading at Hoek’s Corner, www.rocscience.com). Hoek, E & Diederichs, MS 2006, ‘Empirical estimation of rock mass modulus’, International Journal of Rock Mechanics and Mining Sciences, vol. 43, Nº2, pp. 203–215. Hoek, E. Kaiser, PK & Bawden, WF 1995, Support of Underground Excavations in Hard Rock. Rotterdam: Balkema. Hoek, E 2007, Practical Rock Engineering, course notes available on line at http://www.rocscience.com. Hönisch, K 1988, ‘Rock mass modelling for large underground powerhouses’, Numerical Methods in Geomechanics, Edited by G. Swodoba, Innsbruck, Austria, vol. 3, A. Balkema, Rotterdam. Itasca 2007, FLAC3D. Fast Lagrangian Analysis of Continua. Version 3.1. User’s manual. (www.itascacg. com). Minneapolis, Minnesota. Jennings JE 1970, A mathematical theory for the calculation of the stability of slopes in open cast mines. Planning Open Pit mines. Proceedings of International Symposium (ed. PWJ Van Rensburg), Johannesburg, pp. 87-102. Balkema, Cape Town. Karzulovic, A 2000, Estimación de las propiedades geomecanicas de las brechas que conforman la pipa Braden, Technical Note Nº DT - CG - 00 – 04 A. Karzulovic & Asoc. Ltda. Chile, submited to Division El Teniente, Codelco. Palmström, A & Nilsen, B 2000, Engineering Geology and Rock Engineering Handbook. Norwegian Rock and Soil Engineering Association. Pariseau, W 2007, Design analysis in rock mechanics, Taylor & Francis / Balkema. Pereira, J, Russo, A & Karzulovic, A 2003, ‘Geomechanical Properties of the Braden Breccias at El Teniente Mine, Chile’, Soil and Rock America 2003. 12th Panamerican Conference on Soil Mechanics and Geothecnical Engineering. 39th U.S. Rock Mechanics Symposium. Cambridge, EEUU. 22-26 june 2003, pp 723-727. Rocscience 2009, Unwedge. Underground Wedge Stability Analysis, Version 3.0, Toronto, Canada. Rocscience 2010, Phase 2, Finite Element Analysis for Excavations, Version 7.0, Toronto, Canada. SGM-I-011/2006, Definición de Estándares de Calidad para Elementos de Fortificación y Soporte, Internal Report. (in spanish)

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Caving 2014, Santiago, Chile SRK 2014, ‘Análisis de Secuencia Constructiva y Diseño de Soporte Sala de Chancado Nº1’, Technical report submitted to VP- NNM Codelco, Abril. (in spanish) Unal, E 1983, Design guidelines and roof control standards for coal mine roofs. PhD Thesis, Pennsylvania State University. VCP 2010a, ‘Análisis Geomecánico Caverna de Chancado’, Technical report T09E205-F1-VCPNNM36000-INFGE04-3100-001-P. Feasibility Stage NLM project, Codelco. (in spanish) VCP 2010b, ‘Caracterización geológica y geotécnica Sala de Chancado N° 1 - Fase II’, T09E205-F1VCPNNM-36000-INFGO04-3100-002-P. Feasibility Stage NLM project, Codelco. (in spanish) VCP 2010c, ‘Validación Diseño de Cavernas’, Technical report T09E205-F1-VCPNNM-36000NOTGE04-3110-002. (in spanish)

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Design of 3-D models in mining E Córdova Codelco, Chile P González, Codelco, Chile C Pardo Codelco, Chile

Abstract The importance of planning and designing and optimized model form its conception, has the advantage that the model is developed and thought from the beginning to be interchangeable between software used in the analysis, making the model transfers easier among the different applications (AutoCAD, Vulcan, 3D Studio Max, Mine2-4D, Abaqus, etc.), while minimizing the information re-interpretation time. The process of learning and understanding the adaptability of the different applications must consider an initial trial period to verify the interactions between them. The future of modeling is in being able to develop an interactive unified model that can easily be adapted and transferred, maintaining an acceptable resolution for the different types of analyses required. In the search of optimizing the creation of models a PLM (Product Lifecycle Management) philosophy can be adopted and modified to establish a MLM (Modeling Lifecycle Management) philosophy that can assure that the different models created are related between them, having parent models that serve as a foundation to create detailed models (child models).

1 Introduction A model can be thought as a representation of reality, that could vary from a very simple and basic model to a detailed and complex one. As the required detail increases and more characteristics from reality are needed as part of the model, the complexity and time required to develop it also increases substantially. The knowledge of the different software to be used in the analyses and the way the model is conceptualized from the beginning can play an important role in the final efficiency of the modeling process. Since the same model might be used with different applications that sometimes do not work seamlessly with each other, it is important to spend time figuring out what is the best way to develop a model and what is also the best technique to transfer the work from one application to the other. A robust model is built from the beginning by understanding the pros and cons of a model, the way the information is transferred between applications, the changes required to make an available model work when is sent to a different application, while optimizing the modeling process by avoiding the duplication of work. A unified model should have an inherent combination of complexity and simplicity where the result comes from transforming something detailed into something simple that captures the most important aspects from reality while simplifying the parts that might not be needed in the analyses. As an example, depending on the resolution of the problem being analyzed, a tunnel might be a simplified regular shape (like a square of 4 m on each side) or a more complex primitive, with a square shape at the bottom, and the upper side curved with a series of points that really represent the shape the tunnel will have in the end, a more refined approach to this detail would be to have actual laser scans of the tunnel joined together to simulate the real shape at a certain interval of meters. As the shapes get more and more complex, usually the number of nodes or points involved also increases, this produces an increase in the number of triangles created to form a triangulation with the 3-D information, and if the same volume being developed, is used in a finite elements application, the number of elements also increases.

501

Caving 2014, Santiago, Chile The best approach is to plan ahead and understand the final purpose of the model.

2

Model conception

Models usually start on paper and are first represented in two dimensional views (2-D) that are built to develop a simple representation on of the main aspects of the final design.

Figure 1 Representative vertical section of a Crinkle-Cut mining method combined with conventional undercut

Figure 2 North-South Section of a Crinkle-Cut method and the connection to an existing cave

Figure 3 Plan view of the production level

The first task to build the model is to take what is in 2-D and use it to build a 3-D representation of it. The “basic model” can consist of the main general areas to model in detail just to provide a feeling of how everything should look in 3-D in the end. To build the first model different software packages can be used, from the most common commercial package such as AutoCAD (with increased 3-D modeling tools

502

Numerical Modelling in its latest versions), to more advanced and specialized mining packages such as Datamine, Gemcom, Minesight, or Vulcan. A plan view of the different areas to model (production and undercut level) is useful to define the main extents from the model, and to visualize the information that sections in 2-D can´t capture such as spacing between the developments, and shape of the undercut and production level.

3

Three dimensional modeling

Before starting to develop the 2-D information into a 3-D model, it must be decided the end result that is required for the model. Depending on the final result needed, the conceptualization of the model will change. A robust model will try to combine and plan for different options and future requirements, taking into account that if time permits it, it is much easier to rebuild a simpler model from a more detailed one than the other way around. Building a general model of the area as a visual model is a good practise that will provide valuable information on where to focus when building a model with more details. 3.1

Visualization models

Visual models can be thought of as a model that is built to place it in 3-D where everything can be visualized to give an impression of how it looks in reality. These models are usually focalized in the external detail of the geometries and based in achieving an optimal external look. The focus on the external looks sometimes means that not enough care is taken to obtain consistent geometries and solids that can be easily transferred without errors.

Figure 4 Isometric view of an Autocad model of the Crinkle Cut and conventional area

3.2

Time dependent models (TDM)

These models might be a variant of the visual models and are different in the sense that geometries and solids are cut or sectioned at certain time intervals. As an example in a model at a monthly resolution, one

503

Caving 2014, Santiago, Chile development as a drift could consist of different pieces that represent the monthly advance. This modelling approach is usually used to check if considerations of the mining method used are being correctly followed, to see if shown delays are related to other activities, and to understand a project evolves over time. In a TDM all the activities are separated and then each activity is sectioned on a monthly basis, allowing the analysis of individual activities or a group of them under a certain time frame.

Figure 5 Time dependent model of the area at monthly resolution

3.3

Design Models (DM)

These models are based in achieving a reasonable overall geometry that will represent the main aspects of the area under study. The end use of these models are engineering and modelling applications like Boundary Element (BEM) or Finite Element (FEM), where the quality of the geometries and volumes play an important role in the expected result. The creation of these models should take into account the following aspects:

• Type of geometries and its complexity: should laser scanned topography be used to analyse developments that lie 400 m below into the ground or most

• Solids definition: solids should comply to certain standards to ensure the best compatibility between applications. Solids should at least be closed (having all its triangles connected creating a closed shell), consistent (making sure there are no overlapping triangles or one edge connected to more than two triangles), and without crossing triangles.

• Contacts: for most engineering applications the contact between different solids must be consistent to avoid having one solid overlapping in the space with another body. Special care must be taken when doing “boolean” operations within two geometries to make sure that the original geometries and the boolean result complies with the basic solid geometry quality.

• Intersections: creating clean intersections it is very important to make sure volumes are not counted twice in space and to make sure errors are not present when building elements inside the sold geometry.

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Numerical Modelling

Figure 6 Crinkle-Cut geometries to be used in a FEM model

4

Unified models

The unified model presented in Figure 6 was built with a specialized mining package. The main reason for using a this tool was that all the general information specially geological and structural was being already developed with it, so it was only natural to use the underground developing tools already available in the application. A few solid geometries were imported from other CAD applications, but they were only used as a base for building a final geometry within the mining tool. The main reason to rebuild the geometries was to make sure that the new volumes would fully comply with the mining package definitions for consistent geometries (closed, not crossing, edge compliance). The main concern at this stage is to make sure not to go into great details if these details are going to be actually lost in the end. A good way to see if the modeling will be effective is to compare the original model resolution to the required resolution in the future analyses, as an example a model can have the greatest detail in the intersections and the development sections could be perfectly shaped to correspond as much as possible with reality, but if in the end in the FEM model the smallest element will be of one or two meters (because the analysis is not centered in the developments), all this detail will be lost and similar results could be achieved with simpler shapes (Figures 7 and 8). As the modeling progresses and more detail are needed, “child” models can be created using the original simplified “parent” models. This technique builds a “family” of models at different resolutions depending on their simplified predecessors.

5

Model application

The first model generated was focused on explaining operational challenges encountered in the area by the LHD operator and the removal of material from the flat and inclined undercut in the area where the CrinkleCut method was tested (Figure 9).

505


Figure 7 Detailed modeling of crossing intersections

Figure 8 Detailed modeling of crossing intersections

Figure 9 Side view showing the LHD position with respect to the flat (red) and inclined (yellow) undercut

506

Numerical Modelling A line of drawbells in the west area was isolated to show the use a drawbell with the Henderson layout, to assure the connection to the existing cave from the South area (Figure 10).

Figure 10 NS Section showing the different drawbells used in the west area of the model

The different types of crown pillars (CP) associated to the Conventional and Crinkle-Cut undercut were modelled in 3-D to establish the approximate volume of CP left by each variant.

Figure 11 Different drawbells and crown pillars used in the model

The model was also used to show the position of the undercut with respect to the drawbell incorporation at certain times (Figure 12).

Figure 12 Position of the undercut front v/s drawbell incorporation at a certain period in a time dependent model

507


Finite element models

When building solids for numerical models, special care must be taken to ensure the model being created is very consistent in the areas where the different pieces are touching each other. The essence of the finite element model is that the solids will be filled by smaller elements, such as tetrahedrons, and they will be all interconnected between them through the nodes in the elements. In complex areas where boolean operations (difference or intersection between solids) took place, a thin layer of the solids will create complex elements that might have a near zero volume, or very long edges that will create distorted minor elements, sometimes increasing the overall number of elements in the model. Nodes between two different solids must connect with each other, aligning the elements between them. In finite element applications, a rule that might come out very often is that “you get what you pay for”, depending on the specific task on hand, if a very complex mesh must be built for the model, sometimes a pre-processor must be used to create such a mesh. The time spent developing a model and meshing it all together might take sometimes 50% or more of effort to do an analysis. The model in 3-D can be simplified depending on the scale of things that are needed to analyze. If a model is built to analyze the stability of 20 m benches in an open-pit, a small scale of element of 10 cm in the near surface of areas that are not of interest to the analyses will create millions of elements that will only slow down all the calculations. The resolution must be increased always thinking about the problem and accuracy needed on the results, while taking into account not to crowd the model in areas where no detail is needed. The number of elements can also be managed on the internal growth of each solid, this means that a solid can have a certain size of elements on the surface, and as more elements are created inside of the solid, and get away from the surface, they start increasing in size to optimize the overall number of elements in the model.

7

Finite element model characteristics

The model developed consists of an area of 5.3x5.3 km, and a total elevation of around 3 km (Figure 13) The solids inside of the model represent a central pipe of a different material than the host rock, and a subsidence cave surrounding the pipe (Figure 14). The main lithologies added to the model besides the base rock of the model are dacite, tonalite, and four separate diorite bodies (Figure 15), where the dacite surrounds part of the pipe (that appears translucent in Figure 15), and a tonalite on the south side.

Figure 13 View of the area modeled in Abaqus

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Numerical Modelling

Figure 14 View of the subsidence cave and the central pipe

Figure 15 Top view of the main lithology of the model

A central diorite intersects the place where the mine design to be studied is placed, the mine design takes an approximate area of 250x250 m (Figure 16).

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Figure 16 Top view of where the mine development is located intersected by a central diorite

The mine developments built into the model represent a production level with connections for the drawbells, an undercut level (UCL) with a crinkle-cut undercut design, an apex level on top of the UCL, and a series of drawbells between the production and UCL level.

Figure 17 View of the mine design elements of the model

8 Conclusions Planning a robust model allows the use of the same base model to generate the needed geometries for the different analyses required (general visualization, time dependent, numerical modeling, back-analysis). Models must be centralized and developed having in mind the required resolution for the different submodels that might be generated from the base model. A robust model will capture the essential details in the geometries while optimizing them for ease of translation form one analysis package to the other, without losing the essential characteristics in them.

510

Numerical Modelling The generation of a base model using one design package will serve to minimize the uncertainty generated when developing models from different sources, and optimize the time required to create sub models used in different analyses. Finite element modeling can be a time consuming task especially if the geometries are complex and not optimized to speed up the modeling process. An optimized set of geometries can be almost automatically meshed without running into any kind of trouble, allowing the modeler to focus in other tasks of the process, and reaching the results in a shorter time. A modeler must always think on how the solids that are being created will interact in the future, taking special care on what is being simplified or designed at a higher resolution, also preserving the integrity of the original data in case the model needs to be updated and re-created in the future. The modeling process will be optimized in the future by having models that can be traceable and linked between them, so changes are updated automatically between the solids and their interacting meshes. The future modeling philosophy is based in the correct management of the life of the models, where the modeling process is divided into a coherent structure where the interactions between the elements and their properties are well defined. The idea is to apply what other companies (aerospace and automotive) already use and have learned to build complex models with thousands of elements interacting between them, and to expand this philosophy to mine models. In the end, the main difference between the aerospace and automotive models and the ones being developed in mining is the size of the elements being created, where most of the time in mining, large global models ranging from kilometers to meters are developed first, and sub-models are done in a much more detailed scale (cm) for very specific areas or analyses. The modeling philosophy allows the rapid development of different finite element analyses to understand and study problems such as macro-sequences definition, undercutting geometries effect, undercut advance, crown-pillars, and expected stress distribution from a range of undercutting designs.

References Beck, D 2012, ‘Applications of Rock Mechanics’, Geotechnical Engineering Centre Presentation, The University of Queensland, September 2012. Córdova, EA, Constanzo, HE 2013, ‘Optimized Design of Models in Mining’, Mine Planning 2013 Conference, 24-26 July, Santiago, Chile. Córdova, EA 2012, ‘3-D Modelling of the Crinkle-Cut test in TTE4 South Extension Area’, SIN-I-005/2012, Internal Report, Division El Teniente, Codelco, Chile.

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Mine Operations

Preconditioning

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Preconditioning

Study of the impact of rock mass preconditioning on a Block Caving Mine operation C Castro IM2-Codelco, Chile F Báez Codelco, Chile E Arancibia Codelco, Chile V Barrera, Im2-Codelco, Chile

Abstract Nowadays, Codelco´s underground mines that apply caving methods are located mainly within primary ore. This extremely hard environment needs the application of rock mass preconditioning techniques (PC), to improve its caveability and the extraction process. To achieve this objective, two main technologies are applied to the rock mass: hydraulic fracturing (HF) and confined blasting (CB). In this work, six variations of the preconditioning technologies are simulated to study their effects on the mine operation: HF with a distance of 0.5 m between fractures, HF with 0.75 m between fractures, HF with 1.0 m between fractures, HF with 1.5 m between fractures and two applications of both techniques (HF + CB): HF with 1.0 m between fractures + CB and HF with 1.5 m between fractures + CB. The effect of each variation is simulated for the predicted secondary fragmentation, hang-ups and over sizes at the draw points, productivity and operation costs. The results are compared with a base case without preconditioning.

1 Introduction Since 1999, Codelco has been developing preconditioning techniques (PC) for the primary ore of its underground mines, Andina, Salvador and El Teniente. Two main technologies have been applied to the rock mass: hydraulic fracturing (HF) and confined blasting (CB) or combinations of both. The conclusion obtained is that there are benefits in terms of the seismic magnitude and frequency, caveability, draw rate, fragmentation, hang-ups and oversize occurrences at the draw points. However, the final fragmentation is not optimal, thus Codelco decided to begin a research effort to determine how to improve the current preconditioning techniques to obtain a better fragmentation at the draw points. The main objectives for this study are:

• To perform a comparative assessment of the preconditioning improved techniques for the fragmentation, flow interruption events frequency, productivity and operating costs.

• To develop a tool to make technical/economical comparisons in different scenarios for the application of the PC-improved techniques.

2 Methodology Six variation of the preconditioning technologies are simulated to study their effects on the mine operation: HF with a distance of 0.5 m between fractures (hereinafter, HF 0.5), HF with 0.75 m between fractures (HF 0.75), HF with 1.0 m between fractures (HF 1.0), HF with 1.5 m between fractures (HF 1.5) and two

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Caving 2014, Santiago, Chile applications of both techniques: HF with 1.0 m between fractures + CB (Mix 1.0) and HF with 1.5 m between fractures + CB (Mix 1.5). The effect of each variation is simulated for the predicted secondary fragmentation, hang-ups frequency and oversize at the draw points, productivity and operating costs. The results are compared with a base case without preconditioning. The model consider a LHD production module of a typical block caving mine, with eight draw points and a dumping point at the production drift end and an ore pass with a grizzly limiting the size of rocks up to 1.4 m in diameter. Two cases are studied: unloading to an ore pass and unloading to a sizer crusher. In both cases, new technologies for rock oversize reduction at draw points are considered. The primary and secondary fragmentation curves for the HF and base cases were simulated using the Block Caving Fragmentation software (BCF). The geotechnical and geological input parameters were obtained from a real production area from Codelco’s Andina mine. The fragmentation curve for the CB case was obtained from a JK Simblast software simulation. The real input parameters for the preconditioned projected area in Andina mine were considered. For mixed cases (HF + CB), a composite fragmentation curve is constructed by considering the influence of the volume of the rock mass for each PC technique (in this case, 68% for CB and 32% for FH). From the fragmentation curves, flow interruption events are obtained. Then, production simulations are performed for each case of preconditioning technologies. Finally, the operating costs are calculated considering development, LHD extraction, secondary blasting, ore passing, haulage, mine services, maintenance and repair, crushing, belt conveyor and manpower.

3 Data 3.1 Fragmentation The fragmentation curves are in order from finer to coarser as expected from the hypothesis for the alternative PC techniques. FH, with the shortest distance between fractures (0.5 m) has the finest fragments for the hydraulic fracturing cases, the mixed cases have the finest fragments, while the base case is the coarsest (Figure 1).

Figure 1 Fragmentation curves for preconditioning technologies alternatives

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Preconditioning To transform the volume of particles in m3 to length in m, the parameters listed in Table 1 were considered. Table 1 Transformation factors

Density

2.65 (t/m3)

Form factor 1

1.00

Form factor 2

0.85

Form factor 3

0.75

Then, the length of the particle L in m was obtained using the formula: V(m3) = L (m) 3 * 1 * 0.85 * 0.75 Where V is the volume of the particle. 3.2

Definition of flow interruption events

3.2.1

LHD dumping to an ore pass

High hang-up occurs at the top of the draw bell, obstructing the draw point and stopping the normal flow of ore. It corresponds to 100% of the rocks with sizes smaller than 4.65 m (64.11 m3). The higher area of the draw bell is 173 m2. Low hang-up occurs at the bottom of the draw bell and it also stops the flow of ore. It corresponds to 50% of the rocks with sizes between 2.37 m and 4.65 m (8.50 m3 up to 64.11 m3). The lower area of the draw bell is 45 m2. Big boulder corresponds to 67% of rocks with sizes between 1.4 m and 2.37 m (1.75 m3 up to 8.50 m3) and 50% of the rocks with sizes between 2.37 m and 4.65 m. It stops the flow of the ore. Small boulder is a rock at the draw point able to be moved to another place by the LHD. It corresponds to 33% of the rocks with sizes between 1.4 m and 2.37 m. A seven cubic yard LHD is considered, as shown in Figure 2.

Figure 2 Flow interruption events, LHD to ore pass case

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LHD dumping to a Sizer Crusher case

In this case, the definition of oversize is increased, where the maximum size of ore the sizer accepts is 1.8 m. 3.3

Simulation area

The production of eight draw points was simulated using heuristic techniques considering two cases: LHD (7 yd3) unloading to an ore pass and LHD unloading to a sizer. New technologies for the secondary reduction of oversizes are considered: a boulder breaking equipment and a hang-ups breaking equipment, both conceived, designed and constructed by Codelco. The influence area of each draw point is 13 m x 17 m (221 m2), and the total area is 2210 m2, considering the ore pass or sizer area at the dumping point. Figure 3 shows the simulated extraction module.

Figure 3 Extraction module simulation

4 Results 4.1

Flow interruption events

Figure 4 and 5 show the frequency distribution of flow interruption events at the draw point, in number of events for 1000 tonnes of ore passing, for the case of LHD dumping to the ore pass and the case of LHD dumping to a sizer crusher.

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Preconditioning

Figure 4 Flow interruption frequency, LHD to ore pass option

Figure 5 Flow interruption frequencies, LHD to sizer crusher option

4.2 Productivity Figure 6 and 7 as well as Table 2 and 3 shows the productivities in tonnes per day for each technology and dumping options. The differences in percentages are obtained uncompared to the base case without preconditioning.

Figure 6 Extraction module productivity, LHD to ore pass case

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Caving 2014, Santiago, Chile Table 2 Extraction module productivity comparisons, LHD to ore pass

CASE Base HF 1.50 MIX 1.50 HF 1.00 MIX 1.00 HF 0.75 HF 0.50

tpd/module

Rate (tpd/m2)

Dif.(%)

1.185 1.577 2.093 2.480 2.506 2.756 2.964

0,54 0,71 0,95 1,12 1,13 1,25 1,34

33% 77% 109% 111% 132% 150%

Figure 7 Extraction module productivity, LHD to sizer crusher case Table 3 Extraction module productivity comparison, LHD to sizer crusher case

Case Base HF 1.50 MIX 1.50 HF 1.00 MIX 1.00 HF 0.75 HF 0.50

tpd/module

Rate (tpd/m2)

Dif (%)

1.360 2.777 2.907 2.984 2.901 2.955 2.928

0,62 1,26 1,32 1,35 1,31 1,34 1,32

104% 114% 119% 113% 117% 115%

4.3 Costs In Table 4, the mining cost is broken down in its different items with values for the conventional panel caving method (base case). The total operating cost is 8.86 US$/t.

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Preconditioning Table 4 Operating cost itemization for the base case

Mine Cost

Value

Units

Development Extraction Secondary Blasting Ore pass Haulage Mine services Maintenance and repairs Crushing Belt conveyor Workforce TOTAL

2,42 0,55 0,51 0,25 0,65 0,25 0,31

US$/ton US$/ton US$/ton US$/ton US$/ton US$/ton US$/ton

0,30 1,10 2,53 8,86

US$/ton US$/ton US$/ton US$/ton

Figure 8 and Table 5 show the comparison of the mining cost between the different scenarios for the dumping to ore pass case.

Figure 8 Mine Cost comparison, LHD to ore pass case Table 5 Mine Cost comparison, LHD to ore pass case

Mine Cost HF 0.75 HF 1.00 HF 0.50 HF 1.50 Base Mix 1.00 Mix 1.50

Value 8,53 8,54 8,58 8,81 8,86 9,13 9,19

Unit Dif. (US$/t) Dif.(%) US$/ton -0,33 -3,77% US$/ton -0,32 -3,65% US$/ton -0,28 -3,21% US$/ton -0,05 -0,61% US$/ton 0,00 0,00% US$/ton 0,27 3,03% US$/ton 0,33 3,68%

Figure 9 and Table 6 show the comparison of the mining cost between the different scenarios for the LHD dumping to sizer case.

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Figure 9 Mine Cost comparison, LHD to sizer crusher case Table 6 Mine Cost comparison, LHD to sizer crusher case

Mine Cost

Value

Units

Dif. (US$/t)

Dif.(%)

HF 1.50

8,74

US$/ton

-0,12

-1,39%

HF 1.00

8,76

US$/ton

-0,11

-1,21%

HF 0.75

8,80

US$/ton

-0,06

-0,72%

Base

8,86

US$/ton

0,00

0,00%

HF 0.50

8,91

US$/ton

0,05

0,52%

Mix 1.50

9,33

US$/ton

0,47

5,26%

Mix 1.00

9,36

US$/ton

0,50

5,59%

5 Conclusions Simulation results show a fragmentation curves order from finer to coarser as expected for the alternative PC techniques. FH has the shortest distance between fractures (0.5 m) and has the finest fragments for the hydraulic fracturing cases; the mixed cases have the finest fragments, while the base case is the coarsest. The different cases of alternative technologies are ordered by the size of the fragmentation from finer to coarser as follows: Mix 1.0, Mix 1.5, HF 0.5, HF 0.75, HF 1.0, HF 1.5 and the base case. For the flow interruption events frequency, the less favourable case is the base case, with the highest value for this indicator. The frequency descends in the following order for HF 1.5, Mix 1.5, HF 1.0, Mix 1.0, HF 0.75 and HF 0.5, the latter with the lowest value. The mixed cases improve the results compared to the HF cases, with shorter frequency for hang-ups and oversizes. The high hang-up occurrences are rare for each one of the studied alternative technologies. These trends are also observed in the LHD to sizer case. In the LHD to OP scenario, the productivity shows its smallest value for the base case (0.54 tpd/m2); then the values increase in the following order: for HF 1.5, Mix 1.5, HF 1.0, Mix 1.0, HF 0.75 and HF 0.5 with the largest value (1.34 tpd/m2), 150% above the base case. The mixed cases also improve the productivity compared to the HF cases.

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Preconditioning From the itemization of the operating cost, we observe that preconditioning techniques (without distinction between the LHD dumping to ore pass or sizer cases) have higher development costs than the base case, with the highest value for the mixed cases. However, the costs associated to secondary blasting, ore pass and maintenance and repair, are extremely smaller than the base case values. We concluded that the lower frequency of flow interruption events and higher draw rate of the preconditioning techniques, have an impact over the mentioned costs, reducing them up to 50%. The comparison of the operating costs for the preconditioning techniques in the LHD to ore pass case, indicates that the HF cases values are smaller than the base case values, with a minimum value of 8.53 US$/t for HF 0.75 and a reduction of 0.34 US$/t (3.80%) compared to the base case (8.86 US$/t). The largest values of the operating cost are obtained for the mixed cases, with the highest value for the Mix 1.5 with an operating cost of 9.19 US$/t, 0.33 US$/t above the base case value (3.69%). These operating costs trends are also observed in the LHD to sizer case, but with larger values for this variable, due to the higher development cost associated to the excavation this equipment needs for its operation. In this case, the smallest value is for HF 0.75 with 8.74 US$/t, with a reduction of 0.12 US$/t compared to the base case (1.39%). The Mix 1.0 is the less favourable case with an operating cost of 9.36 US$/t, 5.63% higher than the base case value. The smaller values for the operating cost in the HF cases compared to the base case are obtained due to the secondary blasting and maintenance and repair lower costs. The mixed cases have higher costs than the base case due to the higher development cost of these alternatives. In brief, for the simulated preconditioning technologies, the HF with a distance of 0.5 m between fractures shows the smallest sizes for fragmentation amongst the hydro-fracturing techniques, the largest productivity and the lowest frequency for the flow interruption events (hang-ups and over sizes). The base case is the less favourable, with the largest fragmentation size, the lowest productivity and the highest frequency for interruption events. In general, the mixed cases show the finest fragmentation and larger productivity (draw rate) than the cases without confined blasting. HF techniques show the smallest operating cost while the mixed alternatives are the most expensive ones, both compared with the base case.

References Raña, F 2011, ‘Análisis de la Implantación de Nuevas Tecnologías en los Proyectos Subterráneos de Codelco’, IM2, Chapters 3, 4.

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Pre-conditioning with hydraulic fracturing — when and how much? C Valderrama Pontificia Universidad Católica de Chile-IM2 Codelco, Chile F Báez Codelco, Chile E Arancibia Codelco, Chile V Barrera IM2-Codelco, Chile

Abstract Rock mass pre-conditioning by means of hydraulic fracturing is increasingly used, generating several benefits in caving mines, one of which is the reduction of fragments size. However, what conditions of the rock mass pre-conditioning will be more useful? What is the optimum reduction of spacing between hydraulic fractures? Through a numerical fragmentation assessment tool, we analyse how successful is pre-conditioning depending on two characteristics of the rock mass: the orientation and density of the pre-existing discontinuities. Furthermore, we examine the influence of the hydraulic fracture spacing (a design parameter) in the fragmentation. To analyse the importance of each parameter in fragmentation, a numerical factorial experiment was carried out. General guidelines are given to know in which cases we could expect the largest reductions in fragment size, and when a reduction in the spacing of hydraulic fractures will have a better performance.

1 Introduction The caving industry is moving towards a next generation of deeper and bigger caving geometries and scenarios, where hard rock masses with high stress environments and low density of discontinuities (or with strong infill) are encountered (Chitombo, 2010). These unfavourable conditions generate problems like, such as:

• Increase in seismicity due to the more brittle behaviour of the rock mass and high stresses. • Slowness or stalling of caving, which could to produce a reduction in production rates or air blasts. • Increase in fragments size (fragmentation), requiring a subsequent comminution. Particularly, fragmentation is fundamental in the design of the mine layout, dimensions of draw points and their spacing, and additionally, it is important in the material handling scheme (Brown 2007). To avoid the problems from the new surroundings in cave mines, the pre-conditioning of rock masses by hydraulic fracturing is being used, and the results have been positive (Araneda & Sougarret 2007). However, the requirements of the current mining make it necessary to study which is the limit of preconditioning in more detail and in which conditions it is most favourable to apply it. If we can reduce the size of fragments, the obvious solution is to decrease the spacing between hydraulic fractures, however, how good is this solution? For all these reasons, we study how hydraulic fractures change the in-situ fragmentation for different scenarios of pre-existing discontinuities. The different scenarios were constructed varying the orientation

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Preconditioning and density of pre-existing discontinuities and the spacing of hydraulic fractures. For all the variables we choose levels or cases, and the factorial combination of these cases generates a set of scenarios for which we carried out numerical experiments to compare the importance of each one of the fragmentation variables. The numerical experiment consists on building 10 Discrete Fracture Networks (DFN) for each scenario and calculating their in-situ fragmentation. After that, we analyse the 80% and 60% passing size (P80 and P60, respectively). To measure the uniformity of gradation, we also consider the uniformity coefficient UC=(P60/ P10)1/3. With these results, we present general guidelines about the rock mass conditions in which hydraulic fracturing pre-conditioning has better results. The paper is organized as follows: in Section 2 we describe the methodology, indicating the main assumptions, the cases that were considered and how the fragmentation curves were obtained. In Section 3, we show and analyse the results, to conclude with Section 4, where the conclusions and a discussion about the future trends are presented.

2 Methodology This study tried to be conceptual; therefore, the characteristics of rock mass discontinuities are not related with any particular case study. However, the values used are representative of conditions generally encountered in caving mines. The analysis presented is based on the factorial design methodology, which consists of determining the factors that influence the response of the studied parameter, assigning them discrete values or levels, and take on all possible combinations of these levels in the experimentation. The studied parameters are the 80% and 60% passing size, P80 and P60, which are considered representative of the biggest and medium blocks portion, respectively, and the uniformity coefficient UC= (P60/P10)1/3, which is a measurement of the particle size range. On the other hand, the chosen factors that influence the response are: dip of pre-existing sets, density of pre-existing discontinuities in the rock mass (measured through the average fracture frequency per meter) and spacing of hydraulic fractures generated in the preconditioning. The selected levels for each factor are presented in Table 1. Table 1 Levels selected for the factors to be studied

Dip (°)

Average Fracture frequency per meter Hydraulic fracture spacing (m)

Low value

Medium value

High value

4

-

6

No HF

0.7

1

10-30

-

60-80

Furthermore, three sets of pre-existing fractures are considered: S1, S2 and S3, which orientation is modelled by a Fisher distribution, with mean dip directions of 0°, 60° and 120°, respectively. The range of 20° for the dip is simulated by means of a Fisher parameter of K=100. In this paper, the lowest dip case will be called gently dipping (GD), while the highest dip case, steeply dipping (SD). Figure 1 shows the pole density plots of the sets S1, S2, S3 and hydraulic fractures when all are steeply or gently dipping. The hydraulic fractures are considered nearly horizontal (dip=0° with K=1000) and their radius was assumed to be 20 m according to Codelco’s field experimental results. Additionally, based on the method proposed by Bunger et al (2012), we estimate that, under the usual characteristics of Chilean mines and preconditioning, no-curving of hydraulic fractures occurs due to their interaction, therefore hydraulic fractures were considered as straight fractures. Certainly, the hydraulic fractures that are perpendicular to the minor principal stress α3, are not necessarily nearly horizontal, but the idea of our analysis is to define the results with respect to the direction of α3.

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Caving 2014, Santiago, Chile On the other hand, the given values for the average fracture frequency is the one measured for all the preexisting sets, and the spacing of each set is modelled by an exponential probability density function (pdf). For the trace length, an exponential pdf is used, with a mean value of 15 m.

Figure 1 Pole density plot of hydraulic fractures with: (a) gently dipping sets, and (b) steeply dipping sets

We choose two levels for dip and fracture frequency and three levels for spacing between hydraulic fractures; therefore, we need to model 48 different scenarios. The 48 scenarios studied are the combinations of the cases presented in Table 2 (numbers) and Table 3 (letters). Table 2 Scenarios for orientation of pre-existing discontinuities

S1 (DDIR=0°)

Case 1

Case 2

Case 3

Case 4

Case 5

Case 6

Case 7

Case 8

SD

SD

GD

GD

GD

GD

SD

SD

GD

GD

SD

SD

GD

GD

SD

GD

SD

GD

SD

GD

SD

GD

S2 (DDIR=60°)

S3(DDIR=120°)

SD

SD

Table 3 Scenarios for density of pre-existing fractures and hydraulic fracture spacing

ff per meter

Spacing of HF

Case A

Case B

Case C

Case D

Case E

Case F

4

6

4

6

No HF

No HF

0.7

0.7

1

1

4

6

Moreover, given the stochastic nature of DFN, it is necessary to make at least 10 runs for each scenario, resulting in a total of 480 runs. This analysis does not include the propagation of pre-existing fractures generated by the interaction with hydraulic fracturing, due to the required computational efficiency is restrictive for the number of cases to be studied. Nevertheless, this limitation is not so restrictive in the assessment of in-situ fragmentation, because the pre-existing discontinuities in the current caving mines usually are closed or sealed with strong infill, which avoids propagation. A simplified analysis was conducted through the JointStats (Eadie 2002)

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Preconditioning software, which takes as input core loggings or scanlines, generating statistics for spacing, orientation, trace length and termination, and later, it creates the DFN. Finally, by means of a tessellation method (constrained Delaunay triangulation) it calculates the curve of fragments size. For this study, we have prescribed statistical characteristics of the discontinuities instead of field scanlines. For this reason, a method was developed to generate artificial scanlines, which follow the desired statistical parameters.

3 Results Because the model is stochastic, it is necessary to study the data variability. Figure 2 shows the boxplots obtained for P80 and P60 in one of the studied cases (case 1), where the lower quartile, median and upper quartile are shown, and the + sign represents outlier data. A high variability exists in the results of the bigger fraction of fragments when the total density of fractures (pre-existing and hydraulic fractures) is low, which happens in case A. The same trend was observed for cases 2, 3, 4, 5, 6, 7 and 8. However, for the medium fraction of fragments (P60), the variability is more similar between the different cases. Another observation is that the results not necessarily distribute normally and, usually, they have an asymmetry that favours the smallest sizes.

Figure 2 Scatter of the P80 and P60 results between runs

Figure 3 shows the average uniformity coefficient (UC) obtained in each scenario, and also shows fragmentation curves for different uniformity coefficients, one for the obtained for the average UC≈3.8 and the other for the outlier UC≈5.4. In spite of the variability of the data shown in Figure 3, we can conclude that: a) the highest variability is obtained for the case with low total density of fractures (Case A); b) largely, UC values are within the 3.2 < UC < 4.5 range, which is a very uniform gradation and c) on average, the smallest values of UC were obtained for cases C and D, which correspond to the intense pre-conditioning (spacing of 0.7 m), therefore, hydraulic fractures contribute to improve the uniformity of the rock blocks. To clarify the display of results, the obtained passing size of 60% and 80% were divided into three groups: 1) Cases 1 and 8, where the three sets have the same average dip; 2) Cases 2, 3 and 5, where two steeply dipping sets exist; and 3) Cases 4, 6 and 7, where two gently dipping sets exist.

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Figure 3 Uniformity coefficient UC for the studied cases, and fragmentation curves for different values of UC

In Figure 4, 5 and 6, for each of the aforementioned groups, we show the variation of P60 and P80 depending on the pre-conditioning characteristics, for the two selected levels of fracture frequency per meter.

Figure 4. P60 and P80 obtained for the cases where the three sets have the same average dip

Figure 4 shows the cases where all the sets are steeply or gently dipping. Both conditions are the worst for in-situ fragmentation, generating the bigger blocks. The addition of a horizontal set, as hydraulic fractures, in a rock mass that only has steeply dipping sets, obviously has a strong influence because this new set cuts

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Preconditioning the blocks in a different direction. Surprisingly, the same effect is observed when all the pre-existing sets are gently dipping. On the other hand, a reduction in the spacing of hydraulic fractures is useful too, obtaining reductions between 1.5 – 5 m3 in the P80, decreasing 30 centimetres of spacing.

Figure 5 P60 and P80 obtained for the cases where two steeply dipping sets exist.

In Figure 5, we show the results when two steeply dipping sets exist. In this scenario, the in-situ fragmentation without hydraulic fractures is much finer than the one obtained in cases 1 and 8. Despite this, the addition of hydraulic fractures has very good effects. However, the reduction of the hydraulic fracturing spacing in this case has small effects and reductions between 0.2 – 0.5 m3 were obtained in the P80.

Figure 6 P60 and P80 obtained for the cases where two gently dipping sets exist.

When two gently pre-existing sets exist, the results (Figure 6) show two different behaviours: the first for the case 4 which is similar to the one observed in cases 1 and 8, and second, the behaviour of cases

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Caving 2014, Santiago, Chile 6, 7 which is similar to cases 2, 3 and 5. We believe that the results obtained for case 4, can be due to the particular values of the dip directions considered, in which the cutting effect of the steeply set over the other two gently sets is not correctly addressed, therefore, the analysis is made take into account the results of cases 6 and 7. This scenario was found to be the most favourable in terms of the in-situ condition, obtaining the smallest fragmentation. The adding of hydraulic fractures improves fragmentation but not in the same magnitude than for the other cases, and the same conclusion can be made for the reduction of hydraulic fracture spacing. The former results are in terms of absolute values. For this reason, in Figure 7 we show the P80 obtained with pre-conditioning, normalized by the P80 obtained from the in-situ condition. The effect of hydraulic fractures in cases 1 and 8 remains being the best one. The effect in the cases 2 and 5 are bigger than the one observed previously in terms of absolute values.

Figure 7 Normalized results of P80 for all the scenarios, and fracture frequency per meter of 4

4 Conclusions The analysis made on the influence of pre-conditioning with hydraulic fracturing on the in-situ fragmentation allows us to point out several ideas about this procedure. We indicate the importance of taking into account the variability of the fragmentation curves when we use DFN simulation, mainly when the density of the fractures considered is low. This variability is related to the stochastic nature of the DFN generation and not to the uncertainty of the input data. In some cases, for example, big blocks, it may take a lot of time to obtain a fragmentation curve. Despite that, we emphasize the fact that it is necessary to do more than one simulation. The uniformity in the gradation measured by UC is improved with the hydraulic fracturing. The smaller is the spacing between hydraulic fractures the more homogeneous are the size distribution of the curves. Considering three main sets, the worst in-situ condition for fragmentation is in which all these main sets of discontinuities have a similar dip. When two sets are steeply dipping or two are gently dipping, the in-situ fragmentation is much better, with P80 values that can be even 10 – 20 times lower than the former case. This initial condition is very important in the evaluation to know if pre-conditioning will be necessary or useful. In absolute terms, the bigger the in-situ blocks, the better is the performance of hydraulic fracturing, which is to be expected.

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Preconditioning From the P80 normalized curves, we can conclude that in all the cases, the addition of hydraulic fractures has a similar good performance in relation to the in-situ condition. On the other hand, the reduction in the hydraulic fractures spacing from 1 meter to 0.7 m generates a decrease between 5 – 30% in P80. The lower is the density of pre-existing fractures, the better is the effect of hydraulic fracturing and, with an increase in density, the effects of hydraulic fracturing are lower. As a summary of the results, for three defined cases, general guidelines are given for the application of preconditioning by hydraulic fracturing:

• All sets with similar dip: This is the best case to apply pre-conditioning and the reduction in spacing is very effective, too. The only exception is when these pre-existing sets are perpendicular to s3.

• Two sets nearly parallel to s3 and one nearly perpendicular to s3: This in-situ condition is good, but

the addition of hydraulic fractures has very good results. A reduction in the spacing is not effective.

• Two sets nearly perpendicular to s3 and one nearly parallel to s3: The in-situ condition is mildly better than the former situation and the addition of hydraulic fractures has good effects but less effects than the other conditions. A reduction in spacing is not effective.

References Chitombo, G 2010, ‘Cave mining – 16 years after Laubscher´s 1994 paper Ćave Mining – state of art´’, Caving 2010 (Potvin, Y.ed), Australian Centre for Geomechanics, Perth, pp. 45-61. Brown, ET 2007, Block Caving Geomechanics, Julius Kruttschnitt Mineral Research Centre, Brisbane, 696p. Araneda, O & Sougarret, A 2007, Keynotes address: Lessons learned in cave mining, El Teniente 19972007, Cave Mining, SAIMM, Cape Town, pp. 59-71. Bunger, AP, Zhang, X & Jeffrey, RG 2012, ‘Parameters affecting the interaction among closely spaced hydraulic fractures’, SPE Hydraulic Fracturing Technology Conference, The Woodlands, pp. 292-306. Eadie, BA 2002, Modelling primary and secondary fragmentation for block caving, PhD Thesis, University of Queensland, Brisbane.

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Caving propagation and dilution control through the pre-conditioning technology V Barrera Codelco, Chile C Valderrama Codelco, Chile P Lara IM2 Codelco, Chile E Arancibia Codelco, Chile F Báez Codelco, Chile E Molina Codelco, Chile

Abstract Caving propagation and dilution control are extremely important phenomena in cave mining, because their correct estimate improves the ore body recovery. The pre-conditioning technology, based on the decrease of the mechanical competence of the rock mass through the creation of fractures, allows the in situ stress redistribution to enhance the ore body caveability and fragmentation. This paper presents the influence of pre-conditioning, namely, hydraulic fracturing, in these phenomena through the analysis of the operational data on draw points obtained during the application of this technique in North Inca, West Central Inca and West Inca sectors at El Salvador mine in the 2011-2012 period.

1 Introduction The planning in cave mining, which aims to recover a large volume of resources, must predict the flow phenomena of the ore body with certainty. By using gravity as the driving force and a finite number of draw points, selectivity in this flow is an important parameter to consider. However, both the entrainment of waste material during dilution and the precise control of the caving propagation when a material with inadequate particle size flows, introduces uncertainty in the process with the consequent loss of selectivity. Prior to the start of the extraction phase of a new area, in order to control the mineral flow, the preconditioning technique was applied, specifically hydraulic fracturing (HF). HF is a technique that involves pressurizing a section of an existing drillhole or fracture with a specific fluid, usually water, which is injected until a net pressure enough to initiate a tensile fracture and propagate it into the rock mass is reached. New fractures produced by HF act as free surfaces that facilitate or increase the formation of a block, thereby reducing the size of the fragments to cave (Baez 2011). At El Salvador, Codelco - Chile Division, in the 2011 - 2012 period, the expansion of the mine was projected to nearby zones in North Inca, West Central Inca and West Inca sectors with challenging production plans, taking mining to the limit rates. These areas consisted on very competent rocks, which added to the information about the hang-ups (20,000 m2 involved area) and subsequent air blast in 1999 at North Inca mine introduced limits to the ambitious mining plan. Additionally, in the area surrounding West Central Inca, the orientation of the draw point drifts was changed causing the formation of irregular pillars with the consequent appearance of areas of high stress concentration. For this reason, as a way to stimulate the caving and improve the particle size of the broken material, HF was used.

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Preconditioning

Figure 1 Inca level layout and commissioning of new areas at El Salvador mine

2 Methodology To achieve the optimum from the HF technique, the following parameters should be considered: 2.1

Mining layout

The mining layout determines the drilling parameters needed for HF, i.e., length and spatial orientation, because the HF design must enhance the stability of the existing works at the expense of the area projected to collapse. The infrastructure arrangement is also relevant because the proximity of the HF equipment to supplies (air, water and electricity) is an operational variable to consider.

Figure 2 Mining layout conditioning design of HF drillholes (North Inca sector)

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Primary – secondary contact zone and subsidence effect

Although Codelco currently operates its mines in a primary enrichment zone in the ore column, the distance from the current works to the draw points generated by the primary - secondary contact zone and the subsidence controls the length of HF drillholes. 2.3

HF´s influence radius

It has been determined [1] that fractures generated by the HF with a radius of 20 m provide an optimal interaction of the drillholes. The influence radii designed for North Inca sector can be seen in Figure 3.

Figure 3 Drillhole design for HF (Plano 2012)

2.4

Fracture spacing

The spacing of the HF fractures affects the block size to cave. This parameter is operationally limited by the minimum distance allowed by the straddle packer system (in this case, 1.5 m). The spacing also depends on the borehole conditions (borehole stability). A section in the drillhole with the presence of important structures (deep discontinuities or existence of fragments that can damage the packer systems) suggests that we should omit these sectors and continue the fracturing where the drillhole is in good condition.

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Stress condition

El Salvador mine has a rich geomechanical database that provides information on the status of stress in mining sectors, which can predict the orientation of fractures generated by HF. This information is shown in Figure 4.

Figure 4 Stress measurement plane at Inca level, El Salvador mine

Through the analysis of the draw curves and the particle size summary, the influence of HF on caving propagation and dilution control was studied.

3 Results The analysis performed after pre-conditioning and undercutting in the West central Inca sector, showed that of the planned 2,303,255 tons, 2,317,959 tons were extracted, which corresponds to the entire mineral block. This is shown in Figure 5, where the percentage of drawn tonnage (blue curve for the mining plan and red curve for the actual draw curve) is shown.

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Figure 5 Draw curves, actual (red), scheduled (blue)

The fact that the two curves coincide reflects that the dilution phenomenon is prevented. The information gathered at the draw points in West Central Inca sector (crosscut 5 to 16, between January and July 2012) allows identifying the fragmentation obtained for the four size classes (<3 “, 6” to 12 “, 12” 32 “> 32”) in the mineral column, as shown in Figure 6.

Figure 6 Particle size summary at West Central Inca sector

The uniform grain size shown in the whole column causes a slight increase in oversize and fine material. The overall grain size was obtained according to the expected contribution of HF.

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Preconditioning 4 Conclusions With the information available, it can be concluded that HF allows an efficient caving propagation, considering that it was possible to extract the mineral column committed in the mining plan completely avoiding or delaying the entry of dilution. Regarding the sieve analysis, with the application of HF, it is possible to obtain a uniform draw fragmentation at all draw points. Less than 25% of the mineral drawn in the last period corresponds to fragments larger than 32” and approximately 70% of the draw was smaller than 12”.

Acknowledgement The authors acknowledge the sponsorship of IM2 in the context of the completion of the IM2 P-64/10 project, “Application of New Technologies in Preparation and Extraction Systems, El Salvador Mine”.

References F. Báez, Preacondicionamiento del Macizo Rocoso – Desarrollo Tecnológico 1999-2010, Codelco, 2011.

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Numerical analysis of pre-conditioning using blasting and its relationship with the geomechanical properties of the rock mass and its interaction with hydraulic fracturing F Báez Codelco, Chile E Arancibia Codelco, Chile I Piñeyro IM2 S.A., Chile J León IM2 S.A., Chile

Abstract In caving operations, the rock mass pre-conditioning (PC) has been adopted in a variety of situations, mainly to mimic the geotechnical properties of the secondary rock. The two techniques used to PC the ore body are confined blasting (CB) and hydraulic fracturing (HF). In this work, a numerical analysis of the technique was done in order to identify the key optimal design parameters considering the in-situ geomechanical conditions for the CB and its interaction with the HF. The analysis was done with the Hybrid Stress Blasting Model (HSBM), which is a blast simulation tool aimed to analyse the role played by different explosive formulations in fragmenting and/or damaging various rock types under different degrees of confinement. The criteria used in this analysis to evaluate the impact of the blasting mainly due to the shock wave, was the peak particle velocity (PPV) response of the medium. Initial results show that the stresses present in the medium are the main geomechanical conditions that impact the extent of the damage. The effect of the presence of joints sets and also Hydraulic fractures in the extent of the damaged zone can be identified only when the orientation of both of them is against the propagation of the shock wave. Simulations were done with different scenarios looking for: interaction between blast holes, interaction of blast holes with free faces and also changing the distance between primers. A strong inverse correlation has been found between the primers distance and the damaged zone. The results of this work are key elements to consider for an optimal PC campaign where the design can be adjusted to the specific conditions of the ore body and the mine requirements.

1 Introduction The current and future caving operations are faced to greater challenges mainly due to a fundamental change in the conditions of the rock that is caved. This is the case of Codelco and a group of mining companies which operations have evolved from secondary rock deposits to deeper deposits where the geotechnical and mining conditions are challenging because of the strong rock masses and the presence of high stresses, conditions that are characteristics of a primary rock. Some of the problems faced in these conditions are of a safety and also operational nature, where stability and fragmentation are key issues that need to be solved in order to ensure production. To address these issues, methodologies have been adopted such as the rock mass pre-conditioning (PC), to mimic the geotechnical properties of the secondary rock. There are two techniques used to precondition the ore body, namely, confined blasting (CB) and hydraulic fracturing (HF), which are used independently and also with a combined configuration. Because the nature of the impact in the rock mass of both technologies

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Preconditioning is different, the benefits of the different configurations of these combined technologies are under analysis. Major mining companies are doing experimental trials in order to look for general guidelines to implement these PC methodologies. As a complement to these field trials, computer models have been introduced mainly due to their versatility to represent a wider configuration and condition spectrum for analysis, as pointed out by (Catalan et al. 2012). This work uses the Hybrid Stress Blasting Model, HSBM, as a tool for the analysis of the impact of the blast under different rock conditions that include rock stress, structures, and also hydro-fractures. The HSBM is a numerical model for the entire blasting process that in its current form represents the rock mass by a continuous medium near the blast holes and a lattice scheme elsewhere. The software takes into account the rock mass geotechnical properties and its capabilities allow a vibrational analysis that can be done through simulated geophones, a 3D map of the Peak Particle Velocity (PPV) reached by every point of the simulation and also includes a rock mass breakage criterion. In its current state of development, the HSBM software does not intend to give specific quantitative results, but it can give important insights about the impact of key parameters that govern the blasting. Therefore, the software can be used to look for blast design guidelines.

2 Methodology This paper looks for some general guidelines on how to maximize the impact of PC under different conditions; so different blast scenarios were simulated in order to compare the results between them. There was no comparison of results between field trials with the simulations, except for calibration purposes. Every simulation consists on a volume that represents the rock mass by the inclusion of its specific rock mass properties, the location of the blast holes, their primers and timing. The dimensions of the volumes correspond to those of actual trials in order to represent real PC implementations. While the HSBM model can consider the impact of the gases in blasting, this study only makes an analysis of the impact of the stress wave. To measure the impact of a blast in the rock mass, two criteria where considered; the Holmberg & Persson Criteria (PPV) and damage. In order to ensure that the results obtained with the model are close to what can be expected in field trials, the model was calibrated using data from a PC field test done by Division Andina of Codelco Chile in 2001. With the model already calibrated, simulations were done with different scenarios in order to analyse the impact along a blast hole. 2.1

Damage Criteria

2.1.1

Holmberg & Persson Criteria (PPV)

This software allows the use of the PPV criteria (Holmberg & Persson 1979), which relates a critical PPV with the damage induced to the rock mass. The critical PPV is obtained from the following expression:

PPVmax =

Cp ×s t E

(1)

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Caving 2014, Santiago, Chile Where: Cp=Compressive wave velocity, σt=Tensile strength E=Young`s Modulus 2.1.2

Internal HSBM damage

The code defines a fragment as a collection of lattice nodes that are connected through bonds, so the breakage of these bonds allows the generation of damage, fractures and new fragments. 2.2

HSBM calibration methodology

For calibration purposes a field trial done by Division Andina of Codelco Chile was simulated. In this trial, an array of geophones was installed in the near a blast hole, allowing the intensity and decay calibration. The blast design considered a blast hole of approximately 23.3 m in length, with a stemming of 12 m. Three primers were used and they were placed in contact with the stemming, at 12[m], in the centre of the hole, at 18 m and down the hole 23.3 m. Two boreholes were drilled parallel to the blast hole to install a sensor array. The first hole was drilled at 9 m from the blast hole and the second 17 m farther from the second blasthole and in line with both of them. The general design is showed in Figure 1.

Figure 1 Calibration test blast design

The calibration was done matching the PPV results obtained from the field trial and from the simulations. The rock properties and explosives used in the test are presented in Table 1.

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Preconditioning Table 1 Calibration parameters

Parameter Uniaxial Compressive Stress (UCS) Tensile Strength (σt) Young’s Modulus (E) Poisson’s ratio (ν) In-situ Stress (σH, σh, σV) Compressive wave velocity (Cp) Explosive type Explosive VoD Explosive density 2.3

Value 150 MPa 17.6 MPa 60 GPa 0.2 30 MPa, 20 MPa, 15 MPa 4,979 m/s ANFO 4,000 m/s 0.78 g/cm3

Rock mass response analysis

Different scenarios were simulated in order to analyse the extent of the impact of the blast hole (damage). The parameters analysed were:

• Stresses of different magnitude and orientation: two main situations were considered in order to analyse

the impact of the stresses in the extent of the damage. The first of them is the one present in most of the Chilean copper ore deposits and corresponds to a horizontal principal stress. The second situation is present in other ore deposits, where the main reason for stresses is gravity, thus the principal stress is vertical.

• The presence of joints and their orientation: the simulations were done considering only the Chilean

case for s1, i.e., horizontal. Each joint set considered in this analysis was a set of planes that change the rock mass properties in each point where they interact with the lattice that defines the volume of analysis. Three orientations for the joint sets were considered in this analysis: horizontal joint sets, and addition, joint sets parallel and perpendicular to s1.

• The distance between primers (boosters): looking for a mechanism to extend the radius of influence of the blast, the impact of the distance between primers was analysed. For this analysis, two sets of simulations were made; both sets considered differences in the length of the blast hole, 50 m for the first one and 70 m for the second. The distance between primers varies from 2 m to 12 m.

• The presence of free faces: simulations with free faces were done in order to look for some new blast designs. The simulated blast designs were against a full free face and against the raise.

• Interaction between blast holes: simulations where the distance between the blast holes were analysed looking for the best interaction between them.

• Presence of HF: because a mixed approach of PC is currently being adopted by the industry, which includes blast and HF, some simulations were done looking for the interaction between them.

Andina mine provided the information about explosives and it is summarized in Table 2. The rock properties are the same ones used in the calibration model.

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Caving 2014, Santiago, Chile Table 2 Simulations parameters

Parameter

Value

PPVmax

1,461 mm/s

Explosive type

Emulsion

Explosive VoD

5,600 m/s

Explosive density

1.15 g/cm3

Primer Timing

Simultaneous

3 Calibration The field measurements of the PPV of each geophone and its corresponding simulation are listed in the following table where the difference obtained after calibration is also indicated. Table 3 Calibration results

Geophone

Measured PPV (mm/s)

Simulated PPV (mm/s)

Difference

G1

359.03

307.43

-14%

G2

683.74

659.73

-4%

G3

649.15

620.78

-4%

G4

434.28

373.35

-14%

G5

205

128.71

-37%

G6

259.81

213.47

-18%

G7

321.87

180.26

-44%

G8

220.45

132.25

-40%

From these calibration results, we can expect a very close correspondence of the simulations in the near field of the blast with reality, even though some consistent negative bias can be expected. So, in the near field we can expect a good correspondence with real trials even though some underestimation can be expected. For distances farther than 25[m] we can say that the model is underestimating the damage.

4 Results 4.1

Stresses of different magnitude and orientation

The impact of the variation of the direction of the principal in-situ stress could be seen in Figure 2. In this the damage zone or its radius is calculated from the PPVmax criteria.

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Figure 2 Damage extent vs. σ1 orientation

From Figure 2, we can see big difference between the two orientations, finding that the vertical orientation generates less damage. In the case of the vertical σ1, a closer look at the damage was done considering now the broken links criteria (Figure 3).

Figure 3 Damage extent vs. Vertical σ1

The results show that even though the extent of the damage is similar in all the cases, the level of damage inside the damaged zone varies as the vertical stress changes. Thus, less broken links are found when the vertical stress is bigger. 4.2

Presence of joints and their orientation

Four sets of simulations were done: without joint sets, horizontal joint sets, parallel to σ1 and perpendicular to it. The extent of the damage was measured along the σ1 direction and perpendicular to it.

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Figure 4 Damage extent vs. Joints Orientation

The results show in Figure 4 that the horizontal structures limit the extent of the damage but only in an order of a few centimetres, which is of no real impact. In the case of vertical structures, the results show a loss of symmetry in the extent of the diameter, where the biggest impact is in the direction that is perpendicular to the plane that contains the joints. This effect was found in both cases analysed regarding the principal stress been vertical or horizontal. 4.3

Distance between primers boosters

The results show that the extent of the damage increases as the distance between primers decreases, and also that the length of the column is of no real impact. The PPV criteria for the two sets of simulations are shown in Figure 5.

Figure 5 Damage extent vs. distance between primers

4.4

Presence of free faces

Figure 6 shows a section perpendicular to the free face. The PPV criteria used considered.

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Preconditioning

Figure 6 From left to right: Base Case, blast hole against full free face and against raise (right)

Compared to the base line, in both cases (full free face and raise), some interaction can be observed, the actual impact of it needs to be analysed considering the distance to the free face as a design parameter. 4.5

Interaction between blast holes

The variation of the distance between two blast holes shows some interaction (Figure 7).

Figure 7 Distance between blast holes and it’s relation with damage extent

4.6

Presence of HF

The interaction between blast and HF was analysed considering the change in the extent of the damage compared with a baseline with no HF (Figure 8). The results show some minor impacts in the extent of the damage. In the case of a non-horizontal HF, some bigger impact can be found, and also some loss of symmetry as in the case of interaction with joints.

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Figure 8 Damage extent vs. HF orientation

5 Conclusion The modelling of different blast scenarios provides some important insights about the relevance of the different parameters involved in the design of PC.

• Stresses and their orientations play an important role limiting the extent of the damage. • The non-horizontal joints also limit the extent of the damage, and some loss of symmetry is found. • The distance between boosters has an important role in the impact of PC. • Free faces can be an alternative to increase the impact of PC; a deeper analysis should be done. • No big interaction between HF and blast has been found in the different scenarios modelled. • Some interaction between blast holes has been found, but not always enough to satisfy some of the damage criteria selected.

The modelling of PC has shown to be an important tool to identify the key parameters for the results of a PC campaign. Most of the parameters analysed are already fixed from the ore conditions and cannot be fine tuned, so important differences should be found if a single PC design is used. New designs considering the distance between boosters and free faces need to be included in the battery of options to consider. Recommendations about how to implement these results may vary from site to site, therefore, a deeper analysis is needed.

References Catalan, A, Onederra, I & Chitombo, G 2012, ‘A proposed methodology for evaluation of the preconditioning by blast at the Cadia East panel cave mine’, Massmin 2012. Holmberg, R & Persson, PA 1979, ‘Design of Tunnel Perimeter Blasthole Patterns to Prevent Rock Damage’, Tunnelling’79’, Proceedings of the Second International Symposium, London, England, 12-16 March, London: Institute of Mining and Metallurgy, pp. 280-283.

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Intensity rock mass preconditioning and fragmentation performance at the El Teniente Mine, Chile A Brzovic Codelco, Chile JP Hurtado Universidad de Santiago de Chile, Chile N Marín Codelco, Chile

Abstract Fragmentation measurements have been undertaken at the Sur Andes Pipa mine sector (SuaPi) within the El Teniente mine to validate the effect of rock mass preconditioning. SuaPi mine sector has been mining out primary and secondary ore since September 2010 at around 6000 tpd. The primary ore is called Dacita, which is considered as the stronger and massive rock mass for caving at the El Teniente mine, and that is one of the main reasons for preconditioning (to improve caving and fragmentation performance). Two different preconditioning techniques were implemented over the Dacita rock mass; hydraulic fracturing (HF) and confined blasting called DDE (Debilitamiento Dinámico con Explosivos). Fragmentation analyses were undertaken considering main geological features of the sector, and finally compared/correlated to the variable intensity of rock mass preconditioning undertaken over the primary rock mass. This paper describes the applied methodologies and main results of the investigations, which shows a clear and direct relationship between preconditioning intensity and fragmentation performance at the El Teniente mine.

1 Introduction The primary copper ore at the El Teniente mine is described as very competent and massive, it exhibits a brittle behavior, often violent failure under high stress conditions (Rojas et al., 2001). This description is coherent with the geological description of the rock mass, which does not have discontinuities (joints) that match as the definition provided by International Society of Rock Mechanics (ISRM, 1981). Only faults can be classified as discontinuities, but they are widely spaced within rock mass. The primary copper ore has a high frequency of veins, where the cooper mineralization is hosted, these vein network structures are known as stockwork (Figure 1). It has been observed and documented that soft veins containing weak minerals as infill (chalcopyrite and anhydrite mainly) control the disassembling of the rock mass during caving (Brzovic and Villaescusa 2007; Brzovic 2011). Different preconditioning techniques have been applied at the mine site, aiming moistly to reduce seismic hazard, but also to improve cavability and fragmentation. Hydraulic Fracturing (HF) is currently applied mine wide since 2008, and the confined blasting called DDE (Debilitamiento Dinamico con Explosivos) have been applied only as industrial trial to study its impact on fragmentation performance. This paper describe the result of the industrial trial of preconditioning (HF+DDE) applied at the Sur Andes Pipa (SuaPi) mine sector, mainly over Dacita rock type, which is the stronger and massive rock mass for caving at the El Teniente mine. A fragmentation measurements campaign at the draw points of the production level was implemented to evaluate the fragmentation performance. Fragmentation measurement, undertaken by mining engineers, started in October 2010 and finished in July 2013 considering two main rock size distributions; a) the fine fraction, which is collected at the production draw point itself by visual inspection using flipchart techniques

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Caving 2014, Santiago, Chile and backed with photographies, and b) the coarse fraction, that represent all big blocks undertaken for secondary blasting and hang up reduction. Both combined data provide the final fragmentation curve observed at draw points of the preconditioned volume.

Figure 1 a) Panel caving method currently used at the El Teniente Mine (b) Intense vein network “stockwork” at a development ahead of the cave front (c) Rock block found in the draw points at the production level (d) Weak Veins as faces of caved rock blocks (e) Laboratory scale sample showing a Weak veins (from Brzovic & Villaescusa 1997)

Preconditioning intensity is calculated by counting the area of created new fractures per unit volume, which is a parameter used commonly in structural geology; it is called P32 m2/m3 according to Dershowitz and Einstein (1988). Detailed logging of cores bored over the preconditioning volume provide the insight of the rock damage by those techniques, which was used to build up Discrete Fracture Network (DFN) model of the created new fractures. The value of P32 m2/m3 that represents the preconditioning intensity is finally obtained from the DFN model. The aim of this paper is to compare and correlate fragmentation performance observed against preconditioning intensity applied over Dacita rock type at the El Teniente mine.

2

Fragmentation measurements methodology

Fragmentation measurements have been collected continuously since October 2010 considering two main rock size distributions; a) the fine fraction that represent the muck material at the draw point, and b) the coarse fraction, that represent all big blocks undertaken for secondary blasting and hang up reduction. The methodology procedure to collect fragmentation information and undertake data analysis is a follow:

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Preconditioning • The size distribution of the fine fraction are measured by visual inspection of fully draw points

(“puntos abocados”) in four size ranges; [<0.25m], [0.25m to 1.0m], [1.0m to 1.5m] and [1.5m>]. A comparative “flip chart” is used to help to estimate the percentages of each size range (Figure 2).

• The coarse fraction represent all rock block identified during secondary blasting; the ones that form

the hang up at the draw bell, and all large rock block over the production level floor that the LHD cannot carry out. The coarse fraction is counting in ranges of; [1.0m to 2.0m], [2.0m to 3.0m] and [3.0m>]. Each single rock block is characterised by its size dimensions (three mayor axis).

• With the rock block size data, the shape factor “f” (Gy 1967) is calculated. “f” is a dimensionless

“particle shape parameter”, which varying between 0 and 1. The shape factor is obtained by the multiplication of the ratio of rock block major axes divided by the large axes recorded. This parameter is necessary to convert the two dimensional observations of a rock block, in hang up for instance, in to a three dimensional volume and further tonnage. It is important to note that all large rock block over the production level floor that the LHD cannot carry out were fully characterised, then statistical analysis is undertaken to estimate the fine and course fraction tonnage.

• Both size data are combined and correlated to the data base of the mine production, which allow to correlate each draw point with; date, shift, column height, extraction tonnages among others parameters. Data analysis is undertaken for a certain number of draw points that have similar geological and preconditioning conditions.

Figure 2 Scheme showing fragmentation measurement methodology. A fully draw points is shown at the left (upper) and a rock block for secondary blasting at the left and right bottom. Hung up at the upper-right and the Flip-Chart at the centre

3

Rock mass damage by preconditioning

Core logging and bore hole camera (BHC) records of several drill cores bored after preconditioning was applied to the primary rock mass allowed to identify and to characterize the rock damage by the application of both techniques. Rock mass preconditioning resulted in a creation of new and fresh open fractures (Figure

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Caving 2014, Santiago, Chile 3), that the natural primary ore does not contain. HF fractures tend to have a sub-horizontal orientation according to the induced stress field at the mine sector. DDE fractures tend to have sub-vertical orientation according to a typical pre-split blasting technique (Figure 4) rather than micro cracks within the intact rock. Micro crack never were observed neither measured. HF is also characterised with a low roughness profile than the DDE fractures as can be seen in Figure 3.

Figure 3 Pictures of both core and bore hole camera showing the fresh and new fractures created by preconditioning techniques. Primary rock mass without fractures (only stockwork veins) is also shown at the BHC´s pictures

Figure 4 The progressive creation of a fracture plane during pre-split blasting technique (from Matheson 1983 in Hudson & Harrison 1997)

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Preconditioning 3.1

Intensity preconditioning estimates

Preconditioning by both techniques were not homogenous at SuaPi mine sector, because HF injection cores and DDE blast hole were placed with different spacing through the entire Dacita rock mass (Figure 5). Closer spacing of DDE blast hole and large number of them was considered at the north part of SuaPI in comparison with the south part. DDE blasting performance were also different at the north parte, more blast hole were detonated at the same time in this mine sector too. HF was also not homogenous through the column height, because some FH could not be created by operational issues as can be seen in geological cross section of Figure 6 (left).

Figure 5 Geological plan view of SuaPi mine sector showing; rock types (Dacita as yellow and Cmet as grey colours), HF injection core (black dots) and DDE blast hole (red and blue dots). North direction is along SuaPi (from bottom to top in the plan view)

Figure 6 Geological cross section showing different HF intensity (left) and isometric 3D view of the HF created at SuaPi mine sector based on mine design and real HF performance

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Caving 2014, Santiago, Chile Mine design and real HF performance informed by the operation staff was used to estimate HF intensity as is shown in Figure 6 (left). HF were assumed as circular disc with 20 meters radio (φ) according mine desing. Data analysis (Figures 4 and 5) and core logging (Figure 7) were used to build up DFN model of the new fractures created by DDE precondition technique (from Brzovic et al, 2014a). DDE data analysis could only be done at the north part of the studied mine sector, and then assumed similar at the south part.

Figure 7 (Left) Different isometric 3D view of DDE blast holes (green colour) showing DDE fractures identified on both Cores (black) and BHC. (Right) Final DFN model of both HF (sub-horizontal) and DDE (sub-vertical) fractures

Different sub-sectors were redefined within SuaPi mine sector considering the following criteria: Preconditioning technique applied (only HF and HF+DDE) and DDE spacing. At each sub-sector and based on the DFN model, intensity preconditioning was calculated as the P32 parameter. At each sub-sector the structural geological intensity (in situ or within rock mass) also was calculated based on the DFN model (Brzovic and Schachter 2013, Brzovic et al, 2014b) as the P32 parameter too. Vein and fault intensity are assumed as similar through the entire area, preconditioning intensity were estimate only over primary ore. The complete intensity information of each sub-sector is shown in Table 1. The fragmentation performance is then studied at each sub-sector. Table 1 Structural Intensity (rock mass and preconditioning) at SuaPi mine sub-sectors

SuaPi mine Sub-Sectors Dacite Primary with HF+DDE (closer spacing)

Veins 3.1

0.06

0.39

0.11

0.50

Dacite Primary with HF

3.1

0.06

0.22

-

0.22

Dacite Primary with HF+DDE

3.1

0.06

0.35

0.08

0.43

HF+DDE

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Structural Intensity P32 (m2/m3) DDE Faults HF (φ 20m)

HF+DDE

Preconditioning 4

Fragmentations results

Fragmentation data analysis were carried out considering different variables such as hang up frequency, large rock block occurrence in hang up, tonnage per hang up, hang up height, explosive consummation, fragmentation curve, among other, but only the two first ones are shown here. 4.1

Hang up frequency

The hang up frequency of the 3 studied sub sector as the extracted column height increase can be seen in Figure 8. Primary ore and preconditioning effect are observed during the first 100 meters of the column height, secondary ore influences by fine migration occur above that height.

Figure 8 Hung up frequency observed of the 3 studied sub sector through the extracted column height. Above 100m of the column height the secondary ore influence appear

Figure 8 clearly shows that decreasing DDE spacing (closer blast hole) improved fragmentation performance. It is also shown in Figure 8 that FH plus wider DDE spacing (red line) does not differ much in comparison with Dacita only with HF in term of the number of hang up performance. However, Dacite primary only with HF tends to be less productive above 80 meters of column height, and even during the secondary ore influence. 4.2

Large rock block in hang-up

During the fragmentation measurement campaign a special attention was made over the large rock blocks identified in hang up, especially of those in which took more than one shift to bring down from the draw bell. Those large rock blocks were also defined when the large axes observed was above 6 meters long. More than 40 cases were reported during the study, some of them took more than 12 shifts to clear the draw bell, and large axes measured were up to 14 meters long (called as extreme cases hereafter).

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Caving 2014, Santiago, Chile Based on DFN approach, it was also possible to create a map of the local preconditioning intensity model (3D 10mx10mx10m block model) as is shown in Figure 9 (plan view of level 30 meters above UCL), The local preconditioning intensity was also correlated to the large rock block occurrence at the studied area of SuaPi mine sector. It is very clear from Figure 9 that large rock block occurrence at the SuaPi mine sector is controlled by preconditioning intensity. Where there is low preconditioning intensity, more number on large rock blocks appeared at the draw points of the production level. In other words, there is a direct relationship between precondition intensity and fragmentation performance in the stronger and massive Dacita rock type at the studied mine sector. It can be inferred from data analysis that DDE fractures help in fragmentation reduction. Despite that DDE fractures have less intensity (fewer and shorter than HF fractures), these are positioned in a perpendicular orientation respect HF fractures helping to define smaller rock block sizes.

Figure 9 Plan view of SuaPi mine sector showing local preconditioning intensity and large rock block occurrence in hang up at the production level draw points. It is also shown detailed location and information of the extreme cases

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Preconditioning 5 Conclusions Fragmentation measurements were undertaken at the Sur Andes Pipa mine sector within the El Teniente mine to validate the effect of rock mass preconditioning. Two different preconditioning techniques were implemented over the Dacita rock mass, which is considered as the stronger and massive rock mass for caving at the mine site. These were hydraulic fracturing (HF) and confined blasting called DDE. Main conclusions of the work were:

• Rock mass preconditioning resulted in a creation of new and fresh open fractures (Rock mass damage). This is the most important finding since primary rock mass practically does not contain open fractures.

• HF fractures tend to have a sub-horizontal orientation and DDE fractures tend to have sub-vertical disposition. Rock mass fracturing by pre-split blasting techniques is a close comparison to the fracturing by DDE.

• Rock mass damage by preconditioning was quantified by an intensity parameter called P32, which represent the area of fracturing (m2) per volume unit (m3). Rock mass fracturing by HF resulted 4 times greater than DDE fracturing. Then preconditioning P32 of studied SuaPi sub-sector was correlated to fragmentation performance.

• It was measured a considerable reduction (50%) of hang up frequency by closer DDE blast hole, but

it was not observed to much difference between primary rock mass with HF+DDE and only with HF. The amount of blast holes blasted during DDE implementation may also played an important role in rock damage, analysis that was not undertaken in this work.

• Large and extreme rock block in hang up appeared where low intensity of preconditioning was

identified, that confirms the clear and direct relationship between preconditioning intensity and fragmentation performance.

Acknowledgement The authors acknowledge the El Teniente Division of Codelco-Chile for their permission to publish the data and for supporting this work. This study was funded by Dacita Proyect (contracts 4501138457 and 4501236828) and by API T10E202 both of Codelco-Chile. Paulina Schachter, Jose Alvarez, Miguel Castro, Brenda Cerda, Cristobal Ignacio Riquelme are also acknowledged for their contribution to this work.

References Brzovic, A & Villaescusa, E 2007, ‘Rock mass characterization and assessment of block-forming geological discontinuities during caving of primary copper ore at the El Teniente mine, Chile’, International Journal of Rock Mechanics and Mining Sciences’, vol. 44, pp. 565-583. Brzovic, A 2009, ‘Rock mass Strength and Seismicity during Caving Propagation at the El Teniente Mine, Chile ‘, In Proceedings of 7th International Symposium on Rockburst and Seismicity in Mines (RaSiM07), (Tang, C.A. ed.), Dalian University, vol. 2, pp. 838-52. Brzovic, A & Schachter, P 2013, ‘Rock Mass Geotechnical Characterization based on the Weak Stockwork Veins at the El Teniente Mine, Chile’, Proceedings of 3th International Seminary of Geology for the Mining Industry, GEOMIN, Santiago, Chile.

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Caving 2014, Santiago, Chile Brzovic, A, Alvarez, J, Schachter, P, Webb, G, & Rogers, S, 2014a, ‘Discrete Fracture Network Modelling to Quantify the Impact of Intensive Rock Mass Preconditioning at the El Teniente Mine, Chile’, Abstract accepted for the 1st International Conference on Discrete Fracture Network Engineering, Vancouver, October 2014. Brzovic, A, Schachter, P, de los Santos, C, Vallejos, J & Mas Ivars, D 2014b, ‘Characterization and Synthetic Simulations to Determine Rock Mass Behaviour at the El Teniente Mine, Chile. Part I’, Proceedings of the 3rd International Symposium on Block and Sublevel Caving, Santiago, Chile. Dershowitz, W & Einstein, H 1988, ‘Characterizing rock joint geometry with joint system models’, Rock Mechanics and Rock Engineering, vol. 21, pp. 21-51. Gy, PM 1967, ‘L’échantillonnage des minerais en vrac’, Int. Rev. Ind. Miner., Jan. 1967, 188p. Hudson, J, and Harrison, J 1997, ‘Engineering Rock Mechanics, an Introduction to the Principles’, Oxford, Pergamon Press. ISRM 1981, ‘Suggested methods for the quantitative description of discontinuities in rock masses’, Rock characterization, testing and monitoring, ISRM Suggested methods, (edited by ET Brown), Pergamon Press, pp. 3-52. Matheson, GD 1983, ‘Presplit blasting for Highway Road Excavation’, Department of the Environment, Depatment of Transport and Road Research Laboratory Report LR 1094. Rojas, E, Cavieres, P, Dunlop, R, & Gaete, S 2000, ‘Control of Induced Seismicity at the El Teniente Mine, Codelco Chile’, In Proceeding Massmin, (Chitombo, G ed.), Brisbane, Australia, AusIMM, 777-781. Vallejos J, Suzuki, K, Brzovic, A & Mas Ivars, D 2014, ‘Characterization and Synthetic Simulations to Determine Rock Mass Behaviour at the El Teniente Mine, Chile. Part II’, Proceedings of the 3rd International Symposium on Block and Sublevel Caving, Santiago, Chile.

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Improved microseismic event hypocentre location in Block Caving Mines using local earthquake tomography J Philippe Mercier, Golder Associates, Canada W de Beer Golder Associates, Canada J Pascal Mercier Advanced GeoScience Imaging Solutions, Canada

Abstract In production data processing, event hypocentre locations are usually calculated by considering a homogeneous (constant) velocity within the volume of rock monitored. However, the rock mass is far from homogeneous and, in the block caving context, its state can change rapidly as caving progresses. The consequent large discrepancies between the homogeneous velocity approximation and the true velocity distribution can considerably hamper the characterization of cave induced microseismic activity. Local earthquake, or passive source, tomography provides an efficient way to estimate the 3D seismic velocity distribution and simultaneously refine estimates of microseismic event hypocentre locations. It is a robust inversion method that uses information readily available in the microseismic data. It requires no a priori knowledge of the rock mass composition and stress state and provides a comparatively easy way to estimate the 3D velocity distribution using only seismic data. We present the results of locating microseismic event hypocentres in a block cave using local earthquake tomography. In addition, the 3D velocity model(s) calculated provide information on the rock mass state and the distribution and evolution of stresses as caving progresses. We first use a synthetic example to demonstrate the method’s ability to estimate the 3D seismic velocity distribution and simultaneously correct the hypocentre location. We then discuss results obtained using real data collected at a block caving operation.

1 Introduction In hard rock mines, microseismicity provides useful information on the behaviour and response of the rock mass to mining. In block caving, it is recognized that the location and characteristics of microseismic events induced by mining could be used to better understand the evolution of the caving process and the overall rock mass response, both during the development of the undercut and extraction levels and during production. This has been put into practice at several block caves (e.g., H. White et al. 2004; Hudyma and Potvin, 2010a, 2010b) (Glazer & Hepworth, 2006; Glazer & Townsend, 2008; Glazer 2008; Hudyma and Potvin, 2008; Hudyma et al. 2007a, 2007b; Potvin & Hudyma 2008; Trifu et al. 2007; Hylton White et al. 2004). The amount and quality of information extracted from the microseismicity largely depends on the ability to accurately calculate the event hypocentre locations. In turn, the accuracy of the event hypocentre locations is directly related to how representative of reality the velocity model used to calculate the locations is: the more representative the model, the more accurate the location of the microseismic events. In block cave mines (as in other type of mines), the event hypocentre locations are usually calculated by considering a homogeneous (constant) velocity within the volume of rock monitored. In the block caving context, the rock mass can be far from homogeneous, and its state can change rapidly as caving progresses. Potentially large differences between the homogeneous velocity used to calculate the event hypocentre locations and true velocities at different locations in the rock mass can considerably limit a microseismic monitoring system’s ability to characterize cave-induced microseismic activity, yielding significant errors

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Caving 2014, Santiago, Chile in event hypocentre locations and source parameter calculations. For that reason, more sophisticated 3D heterogeneous velocity models that better represent the rock mass should be used. Local earthquake, or passive source, tomography (LET) provides an efficient way to estimate 3D seismic velocity distributions and simultaneously refine estimates of microseismic event hypocentre locations. Compared to other approaches, LET provides an easy way to estimate the 3D velocity distribution employing only seismic data. It has been applied using mine-induced seismicity ((Huang et al. 2013; Maxwell and Young, 1996, 1993; Maxwell et al. 1998)). Our LET method is computation-efficient. It uses only readily available information collected from microseismic data, namely initial event hypocentre locations and Pand/or S-wave onset times. It requires no a priori knowledge of the rock mass composition or stress state. We first verify the capabilities of this technique by applying it to a synthetic example. We then show how it can be applied to real data collected at a block caving mine during the caving process. Our results clearly show that our method helps to improve the accuracy of microseismic event hypocentre location estimates and obtain information on the 3D velocity distribution, yielding a better understanding of the rock mass state and the distribution and evolution of stresses as caving progresses. We show that LET provides an alternative to an approach that involves manual building of a 3D velocity model from available geotechnical information. A note on terminology: by “location error” we mean the difference between a real source location and the calculated location for the same source. In practice, location errors can only be determined for synthetic sources, controlled sources (e.g., surveyed blasts or mechanically-induced vibrations) and mined-through induced or natural seismic event sources. “Location uncertainty” refers to a statistical measure of the size of error ellipsoid within which, to a high degree of confidence, the actual location of the source is. “Residual” or “travel time residual” refers to the goodness-of-fit measure employed in an inversion.

2 Method The relation between arrival time, , velocity, recorded at a sensor located at is as follows:

, and origin time,

, for an event located at

(1)

Where:

represents the travel-time, , and

is the ray-path.

Equation 1 is non-linear, since the trajectory between a source and a receiver along which the seismic energy travels depends on the underlying velocity model, , the event hypocentre location, and the sensor location, , and since the calculated hypocentre location and event origin time depend on the velocity model. The inverse problem consists in calculating simultaneously the 3D velocity distribution, event hypocentre location and event origin time corrections from travel-time measurements. To solve this inverse problem, we adopted a popular approach (e.g., Eberhart-Phillips, 1993, 1993; Kissling et al. 1994; Thurber & EberhartPhillips, 1999) consisting of linearizing Equation 1 and correcting the model parameters (velocities, event hypocentre locations and event origin times) to reduce the difference between the observed and predicted arrival times while imposing constraint on the resulting model in a series of linear inversions and forward modellings.

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Seismicity 3

Synthetic example

3.1

Synthetic examplesetting

The purpose of the synthetic test is to show that LET can recover complex velocity distributions and correct event hypocentre locations without any a priori knowledge of the velocity distribution. For the synthetic test, we built a 100x100x100 m3 synthetic velocity model with a 20x20x20 m3 cubic low velocity anomaly in the middle. We set the velocity of the background and the low velocity anomaly to 5,000 ms-1 and 1,000 ms-1, respectively. We then distributed 25 sensors and 200 events inside the model but outside the low velocity anomaly using uniform and Gaussian random distributions, respectively. Figure 1 shows the synthetic velocity model, the location of sensors and the microseismic events.

Figure 1 Oblique view of the synthetic velocity model. Blue and red represent low and high velocities, respectively. Inverse cones represent sensors and dots microseismic event hypocentre locations

3.2

Synthetic travel time data and initial event hypocentre locations

Using the settings discussed previously, we generated a set of synthetic travel times using a Fast Marching Eikonal solver (Sethian 1999) along 40% of the all possible ray paths, which represents on average 10 travel times per event. Note that every event-sensor pair yields one ray path. Employing the synthetic travel times, we then calculated the event hypocentre locations in a homogeneous grid with a velocity of 5,000 ms-1. 3.3

Inversion setting

Joint velocity, event hypocentre and event origin time inversion was performed on the synthetic travel time data set. We used a homogeneous grid, with a velocity of 5,000 ms-1 as a starting velocity model and the event hypocentre locations calculated from the synthetic travel time on this homogeneous grid as

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Caving 2014, Santiago, Chile the starting point for event hypocentre locations. The spacing of the inversion grid was set to 1 m in every direction, yielding a little more than 1 million grid nodes. 3.4

Synthetic test inversion results

Figure 2a compares the true velocity profile and the velocity profiles obtained after 1, 10 and 50 iterations measured along the Z axis for X and Y constant and equal to 50 m (middle of the model). The recovered velocity model converges towards the true velocity. The mean relative error in percent ( ) between the true, , and recovered, 1.5% and 0.8% after 1, 10 and 50 iterations, respectively.

, velocity model is 7%,

Figure 2b shows the evolution of the mean event hypocentre location error during the inversion process and compares the results to the mean location error for an event hypocentre located using the initial model homogeneous velocity (~10.5 m) and the true velocity model (~2 m). Note that a non-linear location procedure (Lomax et al., 2000) was used to locate the event hypocentre in the true velocity model. The mean error for the event hypocentre location calculated using LET decreases from approximately 10.5 m, the mean hypocentre location error in the homogeneous model, to slightly more than 3.5 m. The mean location error obtained after 50 iterations is roughly 1.5 m higher than the mean hypocentre location error calculated using the true model, and about 3 times smaller than the initial mean error for the event hypocentre located in the homogenous velocity model.

Figure 2 Comparison of original and recovered model parameters: (a) Velocity along the Z-axis for constant X and Y in the middle of the model at three stages of the inversion process. (b) Evolution of the mean event hypocentre location error during the inversion process

4.0

Real data example: Block Caving

4.1 Context We used a data set containing P-wave onset time measurements collected over a week at the height of seismic activity during production, and corrected the event hypocentre locations and event origin times.

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Seismicity 4.2

Microseismic system

The microseismic activity at the mine was monitored by an array composed of 19 triaxial accelerometers and nine uniaxial geophones. The sensors were deployed relatively close to the ore body in a 3D geometry designed to ensure accurate detection and location of microseismic events throughout the caving process and to mitigate the shadowing effect expected from the growing cave. 4.3

Inversion setting

The P-wave velocity models cover a volume extending over 550 m in the north and east directions and 500 m in the Z direction, fully encompassing the ore body being mined. The spacing between adjacent nodes was set at 5 m in every direction, yielding a model comprising 1.21 million nodes. The starting velocity model was chosen to be homogeneous (i.e., constant), with a velocity value of 3,900 ms-1 attributed to every node. This velocity corresponds to the average P-wave velocity obtained with calibration blasts. The inversions were performed independently on each of the velocity models, and 20 non-linear iterations were used. The regularization parameters were set to 1x10-13 and 1x10-7 for velocity and event hypocentre correction, respectively. These values were selected using a heuristic approach based on the so-called trade-off curve (see Rawlinson and Sambridge, 2003). 4.4

Inversion results

Figure 3 presents the inversion results and shows in (a), the evolution of the cumulative travel time residual, (b), the difference in the estimated event hypocentre location uncertainty at the beginning and end of the inversion process, and (c), (d) and (e), three perpendicular cut-slices showing the resulting velocity models and the location of the corrected microseismic event hypocentres. The inversion yielded a decrease in the cumulative travel time residual from the initial value of 0.65 ms to 0.25 ms at the end of the inversion process. Note that the cumulative travel time residual measures the

fit between the predicted and observed travel times ( ). The event hypocentre location uncertainty, which is estimated from the covariance matrix, decreased from more than 30 m for events located in the homogeneous grid to approximately 10 m at the end of the inversion process. This represents a three-fold improvement. In addition, the 3D seismic P-wave velocity distribution was calculated. The velocity model features a large, low velocity (blue) region surrounded by high velocity (red). The main low velocity region is located at the bottom, near the extraction level. The range of recovered velocity extends from approximately 2,900 ms-1 to 4,550 ms‑1, with a standard deviation of close to 150 ms-1. The synthetic and real data examples presented in the previous sections show that LET can be used to significantly improve event hypocentre location in complex media with strong velocity contrast, using information readily collected by a microseismic monitoring system and without the need for explicit manual construction of a velocity model using estimates of cave geometry, rock mass properties, stress state and (simplified) geological units.

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Figure 3 Tomographic inversion results for the period extending from 6 October 2004 to 13 October 2004. (a) Event hypocentre location uncertainty distribution at the beginning and at the end (after 50 non-linear iterations). (b) Travel-time residual evolution during the inversion process. (c), (d) and (e) Three perpendicular slices in the velocity model perpendicular to the east, north and z directions, respectively. The black rectangles give an indication of the volume encompassed by the cave during the time spanned by the data

5

Discussion

For the synthetic case, LET was able to reduce the location error by a factor of three compared to the homogeneous velocity model. In addition, the location error achieved by LET is only slightly higher than the smallest possible location error obtained using the true velocity model. In the case of the real data example, although the vast majority of location errors cannot be determined since the true locations of the microseismic events in question are generally not known, we have shown that the estimated location uncertainty was also improved by a factor of about three compared to the homogenous velocity model. Apart from allowing relocation of the event hypocentre, LET images the 3D velocity distribution of the rock, providing insights into stress distribution and cave geometry. The velocity distribution can be used to supplement the geotechnical data collected during the caving process and provide insight into the rock mass response to mining activity, the progression of the caving front and the geometry of the cave. When inversion is repeated for data sets covering different time periods, LET can also provide information on the variation of the 3D velocity distribution. The explicit construction of a model representing the 3D velocity distribution during the caving process requires considerable logistics and adds to the burden of duties of a technical services department. Large amounts of geotechnical and geological data must be collected (generally manually), rapidly qualitycontrolled and then distributed and managed. To properly build a velocity model that is representative of the true rock mass velocity, precise information is required on rock mass properties, the caving front location, the cave geometry and the stress state of the rock mass. In addition, velocity models need to be

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Seismicity updated regularly to account for the progression of the caving front, the changing cave geometry and stress redistribution around the cave. Even after this effort, with the best data quality and density, inaccuracies in the velocity model are inevitable.

6 Conclusions In this paper, we have demonstrated that local earthquake tomography (LET) can be used to improve the accuracy of event hypocentre location with very little information about the velocity model and no information on the cave geometry or stress state of the rock mass. We have applied LET to two data sets, one synthetic and one from a real block cave. Our results show that event location uncertainty can be significantly improved by using LET rather than homogeneous velocity models. An additional outcome of LET is a 3D velocity model that provides important insights on the rock mass response to mining, complementing other geotechnical data collected. In summary, our results show that LET can provide an alternative to an approach involving the manual building of a 3D velocity model from available geotechnical information.

References Eberhart-Phillips, D 1993, ‘Local earthquake tomography: earthquake source regions’, Seism, Tomogr. Theory Pract, pp. 613–643. Glazer, S, Hepworth N, 2006, ‘Crown pillar failure mechanism–case study based on seismic data from Palabora Mine’, Min. Technol, vol. 115, pp. 75–84. Glazer, SN 2008, ‘Seismically active volume around the cave and its relation to the caving stages’, MassMin 2008, Luleå Sweden 9-11 June 2008, Luleå University of technology, Luleå Sweden, pp. 983– 992. Glazer, SN, Townsend, P 2008, ‘The application of seismic monitoring to the future Lift 2 block cave at Palabora mining company’, MassMin 2008, Luleå Sweden 9-11 June 2008, Luleå University of technology, Luleå Sweden, pp. 919–930. Huang, J.-W, Reyes-Montes, J, Young, R 2013, ‘Passive three-dimensional microseismic imaging for mining-induced rock-mass degradation’, Rock Mechanics for Resources, Energy and Environment, CRC Press, 1000p. Hudyma, M, Potvin, Y, 2008,’Characterizing caving induced seismicity at Ridgeway gold mine’, MassMin 2008, Luleå University of technology, Luleå Sweden, pp. 931–942. Hudyma, M, Potvin, Y 2010a, ‘An Engineering Approach to Seismic Risk Management in Hardrock Mines’, Rock Mech. Rock Eng., vol. 43, pp. 891–906. Hudyma, M, Potvin, Y 2010b. ‘An Engineering Approach to Seismic Risk Management in Hardrock Mines’, Rock Mech & Rock Eng., vol. 43, pp. 891–906. Hudyma, MR, Potvin, Y, Allison, DP 2007a, ‘Seismic monitoring of the Northparkes Lift 2 block cave - part 1 undercutting’, 1st International Symposium on Block and Sub-Level Caving Cave Mining, Cape Town, pp. 303–334. Hudyma, MR, Potvin, Y, Allison, DP, 2007b, ‘Seismic monitoring of the Northparkes Lift 2 block cave part 2 production caving’, 1st International Symposium on Block and Sub-Level Caving Cave Mining, Cape Town, pp. 335–354.

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Caving 2014, Santiago, Chile Kissling, E, Ellsworth, W, Eberhart-Phillips, D, Kradolfer, U 1994, ‘Initial reference models in local earthquake tomography’, J. Geophys. Res. Solid Earth 1978–2012, vol. 99, pp.19635–19646. Lomax, A, Virieux, J, Volant, P, Berge-Thierry, C 2000, ‘Probabilistic earthquake location in 3D and layered models’, Mod. Approaches Geophys, vol. 18, pp. 101–134. Maxwell, S, Young, R 1993, ‘A comparison between controlled source and passive source seismic velocity images’, Bull, Seismol. Soc. Am. , vol. 83, pp.1813–1834. Maxwell, S, Young, R 1996, ‘Seismic imaging of rock mass responses to excavation’, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., vol. 33, pp. 713–724. Maxwell, S, Young, R, Read, R 1998, ‘A micro-velocity tool to assess the excavation damaged zone’, Int. J. Rock Mech. Min. Sci., vol. 35, pp. 235–247. Potvin, Y, Hudyma, M 2008, ‘Interpreting caving mechanisms using microseismic monitoring data’, MassMin 2008, Luleå University of technology, Luleå Sweden, pp. 971–982. Rawlinson, N, Sambridge, M 2003, ‘Seismic traveltime tomography of the crust and lithosphere’, Adv. Geophys, vol. 46, pp. 81–198. Sethian, JA 1999, ‘Fast marching methods’, SIAM Rev., vol. 41, pp. 199–235. Thurber, C, Eberhart-Phillips, D 1999, ‘Local earthquake tomography with flexible gridding’, Comput. Geosci, vol. 25, pp. 809–818. Trifu, C-I, Shumila, V, Burgio, N 2007, ‘Characterisation of the Caving Front at Ridgeway Mine, New South Wales, Based on Geomechanical Data and Detailed Microseismic Analysis’, Challenges in Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, Australia, pp. 443–453. White, H, de Beer, W, White, H, van As, A 2004, ‘Design and Implementation of Seismic Monitoring Systems in a Block-Cave Environment’, MassMin 2004, Santiago, Chile. White, H. de Beer, W, White, H, van As, A, Allison, D 2004, ‘Implementation of seismic monitoring systems in a block-cave environment’, Presented at the Massmin 2004, Santiago, Chile, pp. 559–554.

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Seismic risk management for underground mining projects - Codelco Chile Division El Teniente AE Espinosa CODELCO Chile Division El Teniente, Chile RA Fuentes CODELCO Chile Division El Teniente, Chile EG Moscoso ERDBEBEN Ltda, Chile

Abstract The main objective of this article is to propose a hazard seismic description, for each project stage, so they are useful in taking decision at the scopes of design and planning. The final purpose is to minimise the exposure and increase the safety conditions according to each engineering project’s stage. As an example of this proposal, the application of this method for Dacita project is presented. This process has allowed apply a methodology based on geomechanical vulnerability descriptions for mine design and mitigate those vulnerabilities by installing a proper ground support system and safety re-entry times after blasting.

1 Introduction Mining projects need a seismic risk management. The risks are presents in mining projects since these consist in a unique process with objectives and time spans well defined. Then, they have uncertainty. The mining projects management consists of applying knowledge, skills, tools and techniques to achieve the production objectives, assuming uncertainties and costs. According to the Project Management Institute (PMI 2008), the risk management basically consists of identifying hazards, risks evaluation under certain criteria, and their impacts and administration. This process must be iterative and fed-back with results. The UK Association for Project Management establishes that the risk management and its implementation must be carried out during the early stages of the project, when its development is more flexible. The risk analysis must be done in these stages and must be upgraded in the next stages (Brown 2003). In general, the risk management of a mining project takes into account not geotechnical issues, such as price variations and exchanges rates (Butcher & Smith 2010). However, some researchers suggest that geotechnical issues are the most important to be considered in a risk management of a mining project (Bartlett 2010; Catalan et al. 2010; Hormazabal et al. 2010). A mining project development involves risks during mining method selection, mine design, and operations. The risks could be: geological and geotechnical data, cave-ability, cave propagation, fragmentation, excavation stability, and operational and environmental hazards. A methodology called CaveRisk was proposed during the International Caving Study to manage the risks in block caving projects, which considers the previous geomechanical topics, and the more dangerous risks like rock burst (Brown 2003). The seismic risk is related to rock burst. This occurs in underground mining as a combination of stresses and rock mass conditions. Seismic hazard requires management from the early project stages. About the conceptual framework, this proposal is based on Dunlop & Gaete (2000) concept. They propose that the induced-seismicity control must be done considering undercut and extraction rates, in order to reduce the active rock mass volume, according to its own mechanical characteristics and induced stresses (Dunlop & Gaete 2000). Currently, at El Teniente mine, the seismic hazard estimation for productive sectors is based on the maximum magnitude expected by using the Gutenberg-Richter law. This estimate considers a volume and

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Caving 2014, Santiago, Chile time period assuming this rock mass volume has been subjected to the entire caving mine process during that time period, up to its total fragmentation. Besides, it assumes the mine maintains similar undercut and extraction rates. This method allows to obtain a certain dangerousness level to a project in evaluation, but is not enough to take some control actions according to geological conditions, geomechanical environment, design and mine sequence, for each engineering stage, construction and execution of an underground mine. The main objective of this article is to propose a hazard seismic description for each project stage to make decisions in design and mine planning, which allows to carry out a seismic risk management in each project engineering stage to minimise the exposure and recommend ground support systems according to expected requirements. Finally, it is necessary to monitor the rock mass response to mining. As an example of this proposal, the evolution of decisions for Dacita project is presented since the first stage. This process has allowed an application of a methodology based on geomechanical vulnerability descriptions for mine design and mitigate those vulnerabilities by installing a proper ground support system and safety re-entry times after blasting.

2 Methodology From a geomechanical point of view, one of the main threats for an underground caving project is the induced seismicity and rockburst. This methodology presents how each engineering stage takes into account the risk management of seismic hazard according to available data and mine design requirements. The pre-feasibility and feasibility are the main stages considering the seismic hazard. In these stages, the following issues must be considered:

1. Ground support in galleries. 2. Growth strategies and extraction rates. 3. Tolerable distances and alternative drifts. 4. Post blasting isolated times for re-entry. 5. Geomechanical monitoring. In this way, the seismic hazard management has objectives well defined. These objectives must be achieved in each engineering stage according to available data and analysis tools. In the following, the purposes, scopes and analysis tools to manage the seismic risk are described for each project stage. The main concepts of this methodology are:

1. Seismic Event: rock mass fracture that releases energy in elastic waves. These elastic waves are detected by a seismic network.

2. Seismic hazard: it is a threat for people and mining plan performance due to seismic events. The maximum magnitude expected is a measure of seismic hazard.

3. Seismic risk: combination of seismic hazard probability and negative consequences for people and mining plan.

4. Risk Control Plan: management to avoid, transfer, reduce and/or accept the hazard consequences. 5. Residual seismic risk: quantification of seismic hazard after taking the control actions. In the following, activities, methodology and final products for each topic are described to finally integrate everything in a strategic plan matrix of resources.

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Seismicity 2.1

Profile engineering

The main objective of this stage is to have a qualitative description allowing identify relevant faults about mine exploitation y achieve a 50% of risk certainty. The applied methodologies are hazard description regarding geological and geotechnical data and stresses. 2.1.1

Seismic hazard description

Seismic hazard depends on these parameters:

• Column height: estimate of pre-mining stresses. • Cavities interaction: estimate of induced stresses. • Rock mass geotechnics (lithology and main geological faults): estimate of rock mass response to mining, strain and fracture.

• Gutenber-Richter distributions for seismic data from productive sectors next to the project. 2.1.2

Identify risk potentials

The risks evaluation is based on design elements being considered in this stage. Some elements in this risk qualitative evaluation are:

• Undercut level elevation y vertical distance among levels. • Pillars size and shape in extraction and undercut levels. • Location of caverns and other civil buildings. • Alternatives for haulage level and mineral transport. • Starting point and growth sequence to cave propagation. 2.2

Prefeasiblity Engineering

Design options are evaluated in this stage. Therefore, the seismic hazard and risk must be done for very different design options. This idea could be ambiguous, but it is necessary to carry out the analysis case by case, because different mining methods could induce similar seismic rock mass response. 2.2.1

Seismic hazard estimate

In this stage, a geographical location of the mining project, and some spatial and time limits are defined. With this background, the methodology proposed by Gaete (2009) is applied by Dunlop (Dunlop 2010) to estimate the maximum magnitude for a seismic event, by using some parameters comparing stress, mining, rock mass properties, and a seismic data catalogue. Induced seismicity is located around the cavity generated by mining. This seismicity defines an active rock mass volume in failure, Vf, due to induced stresses. Then, the maximum seismic moment expected, M0, inside of this active volume is: M0 = kVf ( 1 )

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Caving 2014, Santiago, Chile where k is a constant. The active volume mainly depends on pre-mining stress, mining rates and rock mass characteristics. If these parameters are known for the project (new mine), its volume in failure

V fp is:

p

V f = (1+α r)Vf s)(1+α

(2)

where aS is the pre-mining stress variation factor, am is the mining rate factor, and ar is the rock mass p variation factor. Therefore, the maximum seismic moment expected, M 0 , for the project is:

r)Vf M 0p = k = V fp k (1+αs)(1+α m)(1+α

(3)

Assuming the structural properties of the rock mass control the fractures generation and caving propagation, and similar mining rates between the reference mine (known) and the mining project:

() () V

p () = kk (1+α 1 + as)(1+α a cf 1ff)Vf +a cf)(1+α Mf = s 1+

f

( 4 )

f

where αcf is related to cohesion and friction angle of structures, and αff is related to fractures frequency. The moment magnitude used at El Teniente´s mine is: 2_ m = logM 0 -6.01 (5) 3 Then, the maximum magnitude expected for a mining project is:

p mmax

p 2_2 log k () m max = 1 +s)(1+α a s () 1cf+)(1+α 1 + f]-6.01 a f V f � 6.01 a cf ff() = log[k(1+α )V 33 2 = 1 + a s 2_() + a fcf)(1+α V 1 = log[kV ]-6.01+ log(1+α 2_ log k f() 1 + a cf () s1)(1+α f � ff)]6.0 33 3

[ [

] ]

(6) (7)

mr

The first term of the second member in equation (7) is the maximum magnitude expected max for the reference mine. This magnitude can be estimated by applying a Gutenberg-Richter law to seismic data. Therefore,

2.2.2

Qualitative risk evaluation - risk matrix

( 8 )

The qualitative evaluation of risk consists in obtain the probability of occurrence and its economic impact in the mining process, due to induced-seismicity not expected. Some control actions are developed and applied to the next engineering stage. In this example, the risk are classified in:

• Tolerable (Probability x Impact ≤ 2 • 2 < Moderado (Probability x Impact < 4 • Unacceptable ≥ 4 2.2.3

Seismic risk control - instrumentation requirements

Considering the hazard antecedents and qualitative evaluation of seismic risk, a first approach for instrumentation requirements can be done to different alternatives of the mining project.

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Seismicity 2.3

Feasibility engineering stage

The feasibility stage does the design of the mine alternative chosen or selected from the previous stage. In this step, it is necessary identify sectors well defined where there are suitable geological, geotechnical and stress conditions. These conditions could assist in getting a different seismic risk response. Besides, it is necessary to establish action controls and anticipate possible negative effects in the mining plan. 2.3.1

Seismic risk - application

The objective in this stage is identify risk zones, applying the previous concepts and elements, which were estimated. The results are vulnerability maps allowing guide the action controls in mine design as well as extraction process. In this stage, the location and sizes of galleries and drifts must be revisited, with modifications in layouts. 2.3.2

Seismic risk control - application on mine design

The mine design must incorporate solutions to control the seismic risk. This is applied in location and geometry of excavations in order to reduce the negative consequences in case of severe seismicity. This process is iterative because the vulnerability maps must include the mining layout, which could be modified according to risk evaluations. 2.3.3

Residual seismic risk

The residual seismic risk estimate and the control actions must be considered in geomechanical guides for mining plan. For example, in this section, the transition zones, the re-entry times, the maximum extraction rates, the advancing front orientation and geomechanical monitoring tools are defined and implemented. 2.4

Detail engineering stage

The previous results are consolidated in technical specifications, design planes, budgets and constructions issues of the project. About seismic risk management, the vulnerability must be considered in layouts, mine advancing and control actions. 2.4.1

Seismic risk control – specifications

These mainly are:

• Type (brand and model) and location of seismic sensors and sesmic network. • Define seismic network sensibility • Seismic network installation on field according to developments and energy power availability.

It would be useful an evaluation of geomechanical stability conditions of older galleries to install seismic sensors there.

• Hardware and software requirements for properly performance of seismic network. Suppliers availability and delivery times of products.

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Caving 2014, Santiago, Chile Table 1 Sumary of principal elements to be incorporated in each engineering stages.

Engineering stage

Key aspect in the seismic risk control

Products

Scope of the evaluation

Value for the project

SCOPING

Location, size and temporality for mining (developments and extraction)

Sizing Impact of seismic risk

Quantify the expected seismic risk,.

Decision on the technical feasibility of the project

Desk reviews for dimensioning seismic response (Deterministic, statistics.)

Using knowned methodologies. At least 50% of the background must come from the sector under study.

Evaluation and consideration for alternative trade off

PREFACTIBILITY (Conceptual)

FACTIBILITY (Básica)

conceptual geomechanical model Identify key aspects in seismic response Geological, geotechnical and mining infrastructure layout models.

DETAIL

3

Vulnerability plans according to plan growth mining.

To develop plans Provides control for evaluation and elements for control of seismic dimensioned risk due to mining seismic risk

Technical specifications and budgets directed to management of seismic risk

Provides all the technical background for acquisitions and operational implementation of control measures

Application to Dacita´s project

A first approach to apply this methodology was done during the feasibility stage of the Dacita project. In this stage, the seismic hazard concept carried out at two stages. First the definition of the maximum magnitude expected and vulnerability plans constructions according to geotechnical data and mining. Table 2 presents the values of the seismic coefficients (Equation 8) as used in the project for the Dacite and the Andesite lithologies. It is noted that acf decreases from 3.7 to 1.6 (131% increment regarding 1.6 ) and that aff increases from 0.62 to 0.85 (37% increment). Both numbers means an increase for the “seismic hazard”. Table 2 Values to estimate maximum magnitude at Dacita project.

Lithology Dacite CMET

FF/m weak veins (αcf)

Ff/m strong veins (αff)

Maximum Magnitude

1.6

0.85

Table 3

3.7

0.62 (*) PA: Preconditioning

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1.5 (Above PA(*)) 1.1 (In PA)

2.2 (Below PA)

Seismicity In relative terms, the maximum magnitude increment could be obtained by replacing into Equation ( 8): Then, the values for the project are derived (as shown in Table 3). Table 3 Values for expected maximum magnitude at Dacita´s project

Zone Above PA In PA Below PA

Maximum magnitude expected 1.5 + 0.3 1.1 2.2+0.3

Regarding the vulnerability plan these indicate specific locations for each mine level where it could be affected by seismic activity owing to factors, such as: excavation geometry, litholigic environment, presence of geological faults and contact zones. This meant changes in the design according to the identified vulnerabilities. Otherwise, in sectors where would impracticable to make changes of layout were implemented control measures. These measures include installing additional fortification geometries of greater vulnerability and a change of the starting point of mining and conditions for continuity of production.

4 Conclusions The seismic risk must be conducted from the profile engineering stage and increase the control level over potential looses while going forward in the different stages of engineering. The estimation of magnitude for maximum expected seismic event is not useful if it is not accompanied with estimation of location, mining condition (mining advance) and control measurements. This control measurements include alternatives in the layout design to mitigate looses in mineral extraction, use of support system according to the risk potential identified and use of different isolation times before blasting according to geological or geotechnical conditions.

References Project Management Institute PMI, 2008, Guía de los fundamentos para la dirección de proyectos, PMI Inc, Pennsylvania. Brown, ET 2003, Block caving geomechanics: International Caving Study 1997 - 2004, JKRMC (ed), Queensland. Butcher, RJ, & Smith, G 2010, ‘Strategic considerations in block caving’, Proceedings of the Second International Symposium on Block and Sublevel Caving, (Potvin ed), Perth, pp. 231 - 236. Bartlett, PJ 2010, ¡Considerations in planning and implementing massive underground mines at depth’, Caving 2010: Proceedings of the Second International Symposium on Block and Sublevel Caving, (Potvin ed), Perth, pp. 359 - 370.

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Caving 2014, Santiago, Chile Catalan, A, Sinaga, F, & Qudraturrahman, I 2010, ‘The role of geotechnical engineering during the prefeseability studies and early works of Canadia East panel caving project’, Caving 2010: Proceedings of the Second International Symposium on Block and Sublevel Caving, (Potvin ed), Perth, pp. 389 - 406. Hormazabal, E, Villegas, F, Rovira, F, & Carranza-Torres, C 2010, ‘Geomechanical evaluation of macroblock caving options using 3D numerical modelling at Chuquicamata underground project in Chile’, Caving 2010: Proceedings of the Second International Symposium on Block and Sublevel Caving, (Potvin ed), Perth, pp. 469 - 482. Dunlop, R & Gaete, S 2000, ‘An estimation of the induced seismicity related to a caving method, in Dynamic rock mass response to mining’, Proceedings of the Fifth International Symposium on Rockburst and Seismicity in Mines Proceedings, RaSiM5, (van Aswegen, Durrheim and Ortlepp eds), Johannesburg, pp. 281 - 285. Van Aswegen, G 2005, ‘Routine seismic hazard assessment in some South African mines, in Controlling Seismic Risk’, Sixth International Symposium on Rockburst and Seismicity in Mines Proceedings, RaSiM6, (Potvin & Hudyma eds), Perth, pp. 437 - 444. Durrheim, RJ, Cichowicz, A, Ebrahim-Trollope, R, Essrich, F, Goldbach, O, Linzer, LM, Spottiswoode, SM, & Stankiewicz, T 2007, ‘Guidelines, standards and best practice for seismic hazard assessment and rockburst risk management in South African mines’, Deep Mining Proceedings, (Potvin ed), Perth, pp. 249 - 262. Spottiswoode, S 2009, ‘Mine seismicity: prediction or forecasting?’, Hard Rock Safe: Safety Conference, The Southern African Institute of Mining and Metallurgy, pp. 81 - 98. Kijko, A & Funk, CW 1994, ‘The assessment of seismic hazard in mines’, The Journal of The South African Institute of Mining and Metallurgy, July 1994, pp. 179 - 185. Hudyma, M & Potvin, Y 2004, ‘Seismic hazard in Western Australian mines’, The Journal of The South African Institute of Mining and Metallurgy, June 2004, pp. 265 - 276. Albrecht, J & Potvin, Y 2005, ‘Identifying the factors that control rockburst damage to underground excavations’, Controlling Seismic Risk: Sixth International Symposium on Rockburst and Seismicity in Mines Proceedings, RaSiM6, (Potvin & Hudyma eds), Perth, pp. 519 - 528. Heal, D, Potvin, Y & Hudyma, M 2006, ‘Evaluating rockburst damage potential in underground mining’, Proceedings of the 41st U.S. Symposium on Rock Mechanics (USRMS), American Rock Mechanics Association, Colorado. Mendecki, A 2008, Forecasting seismic hazard in mines, in The First Southern Hemisphere International Rock Mechanics Symposium Proceedings, Perth, pp. 1 - 17. Mendecki, A & Lötter, E 2011, ‘Modelling seismic hazard for mines’, Australian Earthquake Engineering Society 2011, Conference Proceedings, Barossa Valley, pp. 1 - 20. Van as Aswegen, G & Mendecki, A 1999, ‘Mine layout, geological features and seismic hazard’, Final Report, Safety in Mines Research Advisory Committee, SIMRAC. Wang, JA & Park, HD 2001, ‘Comprehensive prediction of rockburst based on analysis of strain energy in rocks’, Tunnelling and Underground Space Technology, Elsevier, vol. 16, pp. 49 - 57.

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Seismic hazard analysis at the El Teniente Mine using a clustering approach J Cornejo Codelco, Chile J Vallejos University of Chile, Chile X Emery University of Chile, Chile E Rojas Codelco, Chile

Abstract Cave mining methods induce changes in rock mass equilibrium conditions around cavities created by mining exploitation. These changes are often represented as seismic activity and its characterization is one of the major challenges related to control people exposure to rock burst risk. At El Teniente mine, important advances have been reached on this field with monitoring and short term protocols, but the study of characterization and identification of zones with higher seismic latency and/or with higher rock burst probability of occurrence is still on progress. This work explains a proposal for the identification of different levels of seismic hazard, using the agglomerative hierarquic clustering technique. This methodology includes the application of reliability filters and temporal locations, besides the use of pre-processing of residual estimation of hypocenter positions. Finally, by using back analysis, it is possible to separate groups of seismic events with different characteristics and hazard levels based on statistical criteria, allowing to improve actions in order to mitigate rockburst risk.

1 Introduction The changes induced by the application of a massive caving method, such as panel caving, in a primary rock mass, generate an important redistribution of stresses around the cave-back. This can be seen mostly in the seismic activity, generating relevant interferences with the mining business, mainly for risks related with people, which are a constant factor of analysis at CODELCO – El Teniente. In this context, zones next to the main faults and lithology contacts with different geotechnical characteristics are among the most complex zones for mining. For that reason, it is primordial to identify the most dangerous for exposure to people to sudden energy releases, to apply mitigation actions. From the nineties, different authors have investigated the spatial distribution of seismic activity through cluster analysis, making significant advances in the inclusion of clustering patterns related with the genesis of seismic activity. At El Teniente, this analysis technique was introduced only in the last five years, achieving significant improvements in the identification of the seismic source associated with each cluster. However, the position of a seismic event is an estimated parameter subject to uncertainties and errors, which can be minimized but not removed. One way to minimize the effect of uncertainties is not to consider the locations where they appear far from the seismic source. This methodology is completely subjective, can produce a loss of information and strongly influence the results of analyses. For the above reasons, in this research, a methodology of repositioning seismic events is proposed, based on the uncertainty in the position estimation, called statistical collapse. This solves the problem of the information effect and

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Caving 2014, Santiago, Chile integrates the uncertainty in the analysis. The research was made in a sector called Esmeralda Sur Bloque 1, in the south east of the deposit. The results permit to apply risk mitigation measures in the production process and minimize personnel exposure, knowing beforehand the areas with less favorable seismic responses.

2

El Teniente Mine overview

El Teniente Mine is a Codelco Chile underground copper mine. It is located in the Andes range in the central zone of Chile, about 70 km SSE from the capital city, Santiago. El Teniente is the largest known copper– molybdenum deposit in the world. It is hosted in a copper porphyry system. The main rock types include andesite, diorite and hydrothermal breccias of the Miocene era. Since 1906, more than 1,100 million tons of ore have been mined. The mine is currently extracting around 140,000 tons/day using mechanized caving methods. Panel and post-undercut caving methods, variations of the standard block caving, were introduced in 1982 and 1994, respectively, to exploit primary copper ore (Informe Plan Minero 2014).

3

Background

3.1

Spatial identification of seismically active zones

Mendecki et al. (1999) arguet that the “main purpose of spatial analysis of the seismicity is to delimit the areas or volumes of interest from the point of view of stability.” In the medium-term seismic hazard, the spatial locations of seismic events were visually identified, or are delimited using techniques such as clustering or control polygons (van Aswegen 2005). The contour of the seismic parameters in the spatial data sets can be carried out to identify areas of maximum values of the parameters or to find gradients of change in the parameters. Both anomalous variations in the local maxima and the area of greatest change have been identified as areas with rock burst potential. Clustering is the search for sets of objects, such that the objects in a group will be similar to (or related) with each other and different (or unrelated) to the objects of other sets (Haldiki et al. 2001). The definition of the groups may be imprecise and depends on the nature of the data, and expected results (Witten et al. 2011). In this way for a reliable method and is not dependent on the user’s influence is crucial because it allows the reproducibility of analysis. Clustering methods have a large number of applications, which have been documented and published especially in the areas of computing, biology, economics, rock mechanics, among others. These methods can be classified into two ways of grouping; agglomerative and divisive. In the case of the agglomerative (or also known as hierarchical) called, pairs of individual data are combined based on a criterion of similarity to create groups. The similarity criterion is then applied to the groups to create a hierarchy of Ascending closeness. In Figure 1, seven elements A, B, C, D, E, F and G with their corresponding map of hierarchy or dendrogram are shown (from Jain et al. 1999).

Figure 1 Hierarchical clustering of individual items (a) and its corresponding hierarchical dendrogram (b) their relative levels of similarity (Jain et al. 1999)

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Seismicity In literature several methods of clustering analysis applied to seismology and seismicity in mining can be found. These studies have sought to main objective investigation of the distribution of space-time, energy and magnitude of seismic events in different regions and scales (Frohlick and Davis 1990; Kijko & Funk 1996; Falmagne 2001; Vasak et al. 2004; Hudyma 2002, 2008, 2009; Fuentes 2008). Clustering conventional methodologies, using the similarity measure of distance between the data, have advantages and intrinsic limitations. However, all the techniques developed in this field concur that when used in clustering data associated with seismic events, assume that the position of the seismic events is unequivocal. The assumption that the estimated positions of seismic events are unequivocal is far away from reality. This is because the position of a seismic event is estimated and is subject to uncertainties and errors, which result in, for example, poor estimate of the time of arrival of P and S waves, incorrect velocity structure or poor geometry of network monitoring. These estimation errors could be minimized by performing the location process carefully, but the uncertainties can never be eliminated from the observed data, i.e., the arrival times of the wave are themselves subject to uncertainties. The collapse statistical methodology incorporates uncertainty in the estimation of hypocenter of the seismic event as a fundamental part of the analysis (Jones & Stewart, 1997). Peters and Crosson (1972) propose a methodology to estimate uncertainty from the residue of location estimation algorithm to data from a seismic monitoring network. Thus the standard error in each of dimensions, is equals the sum of squares of residual divided by (n-4), where n is the number of stations used to locate the seismic event. Given the above, for each seismic event will feature an ellipsoid of uncertainty, which equiprobable hypocenter location can be relocate without losing its characteristics. The purpose of this approach is to “collapse” the location of seismic events to simplify the analysis of the source or spatial interaction. In particular, this method uses a distribution fit performing a hypothesis test after each iterations, where the hypocenter is varied inside the uncertainty ellipsoid, interacting with environment events until adjustment of the distribution of overall uncertainty of events is made relative to a given distribution (see Figure 2).

Figure 2 Operation scheme statistical collapse methodology (Jones & Stewart 1997)

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Caving 2014, Santiago, Chile The algorithm consists of the following stages:

1. Optimize the objective function of the location of a particular seismic event. 2. Search events, where their uncertainty ellipsoids interact with the event defined in step 1. 3. Calculate the centroid of the events, where their uncertainty ellipsoids intersect, given the same weight for each of them.

4. Relocate the seismic event defined in step 1 in the centroid calculated in step 3. The output of the algorithm consists of 3 steps, which are repeated for each generation of collapsed hypocenter:

1. Perform steps 1-4 of the algorithm to create a generation of collapsed hypocenter. 2. For each seismic event, calculate the distance between their original location and the new hypocenter, in terms of standard deviation of the original error ellipsoid.

3. Compare the distribution of movements with a Chi-squared distribution with three degrees

of freedom, using a Kolmogorov hypothesis test, which is based on the large differences in the cumulative probability distribution. This output algorithm is repeated until the differences are not significant according to the test.

3.2

Analysis methodology

To identify relevant groups in the database of seismic data used in this paper, the uncertainty in the estimation of the location for repositioning through the statistical collapse algorithm is used, then apply the agglomerative hierarchical Clustering methodology. The approach consists of the following steps:

1. Definition of period, study area, and volume of seismic monitoring, which completely encloses the area of concern.

2. Exploratory data analysis, reliability and filters spacetime. 3. Relocation using statistical collapse and clustering by complete link clustering 4. Categorization of seismic hazard for the identified clusters using historical information. 5. Identification of seismic hazard zones.

4

Application of the methodology to El Teniente mine

For the current study, was defined an analysis zone Block 1 of the Esmeralda Sur located in the southern part of the El Teniente mine, which is operated by panel caving conventional pre-conditioning by hydraulic fracturing, this sector has about 26 Mt in an area of approximately 42,500 m2 (Gallardo et al. 2010). In this area, was isolated a volume centered on the production level with a radius of 200 m, forming a sphere, which encloses the lithological bodies and major faults that are located in the area, which directly affect the seismic behavior of Block 1 associated with mining performed in the initiation stage of caving (between February and September 2012), which correspond to 10,123 records. Table 1 show a summary of the exploratory study results, using these results was defined as minimum threshold magnitude for the filter data reliability of -0.65 and an uncertainty of 34 meters, uncertainty that allows having at least 95% of the data and also deletes data by over 2.5 times the mean uncertainty.

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Seismicity Table 1 Exploratory study results

Variable / statistical

average

Standard deviation

Mode

P95

Error

12.87

6.776

13.15

34

Mw

-0.59

0.306

-0.65

-0.2

The initial filter is related to the spatial location of each event and overall uncertainty in the location estimation; thus, such a distance is estimated that 95% of the data have an uncertainty of less than 34 meters. Moreover, the minimum sensitivity of seismic monitoring system calculated for the study area was used, which corresponds to the mode of the distribution recorded in the period of analysis, which in this case corresponds to the local magnitude -0.65, excluding 4,331 events (42.8% of the database). In order to space-time filter, a time corresponding to six months recurrence was used, considering accepted all events contained within an area, which in this case corresponds to the sum of the average uncertainty of the seismic system and the standard deviation of the overall distribution of estimation uncertainty. In this way any event that meets these characteristics is considered to interact with the events of their surroundings effectively, eliminating just so you have no interaction with any events in their environment, in total 138 records were excluded (1.4% of the database). Finally, after application of the filters 5,654 events database were accepted (55.9%), the results of applying the filter is displayed in Figure 3.

Figure 3 Applying reliability and space-time filter.

Then a relocation of the position of seismic events is performed using the algorithm called “statistical collapse” (Jones & Stewart 1997). This uses the uncertainty in the estimate of hypocenter as neighborhood search for a global optimization of the position, searching to converge to a known distribution of the estimation errors (in this case, is the square root of a chi-square distribution of three degrees of freedom) and by a Kolmogorov hypothesis test evaluates goodness of fit of distributions. Once the test result under a given range is found, it is the spatial configuration of the seismic events which best represent their position within ellipsoids of uncertainty, the results are shown in Figure 4. After repositioning, we proceeded to group events using the hierarchical agglomerative technique, called clustering by complete linkage (CLC). In Figure 5, the summary of the Euclidean distances of seismic events standardized dendrogram shown, applied after the re-positioning algorithm. It can be seen that under the threshold 2.5 at least three distinct groups and more diffuse two separate groups. Then the upper bound rule was applied to identify the optimal number of clusters (Figure 5), where one can observe that the first change in trend is produced after the construction of five clusters. However, between five and seven groups have the same coefficient of pure bond, which is considered as the optimal value as many groupings before again changing trend coefficient level pure bond (Mojena, 1977). Clustering of seismic events is

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Caving 2014, Santiago, Chile then performed in seven groups, obtaining the distribution of the number of events per group shown in the histogram in Figure 5, where one can see that of the seven groups identified only four have the number of minimum elements for the estimation of seismic hazard (at least 250 events (Felzer 2006)), thus discarding groups 1, 6 and 7. Whereupon, for subsequent analysis is considered as valid clusters 2, 3, 4 and 5.

Figure 4 Outcomes from application of statistic colapse

Figure 5 Identification of main groups

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Seismicity The obtained results were compared using the following concepts:

1. Goodness-of-fit of the linear portion of the cumulative frequency distribution - event magnitude (Gutenberg-Richter relationship) for each group.

2. Association with mining in terms of its temporal trend. Figure 6 shows, the goodness-of-fit obtained in the linear part of the frequency-magnitude relation for the clusters identified. It can be seen that in general the clusters do not present major changes in slope at its bottom (even when adjusting the relation of Gutenberg-Richter to events nonclustered), with the exception of group 3, which also has the second largest magnitude estimated (1.6 MW). As for the estimated magnitudes, which are obtained for groups 3 and 4 the highest estimated magnitudes, and in the case of group 4 to 2.0 MW, and this group is also the highest relative probability of exceeding the estimated value since it has the lowest value in its parameter b (b = 1.11). Furthermore, it can be observed that the group has a lower relative hazard is group 5, with an estimated 1.0 maximum magnitude [Mw] and the higher value b (b = 1.92), which was estimated for this group the least relative probability of exceeding the maximum estimated value. Regarding the goodness of fit obtained for groups, an average value of 0.86, where the maximum value corresponds to group 1 (R = 0.91) and the minimum value to group 5 (0.82) was obtained in which the methodology statistical agglomerative hierarchical clustering collapse and records an average acceptable fit.

Figure 6 Outcomes for Gutenberg-Richter relation fit

In Figure 7, it can be seen that after the first evidence of connection to upper level has a strong change in the slope of the cumulative frequency of groups 2 and 5, and to a lesser extent in group 3, but not in group 4, which is not affected their rate of events during the connection process, and it has been found that the active from the beginning of the incorporation area. Given this, it is possible to associate the seismic activity of groups 2 and 5 directly with the connection process to the upper level, in the case of group 4 with mining

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Caving 2014, Santiago, Chile on the surroundings of undercut level (especially geometrical changes as undercut or incorporation of area) and in the case of group 3 this would be related in a first step to mining activity in the vicinity of the undercut level and then be affected by the connection process. Furthermore is possible to see that group 2 is situated in the surroundings of intrusive body of diorite west limited by faults B and P, mainly near the level of the undercut level. As for group 3, this is located in the north-eastern border of Central diorite intrusive body north of the fault and low altitude J interacting with mining in an environment with stiffness contrast between the host rock (CMET) and intrusive body in the vicinity of major faults, as group 2. As for the group 4, this is located at low altitude, in the surroundings of undercut front, and directly affected their behavior due to undercut task. Finally, group 5 is located south of the fault J (mainly height), and to the north of the P fault on the edge and inside the central diorite intrusive body interacting directly with the spread of the cavity and connected to the upper level.

Figure 7 Evolution of mining and identified clusters

Using the previous results for classifying the clusters obtained in terms of magnitude energy, and geological association of groups, an array of seismic hazard was constructed (see Figure 8). In this matrix, it was considered the highest level of hazard that the case compliance with all conditions defined above as necessary and / or sufficient to generate a rockburst, the average level of hazard was considered the areas where they energy is at least estimated 10 ^ 6 [J] and / or estimated Mw 1.5 or greater, or geologically complex

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Seismicity areas (Landeros & Cornejo 2013; Cornejo 2013). Concerning the seismic hazard areas considered low, considered those without energy and magnitude estimated with the characteristics of rockburst historically recorded in the El Teniente mine, and also not in geologically complex areas. The results obtained are summarized in Figure 8.

Figure 8 Classification of seismic hazard

Finally, a clear differentiation of two zones with different hazards in the production level and undercut level was obtained; these results allow differentiation hazard management depending on the area where mining takes place. With the above, including the possible consequences, as production stop, material damage and / or personal injury, it is possible to estimate the relative risk of areas with active mining and implement mitigation measures which allow risk management systems to through minimizing exposure and / or modification of mining, in order to avoid geometrical changes that unleash an unhandled seismic response.

5 Conclusions Based on the results obtained it may be concluded:

• For seismic applications, the use of cluster analysis as a pre-processing of data, allows a simplification of large databases allowing largely reduce calculation times for further analysis.

• The cluster analysis using the similarity function “Euclidean distance”, performs well in the

characterization of seismic groups. However, given the limitations of the seismic monitoring network of the El Teniente mine, the inclusion of the whole database considerably decreases the recognition performance of interest groups, in this way is important to perform the filters based on characteristics of seismic information thus carry out the identification of groups on a reliable database.

• The cluster methodology with pre-processing using the characteristics of the database and / or the

uncertainty of hypocenter estimation can standardize the analysis and obtain better performance on identifying the most relevant groups within a seismic database. This, since it decreases information bias linked to who performs the cluster analysis, thereby minimizing results based on conclusions preconceived with respect to seismic behavior analysis zones and preventing the analysis be led to an outcome in particular.

Acknowledgement The authors would like to thank Codelco Chile, El Teniente, to authorize the publication of this document and, in particular, to all the people who make the Superintendency of Geomechanics GRMD.

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Caving 2014, Santiago, Chile References Cornejo, J 2013, ‘Identificación de zonas de peligro mediante análisis de agrupamiento de eventos sísmicos, mina El Teniente’, Master Thesis, Departmento de ingeniería de minas, Universidad de Chile. (in spanish) Cornejo, J & Landeros P 2013, ‘Aplicación de criterio sísmico para estimación de la interferencia operacional asociada a tronadura, mina el teniente’, XVIII Simposium de ingeniería de minas, SIMIN 2013, Santiago, Chile. (in spanish) Falmagne, V 2001, ‘Quantification of rockmass degradation using microseismic 370 monitoring and applications for mine design’, PhD Thesis, Queens University, Kingston, Canada. 400 p. Felzer, K 2006, ‘Calculating the Gutenberg-Richter b Value’, American Geophysical Union Meeting 2006, San Francisco, CA, USA. Frohlick, C & Davis, SD 1990, ‘Single-link cluster analysis as a method to evaluate spatial and temporal properties of earthquake catalogues’, Geophysics Journal International, vol. 100, pp. 19-32. Fuentes, RA 2008, ‘Mine Seismicity Risk Analysis Program (MS-RAP) in Reno’, Technical Assessment. Unpublished Power Point presentation, Teniente Technical Advisory Board (TTAB), Rancagua. Gallardo, M, Díaz S, Cuello D & Cavieres P 2010, Ingeniería geomecánica proyecto Esmeralda Sur, Nota Interna SGM-255/2010. Halkidi, M, Batistakis, Y & Vazirgiannis, M 2001, ‘On cluster validation techniques’, Journal of Intelligent Information Systems, vol. 17, Nº 2/3, pp. 107-145. Hudyma, MR, Mikula, P & Owen, M 2002, ‘Seismic hazard mapping at Mount Charlotte Mine’, Proceedings of the 5th North American Rock Mechanics Symposium, Toronto, 07-10 July, (Editors: R. Hammah, W.F. Bawden, J. Curran and M. Telesnicki), University of Toronto Press, Canada, pp. 1087-1094. Hudyma, MR 2008, Analysis and Interpretation of Clusters of Seismic Events in Mines, PhD Thesis – Department of Civil and Resource Engineering, University of Western Australia. Hudyma, MR 2009, Esmeralda seismic risk study, 1999-2000, ACG report submitted to the New Mine Level Project, Perth. Jain, AK, Murty, MN & Flynn, PJ 1999, ‘Data clustering: A review’, ACM Computing Surveys, vol. 31, Nº 3, pp. 264-323. Jones, RH & Stewart, RC 1997, ‘A method for determining significant structures in a cloud of earthquakes’, Journal of Geophysical Research, vol. 102, Nº 134, pp. 8245-8254. Kijko, A & Funk, CW 1996, ‘Space-time interaction amongst clusters of mining induced seismicity’, Pure and Applied Geophysics, vol. 147, Nº 2, pp. 277-288. Mendecki, AJ, van Aswegen, G & Mountfort, P 1999, ‘A guide to routine seismic monitoring in mines’, A Handbook on Rock Engineering Practices for Tabular Hard Rock Mines, (Editors A.J. Jager and J.A. Ryder), Creda Communications, Cape Town.

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Seismicity Mojena, R 1977, ‘Hierarchical grouping methods and stopping rules: An evaluation’, Computer Journal, vol. 20, pp. 359-363. Peters, DC & Crosson, RS 1972, ‘Application of predictional analysis to hypocenter determination using a local array’, Bull. Seismol. Soc. Am., vol. 62, pp. 775-788. Van Aswegen, G 2005, ‘Routine seismic hazard assessment in some South African mines’, Controlling Seismic Risk - Rockbursts and Seismicity in Mines, (Editors: Y. Potvin and M.R. Hudyma), Perth: Australian Centre for Geomechanics, pp. 437-444. Vasak, P, Suorineni, FT, Kaiser, PK & Thibodeau, D 2004, ‘Hazard map approach using space-time clustering analysis of mining-induced microseismicity’, Canadian Institute of Mining and Metallurgy Annual General Meeting, Edmonton, 8p. Witten, IH, Eibe, F & Hall, MA 2011, Data mining: practical machine learning tools and techniques, (3rd ed.), ELSEVIER, 2011.

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Modeling induced seismicity in 4D E Cordova Codelco, Chile M Nelson University of Utah, USA

Abstract A new technique has been developed to estimate how seismicity evolves through the mine, making the technique an interesting addition to defining areas with high, medium, and low damage potential due to their embedded seismic history. The use of solid triangulations in representing the areas of interest makes the developed methodology a simple and powerful addition to the study of seismicity in mines. The research illustrates a new technique to model seismic events and combine them into block models, providing the user with the ability to analyze these data as a function of time (4-D) model, with the possibility of combining different analysis criteria to display the data, create sections of the information in any direction needed, cut the data at any elevation to see what has happened through the life and development of the mine. The seismic history of the mine can be displayed and analyzed using the developed technique, defining areas of progressive deterioration associated to the energy levels released by the seismic events.

1 Introduction Evolution can be defined as a progression or succession of events that have shaped the way something is today due to the changes it has suffered through time. Evolution relates to change, and if change can be studied and understood we might be able to realize how the changes might shape future events. If we apply this thinking to the way an underground mine evolves, all the different activities taking place are related to the final process of extracting the ore. In mines that use caving mining techniques the extraction and evolution of the mine will produce a cave that will interact with the surrounding rock and the rock will also respond and accommodate to the changes taking place.

2 Information The seismic response to the evolution of the mine is captured by a seismic network installed in order to record the seismic events taking place all over the mine due to the workings and developments taking place on a daily basis. The seismic network provides an instant feedback of the pulse of the mine and the working conditions in the areas under development, even reaching a point where the recorded data is used to condition if certain activities might be delayed for safety reasons. The seismic monitoring system provides the date, time, spatial location, associated location error, moment, energy, triggers activated, etc. for each event, making it possible to define their location with respect to different areas of the mine.

3

Conceptual approach

The succession of seismic events can be thought as the responses from the rock to how the mine has changed over time, where areas that presented increased seismic activity have a higher damage potential.

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Seismicity If the seismic history of the mine can be ordered and accounted through time, then it can be used to study how the rock has responded to the modifying conditions of the mining process. Data can be easily displayed but there is a whole new spectrum of information if the same data can be ordered in time and analyzed in different ways to achieve a better understanding of it. Block models have been widely used in resource estimation to establish where the grades are and to define areas of interest where the grades show that there might be a potential for extraction. On a simple definition, a block model is a big box that covers the data that needs to be modeled, with this box composed of smaller blocks, the big box is defined by its origin and extent on each direction (x,y,z) and the smaller blocks are defined by its size and location relative to the origin of the block (Figure 1).

Figure 1 Block model parameters and sub blocks

The main advantage of a block model is that it can be used to store information from different variables located in 3D, since each block can be assigned sets of variables where the information is stored, new variables can be also created to manipulate and perform calculations from the original variables, while using restrictions from other stored parameters. To store information into the blocks, interpolation techniques must be used in order to define which values are used in the estimation of each block. There are different techniques that range from the basic nearest neighbor algorithm that assigns the closest value of data to the center of the block to more complex ones that take into account the different data trends while minimizing the error in the estimations like kriging and its variations. The inverse distance method is a technique that gives more importance to the data that is closer to the center of the block, by using an exponent that when increased assigns a higher weigh to closest samples. (1)

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Caving 2014, Santiago, Chile Where: d = distance from sample to block center. w = sample value to interpolate. x = inverse distance power. n = total number of samples.

4

Modeling methodology

In order to account for the seismic events and their location a restricted interpolation technique is used, where only the values that fall inside of the blocks are used in the estimation. Of course this relates to the selection of the block size, where big blocks might cover too much data and could hide the required changes, while very small blocks might not be able to accumulate a representative number of events over time. A set of variables that represent the time are created to store the estimations at different times depending on the desired resolution of the analyses (weekly, monthly, or yearly). The same time variables are then manipulated to calculate the values on accumulation variables where the effect of the progressive seismicity is stored. Restricting variables are also created to store defining parameters of the block like an associated average location error and the number of triggers for each event, these variables can be used to restrict the estimated data to blocks with a higher location confidence by using only blocks that have a lower location error.

5 Results Figures 2 through 5 show the progressive seismicity accounted for year 1992 up to 2012. Within the model, the same analysis can be carried out for any desired time range or between specific time periods (i.e. 2004 to 2012). The raw interpolation shows the accumulated energy releases for a section of the mine, with higher activity at the 2,400 level between 1,000-1,400 East (Figure 2).

Figure 2 Progressive energy for the model up to year 2012

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Seismicity The same data can be reloaded by restricting that the number of triggers activated by the progressive events should be greater than 5 (Figure 3.)

Figure 3 Progressive energy up to year 2012 with an average of five or more triggers

The data can also be filtered to a show only blocks that have a location error of 20 m or less between the samples used to interpolate the values to each block (Figure 4)

Figure 4 Progressive energy up to year 2012 with location error less than 20 m

The restrictions can be combined to use blocks that have a greater accuracy in the values used to estimate them, with a lower location error and higher number of triggers (Figure 5).

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Figure 5 Progressive energy up to year 2012 with location error less than 20 m and an average of five or more triggers

6

Three dimensional modeling

The data was modeled in three-dimensions using volumes to display the progression of the events magnitude from 1992 to 2012 in a specific area. The different contours show blocks with accumulated magnitude values higher than one (blue), five (yellow), and ten (red) (Figure 6).

Figure 6 Progressive seismic analysis of events magnitude of one (blue), five (yellow), ten (red)

For the same area, the magnitude evolution at a cut-off value of 1 or higher is modeled into volumes to show how the progression occurred for years, 1995, 2000, 2005, and 2010 (Figures 7 to 10).

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Seismicity The modeled volumes at different years show that the evolution of the seismicity has occurred from the top of the area from level 2550 to 2300 up to the year 1995, to continue to level 2200 during the next five years, reaching level 2125 at year 2005 and expanding to the east during the last five years. The advantage of the modeling is that the same type of analyses can be performed for different years; the variables used can be restricted for other values of interest (i.e. accumulated magnitude greater than 5 or energy greater than 10,000 J.

Figure 7 Progressive seismic magnitude up to year 1995 greater or equal than 1

Figure 8 Progressive seismic magnitude of events up to year 2000 greater or equal than 1

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Seismicity The evolution shows that the seismicity has moved on a top-down manner for the volume under study in light red. Once the seismicity reached the bottom of the volume, then it extended to the east side of the volume at year 2010. A better detail of the evolution can be achieved by applying the methodology in a monthly basis over the whole set of data.

7 Applications The defined methodology can be used in different type of analyses to display and understand how seismicity has moved through different periods of time (Figures 11 through to 13). 7.1

Seismicity evolution

Checking how the seismicity has developed over time at a certain area of the mine, the current seismic state of a future area can be developed.

Figure 11 Accumulation of seismicity in the central area due to mining on the yellow project to the right

Figure 12 Accumulation of seismicity in the central area due to mining on the blue (left project) and yellow (project to the right)

7.2

Accumulation of events

Evaluating the accumulation of events according to the magnitude these events have had over certain periods of time.

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Figure 13 Accumulated magnitudes inside the volume under study for different cut-off values

7.3

Relevant events

Evaluating the relationship between relevant seismic events and the accumulated seismic energy before the event took place, to check if the model has blocks that represent sudden increases of energy (Figure 14). Thirty-four geomechanical events in one area were located within the developed block model. The relevant events were analyzed with the modeled data and the locations of the events were used to look for blocks that showed unusual seismic activity prior to the occurrence of each event. A monthly resolution model was used to find the blocks that had sudden increased seismic activity up to one month before the relevant event occurred. The study of the events and their correlations showed the following results:

• There were 34 relevant geomechanical events in the mine area studied, which had a footprint of 160,000

m2. Those events were used to compare the locations of these relevant events and the accumulated energy distributions in the blocks surrounding the event one month before the occurrence of the event.

• Out of 34 major studied for the same level of the mine, 16 events (47%) took place near blocks that showed increased seismic activity prior to the relevant event taking place.

• The 16 relevant geomechanical events took place from March 1997 to December 2012, with two events in 1997, three in 1998, three in 1999, two in 2001, one in 2002 and five in 2003.

• Out of the 16 events, there were four events that showed seismic activity in the 10 months preceding the event, in a block close to the relevant seismic event.

• Out of the 16 events, 12 (called main events) showed an increase of energy in a nearby blocks in a period 4.1 months or less before the event.

• Of these 12 main events, 75% showed an increase in energy from 1 to 6 months before the event took place.

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Seismicity

Figure 14 Seismic event location in green with high energy block on top (103,158 J)

7.4

Undercutting face advance

Checking the behavior of the seismic events in volumes due to the advance of an undercutting front, to determine if the seismicity is moving behind or in advance of the undercut face.

Figure 15 Seismic energy model (for year 2004) with undercut advance from two projects south (blue) and north (red), showing three energy cut-offs in different views (Top, Front, and Left side views)

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8

Discussion of results

The approach of storing the seismic history of a mine in a standard block model creates an effective tool for analyzing and understanding how various seismic events have migrated and affected different areas of the mine over time. The progression of seismicity can be used to establish the seismic history of areas that might have been affected by previous mining activities. Analyzing how the seismicity has affected the surroundings of new areas to be mined by caving techniques can be useful in establishing the most suitable for the start of the undercutting of the block. When the seismicity data are embedded in the blocks, seismic activity can be related in space and time to activities in the mine, and the block viewing filters can be manipulated to display the information contained in certain areas to show the seismic effects in desired areas and times. The behavior of a given area and the energy associated with other caving areas nearby can be tracked to show the effects from the caving process over time.

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Seismicity The analyses can be filtered for the individual blocks to achieve a higher confidence in the data. The two filters studied here, location error and the number of triggers activated by a given event, can be used alone or together to increase the consistency of the data being used in a particular analysis.

9 Conclusions The modeling approach used in this study has shown that the energies and magnitudes associated with seismic events, along with the numbers of triggers and the location errors, can be effectively interpolated into a block model and that the resultant data can be used to determine areas with historic seismicity that may have resulted in accumulated deterioration of the rock mass. The ability to model the seismic energy associated with the blocks over time allows the analysis of the evolution of the seismicity in different areas of the mine. The resulting information can be displayed as two-dimensional sections in any direction required or as triangulated solids to better understand how the seismic events evolve through the mine. The new method presented here for interpolation of the seismic data facilitates the accumulation of the seismic history within a block model, and the modeling of potential deterioration solids. These can be used to study how previous mining activities have influenced areas where new projects are being planned for the future. The seismic data can be displayed and located at different levels of the mine where seismicity has been recorded through time, for example in the undercut, production, haulage, and ventilation levels of a panel or block caving mine. These visualizations can be used to define areas where significant seismicity has occurred in the past, indicating where potential problems may occur in the future. The interpolated data provide a powerful tool that facilitates analysis of how the seismicity has evolved in an area where mining with a caving technique is planned. This will allow the identification of preferred locations for the initiation of the undercutting of the block, leading to optimized caving performance. The progressive analysis of seismic activity as presented shows a new way of looking at the evolution of seismic data by combining the data with inverse-distance interpolations and block modeling techniques. Induced seismicity occurs mainly as a consequence of caving and undercutting, both of which are dynamic processes. Undercutting events can vary depending on the undercut method used and when undercutting takes place in relation to other development activities. These variations in undercutting procedures will affect the “cavability” of the rock mass. The methods presented here constitute a new approach to the study of seismic information, by allowing the association of several variables related to seismicity with the blocks in a block model. This is convenient and useful because mine operators, planners, and engineers use block models regularly, and are familiar with the organization and presentation of data in this manner. The association of seismic variables with the blocks in a model allows the seismic information to be filtered based on one or more parameters. Such filtering can eliminate minor or unimportant seismic events, allowing a much clearer visualization of the accumulation of seismic energy in a particular area of interest. There is a great potential in applying this modeling method to studying the correlation between relevant, geo-mechanical events that have caused problems at the mine and the blocks that have shown unusual increases in seismicity prior to the occurrence of the relevant event taking place. The example presented in Section 7.3 considered 34 relevant geomechanical events, and the model showed that in 16 of them, there was a nearby block that experienced a sudden accumulation of energy prior to the occurrence of the relevant event.

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Caving 2014, Santiago, Chile Though examples are not shown here, it is clear that this modeling approach can be readily used in conjunction with numerical modeling packages, such as, Flac, Map3D or Abaqus, to define zones where rock mass properties have changed, and damage potential might be increased by mining resulting in geomechanical events leading to problems in production and operation. The volumes derived with this approach, showing progressive seismic activity, can be used in finite element modeling analyses to define areas where the rock mass has been changed over time, providing an important tool for enhancing numerical analyses in the future.

References Applied Geostatistics, E.H. Isaaks and R.M. Srivastava, Oxford University Press, 1989. Barnes, MP 1979, ‘Drill Hole Interpolation: Estimating Mineral Inventory’, Open Pit Mine Planning and Design, New York: SME of AIME. Brown, AR 2004, Interpretation of Three-Dimensional Seismic Data, 6th ed. AAPG Memoir 42, Investigations in Geophysics, Nº 9, American Association of Petroleum Geologists and the Society of Exploration Geophysicists. Codelco, 2001, Fundamentals to the Seismicity Conduction Response in a Caving Method, Internal Mine Report, Codelco – División El Teniente, Rancagua, Chile. Durheim, RJ, Spottiswoode, SM, Roberts, MKC & Brink, A.van Z 2006, ‘Comparative Seismology of the Witwatersand Basin and Bushveld Complex and Emerging Technologies to Manage the Risk of Rockbursting’, Journal of South African Institute of Mining and Metallurgy, vol. 105, Nº 6, pp. 409-416. Essrich, F 2005, ‘Mine Seismology for Rock Engineers – An Outline of Required Competencies’, Controlling Seismic Risk, Proceedings of the Sixth International Symposium on Rockburst and Seismicity in Mines, March 9–11, Perth, Western Australia: Australian Centre for Geomechanics. Gibowicz, SJ & Kijko, A 1994, ‘An Introduction to Mining Seismology’, 1st ed. International Geophysics Series, vol. 55, San Diego: Academic Press. Glazer, S & Hepworth, N 2004, ‘Seismic Monitoring of Block Cave Crown Pillar – Palabora Mining Company, RSA’, Proceedings of Massmin 2004, Santiago, Chile, pp. 565-569, Instituto de Ingenieros de Chile. Gutenberg, B & Richter, CF 1954, Seismicity of the Earth and Associated Phenomena, 2nd ed. Princeton, N.J.: Princeton University Press. Hudyma, MR, Frenette, P & Leslie, I 2010, ‘Monitoring Open Stope Caving at Goldex Mine’, Proceedings of Caving 2010, Second International Symposium on Block and Sublevel Caving, Perth, Western Australia: Australian Centre for Geomechanics. Hughes, WE & Davey, RK.1979, ‘Drill Hole Interpolation: Mineralized Interpolation Techniques’, Open Pit Mine Planning and Design, New York: SME of AIME. Moss, A, Diachenko & Townsend, P 2006, ‘Interaction between the block cave and the pit slopes at Palabora Mine’, Symposium Series S44, Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations, Johannesburg: SAIMM.

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Seismicity Spottiswoode, S 2009, ‘Mine Seismicity: Prediction or Forecasting?’, Proceedings of the 1st Hardrock Safe Safety Conference, Sun City-Mine. Stanley, BT 1979, ‘Mineral Model Construction: Principles of Ore-Body Modeling’, Open Pit Mine Planning and Design, New York: SME of AIME. Swanson, PL & Sines, CD 1991, Characteristics of Mining-Induced Seismicity and Rock Bursting in a Deep Hard-Rock Mine, Report of Investigations, RI-9393, Washington, DC: U.S. Bureau of Mines. Turner, MH & Player, JR 2000, ‘Seismicity at Big Bell Mine’, Proceedings of Massmin 2000, Melbourne, Victoria: AusIMM. White, H, Van As, A & Allison, D 2004, ‘Design and Implementation of Seismic Monitoring Systems in a Block-Cave Environment’, Proceedings of Massmin 2004, Santiago, Chile, Instituto de Ingenieros de Chile. Whyat, JK, White, BG & Blake, W 1996, ‘Structural Stress and Concentration of Mining-Induced Seismicity’, Trans. SME, vol. 300, pp. 74-82.

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Subsidence

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Subsidence

Application of InSAR technologies to measure the subsidence at El Teniente´s Mine AE Espinosa Codelco, Chile O Mora Altamira Information, España F Sánchez Altamira Information, España

Abstract The generation of a subsidence crater, derived from an underground mine explotation, has been under study since the 70’s with works done by Peck (1969), Shandbolt (1978), Kvapil et al (1989) and Flores (2005), among the most famous. All of them have been developed using available observations together with the characterization of rock massifs and materials disposition, to elaborate rules that would allow the estimation of the extension of motion caused by the phenomena of subsidence. With the incorporation of data obtained through high resolution radar satellites and the application of interferometry, it has been possible to make precise measurements over large extensions of land and monitor ground motion. The application of this technique in the measurement of deformations in and around a subsidence crater, has allowed estimating quantitatively the extension on the surface of the effect of underground mining. The utility of this type of information can be identified in the following points: 1. Support in the safety of personnel and equipment working near a subsidence crater. 2. Register the limit at the surface of the extension of the subsidence generated and provide with records for estimating the same effect on the surroundings of the underground mine. 3. Indirect monitoring over the evolution of cavities and register the decrease of the crater bottom. The use of this technique at El Teniente Codelco’s division in Chile since 2010, has allowed for, among others, proposing a model of the behaviour of the effect of the underground extraction on the surface. With that information, it has been decided as viable, the exploitation of Rajo Sur mine and the positioning of a waste dump at the base of the subsidence crater. This articles shows the results of the measurements of the subsidence that challenges past thinking at the mine. This document summarizes the process starting with the conceptual preparation done by the Geomechanical Superintendence of El Teniente’s Division, up to the practical application and the elaboration of several products that has been done by ALTAMIRA INFORMATION.

1 Introduction The implementation of techniques that use satellite interferometry to monitor ground motion in the subsidence crater of El Teniente mine has allowed for the visualization of the extension process (or growth) of the edge of the subsidence crater, attributed to the extraction from the underground mine, as well as landslides on the walls and the descent of the crater bottom. This differentiation translates itself in identifying three areas with different motion patterns:

1. Edge of the crater (or zone of theoretical breakage): it is defined between the morphologic edge of the subsidence crater (loss of the original topography) and the detectable limit (visual or through tools) of the subsidence effect.

2. Exposed wall: it is located between the morphologic edge of the crater (Edge of the crater) and

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Caving 2014, Santiago, Chile 3. Bottom of the crater: it corresponds to the surface that modifies its topography due to a direct action of the underground extraction.

With all these data available, a conceptual model of the evolution of the subsidence crater growth has been built, which allows to sustain control measures over its extension, especially in the horizontal component of the growth (strictly over the topographic map). Additionally and based upon this available data, estimations of the vertical descent were done and the location of landslide areas due to the exposed wall instabilities were defined. The result of this project is oriented towards the functional implementation of the InSAR data in the periodic evaluation of the risk conditions in the subsidence crater areas and in the use of prior information to develop control actions for annual and 5-year mining plans. In these mining plans, there is an interaction between underground mining activities and those done in the subsidence crater area.

2

Theoretical background

Spaceborne SARs are active systems on board satellites, which illuminate the Earth’s surface with a series of microwave pulses in a side-looking geometry (Duro 2010). While the sensor is moving through its orbital path, it transmits microwave pulses. The emitted signal interacts with the elements of the Earth’s surface and part of this energy is backscattered towards the satellite. Presently, there is a large number of spaceborne SAR sensors, offering data sets of varying suitability for repeat pass interferometry. SAR images acquired at different wavelengths, with different ranges of swath coverage, resolutions and revisit times are highly accessible. 2.1

SAR interferometry

SAR interferometry (InSAR) is a signal processing technique. It uses two different SAR acquisitions of the same ground surface from slightly different point of views to create an image of the phase differences. This phase difference is known as the interferogram or the interferometric phase. 2.2

InSAR main applications

The main InSAR applications take benefit of the capacity of measurement differences in the travel phase between repeat passes. Within the mining industry, the most important application is the detection of movements of the ground surface. There are other applications based on radar interferometry as for example change detection based on the interferometric coherence, classification, soil moisture analysis, and others. 2.3

Estimation of ground deformation maps

The interferometric phase can be directly related to the difference of travel phase between the two acquisitions. If the two images have been acquired under the same point of view, possible changes in the travel phase would mean that the ground target has changed its position. In other words, that there was a displacement of the illuminated ground slice of terrain between the two epochs. 2.4

PSI technology

Persistent Scatterer Interferometric techniques are very powerful geodetic tools for land deformation monitoring that offer the typical advantages of the satellite remote sensing SAR (Synthetic Aperture Radar) systems: a wide coverage at a relatively high resolution. Those techniques are based on the analysis of a set of SAR images acquired over a given area.

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Subsidence In late 1990s and beginning of 2000s, a new InSAR processing technique appeared based on the multi-image SAR images comparison over stable or coherent scatterers, called Persistent Scatterers Interferometry (PSI). The Permanent Scatterer technique was the first PSI methodology introduced in between 1999 and 2000. During the past years, the Stable Point Network (SPN) technique, a PSI technique developed by ALTAMIRA INFORMATION, has been tested in a very large variety of scenarios and in some cases under very difficult conditions. Therefore, the robustness and the flexibility of the chain is well known, and thus are the main limitations and constrains within the actual processing workflow.

3

Methodological development

The application of satellite interferometry to determine the ground motion around the subsidence crater was the result of looking for tools that would allow:

1. Obtaining ground motion data remotely, without putting at risk personnel. 2. Obtaining millimetric precision of the ground motion estimation. 3. Having at least a weekly measurement frequency. 4. Fully covering the area of interest for each image acquisition. 5. Reducing costs compared to aerophotogrametry measurements (LIDAR, orthoimage corrected) in manned flights.

The data acquired are used to recreate the extraction process of the subsidence crater, taking into account a group of events that cause the modification of the crater edge and the bottom descent. Since the use of InSAR measurements for the subsidence crater of El Teniente mine, the events presented in Table 1 have been developed. Table 1 Stages of development

Phase Assessment of the technical feasibility (Nov 2012 – Feb 2011) First estimation of the ground motion in the subsidence crater area (Jan 2011 – Apr 2011) Application for the crater bottom measurements (July 2011) ALTAMIRA INFORMATION InSAR’s monitoring program(July 2011 to date)

Objective

Tasks

Product

Determine the applicability of InSAR measurements for the crater of el Teniente mine.

Images’ acquisition, satellite geometrical distortions analysis, coherence evaluation.

Decision about the continuity of the project, satellite selection, image acquisition.

Obtain a motion distribution around the crater.

Images’ acquisition and processing covering the period January-April 2011.

InSAR estimation of crater edge (limit of the motion at the surface)

Obtain a motion distribution at the bottom of the crater.

Application of SPN InSAR and classical DInSAR

Estimation of the descent magnitude and direction of the motion affecting the crater bottom.

Monitoring to assess the crater behaviour (bottom and surroundings) according to the mining extraction.

Processing and delivery of reports, database and specific assessments in case of anomalies.

Reports every six months for the accumulative motion, for special events every 8 days (average) if required

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Caving 2014, Santiago, Chile As relevant events, there is the estimation of the areas that are visible by the satellite. The satellite beam has covered completely the crater area. Another factor that reinforces the application of the InSAR technology is when an estimation of the crater edge is done using the ground motion data, making them coincide in shape and dimension with the estimation at the edge done by analysing orthorectified images. As far as the monitoring data is available, it has been confirmed through site visits, a high level of coincidence between the real motion and the data delivered by InSAR; these observations are commented in this paper. 3.1

Phase 1 – Technical feasibility of InSAR application in measuring ground motion around the subsidence crater of El Teniente mine

The area of interest is completely covered by TerraSAR-X and Cosmo-SkyMed satellites, they both work with similar parameters, the data used to define the coverage and visibility are listed in Table 2. Table 2 Data adquisitions

Cosmo-Skymed

TerraSAR-X

02/05/2011

11/27/2010

02/13/2011 02/14/2011

12/19/2010 01/10/2011 02/12/2011

Both satellites covered the area of interest, however, Cosmo-SkyMed offered a better visibility reducing distortions thanks to a more appropriate incidence angle chosen. Together with this advantage and for availability reasons, it was decided to continue the acquisitions with Cosmo-SkyMed, acquiring an image every 8 days. Figure 1 shows, from left to right, the coverage of the satellites and the visibility masks.

Figure 1 Analysis for coverage and masks

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Subsidence 3.2

Phase 2 – First estimation of the edge of the crater

Having clear that the edge of the crater establishes the border between the visual manifestation of the subsidence (surface sinking) and the area where there are only small deformations, as settlements and cracks of a few centimetres, it is possible to estimate which would be the edge of the crater using InSAR data; where the areas with low coherence are associated with deformations at the bottom of the crater and the higher coherence areas correspond to small deformations that are expected to occur at the upper part of the crater. Figure 2 shows an interferogram obtained using two TerraSAR-X images (January 10 – February 12, 2011), the projection on the DEM and a view with the underground mining infrastructures.

Figure 2 Interferogram and crater border estimation

The image on the right in Figure 2 shows the degree of coincidence between the edge of the crater estimated “manually” and the edge obtained through the interferometric analysis. The assessment of the result allows proposing a new edge of the crater, named “coherent”, that incorporates deformation data that is not seen on site, and that are not considered for the definition of the traditional crater edge. 3.3

Phase 3 – Measurements at the bottom of the crater

Once the objective of determining the limit of the subsidence caused by the underground mining was achieved, the focus was on estimating the motion occurring at the bottom of the crater. An image amplitude analysis had to be used for this purpose; furthermore, the result added the motion vectors (Figure 3).

Figure 3 Bottom crater subsidence estimation

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Caving 2014, Santiago, Chile Finally, in the integration of the phases, a product that covers the technical expectations is obtained, in terms of precision, on site checking and extension of the motion area in the same event (Figure 4). In terms of cost, an estimation obtained by applying InSAR technology is around 25% less than applying traditional tools. Figure 4 shows the result obtained in the technical assessment phase.

Figure 4 Summary of InSar technique evaluation and applications

After the three evaluation stages were completed, the decision was made to apply InSAR for three years over the crater area and to obtain the following products: time series of the displacement at the crater edge and bottom and time series of motion for each natural reflector registered. The present status of the InSAR monitoring can be described as successful in terms of fulfilment of radar acquisitions, delivery of reports and consistent data in relation with on-site observations.

4

Current application

Presently, the results of the interferometric analysis are displayed in a very functional application where it is possible to look and identify the general performance of subsidence in and out of the crater and to review time series for displacement (Figures 5 and 6). This application is being used to define the border of movement beyond the crater edge. For the next stages, the movement estimation at the bottom of the crater will be used to relate this data with underground mining, with the idea to correlate descent and extension of subsidence with vertical flow due to extraction.

608

Subsidence

Figura 5 TSViewer data display

Figura 6 TSViewer data display

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Caving 2014, Santiago, Chile 5 Conclusions After the evaluation stage, InSAR technology has proved appropriate to estimate the effect of subsidence around the edge of the crater in terms of satisfying the main requirements for subsidence monitoring:

• Obtaining ground motion data remotely, without putting personnel at risk. • Getting millimetric precision of the ground motion estimation. • Having at least a weekly measurement frequency. • Fully covering the area of interest for each image acquisition. • Reducing costs compared to aerophotogrametry measurements (LIDAR, orthoimage corrected) in manned flights.

This kind of monitoring allows to get an important amount of data which can be used in studies for:

• Identifying risk zones due to water or mud accumulation over the crater surface. • Linking up underground extraction with the subsidence effect over the surface (estimating a rate between mineral extracted and descent and extension on the surface).

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Subsidence

Chuquicamata Underground Project subsidence analysis A Aguayo Codelco, Chile D Villegas Codelco, Chile

Abstract The superficial expression of underground mining mined by Block Caving, is represented by a depression in the ground called subsidence crater, its perimeter is defined by a failure plane that start at the undercutt level and finish at the surface. These planes originate in the undercut level and have an inclination in relation to the horizontal line, this angle is referred to as the collapse angle, and another angle that defines the limit zone of the effect of the subsidence or zone of influence, which is defined by the fracturing angle. In the Chuquicamata Underground Project, where the crater is located within the current open pit operation, the subsidence angles were estimated considering various aspects including the following:

• Benchmarking information from other similar underground mines. • The application of empirical methodologies in order to apply experiences from similar mines. • Results of two-dimensional models, through which it is possible to estimate displacement, settlements and distortions.

The result of the analysis shows a projection for the subsidence angles separated by the current pit wall and elevation, since it depends directly on the geological - geotechnical characteristics of the rock mass in each case. In addition, a zone of influence is defined by the effect of subsidence and a criterion for abandonment of the site is recommended once the underground mining is completed, considering 100 % of the extraction.

1 Introduction After many years of engineering studies and development, Codelco Chile is in the process of constructing an underground mine that will replace the current open pit extraction method with Block Caving Method. One of the planning variables most relevant for this method is the definition of subsidence that will be generated by underground mining, particularly for this project that includes mining in two simultaneous levels. The great amount of interference generated by subsidence on upper levels and infrastructure of División Chuquicamata, require knowledge of the subsidence angles with accuracy greater than the one provided by empirical methods. Therefore, it was necessary to estimate these angles by using two-dimensional numerical models.

2 Methodology In the case of massive underground mining using caving methods, caving generate a cave that ends up connecting to the surface. This connection to surface defines a crater usually called subsidence crater. In the ground adjacent to the crater perimeter a noticeable cracking zone occurs. This noticeable cracking

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Caving 2014, Santiago, Chile corresponds to the maximum expression of displacement and deformations experienced by the land located within the influence zone of the crater, as the presence of the crater enables the converging displacements of land towards it. Figure 1 shows some examples of subsidence caused by caving methods (block and panel caving).

Figure 1 Examples of subsidence caused by caving methods (block and panel caving). Left: Mina Andina, Chile. Right: Mina El Teniente, Chile

The surface expression of mining and underground ore body is represented by a depression in the ground called Subsidence Crater and from a practical point of view, it is interesting to evaluate the magnitude and extension of this subsidence as well as its probable evolution over time. Its surface expression is defined by the intersection of a series of inclined planes with respect to the ground surface. These planes are originated in the undercut level and are inclined with respect to the horizontal; the angle is called: “Collapse Angle” (ΨA). Another angle, which defines the limit zone of subsidence effect or influence zone is called “Fracturing Angle” (ΨB). A brief description of parameters defining the geometry of the subsidence crater for Chuquicamata Underground project as illustrated in Figure 2, includes:

• Height of Broken Material: average height between caving level and surface of broken material column.

• Crater Perimeter: surface contour of zone affected by block falling and spillage inside the subsidence crater.

• Base Perimeter: base contour of subsidence crater, defined by the undercut area in the undercut level (UCL).

• Crater Height: vertical distance between the crater perimeter and base perimeter. • Collapse Angle (ΨA): average inclination of crater walls measured between horizontal line and imaginary line that connects the base and the edge of the crater. Also known as breaking angle.

• Fracturing Zone (Large Scale): zone adjacent to the crater, where the rock mass has large

deformations and there is evidence of large size cracks generated (> 1 m). The width of this zone varies in depth, showing the larger extension in the surface and the smaller extension above the undercut level.

• Fracturing Angle (ΨB): average inclination between the horizontal and imaginary lines connecting the limit of large scale fracturing zone and the base of the crater.

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Subsidence • Fracturing Zone (Small Scale): Zone adjacent to fracturing zone, where the rock mass shows continuous deformation and there is evidence of small size cracks generated (< 1 m). The width of the zone limits the surface subsidence.

• Subsidence Angle (Ψc): Average inclination between the horizontal and imaginary lines connecting the small scale fracturing zone limit and the cráter base.

• Subsidence Angle between Levels (ΨD): Average inclination between the horizontal line of lower

undercut levels (UCL) and its projection to the surface. This angle can connect to the fracturing angle or subsidence angle and should be steeper than both.

Figure 2 Parameters defining the geometry in a subsidence crater (modified from Vyazmensky 2008)

3 Data 3.1

Characterization of rock mass

For geotechnical characterization of Chuquicamata Mine, the following Basic Geotechnical Units (UGTB) have been defined, which consist of relatively homogeneous ore bodies resulting from overlapping of alteration units on lithology units. Based on the aforesaid, considering the gravel units and leached materials, the following UGTB are recognized:

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Caving 2014, Santiago, Chile • Fortuna Granodiorite (GDF) • Elena Sur Granodiorite (GES) • Elena Norte Granodiorite (GEN) • Metasediments (MET) • Moderate Shear Zone (ZCM) • Intense Shear Zone (ZCI) • Breccia Between Faults (BEF) • Quartz Greater than Sericite (Q>S) • Quartz Equal to Sericite (Q=S) • Quartz Less than Sericite (Q
Figure 3 Left: Plan view with geotechnical units of Chuquicamata Mine, pit 2005 (DCN, 2005). Right: Plan view of major VIF structures in Chuquicamata Mine (DCN, 2005).

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Subsidence On the other hand, structural domains identified in Chuquicamata Mine correspond to: Mesabi Domain, Noroeste Domain, Balmaceda Domain, Estanques Blancos Domain, Zaragoza Domain and Americana Domain. While the Fortuna Norte Domain and Fortuna Sur Domain are located in the West Slope , VIF (Very Important Faults) structural systems have a persistency of 500 m or more and FT (Faults) have a persistency of 100 m approximately. 3.2

Geotechnical properties of rock mass

Properties of the rock mass were calibrated as a function of analysis sections covering the different geotechnical zones of Chuquicamata pit. With these analysis sections the behavior observed in slopes of east and west walls of Chuquicamata pit was verified. Table 1 presents a summary of characteristic values corresponding to strength and strain properties for all geotechnical units according to the calibration of properties obtained from back analyses and pit analyses in year 2012. Table 1 Rock Mass Properties at Chuquicamata Pit

UGB

g (kN/m3)

UCS (Mpa)

mi

GSI

GDF

25.3

100

20

43

GES

26

100

14

43

MET

25.9

49

20

40

ZCM

23.5

50

22

50

ZCI

22.6

20

22

40

BEF

24.6

45

20

54

Q
25.4

30

16

50

Q=S

25.8

60

20

60

Q>S

26.2

90

25

65

PES

25

78

20

68

PEK

25.2

90

21

58

PEC

25.8

80

17

53

LIX HOM

25.6

31

20

45

LIX HET

25.6

25

20

30

D

0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00 0.70 0.00

E (Gpa) 8.00 10.00 5.10 8.00 3.10 5.00 5.00 6.00 4.00 5.00 8.50 10.00 5.00 8.00 8.00 10.00 8.20 19.50 15.00 20.00 8.10 10.00 13.40 15.00 8.00 10.00 4.00 6.00

v 0.23 0.24 0.25 0.26 0.27 0.23 0.26 0.25 0.23 0.22 0.23 0.22 0.23 0.23

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Caving 2014, Santiago, Chile 4 Results 4.1

Empirical methods

There are different empirical methodologies used in mining industry to estimate the subsidence associated to massive underground mining, such as: Laubscher (2000), methodology used by División El Teniente and División Andina, being the latter the one used in this analysis. Estimation of the breaking angle is done by a procedure that relates topography or elevation you wish to know the probable effect of subsidence, the height of the primary rock column and the extraction percentage of each production area or macro block at the time the projection is done. It must be mentioned that the predictive design curves of the breaking angle are classified according to the competence of the rock mass, using the RMR index from Bieniawski (1989) (Figure 4).

Figure 4 Methodology of División Andina to estimate the breaking angle or rupture angle (modified from Karzulovic et al. 1997)

Considerations made for estimation of subsidence through empirical methodology of División Andina are the following:

• Extraction percentage of each Macro Block used 10 years periods. • Final topography of pit according to PND 2013 was considered.

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Subsidence It must be mentioned that the methodology of División Andina was prepared and calibrated with subsidence data for depths less than 500 meters, so for bigger depths the method must be extrapolated. 4.2

Numerical methods

For two-dimensional numerical analysis software Phase2 was used, this considers finite elements. The analysis considered the following:

• Geotechnical sections and underground mining sequence were considered • For each of the analysis sections, the in situ tensile status is defined by the following main stresses: οο Gravitational vertical stress. οο Stress ratio in EW direction was KEW = 1.2 and in NS direction was KNS = 0.8. • Structural systems VIF and FT were explicitly considered with the most unfavorable orientations for the slope according to the current structural model.

• Current rock mass properties from División Chuquicamata were considered and calibrated according to the most important instabilities registered in the pit and behavior observed in pit slopes.

• An alteration factor D=0.7 was considered for low confinement zones (< 3 MPa) and D=0 value for

higher confinement areas (> 3 MPa). Thickness of this zone was estimated between 100 and 120 m through an elastic bi-dimensional model that corresponds to the mobilized zone according to the field records from extensometers installed in the area.

First, it was necessary to reach the equilibrium condition of the model, considering the in-situ tensile status or before the mining. Once the model for a given section was balanced, the mining effect was simulated and the mining of benches with the geometry considered. As a result of the analysis for the base case of each section, the strain field, displacements and possible failure mechanisms of the rock mass were obtained for the different years analyzed. Figure 5 shows the distribution of different geotechnical units, major VIF structures, simplifed geometry of slopes and mining sequence of underground mine for section P corresponding to Base Case Year 2060.

Figure 5 Two-dimensional model Phase2 of profile P that shows the mesh of finite elements with the different geotechnical units, major structures and mining sequence corresponding to Base Case for Year 2060

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Caving 2014, Santiago, Chile For the analysis and interpretation of results horizontal displacements were considered as well as maximum shear deformation. The aforesaid according to historical instabilities registered and behavior observed in the slopes of Chuquicamata pit. The threshold value for horizontal displacements for the west wall has been defined as 5 m in order to define the limit of the fracturing zone which in turn will define the fracturing angle. The aforesaid was based mostly on instabilities registered in the west wall and specifically on the instability occurred in November 2006. Likewise, to define the subsidence angle, a threshold value of 1 m has been considered. The threshold value for the east wall has been defined as 2 m for horizontal displacements in order to define the limit of the fracturing zone which in turn will define the fracturing angle. The aforesaid is based mainly on the instabilities registered in the east wall and specifically on the instability occurred in May 2010. Similarly, to define the subsidence angle a threshold value of 1 m has been considered. To define the breaking angle and the angle between levels, we have considered the maximum shear deformation and presence of major VIF structures, which in some cases control deformations or the “connection” to the surface. Figure 6 shows a schematic of structural control that can be present in a subsidence crater due to the presence of major structures.

Figure 6 Schematic that illustrates the structural control in a subsidence crater (modified from Stacey 2007)

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Subsidence In general terms, fracturing and subsidence angles in sector W and NW are less steep than in the east sector mostly due to the presence of the West Fault, poor geotechnical quality units (ZCI and ZCM) and GDF unit which presents a “strong” structural control. Subsidence and fracturing angles in SW sector would be controlled by the presence of the West Fault and poor geotechnical quality units (ZCI and ZCM) and major VIF structures present in the sector. Subsidence and fracturing angles in E and NE sectors would be controlled by poor geotechnical quality units (LIX HOM, LIX HET and MET) and major VIF structures present in the sector. Subsidence and fracturing angles in the SE sector would be controlled by the poor geotechnical quality unit MET and major VIF structures present in the sector. Subsidence and fracturing angles in N sector would be controlled by major VIF structures present in the sector and West Fault. Figure 7 shows an example of the results from finite elements two-dimensional models with an estimation of subsidence and fracturing angles as a function of horizontal displacements corresponding to Base Case and undercut level 1409.

Figure 7 Two-dimensional model of finite elements in Profile P showing the estimation of subsidence and fracturing angles as a function of horizontal displacements corresponding to Base Case and undercut level 1409

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Caving 2014, Santiago, Chile Table 2 Summary of subsidence angles for different sectors of the Chuquicamata pit – Base Case

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Subsidence 5 Conclusions According to the information available and analyses performed the following conclusions can be obtained:

• The methodology used in División Andina was applied for estimation of subsidence envelopes of PMCHS considering the production program and the current mining macro sequence.

• The design criteria defined in the basic engineering stage were applied in order to estimate the subsidence envelopes of PMCHS.

• In order to analyze subsidence through two-dimensional numerical models, various sections of

interest have been defined which are representative of the different geotechnical design zones of Chuquicamata pit. These sections, like Profile P described in this document, consider underground mining of the four exploitation levels based on the current production program of PMCHS, which will be considered as “Base Case”. Interpretation of results indicates the following:

οο In general terms, subsidence and fracturing angles of W and NW sectors are less steep than East sector mostly due to the presence of the West Fault, poor geotechnical quality units (ZCI and ZCM) and GDF unit which presents a “strong” structural control.

οο Subsidence and fracturing angles in SW sector would be controlled by the presence of West

Fault and poor geotechnical quality units (ZCI and ZCM) and major VIF structures present in the sector.

οο Subsidence and fracturing angles in E and NE sectors would be controlled by poor geotechnical quality units (LIX HOM, LIX HET y MET) and major VIF structures present in the sector.

οο Subsidence and fracturing angles in SE sector would be controlled by poor geotechnical quality unit MET and major VIF structures present in the sector.

οο Subsidence and fracturing angles in N sector would be controlled by major VIF structures present in the sector and the West Fault.

• The subsidence analysis shown in this document is look at two different ways. The empirical

analysis take old experiences in another mining operations and use it to plan the new subsidence behavior taking account the mass rock characteristics. By the other side the bidimensional analysis illustrate the stress and displacements occur in the rock mass contour as a result of the caving shown like subsidence. The initiative is synchrony the old experiences and the results of the modeling in the projection of subsidence and both must be calibrated once the caving starts.

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Caving 2014, Santiago, Chile References BCTec Ingeniería y Tecnología 2013, Propiedades de Relaves Filtrados, Nota Técnica. Bieniawski, Z 1989, Engineering Rock Mass Classifications, John Wiley & Sons, New York, 251p. Flores, G & Karzulovic, A 2002, Geotechnical Guidelines for a Transition from Open Cut to underground Mining: Benchmarking report, Report to International Caving Study II, JKMRC: Brisbane. Itasca 2009, ‘Chuquicamata Underground Project, 2009 Geotechnical Update’, Subsidencia por Efecto del Caving Mina El Teniente, XI Simposium de Ingeniería en Minas (SIMIN’99), (Karzulovic, A. Cavieres, P. & Pardo, C. eds), Universidad de Santiago de Chile. Karzulovic, A 1997, Subsidencia Asociada al III Panel de la Mina Río Blanco y su Evolución en el Tiempo, Informe Técnico, A. Karzulovic & Asoc. Ltda. para División Andina de CODELCO-CHILE. Laubscher, DH 2000, Block Caving Manual, Prepared for International Caving Study, JKMRC and Itasca Consulting Group, Inc: Brisbane. Rocscience 2010, PHASE2 v8.0, Finite Element Analysis for Excavations and Slopes, Canada. SRK Consulting Chile 2010, Criterios y Parámetros de Subsidencia, Ingeniería Básica Proyecto Mina Chuquicamata Subterránea, N09DM41-F11-HATCH-7129-CRTGE04-2000-001, Rev. P. SRK Consulting Chile 2013, Estudio de Riesgo Geotécnico Mina Chuquicamata, División Chuquicamata, CODELCO, Informe Técnico emitido en Rev. A. Stacey, TR 2007, ‘Slope Stability in High stress and hard Rock conditions. Keynote address’, Proceedings of the Int. Symp. On Rock Slope Stability in Open pit Mining and Civil Engineering, Perth. Australian Centre for Geomechanics, pp. 187-200.

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Unit Mine Operations

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Methodology for up-hole drilling accuracy measurements at Kiruna SLC mine M Wimmer LKAB, Sweden AA Nordqvist LKAB, Sweden D Billger Inertial Sensing One AB, Sweden

Abstract Blast function, fragmentation and gravity flow are core elements for sublevel caving (SLC). A high ore recovery and a low waste rock dilution is possible if all three elements work as planned. These elements are affected by a number of given factors and controllable factors. The effect of rock mass characteristics and a semi-confined blasting situation are largely unknown. The controllable factors are related to mine planning (SLC layout and ring design), charge and initiation pattern, performance of drill and blast work, and mucking (draw control). The scale and layout of SLC has changed tremendously over the years which makes accurate upwards production drilling outermost important. Undesirable borehole deviations are dependent upon errors related to the collaring, alignment and in-hole trajectory deviations. A methodology to separately measure these different components is suggested. The collar and collar alignment is measured with a newly developed system. A set of two inflatable packers are aligned along a rod which is pushed into the borehole. As the packers are inflated by compressed air they adjust to the irregular borehole wall and centralize the system. Its alignment is then measured along a mounted base with pivoting prisms. In-hole deviations are measured by a gyro based system which allows high accuracy measurements also in a magnetically disturbed environment. The geo-referencing of this trajectory is based upon the collar and collar alignment measurement and the total borehole deviation can be quantified. Its implications on the blast result and subsequent gravity flow can then be analysed. The results of a systematic, in-depth quality control of 282 boreholes are presented.

1 Introduction Sublevel caving (SLC) is a mass mining method based upon the utilization of gravity flow of blasted ore and caved waste rock (Hustrulid & Kvapil, 2008). It relies on the principle that ore is fragmented by blasting while the overlying host rock fractures and caves under the action of mine induced stresses and gravity. Thereby the caved waste originating from the overlying rock mass fills the temporary void created by ore extraction. The SLC extraction process may be simplified as in Figure 1. At the heart of the SLC process lie the three core elements: blast function, fragmentation and gravity flow (Wimmer 2012). These elements are affected by a number of given factors and controllable factors. The use of best available technology (BAT) has resulted in increased mining productivity by decreasing development and mining costs. Thereby, the scale has increased tremendously at the LKAB Kiruna mine, for example from a 12 m sublevel height in 1983 (Hustrulid & Kvapil 2008) to 28.5 m in the mid-1990s. In addition, the SLC layout and ring design was altered (Wimmer 2012). On this account high demands are made on the performance of drilling long boreholes (Ø 115 mm) in terms of quality and quantity, i.e accurately drilled long holes without decreasing the penetration rate. Both given and controllable factors are subject to change. Mining is carried out at greater depths at which the mining method has not previously been tested. In-situ and confining stresses increase at these depths and this affects the blast performance.

625


Figure 1 System description of the SLC extraction process

Based upon these changes in boundary conditions, it would be expected that flow behaviour has also changed during the years. For this reason, a comprehensive study of gravity flow of broken rock was started within the frame of the EU-project “I2Mine” (Innovative Technologies and Concepts for the Intelligent Deep Mine of the Future; Hejny 2013). SLC material flow from a specific test area is thereby monitored by markers based on RFID technique. The markers are installed within the burden of blast rings and later recovered at the draw point. Details about the development and final shape of extraction zone are obtainable based upon the recovered markers. Essential input data for this experiment is good knowledge of the drilling deviations in both the SLC blast rings and marker rings in-between. The methodology to measure up-hole drilling accuracy and its results are described herein.

2

Measurement of up-hole drilling accuracy

Blasted geometry and fragmentation itself are hidden in the controlled SLC operation. Drilling accuracy as an essential controllable factor, is though unpredictable. Without special tools, it is e.g. impossible to say how large the actual burden and spacings for the blastholes are. Generally, borehole deviation consists of various components (Ouchterlony 2002):

• Collar deviation, i.e. component due to set out of collar, set-up and collaring. • Alignment deviation, i.e. component due to collar angle error. • Trajectory deviation, i.e. component due to in-hole deviations. Deviation is defined as “measured” – “planned” throughout the paper. Figure 2 shows the individual components. In particular, a distinction between trajectory deviation and borehole deviation from plan is important. The former is related to the ability to drill straight holes whereas the latter is related to the ability to drill straight holes according to plan. In-hole trajectory deviations for the Wassara DTH drilling system are normally within 1 – 1.5 % of its length for 54 m long boreholes (Quinteiro & Fjellborg 2008). This estimate is based upon the measurement of maximum depth of sight in a borehole (visual observation of reflective material in the hole). With the assumption that in-hole deviations follows a circular arc, the deviation at this maximum depth of sight corresponds to four times the hole diameter. The total borehole deviation was quantified by holes drilled

626

Unit Mine Operations through to upper levels and the superior accuracy of a DTH versus a top hammer was confirmed. However, the lack of proper measuring methods at this time, did not allow for a distinction between alignment and trajectory deviation.

Figure 2 Borehole deviation and definitions of error components in SLC (not to scale)1

The methodology described in the following should though meet these concerns. 2.1

Collar and collar alignment survey instrument (“C2ASI”)

To measure post-drilling collar and collar alignment, a new survey instrument (“C2ASI”, Figure 3) was designed, custom-built by Comdrill Bohrausrüstungen GmbH and commissioned.

Figure 3 Schematic picture of the collar and collar alignment instrument “C2ASI”

1 Terminology and coordinate systems related to an SLC ring are explained in section 6.

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Caving 2014, Santiago, Chile A pair of inflatable hose packers (diameter 72-160, length 500 mm) is axially mounted on a 2.5 m long precision steel tube (40 x 3 mm). After insertion into the borehole, both packers are inflated at the same moment using a small compressor. The system gradually centralizes in the borehole and fastens at a pressure of around 5 bars in a 115 mm borehole. A rod with two prisms (centre-centre distance 1 m) is attached to an adapter at the end of the tube. The prisms are tiltable (± 45°) together, which facilitates measurement with a total station from different viewing directions. Both, collar and collar alignment are deduced from these measurements. With respect to SLC production holes, the instrument is inserted into the boreholes using a telescopic handler platform. Two measurements are performed in the same hole (system rotated by 180°). In this way, the measurement error can be reduced by calculating the average value of measurement 1 and 2. The good repeatability of results is demonstrated by the low angle difference of measurement 1 and 2 which is on average 0.498° ± 0.083° (282 measurement pairs). The lowest measured limit at 0.3° can be related to a marginal false position of the adapter. Based upon the measurement technique in combination with relatively long packers (0.5 m) the system compensates for irregular borehole walls and centralizes well in most boreholes. An exception to that are boreholes with larger cavities. 2.2

Borehole survey system (“isGyroTM”)

The “isGyroTM” (Inertial Sensing One AB) is a system suited for borehole surveying in magnetically disturbed environment, e.g. surveys inside drill rods or in otherwise magnetically disturbed holes. The system has accessories and operational procedures that allows the user to run surveys in vertical, inclined and horizontal boreholes and is commonly used in in mineral exploration, civil engineering as well as oil and gas exploration. The standard system consists of running gear, the survey instrument with rechargeable battery and a rugged computer. The running gear consists of a protective 38 mm pressure barrel for the instrument plus a set of accessories used to winch, pump or by other means getting the system in- and out of the borehole. To facilitate a smoother run in the borehole in-line centralizers (bow springs) might be attached. The instrument itself is based on so-called MEMS (micro-electro-mechanical systems) sensor technology. There are three gyro and accelerometer components inside the system, which are mounted along perpendicular axes thereby providing continuous measurement of rotation speed and acceleration along the x-, y- and z-axes of the instrument. The sensors are mounted on separate boards making the system modular. The system also contains a motherboard which handles the internal processing, data storage and Bluetooth communication. Prior a survey, the computer and instrument are synchronized in time. A communication during survey, while the instrument is encased and in the hole, is thereby not necessary. The system is inserted in the upholes by means of the hose from a charging truck. The actual survey is started at the hole bottom. The system is then slowly and continuously retracted to pre-set survey stations (measured depths) for which the time stamps are recorded on the computer. A measuring length of 2 m proved to be ideal for short holes with desired highly accurate measurement results. The instrument is held stationary at each survey station. The data recorded at these stations is used to compute inclination and gravity high side based on accelerometer data. Gyro data from these stations is used to analyze and compensate for any gyro offset signals. The data recorded when moving the system from one station to the next is used to navigate the attitude of the instrument (integrate gyro signals) in order to compute the change in azimuth and gyro tool face. After the survey, when Bluetooth communication is restored, the data is transferred to the computer. Once the data has been transferred, it is processed by the survey software. A fast data processing (~ 1/60 of survey time) is due to special signal processing algorithms, which include efficient memory handling and

628

Unit Mine Operations navigation filters (Kalman filter). Finally, coordinates for all survey stations are calculated based upon the minimum curvature method. All boreholes were measured twice with the intention to limit possible measuring errors. If the georeferenced measurements (see Section 2.3) did not meet a pre-defined requirement (hole bottoms within a radius of < 0.5 m), another measurement for this specific hole was made. The surveying precision (see Section 2.4) is related to several factors, some of them inherent to the system and some related to the measurement procedure. The accuracy of individual measured components of the borehole survey are as follows: inclination ± 0.15°, gravity high side ± 0.2°, gyro tool face ± 0.2° and for the azimuth ± 0.5°. This should, in principal, allow for a position accuracy in the order of 0.5 % or 0.5 m for a 100 m long borehole. 2.3

Calculation method for final borehole deviation

The survey data (see Section 2.2) is still not geo-referenced since the gyro instrument is not north seeking. Initially, the orientation of the borehole survey system was directly determined as the probe left the borehole (last survey station). Either two points were measured along the pressure barrel (short base) or start and end point of a line projected by a parallel aligned laser (longer base). All coordinates were adjusted based upon the measured azimuth and a 3D point at the collar. This method became though obsolete as it was found that the alignment measurements on-site did not proved to be accurate and reproducible. By contrast, the collar and collar alignment measurements (see Section 2.1) are regarded as highly accurate and are therefore the basis for the calculation of the final borehole deviation. The calculations and visualization are part of a recently in-house developed software. The main calculation steps are:

• A 3D line of best fit is calculated for the first part of the borehole (e.g. 10 m). The assumption of

nearly straight holes close to the collar seems to be valid based upon earlier observations (Wimmer et al. 2012) and as calculated RMS values are still small. If the underlying length for the regression is reasonably long also small irregularities from the run of the probe in the borehole are balanced.

• The borehole survey data is rotated around the z-axis so that the line of best fit is parallel with the measured hole alignment. Its origin is shifted to the measured collar.

• The so shifted data is visualized in a bull`s eye plot, i.e. the borehole trajectory is plotted in relation

to the intended position (centre). The final borehole deviation is then calculated as the mean for all surveys that end within a radius of < 0.5 m.

2.4

Surveying precision

The surveying precision of the entire measurement system was quantified by two tests. 2.4.1

Test 1, survey of boreholes drilled through to upper levels

Two intentionally curved top hammer drilled holes were drilled from 907 m level through to upper levels (Table 1). Hole 1 showed an excessively large deviation in x direction. The computed results are summarized in Table 2. With the described method (rotation round z axis, see Section 3) and standard parameters (highlighted in Table 2), the deviation Δr from breakthrough is 0.41 m for hole 1 and respectively 0.46 m for hole 2. Δx is the major component and suggests that the calculated trajectory additionally dips forward (Figure 4).

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Caving 2014, Santiago, Chile It should be noted that the measured deviation from the real breakthrough is a combination of several measurement errors which might be related to the system or the computational method. Mentionable is also the uncertainty in the setup of the total station and reference to fix points at different mine levels which might be in the order of 0.1 m (Gustafsson 2013)2. By increasing the number of measurements (from 2 to 4) and reducing the used length for linear regression (from 10 to 6 m) Δr could be further decreased below 0.4 m. The latter assumption might well be justified considering that these specific holes were drilled with a top hammer which allows for strongly curved boreholes. The results also indicate that with increased station intervals (from 2 to 4 m) measuring inaccuracy might get larger as well. Table 1 Borehole data for the test of surveying precision3

Borehole id Mine level Hole length Measured collar alignment Verified deviation at breakthrough from alignment

collar breakthrough l side angle, εside front angle, εfront Δx Δy

m m m ° ° m m

1 907 849 55,1 92,5 80,5 3,31 -0,08

2 878 24,3 110,8 79,7 -0,17 -0,32

Figure 4 Surveying precision, X versus Y (left) and Z versus X (right)

2 In particular, there are indications that the fix points at Z = 849 m have an error of around +0.1 m in such a way that the deviation Δr for the longer hole would be further increased. 3 Calculation of side and front angle is based on c angle = 0 (see Figure 13).

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Unit Mine Operations Table 2 Data for surveying precision, test 1 4

Number of measurements N

Hole

2 4

2.4.2

id 1 1 2 1 1 2

Station intervals [m] 2 4 2 2 4 2

Test 1: Deviation from Breakthrough: calculated - real 6 m linear regression 10 m linear regression Δx [m] Δy [m] Δr [m] Δx [m] Δy [m] Δr [m] 0.44 0.15 0.46 0.46 0.05 0.46 0.46 0.42 0.62 0.48 0.31 0.57 0.29 -0.05 0.30 0.40 -0.11 0.41 0.33 0.19 0.39 0.31 0.33 0.45 0.36 0.36 0.51 0.34 0.47 0.58 0.32 -0.20 0.38 0.30 -0.19 0.36

Test 2, survey of an artificial test site

A 55 m long plastic pipe was buried in an underground ramp with the intention to create an extremely large deviation (trajectory deviation of 19.5 m). In order to allow for a linear regression line, the initial 5 m of the pipe were still kept straight. Start and end-point were determined by a traverse line which assures high accuracy. The results (Table 3) show a remarkably well agreement of the calculated and real data for this challenging test site. The calculated end-point was within 0.35 m. The measured deviations occur primarily in z-direction, which suggest that minor problems seem to exist with the accuracy of repeated inclination measurements. Table 3 Data for surveying precision, test 2 5

Number of measurements N 2 2

Station intervals [m] 1 2

Test 2: Deviation from endpoint: calculated - real Δx [m] -0.15 0.03

3

Analysis of measurements results

3.1

Data set

Δy [m] 0.02 -0.05

Δz [m] -0.32 -0.30

Δr [m] 0.35 0.31

An experimental test area for an in-depth study of gravity flow (see Section 1) was established in production block 9 at level 820 m in two adjacent drifts (99 and 101). RFID markers were installed within the burden of 5 consecutive SLC blast rings in each drift and their appearance during extraction at the draw point was recorded. Figure 5 shows the drill pattern for the blast and “marker” rings. Three marker rings are drilled in the burden. Standard blast design, a so-called “silo-shaped” ring design was applied. It involves drilling of 8 holes with fairly steep side angles (73°) and long mid holes (54 m). The individual rings have a front angle of 80° and a projected burden along the drift of 3 m. Specific drilling amounts to about 0.03 m/tonne for a full-sized ring, yielding a tonnage of 10,000 tonnes of ore. Three marker rings with total of 17 holes (diameter 155 mm) were drilled parallel to the blast ring plane in each burden. The design for two rings 4 5

Boreholes are depth corrected (Δz = 0). Geo-referencing is done by 5 m linear regression (see section 2.3). The deviation is calculated as orthogonal distance from the (extrapolated) measured line to the known end-point.

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Caving 2014, Santiago, Chile (ring 1 and 3) was identical with 6 holes each whereas ring 2 was staggered with only 5 holes. All holes had a diameter of 115 mm. They were drilled with an automated drill rig (Simba W6 C, Atlas Copco) equipped with a down-the-hole hammer (W100 DTH, Wassara) and powered by a high-pressure water pump. The rock quality was classified throughout the test area as good.

Figure 5 Drill pattern for blast and marker rings

Knowledge of the actual placement of these holes in relation to the blast holes was of the utmost importance if detailed conclusions on internal flow mechanisms, e.g. shallow draw or backbreak (Wimmer 2012) were to be made. A distinction between blast- or marker holes was not made in the further analysis. 3.2

Collar deviation

Measured collar deviations are plotted in Figure 6 and Figure 7. To compensate for possible irregularities of the drift roof, the measured z coordinate was shifted to the planned one. An offset between subsequent rings towards positive Δx (0.28 m), i.e. forward in the longitudinal direction of the drift, exists. In exceptional cases, it can be as large as 0.85 m. As long as the offset remains constant, this implies essentially an unchanged burden. Deviations in y direction, Δy, are much larger indicated by the variability outside the upper and lower quartiles. Clearly, a trend exists that deviations increase towards the sides in such a way that the borehole collars are successively shifted towards the midline. Primarily, this type of deviation causes a reduced width of the SLC rings at the collar by 0.68 ± 0.09 m. If the alignment deviation (see Section 3.3) does not compensate with flatter side holes, this will ultimately imply narrower boundaries of the blasted ring face. The systematic character of the deviations in both Δx and Δy direction suggests problems related to set out of the drill plane and set-up of the drill rig. On the contrary, collaring errors would be assumed to be more stochastically. With respect to deviations in Δy, the used drill rig (Simba W6 C, Atlas Copco) is a major influencing factor as the drilling pattern was actually planned based upon the capacities for the standard drill rig (Solo 8-W100, Sandvik, see Section 6). For the used drill rig the offset from the mid-line is limited to 1.5 m (instead of 1.8 m) which makes it necessary for the operator to shift the side holes inwards. The side angles are though unchanged for drilling, which then results in a parallel shift of these holes.

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Figure 6 Collar deviation. Data points: 282 (each)6

Figure 7 Collar deviation Δx and Δy versus side angle

3.3

Alignment deviation

Alignment deviations (Figure 13) in terms of holes and rings are plotted in Figure 8. For individual holes it was found that the side angles are slightly larger than planned, i.e. Δε side = 0.14°. This causes the holes to be rotated in the drill plane towards positive y direction. Minimum outliers (31 samples) were identified to occur in particular for holes at the right side of the drift with a side angle from 95 – 107°. Also the front angles are larger than planned (Δε front = 0.30°) which implies steeper holes close at the collar. Some outliers (± 2°) exist both towards larger and smaller front angles. A best fit plane was calculated for all alignment measurements in a single ring. A calculated RMS value of 0.02 ± 0.01 m illustrates that deviations within the ring plane are insignificant. The deviations for the front angle (Δε front = 0.24°) still show that the ring plane at the collar is somewhat steeper but with a smaller variability. Additionally a minor rotation of the ring plane occurs (Δc = -0.17°, i.e. rotation towards negative x). As both of these deviations are referred to the ring they could be considered to be a set-up error.

6 The median is represented with an “x” marker and horizontal markers are used for the first quartile (Q1) and third quartile (Q3). The ends of the whisker are set at 1.5*IQR (interquartile range) above Q3 and 1.5*IQR below Q1. If the minimum or maximum values are outside this range, then they are shown as outliers (min and max values shown only).

633


Figure 8 Alignment deviation. Data points: 282 (holes), 44 (rings)

3.4

Trajectory deviation

The trajectory deviation measured for different hole lengths (20, 30 and 40 m) is shown in Figure 9. It is assumed that the median follow a circular arc and thus being quadratically proportional with length:

Where: k = bending. r= bend radius. d = borehole length. θ = angle at specific borehole length.

Figure 9 Trajectory deviation, measured and calculated. Data points: 275/156/64

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Unit Mine Operations For the longest holes (48 m), the trajectory median deviation is estimated to be 1.56 m if the bend radius is 740 m. The shown deviations are based on all measured holes (Ø 115 mm) and do not distinguish between different side- and front angles. Figure 10 displays the trajectory deviation for an individual ring (blast ring 20, drift 99) in a bull`s eye plot7.

Figure 10 Trajectory deviation, drift 99, blast ring 20, holes 1-8

Foremost, it can be observed that all holes are flatter than planned. This effect is length dependent with the largest deviations for the longest holes (Down = -1.6 m). The mid holes (hole 4 and 5) do not have any significant deviation to the sides. By contrast, the side holes 1-3 deviate towards the right side and side holes 6-8 to the left respectively. Hole 3 has the largest deviation to the side (Right = 0.6 m). Figure 11 summarizes the described effect for all measurements with the deviations plotted at 20 m borehole length and grouped with respect to the side angle.

Figure 11 Deviation at 20 m length for different side angles, confidence region (95%)

The actual mechanisms for the observed deviations are not understood. Further investigations are planned. The symmetric character suggests that the direction of torque has a rather insignificant effect on trajectory deviations. In addition, changes in feed force and rock parameters seem to have minor effect. By contrast, effects related to the actual set-up of the machine, e.g. the fixation of drill boom in the drift, close match 7 The centre represents the measured actual collar alignment (see Figure 2 and section 2.1) as a direction vector ( vector in horizontal plane and perpendicular to vector in vertical plane, perpendicular to (

).

. “Positive” is defined to the right when looking inwards the hole. ) and LR positive upwards (

=

×

is a is a

).

635

Caving 2014, Santiago, Chile of drill bit and hammer, etc. might be relevant. Likely, also the gravitation field plays an important role. It might be seen as a varying lateral load on a beam in bending (DTH hammer - drill rod – feeder) which, is dependent on the local change in angle along the structure. 3.5

Drilled burden

From a blasting point of view, the relative accuracy in between the SLC rings is a decisive factor for completed breakage. This concerns both deviations within and between SLC rings. The drilled burden might be estimated by means of a horizontal xy plot (Figure 12).

Figure 12 Drilled burden for blast ring 20, drift 99, front view (left) and top-view (right)

For the specific example (blast ring 20, drift 99) the maximum projected burden varied at different heights between 2.6 – 3.1 m. It should not infer any breakage problems. Also, the afore mentioned symmetric curvature of holes (see Section 3.4) can be identified which results in a somewhat narrower ring area. In general, the systematic character of deviations between rings, i.e. a positive collar offset (see Section 3.2) and flatter drilled holes, was found (see Section 3.4), which implies a constant offset with an essentially unchanged burden.

4

Conclusions and future work

A methodology to measure up-hole drilling accuracy was presented. It comprises of the determination of all components for borehole deviation, i.e. collar, collar alignment and in-hole trajectory. An instrument was developed to accurately measure post drill-drilling collar and collar alignment. In-hole trajectories were measured using a gyro based system. For the total borehole deviation, these two measurements are linked. Surveying precision was verified and found to be acceptable (< 0.7 % of its length for a 55 m long hole). By contrast, the measured in-hole trajectory deviations normally showed to be a factor of 3-4 larger. The results of a systematic, in-depth quality control of 282 boreholes were presented. Implications on the blast result and later gravity flow was shown based on an example. A more detailed overall investigation is pending. With the presented methodology, it is now possible to reliably and accurately survey boreholes of all types. This also provides a basis for future developments, e.g. to control futuristic SLC designs with curved holes (Hustrulid & Kvapil 2008) or to precisely position sensors in the blasted burden to monitor the effects of confined blasting in full-scale (Wimmer 2012).

636

Unit Mine Operations The terminology and coordinate systems related to an SLC ring are explained in Figure 13.

Figure 13: Terminology for an SLC ring, schematic

References Gustafsson, J 2013, Personal communication. Hejny, H 2013, ‘I2Mine – Innovative technologies and concepts for the intelligent deep mine of the future’, 23rd World Mining Congress, paper 246, on CD, Montréal, Canada: CIM. Hustrulid, W & Kvapil, R 2008, ‘Sublevel caving – past and future’, 5th International Conference and Exhibition on Mass Mining, (H. Schunnesson & E. Nordlund Eds.), pp. 107-132, Luleå, Sweden: Luleå University of Technology. Kvapil, R 1998, ‘The mechanics and design of sublevel caving systems’, Techniques in underground mining. Selections from underground mining methods handbook, (R.E. Gertsch & R.L. Bullock), pp. 621-653, Littleton, USA: Society for Mining, Metallurgy, and Exploration, Inc. Ouchterlony, F 2002, Borrhålsavvikelser vid sprängning av slänter, Erfarenheter från mätningar i Södertäle Drillhole deviations in a road cut perimeter, experiences from measurements at Södertälje, SveBeFo Report 53, Stockholm, Sweden: Swedish Rock Engineering Research. Quinteiro, C & Fjellborg, S 2008, ‘Measurements of borehole deviation in sublevel caving fans at Kiruna mine’, Proceedings of the 5th International Conference and Exhibition on Mass Mining, (H. Schunnesson & E. Nordlund Eds.), pp. 543-551, Luleå, Sweden: Luleå University of Technology. Wimmer, M 2012, Towards understanding breakage and flow in sublevel caving (SLC) – Development of new measurement techniques and results from full-scale tests, PhD Thesis, Luleå University of Technology, Luleå. Wimmer, M, Nordqvist, A, Ouchterlony, F & Selldén, H 2012, 3D mapping of sublevel caving (SLC) rings and flow disturbances in the LKAB Kiruna mine, Swebrec Report 2012:P1, Luleå, Sweden: Luleå University of Technology.

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Analysis of geometric design in ventilation raises for Block Cave production level drifts JP Hurtado Universidad de Santiago de Chile, Chile YH San Martín Universidad de Santiago de Chile, Chile

Abstract Block Cave mining methods and their ventilation systems have both evolved through time, starting in the early 50’s, with manual operation system, until nowadays with highly mechanized LHD system. Ventilation systems are designed according to the mining method and the country of residence legal requirements. In a mechanized LHD Block Cave, the production level drifts are the most polluted area of the mine. Every production drift contributes gases from LHD equipment and dust from extracting, transporting and dumping the ore. As a result production drifts have one of the largest airflow volume requirements at the mine. This paper analyses the impact of different raise diameters in terms of energy consumption of the system through the use of an experimental scale model coupled with a commercial mine ventilation network software. Additionally, different curvature radiuses are introduced to the experimental models generating important improvements in terms of energy consumption. The methodology here developed could be used to improve the future designs of mine ventilation systems to save energy and to help improve the underground mine environment.

1 Introduction Ventilation systems in Block Cave mines have been studied by several authors. Calizaya & Mutama (2004) present a comparative evaluation of four ventilation systems for Block Cave mine operations. The systems are illustrated with real mine examples showing the critical design aspects, the basic requirements, and the limitations beyond which the system becomes inefficient. In this way, mechanized Block Cave involves activities on many levels, all of them required to achieve production. From those levels, the production level drifts are one of the main ventilation concerns, because of the airflow volume requirement needed to dilute and remove contaminants as gases and dust due to load, haul and dump activities (Hurtado et al. 2010). The circuit the air follows from the fresh air intake to the exhaust is very tortuous, with singularities resulting in high shock losses, which are usually not properly accounted for in ventilation models resulting from a lack of available tabulated data for particular geometries. For El Teniente Mine, the air volume circulating in the production drifts can be the order of 14-30 m3/s, depending of the number of load equipment working in the same stretch (usually 1 or 2). Shock losses have been less studied in mine ventilation than in piping, but some previous works have given important information about shock losses for Block Cave mines. Hurtado et al. 2010) studied the intake and exhaust shock losses of Production Level drifts, mainly focused at El Teniente performance ventilation system, by mean of CFD techniques. This work helps understand the turbulent behaviour of airflow in a drift, but the values of shock losses were non-calibrated. Subsequently, Hurtado et al. (2012a; 2012b; 2014) developed an experimental and CFD modelling, which allows calibrating the shock losses values to a real scale size drift. Values of shock losses were obtained and also included the impact of a simple geometry modification to the curvature radius of the Elbow-Split, which was modelled using CFD. The CFD results of these studies were introduced in a commercial ventilation network program, proving an energy reduction of 25% for the particular circuit presented in Figure 1.

638

Unit Mine Operations This study develops a methodology to establish the operational cost per cubic meter of the circulating airflow, in terms of energy used by the ventilation system fans, considering different curvature radiuses and diameters to ventilate a stretch of the Production Level according to El Teniente mine layout. Figure 1 show the circuit studied that considers three main geometrical singularities, namely elbow-split/T-joint, fan-chamber-raise and crosscut (Diaz 2011). The stretch considers four crosscuts drifts (draw points) to ventilate from the inlet ventilation raise to the exhaust ventilation raise. Additionally, it is necessary to include the fan-chamber-raises located in the intake and exhaust airways. The shock loss at the elbow-split/ T-joint and fan-chamber-raise depends on the direction of flow, which are studied separately by Hurtado et al. (2014).

Figure 1 General scheme of production drift ventilation system (Diaz 2011)

In economic terms, the studied geometry (Figure 1) represents the most important circuit to study in Block Cave exploitation systems, because it is repeated dozens or hundreds of times in a Block Cave mine. Concordantly, diminishing the costs associated with operating this circuit will impact the operational total costs, resulting in several USD millions per year in savings.

2 Experimentation 2.1

Experimentation set-up

The singularities experimentally studied consider the geometries mentioned in the previous section. The fan-chamber-raise presents a geometric difference, in the experimental circuit it is opened, as presented in Figure 2, but in the previous works it was closed with an entrance only for the fan. It is important to take this aspect into account in the analysis because it reduces the shock loss in the geometry. Crosscut geometry is not considered in the scale circuit but it is considered further in the resistance estimation. Airway tunnel (intake and exhaust airways) has sections of 5.5 m x 5.5 m, production drifts 3.8 m x 4.0 m and raises 1.50 m in diameter. Figure 2 shows the experimental facilities. They are composed of a scale model (1:52) to keep constant the geometric dimensionless, which were made with even wood, PVC split tubes and PVC tubes for raises; an “American Fan Company” fan model VP0404, with a TD – 5006 impeller model VP1 used to generate the airflow and pressure. A calibrated Venturi flow meter serves to measure the airflow in the system.

639

Caving 2014, Santiago, Chile Modifications in the curvature radius were molded with a heat gun in PVC. Figure 3 shows a modified raise with curvature radius (PVC tube) overlapped in a chamber’s roof. Dynamic dimensionless can’t keep constant because the scale model can reach a Reynolds number near to 50,000 but the mine drifts can reach a Reynolds number near to 200,000. However, previous CFD work has solved this problem (Hurtado et al. 2014).

Figure 2 Experimental facilities for the studied circuit

Figure 3 Modified raise with curvature radius PVC tube

Losses can be obtained from Equations (1) and (2), according to the static and dynamic pressures measured in the different stretches. The pressure loss and power are obtained from the square law Equation (3) and the power Equation (4), in Pascals and kW. Figure 4 shows pressure taps that allows quantifying head losses to obtain the losses of the system (McPherson 1993; Acuña & Lowndes 2014). (1)

640

Unit Mine Operations (2)

(3)

(4)

Where: Ẇ = Power (Watts) V = Average Velocity (m/s) P =Pressure (Pa) p = Density (kg/m3) Re = Reynolds Number (dimensionless) Q = Flow Rate (m3/s) R = Resistance (Ns2/m8) X = Shock Factor (dimensionless)

Figure 4 Studied circuit diagram with measure points

2.2

Raise modifications

Modifications were conceived considering an operational and constructability point of view. First, modification corresponding to the radius of curvature, were done with minor requirements of drilling and blasting. Diaz (2011) obtained energy savings in the order of 25% with a curvature radius of 1.0 R (radius of raise), which was found to be the optimum curvature radius. As a result, modified geometries correspond to a curvature radius of 1.0 R. The second modification corresponds to increasing raise diameters, which we assumed would not generate an excessive extra cost or time to develop. The actual diameter of raises was 1.50 m and the modifications considered 2.0 m and 2.5 m. Scaled raise diameters were 28.4 mm, 36 mm and 48.2 mm, respectively. Figure 5 shows the raises’ modifications. It is important to highlight that, including the tests for the different diameters and modifications to the radiuses for all the tested diameters, a total of six tests had to be implemented.

641


Figure 5 Experimental facilities for the studied circuit

It is known that fans change their operational point according to the circuit losses (Acuña et al. 2010). As expected, the carried out tests show different pressures, airflow rates and hydraulic power according to raise diameter and the modifications of curvature radius. Table 1 shows the obtained results. Taking 1.50 m diameter as the base case and dividing by the hydraulic power to obtain an increment of hydraulic power of the system, Figures 6 and 7 were obtained. Hydraulic power increment with diameter can reach approximately 90%. If increase in diameter and curvature radius is considered, the increment can reach almost 200% at maximum diameter of 2.5 m and 1.0 R curvature radius modification (2.5 m is equivalent to 48 mm). Table 1. Results obtained in the scale tested circuit

Circuit 28.4 mm 36 mm 48.2 mm 28.4 mm Mod. 36 mm Mod. 48.2 mm Mod.

Pressure drop (Pa) 3339 3269 3276 3326 3258 3234

Flow rate (m3/s) 0.0263 0.0379 0.0503 0.0315 0.0539 0.0788

Resistance (Ns2/m8) 4823894 2271463 1293536 3361937 1120777 520574

Hydraulic power (kW)

Figure 6 System hydraulic increment for different diameters tested

642

0.088 0.124 0.165 0.105 0.176 0.255


Figure 7 System hydraulic increment for different diameters and curvature radiuses tested

3

Ventilation modelling

3.1

Ventilation network program

The ventilation circuit was modeled with a commercial ventilation network program commonly used for mine ventilation network modeling (VentSim). One limitation of ventilation network programs is the lack of capability to assign shock losses from complex geometries, usually resulting from the turbulence generated between close singularities, which made shock losses not predictable. That is the reason to determine them experimentally and with CFD techniques, as mentioned in the cited studies in previous sections. The fan used for the ventilation network program simulations is an Alphair 4500-VAX 1800 Full Blade with 30º blade angle, which operates at ranges of 20,000 to 80,000 cfm and 0 to 4 inches of water gage. Figure 8 presents the geometries implemented using the ventilation network program.

Figure 8 Ventilation model of studied circuit

643


Tested modifications

The tested modifications have been selected from previous works which gives the values necessary to estimate the total loss of the circuit. Table 2 shows the values of shock losses (Hurtado et al. 2012). Once the circuit was simulated, the pressure drop, airflow rate and total resistance of the system to each circuit and its modifications were obtained and presented in Table 3. Table 2 Values of shock losses (Hurtado et al. 2012)

Circuit Circuit 1 (Raise 1.5m) Circuit 2 (Raise 2.0m) Circuit 3 (Raise 2.5m) Circuit 4 (Raise 1.5m Mod.) Circuit 5 (Raise 2.0m Mod.) Circuit 6 (Raise 2.5m Mod.)

X1

X2

X3

X4

Fan/Chamber/Raise 2.0 2.0 2.0

Elbow/Split 1.5 1.5 1.5

Elbow/T-joint 1.0 1.0 1.0

Raise/Chamber/Fan 1.5 1.5 1.5

1.2

1.2

0.9

1.2

1.15

1.15

0.9

1.15

1.2

1.2

0.9

1.2

Table 3 Results obtained from ventilation network program

Pressure drop (Pa)

Flow rate (m3/s)

Resistance

28.4 mm

602

31.9

36 mm

233

48.2 mm

(Ns2/m8)

Hydraulic power (kW)

0.59137

19.2

35.7

0.18286

8.3

104

36.8

0.0767

3.8

28.4 mm Mod.

479

33.2

0.43466

15.9

36 mm Mod.

170

36.2

0.13001

6.2

48.2 mm Mod.

77

37.1

0.05576

2.9

Circuit

3.3

Power loss of the circuit

The resistance curves of each case were obtained from the resistance values estimated previously in Table 3, considering pressure and airflow rate. Also, the fan consumed power and delivered airflow was graphed. Dividing the consumed Power by airflow helps determine the energy cost per cubic meter for each resistance curve, in kW h. Figure 9 shows hydraulic power per cubic meter airflow in kW h for the scale circuit and Figure 10 for the ventilation network program.

644


Figure 9 urve of kW-h per cubic meter supplied for the scale circuit

Figure 10 Curve of kW h per cubic meter supplied for the network model

4

Analysis of results

The graphs in Figures 9 and 10 show the same behavior between the scale experimental circuit and the ventilation network program model. There is a magnitude difference in the scale making the air supply more expensive through the scale model because of the high resistance of scaled circuit, which area is very small compared to the real size model. From the exposed results, a notable difference can be appreciated for energy consumption, when curvature radiuses are modified or diameter sizes changed. Concordantly, a larger radius or a larger raise diameter diminishes the energy consumption. However, in the case of mine circuit it is very important to notice that the results here exposed are only useful for the tested circuits. It is because ventilation circuit responds in different way to the turbulence, which depends mainly on velocities and shape of geometry. Additionally, longitudes and dimensions of drifts and raises can vary according to the mine layout.

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Caving 2014, Santiago, Chile 5 Conclusions A methodology was developed and implemented that allows the user to quantify the cost per cubic meter of airflow supply in a Block Cave ventilation system. With these values, it is possible to choose the most appropriate design or improvement to obtain cost effective mining designs to diminish operational cost. To reach these values, it is necessary to calibrate and evaluate the specific layout of each mine in order to not misunderstand or make mistakes in the ventilation design input parameters. Each design and its respective geometry must be separately to obtain shock loss values.

Acknowledgement This research work has been supported by the Fondecyt Research Project 11085050 of Conicyt Chile and Dicyt Research Project 051215HC of Universidad de Santiago de Chile.

References Acuña, E, Hardcastle, S, Fava, L & Hall, S 2010, ‘The application of a MIP model to select the optimum auxiliary fan and operational settings for multiple period duties’, INFOR, vol. 48, Nº 2, pp. 89-96. Acuña, EI & Lowndes, IS 2014, ‘A review of primary mine ventilation system optimization’, INTERFACES, INFORMS, vol. 44, Nº 2, pp.163-175. Calizaya, F & Mutama, KR 2004, ‘Comparative evaluation of Block Cave ventilation systems’, Proceedings of the 11th U.S./North American Mine Ventilation Symposium, (Eds. Ganguli & Bandopadhyay), pp. 3-14, Taylor & Francis Group Plc., ISBN: 9058096335. Díaz, N 2011, ‘Mejoramiento aerodinámico del sistema de ventilación de las calles de producción en mina El Teniente’, Thesis, Universidad de Santiago de Chile Santiago, Chile. (in spanish) Hurtado, JP, Gutiérrez, O & Moraga, NO 2010, ‘Numerical Simulation of Shock Losses at the intake and exhaust Raises of Block Caving Production Level Drifts’, Proceedings of the 13th US/North American Mine Ventilation Symposium, Sudbury, vol. 1, pp. 425-432. Hurtado, JP, Díaz, N & Acuña, E 2012, ‘3D Characterization of Mine Ventilation Circuits for Block Caving Production Levels’, MassMin 2012, Proceedings of the Sixth International Conference & Exhibition on Mass Mining, Sudbury, Ontario, Canada. June 10-14, vol. 1, pp. 896-911. Hurtado, JP, Díaz, N, Maya, C & Acuña, E 2012, ‘Caracterización numérica y experimental de pérdidas de carga en el nivel de producción en método Block Caving’, Proceeding of the 14th US/ North American Mine Ventilation Symposium. Salt Lake City, Utah, United States of North America, June 17-20, vol. 1, pp. 553-559. Hurtado, JP, Díaz, N, Acuña, E & Fernández, J 2014, ‘Shock losses characterization of ventilation circuits for Block Caving production levels’, Tunnelling and Underground Space Technology, vol. 41, pp. 88-94, ISSN 0886-7798. Available from: (http://www.sciencedirect.com/science/article/ pii/S0886779813001946). McPherson, MJ 1993, Subsurface Ventilation and Environmental Engineering, Chapman & Hall, London.

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Simulating the Logistic of an Underground Mine M Moretti Paragon Decision Science, Brazil L Franzese Paragon Decision Science, Brazil M Capistran Paragon Decision Science, Brazil J Cordeiro Alkmim/AngloGold Ashanti, Brazil B Penna Alkmim/AngloGold Ashanti, Brazil G Mendes Alkmim/AngloGold Ashanti, Brazil

Abstract This paper describes a logistic study of an underground gold mine, belonging to AngloGold Ashanti, where four different layout options could be applied to the tunnels with different transportation strategies. Each evaluated layout had its own configuration for shaft and truck fleet. The study was made individually for each year of the mine operation life, determining the necessary transportation capacity to achieve the planned production for that year. Due to the very restrictive traffic options in the tunnels, a framework was developed to represent the tunnels and traffic rules in a discrete-event simulation model. A KPI named Total Transportation Capacity was developed to compare scenarios with different truck types. The results pointed to the scenario with the lowest necessary transportation capacity to achieve the planned production.

1 Introduction The underground mining is a very defying challenge. In addition to all concerns about safety, the tunnel network has to be well planned in order to achieve feasibility of the mining operations. The excavation of galleries is an expensive and complex operation. Thus, the tunnel network has to be designed to minimize its extension, allowing the best possible traffic options. A search for the best layout option to the tunnel network was the problem faced by AngloGold Ashanti, a gold mining company with operations in Brazil. In addition to the tunnel layout itself, the mine could have shafts in different positions, different transportation strategies with intermediary silos, and also different truck fleets. The goal was to find the best layout option to achieve the scheduled production using the lowest investment in trucks. The truck fleet should be sized for each one of the fourteen years of the mining operation. Since the underground traffic is a very dynamic process, it is very difficult to study with deterministic tools, and the discrete-event simulation was the chosen option. The concern about underground traffic in mines is not new. It is also subject of simulation studies since the early days of this technique applied with computers. Hayashi and Robinson (1981) documented a simulation study regarding an underground railroad in a coal mine. They addressed traffic problems in detail, considering crossing lines, single lines and tunnel layouts. Their objective was also to achieve the best train configurations and dispatching strategies to sustain coal production with minimum resources. The study conducted by Miwa and Takakuwa (2011) is also about a coal mine. They have evaluated an underground conveyor network, another option to retrieve minerals from the mine. In this case, the study was focused in the conveyor velocity, working under a predefined layout. Wu et al. (2013) have developed a simulation study regarding tunnel visualization of underground mines, but the transportation and traffic were not discussed.

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Caving 2014, Santiago, Chile When an underground mine uses trucks as the main transportation resource, the tunnel network may have traffic problems similar to a railroad network. Usually, the tunnels are large enough to allow only one truck to pass, sometimes two. Traffic situations, such as, passing or crossing, are not easy inside the mine. Almost every tunnel has structures called “mucking bays” or “passing bays”, which are strategically located spaces that can accommodate one truck, sometimes more than one. When a truck is in a tunnel and another comes from the opposite direction, one of them parks into the passing bay and allows the other to pass. This is similar to a single railroad line with a crossing line, as presented in Figure 1.

Figure 1 Comparison between crossing vehicles in a mine gallery and a railroad

Since the traffic problems are similar, the solutions developed for railroad could also be applied to this case, with the necessary adjustments. Even the prioritization behaviour is the same: loaded trucks should pass and empty trucks should wait. The chosen algorithm was the one proposed by Fioroni et al. (2008), which addresses the line/tunnel restrictions, crossing rules and traffic behaviour. The following sections describe how this study was conducted.

2

Main structures in the mine

The underground mine used to support this study is located in Brazil, in the Minas Gerais state. The available scenarios to be evaluated are a combination of the following components:

• Tunnel layout. • Traffic directions. • Shaft loading position. • Intermediary silos: quantity and position. • Truck type and capacity. The trucks have mainly three tasks to accomplish: carry the gold ore to a shaft or hopper, carry waste to the shaft or hopper and carry waste to some mined out areas that need to be filled again. Trucks never go loaded to surface. The mine has a limited number of loaders, which is the same for all scenarios. The loading points are changed according to the production schedule, going deeper in the mine. After internal discussions and studies, the AngloGold team has selected four scenarios to be evaluated with simulation:

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Unit Mine Operations 2.1

Scenario 1: Original design

This scenario is the original design for the mine, with four main access tunnels and a mix of trucks with capacity of 30 and 45 tons. It is considered the base scenario, and is used as a reference. The schematic of the tunnel network is presented in Figure 2. Each color square is a mining point at the level, and a brown square means a passing bay position. This scenario has a hopper at level 9 and the shaft is positioned at level 11, providing two unloading points to the trucks.

Figure 2 Tunnel schematics for the scenario 1, the base scenario

2.2

Scenario 2: Deeper shaft position

This scenario uses the same mix of trucks, but adds a new unloading position at level 16, providing more options for the trucks, minimizing congestions. It is also nearest to the bottom of the mine. The tunnel layout is the same as for Scenario 1. 2.3

Scenario 3: Intermediary silos

This scenario uses the same tunnel network layout and unloading positions as Scenario 1, but intermediary silos were added at levels 15, 18, 20 and 22. A fleet of 30 tons trucks is used to bring gold ore to these silos and, after that, a fleet of 60 tons trucks is responsible to convey it to the shaft position at level 11.

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Scenario 4: Additional access tunnel and traffic changes

This scenario adds a new access tunnel to Scenario 1 layout, assigning it as unidirectional going down and another pre-existing tunnel, as unidirectional going up. The truck fleet mix is also the same as of Scenario 1 with 30 and 45 tons of capacity.

3

The mine simulation model

The simulation tool chosen to build the models was Arena, from Rockwell Automation. The approach to model the tunnel network was the one described by Fioroni et al. (2013), the Signal Oriented Approach. It was chosen because the network had some particularities that should be addressed locally and this approach allowed that. Situations, such as, prioritization between trucks and the access to the hoppers required a local set of decisions different from the regular truck movement. This approach focused on the signal intelligence, letting them decides if the truck was allowed to pass or not. Signals were distributed along the model network and each one of them had a different decision expression, considering the other signal’s status, the nearby tunnels situation and other relevant factors to its specific location. At the real mine, they don’t really have this amount of light signals, but the truck advance is decided visually or by radio instructions, resulting in the same behavior. The model has considered more than 2000 individual positions, where the truck could load, unload, park or wait for other trucks to cross. The animation structures of the tunnel network are presented in Figure 3, where the signals can be seen along the lines.

Figure 3 Partial view of model animation

The real network was too big to be represented and great part of it was not important to the study. Therefore, not all tunnels were represented but only the ones relevant to the process and with truck circulation. It was further simplified by removing irrelevant connections and aggregating common points. Furthermore, it was assumed that the truck should use only one path/route between positions. This helped to simplify the model and give some “room” to the results, since, at the real mine the trucks could avoid tunnels with more traffic, making better decisions than the model. However, it was not considered relevant enough to affect the decision. The routes were assigned by AngloGold personnel, since they had more

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Unit Mine Operations knowledge about the mine and where the trucks should pass on every trip between positions. More than 10,000 routes were created, covering each possible origin-destination pair in the model. An individual model was built for each scenario, due to structural differences between them. Evidently, the route’s list had to be updated for each model. All trucks and loaders were affected by downtimes and maintenance and every movement of the truck had a chance to be affected by disturbing vehicles, impacting its travel time. Besides the priority in the mine, the trucks, sometimes may be affected by the other vehicles, such as, personnel transportation, tunnel maintenance equipment, cars, etc. 3.1

Model output

A set of KPIs were implemented within the model to help the system validation and comparison between scenarios, especially, travel and activities times and utilizations. Also, the scheduled production and simulated production were compared to confirm the goal achievement. A partial view of the output interface can be seen in Figure 4.

Figure 4 Partial view of the output interface

In addition, the model output included the number of trips performed for each route inside the mine to provide the user with useful information about potential traffic problems and the most problematic routes, as can be seen in Figure 5. 3.2

Model validation

The model was validated by comparing its results with deterministic calculations made for the base scenario (Scenario 1). All results were analysed by the mining experts to check for coherency. The model behaviour was evaluated with sensitivity experiments. Subsequently, AngloGold team has approved the model to proceed with scenario experiments.

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Figure 5 Usage count for each route at the tunnel network

4

Scenario results

Several experiments were made with each scenario to determine the optimal truck fleet for each year of operation. The objective was to find the lowest fleet, able to achieve 95% or more of the scheduled production. In order to compare the scenarios, a new KPI was proposed, since the truck type was not the same for all scenarios and the direct comparison would not be possible. This KPI was named “Total Transportation Capacity” (TTC) and is a sum of capacities of all trucks of the two different fleets measured in tons.

TTC = (F1*C1)+(F2*C2)

(1)

Where: F1= Trucks of fleet 1. C1= Truck capacity at fleet 1. F2= Trucks of fleet 2. C2= Truck capacity at fleet 2. The TTC was calculated for all scenarios and used to generate the chart presented at Figure 6. Evaluating this KPI, Scenario 2 and 4 performed noticeably better than 1 and 3. The production has a peak at 2024 and a reduction at 2025. It can be noted at the transportation capacity required for this year in all scenarios. The following year, 2025, isn’t so demanding, requiring less trucks. These sudden changes in the number of trucks from one year to another are inconvenient and should be avoided. In the comparison between scenarios 2 and 4, is possible to note that scenario 4 is more stable. It requires less changes in the number of trucks during the entire mine operation period. The Table 1 shows another KPI: the peak capacity required for each scenario.

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Figure 6 TTC comparison between scenarios Table 1 Peak capacity required for each scenario

Scenario Peak TTC (tons) 1 918 2 552 3 1004 4 466 By evaluating this KPI, the best is also Scenario 4, which achieved the scheduled production for all years with the lowest TTC, meaning the smallest fleet. Another KPI used to compare the scenarios under the same basis was the tons per kilometer per truck (tkm per truck). It was calculated using the truck cycle times, average distance traveled and truck fleet for each year. These are also model outputs. The result is presented in Figure 7.

Figure 7 Comparison between scenarios

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Caving 2014, Santiago, Chile This KPI also confirms that Scenario 4 has the best operational performance.

5 Conclusions By the results obtained and model behavior, it is possible to conclude that the railroad algorithms and approach adopted were appropriate to represent an underground mine truck traffic behavior. All scenarios could be modeled and considered validated by the mine specialists. This is a relevant achievement, because to model restrictive movement is always a challenge, not the restriction itself, but the entire decision process that have to be present to allow the truck or train to move in this structure. This study have focused on the truck fleet as the main factor to decide which scenario was the best, but there are other factors involved, such as, the investment to implement the infrastructure required for each one of them. For this study, all scenarios were assumed to have similar investment levels. One weak point in this study was the absence of a dispatch system in the model, which will probably exist in the real system. Even if it was not perfect or optimal, this could allow the trucks to choose a better path or decide a different destination depending on the present situation at the mine. In this case, however, as mentioned before, this was not considered relevant to the study. All of the scenarios shared the same weakness, which becomes irrelevant when comparing scenario data. They are all affected in the same way and at the same level, meaning the comparison is very reliable. This study has confirmed the value of a discrete-event simulation tool, such as, Arena, to evaluate traffic problems in underground mines. Computational tools, FPC and TALPAC, are useful to determine the fleet of loaders and trucks for a specific sector, but lack the necessary resources to deeply consider the traffic at the entire mine. This study could be applied to any underground mine using block and sublevel cavingas well as other methods. The conclusion is that this result pointed to the best technical decision. However, the best business decision should be taken after adding costs to all this data.

Acknowledgement The authors thank AngloGold Ashanti by supporting this project and by authorizing the use of its information in this paper.

References Fioroni, MM 2008, Simulação em ciclo fechado de malhas ferroviárias e suas aplicações no Brasil, PhD Thesis, Escola Politécnica, Universidade de São Paulo, São Paulo, SP. Available at: http:// www.teses.usp.br/teses/disponiveis/3/3135/tde-03062008-180002/pt-br.php. [Accessed : February 27, 2014]. (in Portugal). Fioroni, MM, Quevedo, JG, Santana, IR, Franzese, LAG, Cuervo, D, Sanchez, P & Narducci, F 2013, ‘Signal-Oriented Railroad Simulation’, Proceedings of the 2013 Winter Simulation Conference, (Eds. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl), pp. 3533–3543. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. Hayashi, F & Robinson, D 1981, ‘Computer Simulation of Mine Rail Haulage System’, Proceedings of the 1981 Winter Simulation Conference, (Eds. T. I. Oren, C. M. Delfosse, C. M. Shub), pp. 121–127. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

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Unit Mine Operations Miwa, K & Takakuwa, S 2011, ‘Operations Modeling and Analysis of an Underground Coal Mine’, Proceedings of the 2011 Winter Simulation Conference, (Eds. S. Jain, R.R. Creasey, J. Himmelspach, K. P. White, and M. Fu), pp. 1685–1695, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc. Wu, S, Lu, M, Mao, S & Shen, X 2013, ‘As-Built Modeling and Visual Simulations of Tunnels Using RealTime TBM Positioning Data’, Proceedings of the 2013 Winter Simulation Conference, (Eds. by R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl), pp. 3066–3073, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.

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Engineering approach for the design and analysis of drawbell blasting in block and panel caving Á Altamirano BCTEC Ingeniería y Tecnología SpA, Chile R Castro Universidad de Chile, Chile I Onederra University of Queensland, Australia

Abstract In Block Caving development and to achieve planned production, several drawbells need to be built. In recent years, the concept of rapid development have also include the possibility of building drawbells using a single phase. This article describes an engineering approach to design and analyze single phase drawbells. It combines the use of empirically based damage models with estimates of swell factors. A damage/breakage criterion is proposed based on the back analysis of successfully extracted single phase drawbells. Design parameters are evaluated using this criterion and recommendations outlined for implementation in the and further validation.

1 Introduction In mining systems, such as, Block and Panel Caving, it is extremely important to perform drawbell blasting effectively. There are significant productivity benefits if the drawbells blasting is conducted in a single phase. This is also referred to as single shot firing. In addition, single shot firing of drawbells can eliminate risks to personnel working with explosives under fragmented material. Drilling and blasting in caving operations depend on the variant that is used. In the case of a Panel Caving with pre-undercut, the excavation of the drawbell has to be done only from the level of production. Therefore, it is necessary to create a slot to provide the necessary free face for the expansion of fragmented material; in addition, the blast holes have to be about 14 m to 18 m long with an explosive charge adequate to provide fracturing rock and suitable connection to the undercut level. Failure to implement a proper procedure in blasting is going to confine the fragmented material blocking the flow of ore. Additionally, in some cases it can damage the Crown Pillar and the drawpoint. Within the conventional or traditional Panel Caving method, there are two stages to the construction of the drawbell: initially, the development from the production level with vertical blast holes of about 12 m to 15 m above the level of production; secondly, the development from the undercutting level can proceed to drill blast holes with negative orientation to complete the geometry of the drawbell. The current practice of drawbell design has considered the use of different geometries (Jofre et. al. 2000). It should be noted that between 1985 and 1994, different designs of drawbell were implemented in Sector Teniente 4 Sur, with the main objective to deal with specific singularities in the mine design, such as, changes in the direction of drawbell drift, changes in orientation in load and haulage or transport, modification of extraction points positions and connections between two methods of extractions. The applications of single shot firing has been driven by advances in both drilling and blasting technology, in particular by the availability of precise initiation systems. Single shot firing is routinely implemented by some of the major cave mine operators. Experience with this technique is described by Lovitt (2005), where he describes the implementation of one shot designs in Lift #2 at Northparkes Mines. This was possible

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Unit Mine Operations through the use of electronic detonators. The success of the procedure was attributed to a redistribution of the blast holes to improve efficiency. Amongst the operational parameters, highlights include: a reduction in the time blast holes departure from 5 to 20 ms, the density of explosive used between 0.8 and 1.2 g/cm3, the free face to archive the blasting increase to 1,1 m (slot) and, finally, the 64 mm diameter blast holes, which played an important part in this achievement as it reduced the percentage of misfired holes after blasting the drawbells and saved considerable time and money. Popa (2012) proposed a drilling and blasting design for at cone–shaped drawbells Cadia East Project. The operational parameters were: 16.5 m high; 17.5 m diameter and 2,100 m3 drawbell, which was successfully established in a single blast, using 136 blast holes 76 mm in diameter and 7 relief holes 200 mm in diameter. The total drilling was 2,057 m and the charge weight 6,000 kg resulting in a powder factor of 1.0 kg/m3. The review of the literature shows that there is a lack of methodology applied to the design and analysis of drawbell blasting. This paper discusses an engineering approach that could be applied to other mine conditions.

2 Methodology The framework of the proposed approach in described in Figure 1. The first stage begins with the analysis of the rock mass properties. This first step is required to determine key parameters for the calibration of the empirically-based damage models. In this instance, the Holmberg and Persson approach is used (Persson et al. 1994). The second stage is to define the explosive properties, in particular, the material density. The third stage involves the definition of preliminary design parameters such as burden, spacing, and uncharged collar lengths. This is mainly driven by rules of thumb and geometry constraints. Prior to conducting simulations of breakage envelopes, the Holmberg-Persson model is calibrated to determine the main intensity (K) and attenuation (alpha) constants. In this case, the parameters were determined from the back analysis of damage zones generated in production or development drifts. In this work, the simulation stage was conducted with the JKSimBlast (2DRing) software. The analysis gives the estimated damage zones in horizontal and vertical profiles, highlighting the possible effect of blasting on adjacent pillars and the interaction of the blast holes with contiguous free faces. In this process, a criteria defined from previous experience are applied to the analysis. This is based on the breakage coverage and swell. As mentioned previously, the process begins with the collection of geotechnical information that should be relevant for the modelling of blast damage. The following considerations were taken into account during the analysis:

• The calibration of the model parameters (K and α) is performed based on the theoretical diagram of drilling and blasting for drift development; there is no information about the deviation of the blast holes.

• The calibration was performed with ANFO at a density of 0.82 g/cm3, since it is the main explosive used in blast design for this horizontal development.

• It was assumed that estimates of breakage envelopes using this simple model could be extrapolated

to production blasting and undercutting, provided that the rock mass and explosive possessed similar characteristics.

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Figure 1 Design and analysis framework for drawbell blasting

2.1

Evaluation and definition of breakage criteria

A standard drawbell case study was used to evaluate the ability to achieve full breakage and extraction through single shot firing. The approach was applied to the first phase of a standard drawbell, which consisted of 54 blast holes in total, distributed in 9 rows. The burden between rows varied from 1.5 m to 1.8 m. In this case, the analysis of blasting in a single phase was focused on the first phase of a standard drawbell, which consisted of 20 blast holes corresponding to 4, 5 and 6 rows, as seen in Figure 2, with the burden is 1.5 m. Blasting analysis allowed to define the break area and interaction of damage zones with respect to the described Case Study. The results of the analysis are shown in Figure 3.

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Figure 2 Drawbell Case Study

Figure 3 Result of analysis of “Drawbell Case Study” showing Contour of PPV at the mid-plane

The simulation of PPV contours (peak particle velocity) in the mid-plane of Drawbell Case Study (Figure 3) shows that the interaction between the slot (free face) and the area of damage by blast holes is high. In the contours of the slot (red color in Figure 3), particle velocity was four times the critical PPV (PPVc). From experience, this is considered to be a reasonable index that defines the extent of breakage. In this particular case, the coverage is of the order of 53%.

4

Application of proposed methodology to an alternative drawbell design

For illustrative purposes the presented methodology was applied to a synthetic case, where a mine required to design a one shot blasting. In Table 1, the conditions or geometry to be reached are shown.

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Caving 2014, Santiago, Chile Table 1 Geometric conditions and description of example drawbell

Drawbell with Single Slot

Specifications Diameter Slot (m)

Plan View

1,5

Long Drawbell (m)

10

Width Drawbell (m)

5

Diameter Blast holes (mm)

63,5

Drilling Length (m)

11,96

Number blast holes

25

Specific drilling (m/m3)

0,74

The next steps include the calibration of damage model, the design of a new drill and blast pattern and the analysis of swell condition. 4.1

Damage Model parameters

Geomechanics information provided included: properties of the rock mass and intact rock as well as a report of preconditioning by ASP Blastronics, providing the Critical Peak Particle Velocity of the rock (PPVC), as shown in Table 2. PPVC represents the peak particle velocity that can be sustained by the rock before tensile failure occurs. Table 2 Variables for simulation JKSimBlast (Holmberg & Persson 1989)

tensile strength [Mpa]

wave speed propagation [m/s]

17,6

4.979

4.2

Young’s modulus of elasticity [Gpa]

PPVc [mm/s] Disturbed Area is considered

60

1.461

4* PPVc [mm/s] Fracture Zone is considered 5.844

K (Factor of Velocity) 600

α (Attenuation constant of the Rock) 0,9

Analysis of drabell design alternative

A new design for drilling and blasting is considered. Similarly, an analysis of the rate of swelling is performed assuming an angle of repose at the extraction point of about 30°. Finally, the time sequencing between blast holes is considered as a key factor of the blasting in one phase, thus followed by the recommendation for a time and output sequencing of blast holes. Regarding the gravitational flow, conventional caving designs indicate that the apex values must be minimum in order to avoid a point charge due to the geometry of the pillar. The new design proposeschanges in the geometry of drawbells. Basically, it is recommended to adjust the designs and reduce the size of the current apex from 7.0 m to 2.5 m, as shown in Figure 4.

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Figure 4 New geometric configuration of drawbell

The alternative drawbell design considers 48 blast holes loaded with ANFO at a density of 0.82 g/cm3 with 9 rows distributed across the width of the drawbell (Figure 5). The blast holes near the limit have increased offset, in order to protect that area. This design ensures a high grade of fragmentation of the material, facilitating the ejection and flow during blasting, allowing a single shot event. It develops a sequence of detonation in conjunction with the design in order to optimize the interaction between zones of fracturing of each blast hole.

Figure 5 Layout top view of the new D&B design drawbell, View Profile of rows 1 and 9

The analysis of the single phase drawbell extraction is performed in two ways. First, the percentage of breakage area is calculated, then the free face available for the sequence is estimated. Table 3 shows the mid-plane simulation of this drawbell, showing the area subjected to a particle velocity greater than four times the PPVC of the rock. Additionally, the lowest breakage around the limits of the drawbell (Visera) is displayed. Comparative results with index drawbell case study are shown in Table 3.

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Caving 2014, Santiago, Chile Table 3 Comparative index Drawbell Case Study vs new design drawbell apex 2.5 m

Comparative indices Conventional Drawbell Case Study (slot phase) Alternative Drawbell design

Specific perforation (m/m3 rock)

Breakage Area (%)

Interaction breakage area

1,05

53,01

High

0,93

58,5

High

Plane of Study

Sequencing of each blats hole can be seen in Figure 6, where the detonation times are displayed.

Figure 6 Detonation sequence new design drawbell apex 2.5 m

The new design drawbell has a high fracture zone (breakage area). The blast holes are distributed to increase the interaction between the zones of fracturing, while advancing the blasting sequence. This design would ensure a fine fragmentation of the material, improving the ratio of space available for movement of swell material. The percentage of area of break reaches 58.5%, which is well above the 50% defined by other back analysis work and 53.01% when compared to the slot phase “Drawbell Case Study”. 4.3

Swell Factor analysis of alternative drawbell

The objective of this analysis was to investigate if there was enough free space for the fragmented material to move and flow. Initially, it was considered as empty volume, the volume of air available in the drift and the slot, with the final consideration was that the angle of repose of the material was 32˚. According to Hustrulid and Kvapil (2008), in caving blasting there are two main swell modes for the ore, the available free swell space as provided by the sublevel drift, and the confined swell, which is scale independent as long as the design powder factor at the toes of the blast holes remains the same. Newman et al (2008) conducted a field test in which a slice of ore was blasted horizontally towards a cave rock filled drift. This resulted in the confined swell values of around 17%, which is a minimal percentage of swell that is needed to achieve a blast single shot and allow the ore flow.

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Unit Mine Operations Based on the above, it was decided that the swell factor was 20% for the first stages and 30% for the remaining stages. The swell factor was less in the first stage, because the interaction between the blast holes is greater in the zone close to the slot and, therefore, the size of rock fracturing was larger. Additionally, it was considered that there will be a compaction of the material product of the blast in the last stage, which generated an additional 10% of the available volume for stage three; this is supported by blasting test in a sublevel caving mine (Hustrulid & Kvapil 2008). It is considered that the level of interaction in the damage zones is high and the energy level is sufficient to produce a fine fragmentation of the material; these assumptions were supported by the high percentage of breakage estimated and the high level of interaction with the free face. For analysis purposes, the blast was separated into three stages with 3 volumes of material given by the iso-lines of time according to the detonation sequence, as shown in Table 4 (considered Stage I: 180 - 200 ms; Stage II: 480-500 ms and Stage III = 730 ms). Table 4 Volumes of material to move by stage and displacement volumes

Stage I (Swell 20%)

Stage II (Swell 30%)

Stage III (Swell 30%)

104

200

266

Swell (m3)

125

260

345

Volume Available (m3)

108

310

350

Volume Difference(m3)

-17

+50

+5

Volume of Material (m3)

Top View

5 Conclusions This paper discussed the evaluation of methodology to evaluate single phase extraction of Drawbells. A procedure is described incorporating the use of simple breakage criteria. The approach is initially verified with the analysis of the slot phase of a conventional drawbell and then applied to an alternative drawbell design. This new drawbell design is proposed for a site specific layout. The analysis indicated that the alternative drawbell design has a significant breakage zone. The blast holes were distributed in order to increase the interaction between the zones of fracturing. The design indicated the potential for a fine fragmentation of the material. The percentage of area of breakage reached 58.5%, which was well above the 53.01% presented in the slot phase of a conventional drawbell case study. It is also consistent with the back analysis of single phase drawbells in other operations. The analysis did, however, indicated the potential for an increase area of disturbed zone in the apex pillars. In order to reduce damage to the pillars, the use of distributed explosive charges or lower density are recommended in rows near the perimeter of the drawbell, at the time of implementation. The analysis also indicated that it was very important to conduct a detailed study of the stability of the pillars, considering the potential increases in the extent of damage. Further work is currently underway to validate the proposed design and analysis approach to better define drawbell blasting parameters in single phase extraction.

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Caving 2014, Santiago, Chile Acknowledgment Deepest gratitude to my workmate Francisco Marco. Without his knowledge and assistance, this study would not have been successful. Thank you to engineers German Púga, Mario Vicuña and Felipe Diaz who collaborated on the study.

References Jofre, J, Yañez, P & Fergunson, G 2000, ‘Evolution in Panel Caving Undercutting and Drawbell Excavation, El Teniente Mine’, Procedings of Massmin 2000, Brisbane. Qld. pp. 249-260. Music, A & San Martin, J 2010, ‘Great Volume Draw Bells Blast at El Teniente’, CODELCO, Division El Teniente. Dunstan, G & Popa L 2012, ‘Innovative Cave Establishment Practices at Ridgeway Deeps’, Newcrest Mining Limited, AusIMM The Minerals Institute National Congress, Auckland 2012. Lovitt, M & Silveira, A 2005, ‘Off to a good start with Lift #2: Drawbell Extraction – Northparkes’, Proceedings of the Ninth Underground Operators Conference, pp. 75-80. Perth, WA. Holmberg, R & Persson, PA 1980, ‘Design of Tunnel Perimeter Blast Hole Patterns to Prevent Rock Damage’, Trans. Inst. Mining Metall, vol. 89, pp. A37–A40. Villaescusa, I & Onederra 2003, ‘Blast Induced Damage and Dynamic Behaviour of Hangingwalls in Bech Stoping’, Fragblast 2003. Onederra, I & Esen S 2003, ‘An alternative Approach to Determine the Holmberg-Persson Constants for Modelling Near Field Peak Particle Velocity Attenuation’, Fragblast 2003. Villaescusa & Onederra I 2003, ‘Blast Induced Damage and Dynamic Behaviour of Hangingwalls in Bech Stoping’, Fragblast 2003. Hustrulid, W & Kvapil R 2008, ‘Sublevel caving – past and future’, MassMin 2008, Luleå Sweden. Persson, Holmberg & Lee, Rock Blasting and Explosives Engineering, CRC Press, 1994.

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Analysis of induced damage due to undercut blasting D Morales Hatch, Chile R Olivares Codelco, Chile

Abstract Operational issues related to the exposure of personnel to hazards at the undercut face and risks related to a typical caving operation (rockfall, collapses, high induced stresses, damage at excavations due to blasting, among others) impact significantly on both safety and productivity of a Panel Caving operation. The analysis of current practices at undercutting resulted in a proposal to modify the standard undercut drilling pattern with the objective to reduce the damage around the undercut drift (brow and walls) and also to avoid the over break. Since some operations are conducted by personnel at the undercut level, it’s crucial to maintain the working zone in optimal conditions. This paper aims to incorporate a new proposed design for the blasting of a Post Undercutting sequence in order to reduce risks related to this operation and to improve productivity and effectiveness for the advance of the undercut face. The analysis and comparison of both designs (standard and proposed) blasting simulations were conducted using JKSimblast software. The results of the simulations show that the proposed design diminishes substantially the damage around the brow and walls of the undercut drift improving brow conditions. Moreover, the proposed design shows no undercutting blasting induced damage at the extraction level.

1 Introduction Undercutting is one of the critical operations within the productive process of a Panel Caving Operation. The understanding of the undercutting process derives largely from operational experience, and many empirical attempts to improve the process (Rivero 2008). According to Butcher (Butcher 2000), the undercutting process has 3 main objectives:

1. To generate an excavation large enough to allow and ensure the caving process. 2. To achieve the required dimensions of the area to start the caving process, minimizing damage in the proximity of the undercut area.

3. To reach as fast as possible the hydraulic radius required to generate caving; to propagate the caving process and consequently reduce the induced stresses derived from this operation.

If a Post Undercut strategy is used, the production levels must be fully developed and constructed prior to the undercutting process. In order to achieve a continuous advance of the undercut face, the unit operations related to the development of these levels must be carried out optimizing the development and construction rate and the safety conditions for personnel exposed to rockfall hazards, collapses and instability inherent to the exploitation method. In addition, since drawbells are opened before the passage of undercut face, this sequence exposes the production level to high levels of abutment stress during a given time causing potential damage in the production level pillars (Jofré 2000). A major operational safety issue, related to the advance of the undercut face and propagation of caving, is the exposure of the personal to poor brow and walls conditions after firing a ring. Since the charging of the blast holes for undercutting is conducted manually by operators, is common that after blasting a ring, the personnel gets exposed to poor brow conditions and hazards at the undercut face while charging the next rings, as presented in Figure 1.

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Figure 1 Scheme of the side view of a typical cave face, using systematic reinforcement of the top of the drift by means of wooden pillars

Instability conditions at the undercut level in the proximity of a working area, in addition to the risks related to a typical caving operation (rockfall, collapses, high induced stresses, damage at excavations due to blasting, among others), impact significantly in safety and productivity of a Panel Caving operation. These conditions create the imperative necessity to incorporate improvements both in the undercutting design and in operational practices. This study will focus on optimizing the undercut drilling design, analyzing blasting damage at the undercut level, by means of blasting simulation software, with the objective to diminish the damage of the walls and brow of the undercut drift. This will improve safety to the workers and minimize damage at the drawbell.

2 Background 2.1

Undercutting in a Post Undercut mining sequence

A typical undercut design (Andina, Codelco Division) for a Panel Caving operation using a Post-Undertcut strategy, considers an undercut height of 10 m with flat roof, using a fan drilling pattern (Figure 2). The undercut drift dimensions are 4 m x 3.6 m and the reinforcement consist of Split Set Bolts and the installation of a preventive mesh. After blasting a set of rings, and with the purpose to continue the undercut face advance, the undercutting process consists on the following activities:

1. Scaling the top of the undercut drift 2. Set-up of a mucking platform. 3. Installation of temporary reinforcement for the excavation, by installing wooden pillars, whose purpose is to support the brow and the roof during charging of the blast holes (Figure 3).

4. Firing of the blast holes to continue the undercut face advance.

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Unit Mine Operations Figure 2, presents a standard drilling pattern for undercutting in a Post Undercutting Panel Caving operation. The design includes 19 radial blast holes, drilled with a Simba pivoting 1.8 m. This particular design generates a 10 m height flat undercut. The undercut blast holes are charged using ANFO at 4%, leaving a stemming of 1 m loaded with sand.

Figure 2 (Left) Standard drilling pattern for undercutting, (Right) Induced damage due to blasting at undercut and production level

Figure 3 Typical undercut face, using reinforcement during the charging of the blast holes.

2.2

Induced damage relative to blasting in undercutting.

Undercutting standard procedures have resulted in significant damage at the brow and walls of the undercut drift. Figure 2 presents this design and illustrates the damage generated along excavation. When a blast hole is fired, a compressional wave generated by the explosive expands allowing cracks to propagate towards in-situ rock. Afterwards, explosives gasses generated by detonation swell the cracked rock and a negative pressure ejects material towards the opposite direction (Kay 2000) as presented in Figure 4.

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Figure 4 Systematic blasting mechanisms (Kay 2000)

The propagation wave related to blasting, impacts the surrounding working zone by activating existing structures and promoting the generation of instable rocks blocks at the brow (undercutting blast) and walls (drawbells blast) of the undercut drifts. In addition, the high stress environment of a Post Undercut sequence encourages the transmission of induced stresses over the brow of the undercut drift; increasing the risks of rock fall and instability and exposing personnel to this hazard working condition. Risk management strategies consist of restricting access to the working zone, modifying the support and adjusting the mine designs. 2.3

Main criteria for the analysis of blasting damage

In order to prove the hypothesis that drilling and blasting design influences the stability and safety of the undercut face working area, simulations were carried out using the JKSimblast software. The simulations will compare two scenarios: the standard pattern and a new design proposed by the authors. JKSimblast allows to evaluate the induced blasting damage at the undercut drifts resulting from blasting. The attenuation model is based on the Holmer and Persson Model, estimation which estimates the particle velocity in the near field. The main parameters to be used for estimating damage are:

• Drilling pattern geometry. • Explosive characteristics. • Attenuation parameters of the each rock type for estimating the impact of the propagation wave due to blasting.

For the case study, it was considered the attenuation parameters for the “Strong Sandstone” attenuation parameters due to the lack of a propagation model for blasting at III Panel, Rio Blanco Mine. Even though the Andina´s rock type is not Strong Sandstone, the attenuation parameters could be considered very similar, since their tensile strength parameters are within the order of magnitude.

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Unit Mine Operations Table 1 Attenuation parameters for different rock types, according to the Holmer and Person Model

3 Methodology This investigation aims to evaluate and contrast two scenarios (original and proposed design), by means of the JKSimblast software. The software allows quantifying the induced damage due to blasting in the undercut and extraction drifts. The simulation allows comparing both behaviours, in terms of the generating suitable caving connection, and the optimization of the operation of the undercut process by diminishing the damage at the undercut drift excavation face (walls and brow). The methodology is as follows:

1. Analysis of a standard drilling pattern currently used in Panel Caving Operations and the new proposed design, by means of the JKSimblast software.

2. Incorporating both drilling patterns in a simulation, defining boundary conditions. 3. Simulating both designs using ANFO at 4% for undercut and ANFO at 10% for drawbell blasting, with the purpose to quantify damage at the undercut level.

4. Conducting comparative analysis between both designs, checking their performance in terms of the extraction level stability and undercut face advance.

4

Proposed improved design

In order to develop the proposed drilling pattern, the following variables were considered:

1. Undercut height. 2. Undercut side view. 3. Diminishing damage in the undercut drifts. 4. Creation of an optimal operational free face, for next rings to be blasted. 5. Ensuring the advance of the undercut face. The proposed design considers vertical drilling rings including 14 radial blast holes perpendicular to drift walls. The design has the peculiarity of not including blast holes at the top of the undercut drift (Figure 4) and adds new blast holes with negative inclination (-42°) that decrease the drawbell height in 3.5 m versus the original design.

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Caving 2014, Santiago, Chile The undercut height above the undercut level is 3.6 m and 17.6 m above the extraction level.

Figure 5 (Left) Proposed design for a Post Undercutting mining sequence. (Right) Undercutting and blasting of the drawbell resulting geometry

The proposed design is presented in Figure 5. The addition of blast holes with negative inclination allows also shaping the crown pillar by decreasing the creation supporting points as in the case of flat undercutting as presented in Figure 6.

Figure 6 Scheme of the creation and effect of supporting points after undercutting (Karzulovic 1998)

The new drilling design enhances the flow of blasted material into the drawbell by modifying the drawbell geometry; this upgraded flow condition allows blasted material to move downwards easily, creating an optimal free face for the next rings to be blasted.

5

Simulation results

According to the wave propagation model presented in Section 2.3, a damage criterion is presented in Table 2. This criterion will be adopted to evaluate induced damage due to blasting in the simulated scenarios.

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Unit Mine Operations Table 2 Criteria for evaluating damage in an excavation, adjusted for Strong Sandstone

5.1

Simulation inputs

In order to conduct the damage analysis in JKSimblast, certain input parameters are needed; these parameters are related to the drilling pattern and to the explosive used. Table 3 Inputs for JKSimblast

5.2

Simulation results

The results from the simulations conducted in JKSimblast are presented in Figure 7.

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Figure 7 Standard design simulation results

The results from the simulations for the proposed design, conducted in JKSimblast are presented in Figure 8.

Figure 8 Proposed design simulation results

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Unit Mine Operations 6

Conclusions and further work

Based on the conducted analysis, it is possible to conclude that the new design has the following advantages over the standard design:

• The removal from the drilling pattern of the blast holes of the top of the undercut drift improves significantly the stability condition at the brow.

• The propagation wave related to the drawbell firing and its induced damage do not affect the undercut drift.

• There is no evidence of damage due to undercutting blasting at the extraction level. • The proposed design minimizes the exposure of personnel to poor brow conditions and hazards at the undercut face.

• In economic terms, the new design diminishes the drilled meters in about 20 m, lowering the overall development cost.

The authors recommend conducting in-situ measures to determine properly the attenuation parameters from the Holmer & Persson’s model for each particular rock type. Therefore, simulations can be compared to in-situ damage measurements and calibrate the software in a particular scenario for validation purposes. Current drilling practices for undercutting must be reviewed in order to minimize operational hazards and to ensure safety working conditions.

Acknowledgements The authors would like to thank Montserrat Pineda for her valuable and helpful support during the writing of this article.

References Rivero, V 2008, Evaluación Geomecánica de Estrategias de Socavación en Minería Subterránea, Memoria de Titulo Ingeniero Civil de Minas, Universidad de Chile, 32p. (in spanish) Soft-Blast 2006, Underground User Manual, JKSimBlast, p. 57-117. Onederra, I 2010, Apuntes de tecnología y técnicas de tronadura, University of Queensland, Diplomado Ingeniería del Block Caving Universidad de Chile. (in spanish) Butcher, RJ 2000, ‘Block Cave Undercutting-Aims, Strategies, Methods and Management’, Proceedings of Massmin 2000, pp. 405-414, Brisbane, Australia. Jofre, J, et al. 2000, ‘Evolution in Panel Caving Undercutting and Drawbell Excavation, El Teniente Mine’, Proceedings of Massmin 2000, pp. 249-260, Brisbane, Australia. Kay, D 2000, ‘Digital Blasting- An Opportunity to Revolutionise Mass Underground Mining’, Proceedings of Massmin 2000, pp. 155-161, Brisbane, Australia. Karzulovic, A 1998, Evaluación Geotécnica Métodos de Socavación Previa y Avanzada Mina El Teniente, Estudio DT – CG – 98 – 003, El Teniente, CODELCO CHILE, pp. 1-19. (in spanish)

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How high a draw column in Block Caving? C Cerrutti AMEC International, Chile A Ovalle AMEC International, Chile Y Vergara Universidad de Chile, Chile

Abstract The heights of block cave columns have been steadily increasing, since the initial 50 m column heights to the current 400 m heights and in one case heights of between 800 and 1,000 m. Thus, what is the maximum limit? What are the factors driving even higher columns, what are the advantages and disadvantages? The need to achieve the highest possible production capacity is the main driver for higher column heights, as they have a direct relationship with each other. Furthermore, higher column heights reduce preparation costs per tonne of ore. On the other hand, factors that may influence column heights include: geomechanics risks, grade distribution, subsidence, global mine strategy, implementation rates, financial reasons, and cultural reasons. This paper presents a benchmarking study of column heights from different mines as well as a discussion on some of the factors that drive column heights.

1 Introduction The heights of block cave columns have been steadily increasing, since the initial 50 m column heights to the current 400 m heights and in one case heights of between 800 and 1,000 m. What is driving this increase? Undoubtedly, one motivation is to lower cost per tonne, as the base preparation costs are distributed in more tonnes by the increase in column heights. However, the real driving force is that maximum capacity is gained through higher column heights as there is a direct relation between them. Achieving the maximum production capacity possible for massive underground mines is a very important for mining of the future. Many large open pits are decreasing their ore production, principally due to reaching the limits of open pit depths, and this trend will increase in the future. Therefore, it is very important to explore the limits of the maximum production throughput of underground mining methods, in order to continue to feed and fully utilise large processing plants, replacing ore from open pit production as it decreases. Furthermore, in an environment of lowering resource grades, an increase in production throughput is one of the main ways to lower costs and keep the industry operating.

2

Maximum Production Capacity

The maximum long-term production capacity that can be achieved in an ore body mined by block caving directly depends on four factors, as shown in equation 1 (Ovalle and Pesce 2004). It should be noted that the generic term block caving can be used to designate all variants of caving such as block caving, panel caving, macro-block caving and others, and that although the formula provided was developed strictly for panel caving, it can be used for other variants of block caving. MPCLP = H × Vp × γ × RO (1) Where: MPCLP (t/a)

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=

Maximum long term production capacity.

Unit Mine Operations H Vp γ RO

(m) (m2/a) (t/m3) (fraction of 1)

= = = =

Height of ore column. Preparation rate. Density of in situ material. Operational recovery.

The dimensional analysis of this formula yields: MPCLP = [m] × [m2/a] × [t/m3] × [1] = [t/a] The density (γ) has a natural invariant value and cannot be changed. While the operational recovery (RO) should be as close as possible to one, it actually has a lower value and prudence is recommended. Experience shows that it is closer to 0.85 (basically due to losses due to collapses, premature closure of drawpoints, and other operational issues). This therefore only leaves two controllable parameters to determine the maximum long-term production capacity; the height of the ore column (H) and the rate of preparation (Vp). Figure 1 graphically shows the maximum possible long-term production capacity that can be achieved with different rates of preparation. The graph indicates that production capacity can reach 800 kt/d for block caving where the column height is 2,050 m, provided that the rate of preparation is 60,000 m2/a. Is it possible to attain column heights of 2,000 m?

Figure 1 Maximum Production Capacity for BC as a function of column height, for different preparation rates

Of the two controllable design factors, we generally look at the column height, as it is the first factor we should fix in the block cave design. Once the footprint elevation is selected, the maximum production capacity that can be achieved is more or less defined. We then should be able to address the second design factor, the preparation rate, which is not discussed further in this paper. Here we analyse the maximum column height values can be achieved and the factors that determine these limits. We also must highlight that hydraulic preconditioning applied to block caving is the technological advancement that allows an increase in column heights to the values we are looking at.

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Block caving column heights and effects

3.1

Definitions Economic column height In block caving this is defined as the height of column that can be extracted economically. This is done in practice at draw points in the detailed engineering stages, and sometimes feasibility stages, or by evaluating columns formed by available blocks in scoping, conceptual, prefeasibility and occasionally feasibility level stages (blocks between 10 m x 10 m x 10 m and 30 m x 30 m x30 m). Depending on the orebody, mineralization and the existence of production levels above, the individual columns can have a wide range of heights. In order to simplify the problem, the average economically extractable column height is generally considered. However, one should also be conscious of the variability of columns heights that should be considered in order to calculate the variability in the maximum production capacity. Column heights to surface or production level above From a physical standpoint, the height of column which is important is the height to surface or the previous production level above. This height needs to be considered when calculating dilution (for example, dilution models that are applied to the columns are physical models and cannot ascertain what the economic column height will be), or in order to calculate the in situ stresses that mining levels will be subjected to (under-cut, production, ventilation, haulage, etc).

This conceptual distinction between economic column height and height to surface is important to keep in mind when comparing column heights between different deposits. 3.2

Values from some deposits

The evolution of column heights at El Teniente is illustrated in Figure 2. In one hundred years column heights have increased from 60 m to 300 m, yet we know that in the edges of some sectors of the mine, where there are no upper sectors present, mineralized and extracted columns have reached over 800 m.

Figure 2 Evolution of the column heights at the El Teniente mine (low column Pilares sectors needed to compensate production losses due to Sub 6 failure by rockburtsing)

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Unit Mine Operations A comparison of column heights of other deposits is shown in the following diagram:

Figure 3 Mineralized column heights and column heights to surface from other deposits

3.3

Costs affected by variation in column height

The components of cost per tonne that can be affected by variation in column height are:

• Preparation cost per tonne • Extraction cost, affected by variation in fragmentation size with height • Infrastructure maintenance and rehabilitation costs Mine preparation costs per tonne of ore If we assume a unique preparation cost for all columns, then the preparation cost per tonne varies with column height as shown in Figure 4. However, it is noted that preparation costs for higher columns may be higher, due to the effects of higher in situ stresses and higher wearing at draw points. These points are discussed later.

Figure 4 Preparation cost per tonne as a function of column height

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Caving 2014, Santiago, Chile Extraction costs affected by variation in fragmentation size with column height Fragmentation size is an important aspect in engineering design, productivity and operating costs. The initial fragmentation is obviously affected by undercut blasting, in which the initial draw can be easily extracted, after which the rock begins to fragment due to the dynamics of caving. The size of rocks reporting to the draw point is determined by primary fragmentation caused by in situ forces and gravity, and later by secondary fragmentation caused by attrition. As columns get higher, so does the effect of comminution, as rock needs to travel further to the draw point and there is a higher probability of attrition between fragments and consequent reduction in size. Based on this argument, it is reasonable to assume that higher column heights will have a favourable effect on fragmentation size. This reduction in fragmentation size will result in less secondary blasting, lower loading times, higher performance of loading equipment and lower extraction costs.

Figure 5 Grizzly productivity of secondary ore, as a function of percent extraction

Although it was not possible obtain data that shows increased LHD productivity due to increase in column height, there is certain evidence to demonstrate this occurs. Figure 5 shows old El Teniente grizzly production data, where productivity is a function of column height or percentage of ore body extraction. We hypothesize that the shape of these curves can be extrapolated in the some way to represent LHD extraction. Therefore, taller columns should lower extraction cost per ton. Maintenance and rehabilitation of mine infrastructure With increasing column heights, the useful service life of infrastructure will need to be higher, so some components will require being more durable, such as: draw points; ore passes; production level roadways; ground support and rock reinforcement. With respect to draw points, the flow of material from the cave will principally wear draw-point brows and nearby support elements, plus significant damage can occur due to removing hang-ups and secondary breakage in the draw point (e.g. by blasting). As the column height increases so should the number of repairs, however, it is considered that this will not be directly proportional to the tonnes mined, as wear should be less due to reduced fragmentation size with column height. The effect of damage due to blasting hangups and secondary breakage should correspond to a fixed cost incurred for the initial stages of extraction. The later stages should see a decreased cost for higher columns as these costs are distributed over higher tonnages.

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Unit Mine Operations Ore passes suffer from wear when the fragment size is coarse, and especially if they are not operated correctly or left empty, as impact from large fragments does the most damage. As the column height increases, fragment size should decrease, so should the wear rate. With these considerations, it could be argued that the maintenance cost per tonne for both draw-point and ore pass maintenance decreases with increasing column height. However, it should be noted that these costs are not continuous, as they are subject to overhauling, which are time-consuming and cause disruption to production. The hypothesis is that these overhauls are required at lesser intervals as the column height increases. The damage to roadways in the production drives is directly related to the tonnage carried on them, so it could be argued that the cost is fixed in terms of cost per tonne and therefore does not depend on the height of the column. There are other factors that influence roadway damage: design, quality of construction, weight of loaded equipment, and mainly water and poor drainage. Deterioration of ground support and rock reinforcement in production levels, especially in walls, are principally due to in situ stresses and rock mass creep, which would indicate an increase in cost per tonne with increase in column height. This factor is worthy of modelling. Secondly, the cost of repairs to walls is influence by damage by equipment, however, this could be negligible if excavations were designed with sufficient space. Summing up all the factors, we believe that the maintenance costs of infrastructure may decrease in terms of cost per tonne with increasing column height. However, pending the availability of an evidence database to support this, it is hypothesized that the cost of repairs to infrastructure should be fixed based on cost per tonne. 3.4 Dilution The variable that affects block caving revenue as a function of column height is dilution. According to Laubscher (2000), if the ratio of the volume of the orebody to ore-waste contact boundary increases, then the overall dilution decreases. V1/S1 > V2/S2 à D1 < D2 (2) Where: V1, V2: Volume of column for cases 1 and 2. S1, S2: Surface boundary exposed to waste for cases 1 and 2 D1, D2: Dilution for columns 1 and 2, according to conditions of each. We will compare dilution for two cases with main column heights of H and H/2, for case 1 and 2, respectively. The usual situation in panel caving or block caving is shown in the 3-D view of Figure 6. The extraction column under study, in dark, is surrounded by four other extraction columns, in grey. There are two sides exposed to waste (the 2 lower neighbouring columns) and two sides exposed to ore (the 2 higher neighbouring columns). The roof face is exposed to waste.

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Figure 6 3-D view of usual situation for an extraction column or block

In order to obtain the relationship between height and dilution, we have the following values for case 1 (height H) and case 2 (height H/2), according to formula (2). V1 / S1 = H × a × b / (H × a + H × b + a × b) V2 / S2 = H × a × b / (H × a + H × b + 2 × a × b) Assuming careful draw for both cases, the height of the column corresponds to the only variable that defines the higher or lower percentage of overall dilution. Thus, if the column height increases, the V/S ratio also increases and accordingly to statements made by Laubscher, dilution decreases.

4

Geomechanical Aspects

4.1

In situ and induced stresses during development

In situ and induced stresses during development are usually not a problem with increasing ore column height or increasing mine depth, but of course there are limits and special conditions. There are many reported popping and rock bursting phenomena in mine developments of deep mines or with unusual rock conditions, like very fragile rocks and high in situ stresses. These conditions require an extra engineering effort. 4.2

Induced stresses during caving

The induced stresses caused by caving are particularly crucial in the undercutting period of the block cave. The dynamics of this operational step are extremely complex (relative to the amount of different excavations), which causes different variations of stress far greater than those generated by development alone. Furthermore there is an increase in the edges of the cave (abutment stresses) and loss of confinement. After the cave front has passed (i.e. in the shadow zone), stresses decrease, and can cause instability issues and/or collapses in the mine infrastructure and propagation of caving (see Figure 7) With depth (increased column height) the undercut phase can generate higher variations of induced stresses, the abutment stresses increase by around 2 to 3 times the in situ vertical stress, and as the confinement stress

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Unit Mine Operations decreases there is a higher probability of failure (e.g production level pillars). These aspects should be studied in more detail for feasibility level design (e.g. adequate support and reinforcement, and/or strategies pre or advanced undercut, and/or preconditioning studies).

Figure 7 Example states of induced stresses during caving over production level pillars

4.3

Other risks

Rock bursts The risk of rock bursts could be a threat to increasing column heights. Rock bursts are conceptually caused by the release of stored strain energy during seismic events. At the moment of a seismic event a portion of the available stored strain energy is consumed in the collapse of the event source (power collapse), and the other portion is propagated as stress waves that travel through the rock mass, whose pulse energy diminishes (attenuation and dispersion) as it travels through the surrounding rock (vibration energy). When stress waves intercept excavations in the rock mass, depending on its form and energy, they can cause violent rupture and ejection of the rock mass into the excavation (i.e. rock burst). The risk of rock bursts can be reduced with adequate seismic monitoring, to control the rate of production and undercutting, which has a direct relationship on seismicity. In addition, the use of hydraulic preconditioning definitely assists by reducing the likelihood of high energy seismic events. Also, the use of special dynamic rock reinforcement can reduce seismic risks. Cave stalling and air blasts Independently of the hydraulic radius relationship for different types of rocks, above which the caving probability approaches one, apparently there is a relationship between the footprint area and the column height that deters thinking about high columns for small footprints areas, because of the probability of cave stalling, formation of stable arches and probable unwanted air blast phenomena. However, hydraulic preconditioning is the tool to mitigate this situation. Propagation of caving We tend to think that block caving propagates vertically, but we know of cases (Northparkes, Palabora) that the caving has propagated off the vertical, causing unwanted early dilution. Higher columns have greater probability of non-vertical caving propagation, especially if main faults, a close by open pit or special stress

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Caving 2014, Santiago, Chile fields are present. Here again, hydraulic preconditioning is the tool to mitigate this situation. Subsidence The subsidence effect might be a big deterrent for higher ore columns; especially if internal mine facilities or surface installations must be protected.

5

Strategic aspects

We have stated technical factors, most of which point in the direction of increasing column heights in block caving. But there are also strategic factors that must be taken into account in the overall equation to set column heights. The main strategic considerations are grade distribution, global mine strategy, project´s implementation time, financial reasons, and cultural considerations. The grade distribution is an important concern. If higher grades are above, it might be more convenient to have a lower column lift to recover these high grades first, even though you might pay the price of an extra production lift. The global mine strategy is very important. In brown field projects, existing materials handling infrastructure, subsidence effects, overall mining sequence or special production requirements might force the situation for a low column height in a new sector. In green field projects, exploration might not have recognized the bottom of the deposit, or the initiation point of the first caving lift defines, to a great extent, the general sequence of the mine exploitation and the overall mine geometry, and thus the possible heights of future production levels. High column caves take a long construction time, especially if the surface topography is flat. Many companies cannot endure long implementation times (financial or production reasons), and this consideration is important to opt for lower caving columns. Last but not least, there are cultural reasons for not opting for higher caving columns. The mining industry is very conservative, we have always done like this, show me where they have done it, etc. But as engineers, and value providers to the industry, we must safely explore the limits. Higher ore columns for block caving will open the door for very high production underground mines in the future.

6 Conclusions There is great pressure to replace the high production of open pits that will soon reach their economic limit by underground methods, and there is also pressure to lower mining costs, which can be attained by increasing mining size. Here there is an opportunity for block caving to satisfy the challenges of higher production in the extractive mining industry of the future. The main incentive to use higher columns in block caving, is its direct relationship to achieving higher long term production rates. Furthermore, there is a reduction in preparation costs per tonne of ore with increasing column height. The cost of maintenance and rehabilitation of infrastructure per tonne of ore possibly decreases slightly with column height, however, for practical purposes may be regarded as a constant cost per tonne of ore. Dilution is another important variable in block caving, which tends to decrease with increasing column height, provided that draw is carefully controlled as with lower column heights. However, it may be more difficult to have careful draw control with higher column heights, and such these two factors may cancel each other.

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Unit Mine Operations The greatest aspect that inhibits higher column heights are a) geomechanical risks; b) long time needed to develop projects with high column heights require higher initial capital costs (can be mitigated with rapid development or if there is adequate topography to enable lateral access); c) long ramp up time to achieve nominal production capacity (can be mitigated by opening up more initial area); d) other strategic and cultural reasons. With respect to geotechnical risks, we believe that these should be addressed at the early stages of engineering. In this case, quantify the magnitude and likelihood of occurrence of the individual conditions specific to each orebody. This would involve identifying lines of research that will be necessary to increase knowledge and analyse the solutions that need to be incorporated into the engineering and design. As history has shown that block cave column heights have been increasing, and that the limits of their application are temporary and depend in many respects, on other branches of engineering, technological development and generation of knowledge. Given the current technology, especially due to preconditioning, we believe that we can feasibly achieve column heights greater than 1,000 m with adequate designs and precautions, with a potential production capacity of 400 kt/d.

References Ovalle, A 2012, ‘Mass caving maximum production capacity’, MassMin 2012, Sudbury, Canada. Pretorius D. & Ngidi, S. 2008, ‘Cave management ensuring optimal life of mine at Palabora’, Massmin 2008, pp. 63-71. Ross I. & van As, A. 2005, ‘Northparkes Mines-Design, Sudden Failure, Air Blast and Hazard Management at the E26 Block Cave’, Ninth Underground Operators Conference, Perth, Australia, pp. 7-18. Pesce J. & Ovalle, A. 2004, ‘Production Capacity of a Mass Caving’, MassMin 2004, Santiago, pp. 75-78. Laubscher, DH 2000, Cave Mining Handbook, International Caving Study, The University of Queensland, pp. 115-118.

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Many operations are considering, or have decided, to use block caving as their preferred mining method. Currently, about 400,000 tons per day are extracted by caving methods. It is estimated that this figure will increase to a rate of 1 Million tons per day by 2018. Production rates would also increase. This will present new and exciting challenges and opportunities for the mining industry. In June 2014, the Third International Symposium on Block and Sublevel Caving will be held in Santiago, Chile the Block Caving’s country. Chile has three large block cave operations; El Teniente, Andina and Salvador, with an annual production of 74 Mt. Codelco, the largest copper producer, is developing two new block caving mines at El Teniente and Chuquicamata, that will produce additional resources for Chile’s future. This book contains the work of authors from all over the globe which summarizes the international state of the art on Block and Sublevel Caving as in 2014.

Caving 2014, Chile

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