Measurements of Size Distribution of Blasted Rock Using Digital Image Processing

MEASUREMENTS OF SIZE DISTRIBUTION OF BLASTED ROCK USING DIGITAL IMAGE PROCESSING

By: NAME

I.D.

Zubair Ahmed Nizamani (Group Leader)

09 MN 28

Shahzad Ali Rajput (Assistant Group Leader)

09 MN 85

Sanaullah Bhoot

09 MN 58

Manzoor Ali Rahimoon

09-08 MN 44

Nasir Ali Magsi

09 MN 60

SUPERVISOR:

MR. AHSAN ALI MEMON Assistant Professor

DEPARTMENT OF MINING ENGINEERING MEHRAN UNIVERSITY OF ENGINEERING AND TECHNOLOGY JAMSHORO

Submitted in partial fulfillment of the requirements for the degree of Bachelor in Mining Engineering 2013 1

Read

" ... Read in the name of thy Lord who created; [He] created the human being from blood clot. Read in the name of thy Lord who taught by the pen: [He] taught the human being what he did not know" (AL-QURAN) 2

DEDICATION

This thesis is dedicated to MY BELOVED PARENTS who have supported me all the way since the beginning of my studies. & SPECIAL GIRL who was the source of motivation and inspiration for me.

3

MEHRAN UNIVERSITY OF ENGINEERING & TECHONOGY JAMSHORO

CERTIFICATE This is to certify that the thesis entitled “Measurements of Size Distribution of Blasted Rock Using Digital Image Processing ” in partial fulfillment of the requirements for the award of Bachelor of Technology degree in Mining Engineering at Mehran University of Engineering & Technology, Jamshoro is an authentic work carried out following students

NAME

I.D.

Zubair Ahmed Nizamani (Group Leader)

09 MN 28

Shahzad Ali Rajput (Assistant Group Leader)

09 MN 85

Sanaullah Bhoot

09 MN 58

Manzoor Ali Rahimoon

09-08 MN 44

Nasir Ali Magsi

09 MN 60 under my supervision and guidance.

_____________

_______________

Ahsan Ali Memon Assistant Professor (Thesis/Project Supervisor)

External Examiner

________________ CHAIRMAN Department of Mining Engineering

Date _________ 4

ACKNOWLEDGEMENT

First of all we would like to thank Almighty Allah, The most merciful, compassionate, gracious and beneficial Who helped to complete our thesis/project.

We wish to express our profound gratitude and indebtedness to Ahsan Ali Memon, Assistant Professor, Department of Mining Engineering for introducing the present topic and for his inspiring guidance, constructive criticism and valuable suggestion throughout the project work. His able knowledge and supervision with unswerving patience guided my work at every stage, for without his warm affection and encouragement the fulfillment of the task would have been difficult, especially Engr. Fahad Siddiqui, Lecturer, Department of Mining Engineering who guided us and gave effective suggestions at every point of completing this thesis.

We are also thankful to Engr. Mushtaq Ali Abro, Quarry Manager, Dewaan Cement Factory Karachi who co-operated with us and helped us in collection of required information for the completion of our thesis work.

Last but not least, my sincere thanks to Prof. Dr. Mohammad Ali Shah, Chairman, Department of Mining Engineering who provided us better study environment and motivated us to complete our thesis work.

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ABSTRACT

The basis of this analysis was to measure the size distribution of blasted rock using the digital image processing software “Split-Desktop system”. Quick and accurate measurements of size distribution are essential for managing fragmented rock and other materials. Various fragmentation measurement techniques are available and used by industry/researchers but most of the methods are time consuming and not precise.

The size distribution analysis of the rock fragmentation by sieving is a direct and accurate method but it is very time consuming and costly. Fragmentation analysis by digital image processing is a low cost and quick method. Split system is one of the digital Image processing software developed to compute the size distribution of fragmented rock from digital images. Fragmentation is the ultimate measure of efficiency of any production blasting operations. The degree of fragmentation plays an important role in order to control and minimize the loading, hauling, and crushing costs.

In this study, size distributions were analyzed by using Split Desktop ® system. In the analysis, the mean fragment size obtained is 250.75 mm and top-size 941.27mm. 7.45% of the fragments are below 25.40mm. A thorough appraisal of blasting operation is suggested to enhance the efficiency of all the post-blast operations such as Loading, Hauling, crushing and Grinding and also reduces the cost of secondary breakage.

Keywords: Rock blasting, Fragmentation, Digital image processing, Split-Desktop

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CONTENTS CHAPTER # 01

INTRODUCTION

Page No.

1.1

Background

1

1.2

General Description

2

1.3

Optimum Fragmentation

3

1.4

Significance of optimum rock

3

fragmentation 1.5

Achievement of optimum rock

3

fragmentation 1.6

Motivation

4

1.7

Objectives of the work

4

CHAPTER # 02 2.1

LITERATURE REVIEW Mechanism of Rock Fragmentation by

5

Blasting 2.2

Different Parameters of Rock Breakage

6

2.2.1

Explosive properties

6

2.2.2

Rock properties

7

2.2.3

Charge loading and blasting parameters

7

and blast geometry 2.3

Fragmentation Measurement

7

Techniques

7

2.3.1

Sieving or screening

8

2.3.2

Oversize boulder count method

9

2.3.3

Explosive consumption in secondary

9

blasting method 2.3.4

Shovel loading rate method

9

2.3.5

Bridging delays at the crusher method

9

2.3.6

Visual analysis method

10

2.3.7

Photographic or manual analysis

10

method 2.3.8

Conventional and high speed

11

photogrammetric method 2.3.9

High speed photography or image

11

analysis method 2.3.9(a)

IPACS

13

2.3.9(b)

TUCIPS

13

2.3.9(c)

FRAGSCAN

13

2.3.9(d)

SPLIT DESKTOP

14

2.3.9(e)

FRAGALYST

15

2.3.9(f)

WIPFRAG

16

CHAPTER # 03

THE SPLIT SYSTEM AND EXPERIMENTAL WORK

3.1

Introduction

17

8

3.1.1

Software and Hardware Requirements

19

3.1.2

Difference in Version of Split-Desktop

20

3.2

Description of Site

21

3.3

Methodology

23

3.3.1

Image Acquisition at Quarry

24

3.3.2

Image scaling

25

3.3.3

Fragment Delineation

26

3.3.3.1

Noise Size

27

3.3.3.2

Watershed ratio

27

3.3.3.3

Gradient ratio

27

3.4

Computation

of

Size

Distribution

27

Curves 3.5

Sources of error

29

3.5.1

Sampling Errors

29

3.5.2

Poor Delineation of Fragments

29

3.5.3

Missing Fines

29

CHAPTER # 04

RESULTS AND DISCUSSIONS

4.1

Results

31

4.2

Combined Size Distribution

41

4.3

Discussion and Conclusion

42

Bibliography

43

9

List of Figures Figures No.

Figure Description

Page No.

Figure 1.1

Typical image of rock fragmentation by blasting

2

Figure 2.1

Clear view of Blasting

6

Figure 3.1

Simple Image inserting in Software

18

Figure 3.2

Front view of quarry face F5, horizontal and vertical

21

bedding planes are clearly visible Figure 3.3

An image taken at Dewan cement quarry for size

24

distribution measurement. Figure 3.4

Delineation of the particles

26

Figure 3.5

Size Distribution Curves

28

Figure 4.1

Size Distribution Curve of Image “Pic1”

32

Figure 4.2


33

Figure 4.3


34

Figure 4.4


35

Figure 4.5


36

Figure 4.6


37

Figure 4.7


38

Figure 4.8


39

Figure 4.9


40

Figure 4.10

Size Distribution Curve of Combined Images

41

10

List of Tables Table No.

Table Description

Page No.

Table 3.1

System requirements for Split-Desktop

19

Table 3.2

Blasting Parameters

2

11

INTRODUCTION

1.1 Background Rock blasting is one of the most dominating operations in open pit mining efficiency or quarrying. As many downstream processes depend on the blast-induced fragmentation, an optimized blasting strategy can influence size distributions and make safe economical environment.

A successful, complete breakage takes place when the amount of explosive and the geometry of the blast e.g. burden, spacing, height are balanced in a way that the cracks expand all the way to the free face and gases push the rock forward to form a well- swollen pile.

The effect of blasting on fragmentation is assessed in two different aspects: Seen and Unseen. The size distribution of blasted fragments is the “seen” part of blasting results, which can be measured quantitatively by sieving or image analysis techniques. The “unseen” effect of blasting is the fracture generation within the fragments, these fracture can be classified as either macro-fractures or micro-fractures. Macro-fractures are comparatively large and can be seen on the surface of fragments; but micro-fractures are only seen through a microscope. The results of a production blast are mainly presented by fragmentation of the broken rock. The fragmentation is described in terms of geometrical characteristics of the particles i.e. size, angularity or roundness. The cumulative size distribution function, CDF, provides a complete description of the former. It is either obtained from physical sieving of the material, which is very costly in large-scale blasts, or by non-physical sieving methods such as image analysis. 12

1.2 General Description Fragmentation is the process of breaking the solid in situ rock mass into several smaller pieces capable of being excavated or moved by material handling equipment. Breakage of rock mass is assisted by conventional drilling blasting operation which is the most important method of fragmentation in almost every quarry.

Figure 1.1

Typical image of rock fragmentation by blasting

There are a number of controllable as well as uncontrollable parameters that govern the fragmentation of rock. The controllable parameters can be controlled by effective blast designing and use of appropriate explosive for blasting. While the uncontrollable parameters as the name suggests cannot be controlled. But certain measures have to be taken to minimize the effects of these parameters in rock blasting in order to attain an optimum rock fragmentation. 13

1.3 Optimum Rock Fragmentation The rock fragmentation obtained as an outcome of blasting operations is said to be optimum, when it contains maximum percentage of fragments in the desired range of size. The desired size usually means the size that is demanded and can be effectively utilized by the consumers for further operations devoid of any processing. The desired size for different consumers is different. For example, the size of dolomite fragments required for railway tracks is comparatively smaller than the coarser ones those used by a cement industry.

1.4 Significance of Optimum Rock Fragmentation The significance of optimum rock fragmentation is, to fulfill the varying demands of different consumers for assorted sizes of rock fragments, to reduce the cost of crushing and grinding or palletization operations, and finally uphold the economics of mining. For this the rock must be 2 fragmented in such a way that further processing (usually termed as Milling) is not required. In other words, if the cost per ton of broken ore is greater than the price it commands when sold as the final product, then the production is not considered to be economic. Hence the cost of milling should be minimized and,

1.5 Achievement of Optimum Rock Fragmentation To achieve an optimum rock fragmentation a blast with optimized controllable parameters should be designed so that the effects of the uncontrollable parameters could be minimized. The controllable parameters for it should be ensured that the primary blast results in optimum fragmentation. Optimum fragmentation can be fixed after conduction of trial blasts in a mine

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and quantification of fragmentation. Quantification of fragmentation refers to the measurement of fragmentation in order to predict the necessary corrections in the blast design. These corrections when applied to the blast design results in almost acceptable fragmentation.

1.6 Motivation It is well known that rock is generally treated as a heterogeneous material and the heterogeneity of rock causes sizes distribution of fragmented rocks in blasting. Rock fragmentation has been used an index to estimate the effect of bench blasting for the mining industry. The measurement of rock fragmentation using image analysis techniques has become an active research field because of its usefulness. This trend involves an effort to eliminate the need for traditional and costly sieve analysis. Sieving analysis is still used for examining results of image analysis because of its limitations. Among these limitations, small particles that are seldom represented in images of blasted rocks have been a big obstacle in determining fragment size distribution by image analysis, especially, in large-scale blasting.

1.7 Objectives of the Work The objectives of the project are as follows: 

To analyze the fragmentation characteristics of the blasted rock using Digital Image Processing.



To Determine the overall size distribution of blasted muck pile.

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LITERATURE REVIEW

2.1 Mechanism of Rock Fragmentation by Blasting Various parameters like explosive parameters, blast geometry, strength of rock, geo – technical conditions affect the degree of fragmentation of rock. The blasting operation causes the rock fail due to crushing, tensile fracture, release of load, strain energy generation, shearing action, flexural rupture etc.

After an explosive is initiated, the site around the drill hole will crush and will deform plastically. The effects of an explosion can be divided into: 

The charge explodes and it is divided into high-pressure, high-temperature gases.



The gases are applied to the borehole, which contains them .Then it creates a strain field in the rock.



This strain field, due to its impulse nature, generates a strain wave that is propagated in the rock and damages it.



This damage is the centre of the cracks in the rock.



The gas pressure is reduced via the cracks and separates the rock fragments.



The pressure of these gases applied to the face of the fragments, produces forces that propel the fragments.



The fragments adopt a ballistic trajectory.



In areas if the damage to the rock was insufficient to generate fragments, the strain wave continues its trajectory until it runs out of energy that dissipates by making the rock vibrate. 16

Figure 2.1

Clear view of Blasting

2.2 Different Parameters of Rock Breakage The parameters are divided mainly into the following: Properties of explosive, Blast geometry and charge loading parameters.

2.2.1 Explosive properties Different properties of explosive like V.O.D, density of explosive, shock wave energy and gas pressure, volume of gas, composition of explosives, powder factor, and type of detonation, primers, nature and strength of explosives affect the rock fragmentation.

17

2.2.2 Rock properties The properties of rock that affect the rock breakage or fragmentation are dip, strike, compressive strength, tensile strength, shear strength, density, elastic property, bedding plane structure, presence of geological disturbances like faults, folds, fractured ground.

2.2.3 Charge loading and blasting parameters and blast geometry The parameters which are included in this category are diameter and the length of shot holes and charges, stemming material and height of stemming, degree of decoupling, method and sequence of initiation, blast hole diameter, spacing and burden, distribution of explosive along the hole, loading density, angle of blast hole, number of holes in a row, number of rows, sub grade drilling, climate condition, amount of strata to be broken, requirement of shape of the excavation, factors of loading, transporting and requirement of crushing and screening etc.

2.3 Fragmentation Measurement Techniques Blast optimization requires a degree of compromise between the competing objectives of maximum fragmentation, minimum dilution and minimum costs for drilling and explosives. Also, mining companies and quarry operations have to examine and reduce production costs to remain competitive. But no single factor, such as cost of explosives, can be properly evaluated without measurements of fragmentation and rock quality. Hence the need to manage production costs necessitates the need to measure the post-blast fragmentation. Quantification of fragmentation on a larger scale is an extremely complicated task. Because it needs a substantial amount of time to find out manually the grain size distribution in a muck

18

pile. Research has been carried out worldwide with different methods and tools for measurement of fragmentation. These methods are listed below. 

Sieving or Screening.



Oversize boulder count method.



Explosive consumption in secondary blasting method.



Shovel loading rate method.



Bridging delays at the crusher method.



Visual analysis method.



Photographic or manual analysis method.



Conventional and high speed photogrammetric method.



High speed photography or image analysis method.

2.3.1 Sieving or screening Sieving or screening is a direct and accurate method of evaluation of size distribution of particles or fragmentation. However, for production blasting, this method is costly, timeconsuming and inconvenient. This method is feasible in case of small scale blasts. In this method the rock fragments are screened through sieves of different mesh numbers for different fragment sizes. Then the screened out fragments are grouped according to their size and the number of fragments in each size range is counted to predict the nature of the blast.

19

2.3.2 Oversize boulder count method In Oversize boulder count method, manual counting of the oversize boulders in the muck pile which cannot be handled by the shovel is done. This directly gives an over-size index with respect to the total in-situ rock mass blasted. It is a very popular method of determining the post-blast fragmentation.

2.3.3 Explosive consumption in secondary blasting method In Explosive consumption in secondary blasting method, an index regarding the consumption of explosives in secondary blasting by either pop shooting or plaster shooting is determined. This index is then used for comparing the degree of fragmentation of a group of blasts.

2.3.4 Shovel loading rate method The shovel loading rate method assumes that the faster the mucking the better the fragmentation. In this method the loading rate of shovel for a particular muck pile is taken in to account. This technique may be used more accurately for a comparative account of the nature of fragmentation of a group of blasts.

2.3.5 Bridging delays at the crusher method In the Bridging delays at the crusher method, the delay in bridging at the crusher mainly due to oversize boulders is observed. This attributes in determining the number of oversize boulders in the muck pile. This method is usually preferable in a small production site rather than in large scale blasting situations. 20

2.3.6 Visual analysis method The Visual analysis method is a subjective assessment method. In this method the post-blast muck is viewed immediately after blasting and a subjective assessment is made. This technique is not dependable as the superficial view of the muck cannot enlighten anything about the hidden portion.

2.3.7 Photographic or manual analysis method In photographic method delineating of fragments on the photographs of muck pile is carried out manually to determine the number of fragments using a graph paper. For this, 0.15m x 0.10m size photographs of the muck pile are printed. Each photograph is then placed under a transparent paper by fixing it firmly with the help of pins. All the fragments are delineated on the transparent paper. Delineation is started with large fragments because they have more effect on the results. It is tried to detect and delineate fragments as small as possible. The scale placed in the middle of the muck pile is used to convert the measured distance on the photograph to actual distance. Then, a Xerox copy of the traced paper is placed on a graph paper. The area of the reference scale on graph paper is noted down and then a scale factor (actual area of scale/graph area of scale) is determined. For every identifiable fragment, the area covered by the fragment is measured by counting the number of small blocks on the graph paper covered by that fragment. The area is then multiplied with the scale factor. For converting the area into volume, the third dimension is determined using the method of equivalent circle of area. The parameters are calculated as follows:

21

 4  Area  Equivalent diameter     π 

Spherical volume (m3) = Area x Equivalent diameter

Weight of the fragment (kg) = Spherical volume x density of the rock

The manual analysis of each photograph takes about one to two hours.

2.3.8 Conventional and high speed photogrammetric method This method is more reliable and accurate than the photographic method. It can provide three dimensional measurements and thereby helps in the calculation of fragmentation volume.

2.3.9 High speed photography or image analysis method Nowadays High speed photography or Digital images processing and analysis systems emerged with the advance in technology are becoming increasingly popular in fragmentation measurement. This is due to their advantages over photographic methods. Consequently several countries and organizations have developed their own image analysis systems.

There are several methods of size distribution measurement and fall under two broad categories; direct method and indirect methods. The sieve analysis is the direct and accurate method of measuring size distribution. Although it is the most accurate technique among

22

others, it is not practical for such a large scale due to being both expensive and time consuming. For this reason, indirect methods which are observational, empirical and digital methods have been developed. Observational methods include the visual observations of muck-piles immediately following the blasting. It is widely used by blasting engineers to arrive at an approximation. In some empirical models such as Larsson’s equation, SveDe Fo formula, KUZ-RAM model, etc, blasting parameters are considered to determine the size distribution of blasted rock.

Recent fragmentation assessment techniques using digital image processing program allow rapid and accurate blast fragmentation size distribution assessments. Digital image software was developed through the 1990s and at present it is a worldwide accepted tool in the mining and mineral processing industries. Its main advantage is that it can be used on a continuous basis without affecting the production cycle, which makes it the only practical tool for evaluating fragmentation of the run of mine. However, some errors are also associated with the digital image analysis. It is extremely hard to obtain accurate estimates of rock fragmentation after blasting. Following are the main reasons for error in using image analysis programs.



Image analysis can only process what can be seen with the eye. Image analysis programs cannot take into account the internal rock, so the sampling strategies should be carefully considered.



Analyzed particle size can be over-divided or combined; which means larger particles can be divided into smaller particles and smaller particles can be grouped into larger

23

particles. This is a common problem in all image-processing programs. Therefore, manual editing is required. 

Very fine particles can be underestimated, especially from a muckpile after blasting. There is no good answer to avoid these problems.

In this investigation, the SPLIT-DESKTOP system was used for size distribution computation. Some of these systems include:

 IPACS  TUCIPS  FRAGSCAN  SPLIT  FRAGALYST  WIPFRAG IPACS The IPACS consists of grabbing, scaling, image enhancing, grey level image segmentation, shape analysis (merging and splitting) and processing parameters as the software functions. The host computer required for this image analysis system is an industrial PC. Therefore this system is well suited for industrial purposes. The Processing speed and accuracy of IPACS are good, and the system is conducted automatically with a video input picture.

24

TUCIPS The TUCIPS has been developed to measure blast fragmentation at Technical University Clausthal (Germany). This system involves general algorithms specially created algorithm for muck pile image analysis. This system is suitable for practical use because there is just five percent (5%) deviation in the practical test with this program.

FRAGSCAN The FRAGSCAN uses the method of measurement of the size distribution of blasted rock from dumper or conveyer belt with the help of a camera and mathematic morphology technique. The FRAGSCAN equipment is composed of a camera, an Image acquisition card, a control data card, computer type PC and a light. Conversion from surface to volume distribution is made possible by using a spherical model. This operating system is fully automatic tool and provides reliable as well as consistent results because extensive experimentation has provided satisfying results. This system is better for industrial usage. SPLIT DESKTOP The SPLIT Desktop is image analysis software developed by the University of Arizona to figure out size distribution of rock fragment. It is operated with eight bit grayscale images of rock fragments. There are two kinds of SPLIT programs; one is an automatic and continuous program that is used on the conveyor belt and the other is a manual program which uses the saved images. However, the same algorithm is used in both programs. A digital camera is used to get the image of the bench face, which is to be used in SPLIT. The maximum size of image that can be processed using SPLIT is 1680x1400 pixels, so the maximum size of image

25

needs to be considered during sampling images because image editing may be required in SPLIT, and a larger image may not be opened in SPLIT without such editing.

Image samples are obtained during charging the blast holes. Approximately five to seven (57) pictures are taken at each blasting, and three to five (3-5) appropriate pictures for analyzing in SPLIT are chosen. The digital camera should be held such that the long axis of the photograph is vertical. The image should be taken with the camera lens perpendicular to the muck pile surface. An article of known dimensions must be in the picture in order to provide scale. A white fig may be used as a scale material on the bench face. The same scale material must be used from image to image for analyzing all pictures in SPLIT regarding each blasting. Also, the number of scale materials should be the same from image to image for analysis. Fragmentation assessment is achieved by analyzing the scaled photographs of the muck pile.

FRAGALYST The Fragalyst is an image analysis system developed by CMRI Regional Centre, Nagpur (India) and Wavelet Group of Pune (India). This system consists of capturing video photographs of the muck pile, down loading the photographs to the computer, or capturing the photos of muck pile from field by digital camera/ordinary camera then converting the images to grey scale, image enhancement, calibration and blob (grain) analysis. With the aid of menudriven software, it is possible to determine the area, size and shape of the fragments in a muck pile/grain aggregates on the basis of grey scale difference. The 2-D information available from software can further be processed for stereological analysis for 3-D information. 26

WIPFRAG The WipFrag image analysis software uses the technique of analysis of digital image of the blasted rock with granulometry system to predict the grain size distribution in the muck pile. Typically, camcorder images of the muck pile are acquired in the field. A scale device is used in each view to reference the sizing. The muck pile is photographed or videotaped and this image is transferred to the WipFrag system. The broken rock image is transformed into a particle map or network. Network areas are converted into volumes and weights and the resulting data is displayed as a graph. The fidelity and speed of fragment edge detection allow fully automatic remote monitoring at a rate of one image per 3 to 5 seconds. More fragments are resolved, over a greater size range. WipFrag allows comparing the automatically generated net against the rock image. The fragment boundaries are analyzed efficiently using Edge Detection Variables (EDV). Any inaccuracies can be corrected by manual editing with a mouse to improve edge detection. Manual editing, however, is needed only if image quality is poor and is simplified by a "smart edit" function that erases and draws lines, linking them automatically to the existing fragment net.

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THE SPLIT SYSTEM AND EXPERIMENTAL WORK

3.1 Introduction The Split software was originally developed at the University of Arizona, and in 1997 the technology was transferred to a newly formed company, Split Engineering. The Split software allows post-blast fragmentation to be determined on a regular basis throughout a mine, by capturing images of fragmented rock in muck-piles, on haul trucks, or from primary crusher feed or product. The resulting size distribution data can then be used to accurately assess the fragmentation associated with different parts of a shot. And in particular, this data can be used to assess and improve the accuracy of fragmentation models (Higgins et al, 1999). Fragmentation models are also being improved by utilizing drill-monitoring data. Drillmonitoring data includes raw drilling data such as rotary torque, penetration rate, and pull down pressure, as well as calculated quantities such as drilling specific energy or the Aquila Blast ability Index (Peck and Gray, 1995). Because drill-monitoring data is available from every blast hole, it provides data throughout the rock mass to be blasted. As part of this project fragmentation studies are being conducted at several large open pit mines in Arizona. At these mines Split-Online systems are installed at the primary crushers. On these systems, cameras installed at the truck dumps monitor primary crusher feed and cameras installed at the discharge belts monitor primary crusher product. The primary crusher feed information is then traced back to the original position of this rock on the shot using mining dispatch systems. This information is used to assess post-blast fragmentation and can be correlated with rock mass and blasting information on a hole by hole basis.

28

Figure 3.1

Simple Image inserting in Software

SPLIT is an image processing program for determining the size distribution of rock fragments at various stages of rock breaking in the mining and processing of mineral resources. The desktop version of SPLIT refers to the user-assisted version of the program that can be run by mine engineers or technicians at on-site locations. The desktop SPLIT system consists of the SPLIT software, computer, keyboard and monitor. There must be a mechanism (software and/or hardware) for downloading digital or video camera images onto the computer. For digital cameras the software that is supplied with the camera is required and for video camera images a frame grabber board is necessary. For higher resolution images and for ease of image selection, than is available by most frame grabbers, a digital camera is recommended. Resolution of the images should be at least 512x512. The first step is for the user to acquire images in the field and download these images onto the computer. The source of these images can be a muck pile, haul truck, leach-pile, draw point, waste dump, stockpile, conveyor belt,

29

or any other situation where clear images of rock fragments can be obtained. The SPLIT program first assists the user in properly scaling the images. SPLIT can then automatically delineate the fragments in each of the images and determine the size distribution of the rock fragments. SPLIT allows the resulting size distributions to be plotted in various forms (linearlinear, log-linear, log-log, and Rosin- Rammler). The size distribution results can also be stored in a tab-delineated file for access in separate spreadsheet and plotting programs.

3.1.1 Software and Hardware requirements The hardware and software for required the split-Desktop Version 3.0 easily are mentioned in table 3.1

Table3.1

System requirements for Split-Desktop

Computer/ processor

Operating System

RAM

PC compatible with 100 MHz processor of higher 

Windows 7, 32 and 64 bit



Windows Vista, 32 and 64 bit



Windows XP



Windows Server 2003



Windows Server 2008, 32 and 64 bit

64 MB or Higher

At least 100 MB free to load manipulate and process sets of Hard-Disk

Monitor

multiple images

Higher Resolution (16-bit) or higher

30

3.1.2 Difference in version of Split-Desktop If you have used previous versions of Split-Desktop, you may not even recognize this release as the same software. The user interface is totally new, and the process of calculating size distribution results has been streamlined. Previous versions of Split-Desktop created a lot of files … and then left file management up to you. Split-Desktop 3.0 and later now use a self contained project file that includes all of your images, settings and output options. Binary files are no longer part of Split-Desktop. Delineations are simpler and usually better than in previous versions. The sometimes confusing array of delineation

parameters has been reduced to one simple slider bar that will increase or decrease the amount of delineation.

Scaling has been simplified and the scales are now visible in the image. You can insert one to three scales anywhere in the image, and modify or delete them later. The calculations have been improved too. Not only are they faster, but the combining formula used for merging multiple images into one result has been updated and brought more in line with the typical field practice. 31

3.2 Description of Site The limestone quarry belongs to Dewan cement (formerly Pak-land cement) located near Karachi, Pakistan. The limestone deposit is of Miocene age and belongs to Gaj formation. The geology is simple and essentially uniform. In the upper 1-2 m, there is an overburden of weathered clay shale of sandy nature and low cohesion. The limestone formation below this has a thickness of 6-25 m; the bedding planes are horizontal or sub-horizontal and crossed by some nearly vertical joints as shown in Fig. 1. The upper part of limestone deposit is highly fractured causing hole-collaring problems during drilling. The quarry is mined in one bench. Limestone rock is medium-hard and has compressive strength of 87 MPa and density is 2.66 tons/m3.

Figure 3.2:

Front view of quarry face F5, horizontal and vertical bedding planes are clearly visible.

32

Drilling is done with heavy duty down-the-hole hammer drill to a preset blasting pattern. The blasting parameters are designed to suit the rock conditions and gradation requirements. The holes are charged with primed cartridge at the bottom with Shock-tube for detonation. ANFO is filled as column charge. Two types of high explosives are used; Gelatinous dynamite and Emulite. Each hole contains 15 kg of high explosive and 60 kg of ANFO. Other blasting parameters are given in Table No. 3.2

Table No. 3.2

Blasting Parameters Parameters

Description

Hole diameter

105mm

Bench Height

9-10m

Sub –Drilling

0.5m

Burden

4 feet

Spacing

3.5 feet

Stemming

0.5-1m

Blasting pattern

Rectangular

Initiation System

Shock Tube

Powder Factor No. of Holes

0.4kg/m3 32

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3.3 Methodology FLOW CHART OF SIZE DISTRIBUTION PROCESS Acquire Image

Add image to project

Set Resolution

Crop Image (if needed)

Delineation

Scaling

Edit Delineation

Fines Estimation

Show Results

Export Results 34

3.3.1 Image Acquisition at Quarry Image acquisition of blasted rock for size distribution analysis is the most critical phase of the analysis. Important issues in image sampling are: The location of the image, the image angle from the surface of the muck-pile, and the scale of the image. In order to obtain good images, which are both capable of being analyzed and representative of the entire rock assemblage, sampling strategies must be carefully considered. The location of image taking is important, and there are two sampling methods, random and systematic. Both methods have been used for this investigation. Another consideration is the angle of the surface being photographed. Ideally, the surface should be perpendicular to the camera lens.

Figure 3.3:

An image taken at Dewan cement quarry for size distribution measurement. 35

A digital camera was used to get the images of the blasted muck, which were used in SPLIT. Images were taken randomly in the field and balls of 21.9 and 15.9 cm in diameters were used to provide scale in the images. Single and dual scaling object were used in this investigation. Total 15 images were taken for analysis.

3.3.2 Image scaling For material piles, you may need to take images of different scale in order to obtain a decent sample of the material:

1) Large scale including boulders and areas of fines. The horizontal length of the image should be about 20 ft (7 m). These images will contain the top size material and will adequately sample the coarse material as well as provide indications of the large areas of fines.

2) Medium scale of typical regions of 2 to 10 inch (5 to 25 cm) material. The horizontal length of the image should be about 8 ft (3 m). These images will provide a closer look at the medium size material (material in size between the top size and the fines) and will lower the fines cutoff value (the value at which the software stops measuring and begins to estimate).

3) Small scale which is zoomed in images of representative samples of the finer material. The horizontal length of the image should be about 1.5 ft (0.5 m). These images will try to measure the fine material to give an indication of the size distribution within the large areas of 36

fines that may be present on the surface of the large scale images. Many zoomed-in fines images would need to be acquired to change the distribution of the entire sample, but these images can help with measuring the fines and lowering the fines cutoff value as opposed to using the fines estimation equation in the software.

Take approximately equal numbers of images at each scale although if you are not interested in the size distribution of the smallest scale of material and are happy to accept a Schumann or Rosin-Rammler curve in this range, you may omit taking the zoomed-in images.

3.3.3 Fragment Delineation In this step Split-Desktop performs the automatic delineation of the particles. The three most important delineation parameters are Noise size, Watershed ratio and Gradient ratio.

Figure 3.4

Delineation of the particles

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3.3.3.1 Noise Size:

The noise size parameter is used to determine the size, in pixels, of the smallest pixel grouping that is used in the split algorithm. Noise size value may range from 3 to 90 and default value is 7. If the image contains larger rock fragments and boulders, noise value may set to as higher as 80 to 90 and if it contains finer fragments, the value may reduce to 3. The Noise size value for this investigation was found empirically by using various values and finally 22 were found best-fit for the images.

3.3.3.2 Watershed ratio:

The watershed size ratio controls the number of divisions made during the watershed algorithm which is used to make additional divisions based on the shape of the particles. The default value is 1.5, which usually gives satisfactory result for most images. Increasing this number makes fewer divisions and decreasing it makes more. This value can be changed typically between 0.33 and 3. In this investigation, watershed ratio was set at 1.85. 3.3.3.3 Gradient ratio:

The gradient is a numerical measure of grayscale change from light to dark. The typical average Gradient Ratio is 0.14. A higher value will create fewer dividing lines and a lower value will create more. The gradient ratio for this analysis was set at 0.18.

3.4 Computation of Size Distribution Curves Once the delineation of images has been completely done, computation of size distribution can be carried out. In this step, the distribution of fines in each image can be calculated using two approaches Rosin-Rammler or Schumann distribution. In the present study, a 38

combination of these two approaches was used to best-fit the fines distribution. The final step and the most critical influence on the size calculation is the Fines Estimation. Split-Desktop can measure particles automatically, but in every image there is a point below the resolution of the image where particles can no longer be “seen" and delineated. At this point, SplitDesktop will estimate the remaining finer material. The "fines" cutoff chiefly depends on the resolution in pixels/unit of the image. Since the black pixels in the image represent both fines and outlines of particles, a percent of these pixels is included in the fines calculation. This percentage of black to be counted as fines can vary for each muck-pile and can be adjusted by the user. For the images that contain too much fines, the High option can be selected and also other options such as None, Low and Medium can be selected accordingly depending upon the fines percentage in each image. As shown in figure

Figure 3.5

Size Distribution Curves

39

3.5 Sources of error There are potentially three sources of significant error while processing in Split System; sampling errors, poor edge net fidelity, and missing fines.

3.5.1 Sampling Errors Sampling errors, the process of taking an image of the fragmentation have the potential to be the most serious of all the errors. Such errors result if the camera is pointed at a place in the muck pile where the coarse blocks or zones of fines dominate.

3.5.2 Poor Delineation of Fragments Poor delineation of individual fragments results in erroneous results. Poor delineation arises from a combination of two sources: 

Poor images, e.g. contrast too low or high, too grainy, lighting inadequate or uneven, or the size of the fragments in the image is too small.



Highly textured rock, where shadows and/or colorings on the surface of the rocks are as prominent as the shadows between rock fragments.

3.5.3 Missing Fines Where the smallest fragments in a distribution are not delineated on the image, either because they are too small relative to the image to be resolved, or they have fallen in and behind larger fragments, there is clearly a bias towards over representing the size of the distribution. Where the distribution has a relatively narrow size range (well sorted, or poorly graded) this is normally not a problem. However, where the distribution has a relatively wider size range (poorly sorted, or well graded), typically with size differences of more than 1 order of 40

magnitude, missing fines start affecting the measurement results. Split Desktop has the ability to deal with the missing fines problem using either an empirically based calibrations or by using multiple images taken at different scales of observation.

41

RESULTS AND DISCUSSIONS

4.1 Results Total 70 images were taken during the field visit to Pak Land Cement limestone quarry immediately after the blasting. Nine most representative images of blasted muck-pile were analyzed using Split-Desktop Software and mean values were obtained. Following are the obtained size distribution curves of each and finally combined image.

42

CUMMULATIVE SIZE DISTRIBUTION

Picture taken at Site

Figure 4.1:

Picture Delineation


43



Figure 4.2:

Picture Delineation


44

CUMMULATIVE SIZE DISTRIBUTIO


Figure 4.3:

Picture Delineation


45



Picture Delineation

Figure 4.4:


46



Figure 4.5:

Picture Delineation


47



Figure 4.6:

Picture Delineation


48



Picture Delineation

Figure 4.7:

Size Distribution Curve of Image “Pic7

49



Figure 4.8:

Picture Delineation


50



Figure 4.9:

Picture Delineation


51

4.2 COMBINED SIZE DISTRIBUTION

Figure 4.10:

Size Distribution Curve of Combined Images

52

4.3 Discussion and Conclusion The results obtained from the analysis of muck-pile images using Split-Desktop shows that the mean fragment size is 250.75 mm and F20, F80, and Top-size are 108.03 mm, 417.19 mm and 941.27mm respectively.

The primary crusher installed at the quarry accepts the feed size as large as 1000 mm and crush down to the 25 mm. Results indicate that approximately 7.45% of the fragments are below 25.45 mm.

Results also indicate that only 0% of the material is above 1000 mm therefore it doesn’t require secondary breakage. The Rosin-Rammler uniformity index of the entire muck-pile is 0.81. This index is generally used to approximate the size distribution of rock in blasted muck-piles. The value ranges between 0.5 (very non-uniform) and 2 (very uniform). So the obtained index value confirms non-uniform size distribution. Non uniform size distribution affects the loading and hauling operations and crusher’s efficiency.

As the results indicate that 7.45% fragments are below 25.45 mm, which is product size of primary crusher, this percentage can be enhanced by optimizing the overall blasting operation.

The Burden and spacing are two most important factors in the blasting because these factors can be adjusted to obtain required fragmentation. Proper explosive in an appropriate quantity can also results in good fragmentation and reduce the overall cost of production.

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Bibliography



Robert S.lewis, E.M, “Element of Mining”, Reprinted by KHAYABAN pass Lahore, 1983



Dahlhielm.S(1996) industrial application of image analysis-the IPACAS system proceeding measurement of blast fragmentation



Girdner, k.k, kemeny, J.M, Srikant.A & Mcgill.R (1996) The split system for analyzing the size distribution of fragmented rock proceeding measurement of blast fragmentation



Jimeno C.L, Jimeno E.L, Carcedo, F.J.A (1995) drilling and blasting of rocks



Norton B (2005) Private communication with as expert in Split Engineering



Split Enginnering LLC (2001) Split-Desktop Software manual.

Web References 

http://www.mine-engineer.com/mining/open_pit.htm



www.spliteng.com



http://www.miningequipmentforsale.net/mining-equipment-for-sale/resize-blastedrock-to-stone-crusher-size.html

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Measurements of Size Distribution of Blasted Rock Using Digital Image Processing

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