Batch Distillation of Water-Methanol System

Batch distillation of methanol-water system and modeling and simulation of distillation column using ARTIFICIAL NEURAL NETWORK and ChemCAD

Satadru Chakr Chak r abarty, Sayantan Sayantan Roy Choudh ur y, Tapas Saha

DEPARTMENT OF CHEMICAL ENGINEERING NATIONAL INSTITUTE OF TECHNOLOGY AGARTALA-799055, AGARTALA-799055, INDIA MAY - 2013

i

BATCH DISTILLATION OF METHANOL-WATER SYSTEM AND MODELING AND SIMULATION OF DISTILLATION COLUMN USING ARTIFICIAL NEURAL NETWORK AND CHEMCAD

Report Report submi tted to National I nstitute nstitute of T echnology, chnology, Agartala F or the awar awar d of the degree degree Of Bachelor of T echnol ogy By Satadru atadru Chakr abart abart y (09UCH 013) 013) Sayantan ayantan Roy Choudhur y (09UCH 014) 014) Tapas Saha Saha (09UC H 016) Supervisor M r. Bibhab Kumar Lodh Assistant Assistant Pr off essor Chemical E ngg D epartment

Chemical Engineering Department National Institute of Technology, Agartala May- 2013 © 2013 Satadru Chakrabarty, Sayantan Roy Choudhury, Tapas Saha. All rights reserved

ii

BATCH DISTILLATION OF METHANOL-WATER SYSTEM AND MODELING AND SIMULATION OF DISTILLATION COLUMN USING ARTIFICIAL NEURAL NETWORK AND CHEMCAD

Report Report submi tted to National I nstitute nstitute of T echnology, chnology, Agartala F or the awar awar d of the degree degree Of Bachelor of T echnol ogy By Satadru atadru Chakr abart abart y (09UCH 013) 013) Sayantan ayantan Roy Choudhur y (09UCH 014) 014) Tapas Saha Saha (09UC H 016) Supervisor M r. Bibhab Kumar Lodh Assistant Assistant Pr off essor Chemical E ngg D epartment

Chemical Engineering Department National Institute of Technology, Agartala May- 2013 © 2013 Satadru Chakrabarty, Sayantan Roy Choudhury, Tapas Saha. All rights reserved

ii

Dedicated:

To all the teachers of the department of chemical engineering , who have inspired us to make this project successfully

iii

APPROVAL SHEET

This thesis/dissertation/report entitled “Batch distillation of methanol-water system and modeling and simulation of distillation column using ARTIFICIAL NEURAL NETWORK and ChemCAD “by Sayantan Roy Choudhury, Satadru Chakrabarty and Tapas Saha is approved for the degree of ____________ (Degree details). Examiners

________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ Supervisor (s)

________________________

Chairman

________________________ Date: ____________ Place: ____________

iv

DECLARATION

We declare that this written submission represents my ideas in my own words and where others' ideas or words have been included, I have adequately cited and referenced the original sources. We also declare that we have adhered to all principles of academic honesty and integrity and have not misrepresented or fabricated or falsified any idea/data/fact/source in our submission. We understand that any violation of the above will be cause for disciplinary action by the Institute and can also evoke penal action from the sources which have thus not been properly cited or from whom proper permission has not been taken when needed. _________________________________ (Signature) ________________________________ (Name of the student) _________________________________ (Roll No.) Date: __________

v

CERTIFICATE

This is certified that the work contained in the project titled “Batch distillation of methanolwater system and modeling and simulation of distillation column using ARTIFICIAL NEURAL NETWORK and ChemCAD ,” by “Satadru Chakrabarty, Sayantan Roy

Choudhury, Tapas Saha” has been carried out under my/our supervision and that this work has not been submitted elsewhere for a degree

Signature of Supervisor(s) Name(s) Department(s) N.I.T. Agartala May, 2013

vi

PREFACE

Technology is any technique, instrument or device that makes human life on this planet easier. This gives us immense pleasure to be working on a project that deals with a very

important phenomena or unit operation of the industry. This project “Batch distillation of methanol-water system and simulation of working of distillation column using ARTIFICIAL NEURAL NETWORK and ChemCAD” is a humble step in trying to

understand and design a fully operational ba tch distillation column, with the help of software.

The mixture chosen for the simulation is basically methanol- water mixture, which is a very common mixture found in industries and is usually separated by a distillation column.

In this project there would be wide scale use of Artificial Neural Network (ANN) , basically for the purpose of data prediction and data validation .

Finally the simulation and design of the distillation column would be completed using ChemCAD, with the data acquired from the initial experiments and data prediction from ANN.

Hope this humble effort would manage to earn the appreciation of all the readers. Satadru Chakr abarty Sayantan Roy Choudhur y Tapas Saha

vii

ACKNOWLEDGEMENT

We would like to express my sincerest gratitude to our Director Prof. Dr. P.K.Bose, who has been kind enough to provide us with the infrastructural facilities that we have in our labs. Then we would like to give our sincere regards to Mrs. Soma Nag (H.O.D, Dept. of Chemical Engg.), without whose help and support this project would not have been possible.

Next we would like to give regards to our project guide Mr. Bibhab Kumar Lodh (Asst. professor, dept. of chemical engg.) who has been so patient with us, and always giving his advice for the betterment of the project and correcting our errors whenever necessary.

We are also grateful to all the teachers of the dept. of chemical engg. for their valuable advice and insights. We are also grateful to each and every person who has helped us in the project with their valuable supports and advices.

viii

LIST OF FIGURES: 

Fig. 1.1: basic layout of a distillation column

2



Fig. 1.2.1: a typical packed distillation column

6



Fig. 1.2.2 :The basic distillation column

7



Fig. 1.3.1: Typical Process flow diagram in ChemCAD

11



Fig. 1.4.1: the data is entered in this sheet

13



Fig. 1.4.2: the neural network between the inputs and outputs

14



Fig. 1.4.3: the error graph showing the range of errors in the A.N.N prediction

15



Fig. 3.1.1:schematic representation of the multicomponent Batch Distillation Process

22



Fig. 4.3.1: the calibration curve attained from the calibration data at 28⁰ C

30



Fig 4.4.1 : the laboratory packed bed distillation column

31



Fig. 5.4.1: flowsheet of batch distillation as displayed by ChemCAD

37



Fig. 5.5.1(a) : distillation column specification (general model)

39



Fig. 5.5.1(b): distillation column specification (heat and material balance)

40



Fig. 6.1: this figure shows the software’s data entry sheet

43



Fig. 6.2: the internal network of ANN, correlating the output a nd input parameters

44



Fig 6.3: the importance given to the different inputs



Fig. 6.3: the error graph, showing the target error, max imum,

45

minimum and average error values

45



Fig 7.1: T-x-y graph of MeOH-H2O system

59



Fig 7.2: x-y graph

60



Fig 7.3: liquid heat capacity curve

61



Fig 7.4: : liquid heat capacity curve

63 ix



Fig 7.5: Temperature vs. vapour thermal conductivity graphs

64



Fig 7.6: Temperature vs. liquid thermal conductivity graphs

65



Fig 7.7: composite curve

71

LIST OF TABLES



Table 1.1: CRC 44th ed. lists azeotropes for acetic acid/water and acetone/water,

5



Table 4.3.1: calibration curve data for different mole fraction.

29



Table 4.5.1: daily experiment log

33



Table 6.1: A.N.N prediction result

46



Table 7.1: experiment vs. ChemCAD

47



Table 7.2: experiment vs. ANN

72



Table 7.3: experiment vs. ANN vs. ChemCAD

73

x

LIST OF SYMBOLS AND ABBREVIATIONS

M B =liquid holdup in still pot (kmol) M D =liquid holdup in reflux drum (kmol) M n =liquid holdup in the nth tray (kmol) nT =total number of trays QR =Heat input to the still pot (kJ/min)

=Reflux flow rate (kmol/min) R RS =steady state value of R, (kmol/min) VB =vapor boil-up rate (kmol/min) Vn =vapor flow rate of vapor leaving nth tray (kmol/min) Vn T =vapor flow rate of vapor leaving top tray (kmol/min)

=distillate flow rate (kmol/min) D =liquid flow rate of liquid leaving the nth tra y (kmol/min) L n =composition of component i in the still xB,i =composition of component i in the Distillate xD,i =composition of component i in Liquid stream leaving the nth tray xn,i r,i =rate of reaction of component (kmol/lit.min) ε=volume of catalyst (lit)

k1, k2 =rate constants

K1, K2= GMC controller tuning parameters vb =volume of reboiler (lit)

=volume of reflux drum (lit) vd =volume of tray (lit) v multiplication factor (RF=1 for reactive section, RF=0 for non-reactive section) RF = stoichiometric coefficient of i the component of m the reaction. Him= α1, α2, α3=estimator tuning parameters.

rate of reaction of component i in distillate. Radii= = rate of reaction of component i in bottom. Rabbi Roni = rate of reaction of component i in nth tray.

xi

ABSTRACT

This project “Batch distillation of methanol-water system and simulation of working of distillation column using ARTIFICIAL NEURAL NETWORK and ChemCAD” , is an

intensive study to check the chances, whether ARTIFICIAL NEURAL NETWORK can be used in the prediction of distillation column ou tput.

This is a relatively different field, for which A.N,N could be used. So this project tries to answer this question by experimental analysis carried out on a packed bed distillation column. Hope that this project is fruitful and further work is done in the future on this topic.

xii

CONTENTS Title Page

i

Dedication

ii

Certificate of Approval

iii

Declaration

iv

Certificate

v

Preface

vi

Acknowledgements

vii

List of Figures

viii

List of Tables

ix

List of Symbols and Abbreviations

x

Abstract

xi

Contents

xii

Chapter 1 Introduction

1

1.1 Methanol – Water system

3

1.2 Distillation

5

1.3 ChemCAD

10

1.4 A.N.N

13

Chapter 2 Literature Review

17

2.1 Batch Distillation

17

2.2 A.N.N

18

2.3 ChemCAD

19

Chapter 3 Modelling of the distillation column

21

3.1 Process Description

21

3.2 Modeling Equations

23

Chapter 4 Experimental distillation of MeOH- H 2O system

27

4.1 Introduction

27

4.2 Theory

28

4.3 Calibration Curve

29 xiii

4.4 Description of Apparatus

31

4.5 Experimental Observation

33

Chapter 5 ChemCAD simulation

34

5.1 Overview of the Batch Distillation Process

34

5.2 Creating a New Simulation

35

5.3 Drawing the Flowsheet

35

5.4 Selecting Engineering Units

35

5.5 Selecting Components

37

5.6 Run the simulation.

39

5.7 Review the results and print as needed

40

Chapter 6 Artificial Neural Network

43

6.1 Data entry and training

43

6.2 Data prediction and further learning

44

6.3 Error analysis

45

6.4 Procedure

46

6.5 Prediction results

46

Chapter 7 Results and Discussions

47

7.1 Comparison between experimental result and ChemCAD results 47 7.2 ChemCAD generated results

48

7.3 Comparison between experimental result and A.N.N results

70

Chapter 8 Conclusions and Future Scope of Study

73

Chapter 9 References

75 77

APPENDIX

xiv

LITERATURE REVIEW

CHAPTER 2

LITERATURE REVIEW

2.1 Batch distillation Batch Distillation is a separation process based on relative volatilities of the components in the system to be separated. A multi-component batch distillation model deals with a s ystem having more than two components, but this does not preclude it being used for a b inary system. Batch distillation is actually a semi batch process where the system is charged onc e in one cycle time by a fresh feed at the beginning of a batch, while the products can be continually withdrawn from the system and other fresh feed or slop recycles can be introduced to the system during the cycle time. The essential temporal features of the cycle time are the charging period, the start-up period, the topping and feeding period. Due to its flexibility, simple operation, and low capital costs, batch distillation has established itself as a standard unit operation for the separation of small amounts of liquid mixtures. This unit operation has therefore been extensively studied. Minimum energy demand Distillation is an energy-intensive separation process, since heat has to be supplied to the system in order to create the second phase required for separation. Both during the design and operation phases, the energy required by the process plays a fundamental role. The minimum energy required for a given separation task is that needed by a distillation column having an infinite number of stages. This is therefore a theoretical minimum, which can be used during the design phase as a pre-selection tool to restrict the choice among the available batch distillation processes. 17

LITERATURE REVIEW

On the other hand, during operation, the minimum energy demand can be used as a measure of how far the process is from its thermodynamics optimal, and how wide the optimization margins of the process are. The most popular method for the calculation of the minimum energy demand is the one published by Underwood [1948]. This is an iterative method, which, under the assumption of constant molar overflow, allows the calculation of the minimum reflux ratio for ideal mixtures. Offers et al. [1995] describe a direct method to calculate the minimum reflux ratio for a given separation both for ideal and real systems. This method is applicable to multi-component batch distillation operated with constant product composition. The calculation of the minimum reflux and reboil ratios is repeated for every concentration in the still. The still’s concentration can be determined via Rayleigh’s equation, as described in Stichlmair and Fair [1998]. This calculation requires only the knowledge of the relative volatilities of the mixture in the still. Modeling and simulating distillation columns is not a new enterprise. All of the models described in literature either contain algebraic loops or simplifying assumptions that render the model ill-equipped for dynamic simulations. The structure and the equations that represent a tray distillation column are explored using bond graphs. Bond graphs model the power flow in a system, an inherently instructive way to view complex systems. Results of this study by Braden Alan Brooks[1993] reveal several ways of eliminating the algebraic loops and producing a dynamic model. The bond graph model can be expanded by introducing other elements including chemical reactions and thermal interaction with other columns. The design for a new packed distillation column for consideration as a new experiment for the University

Of

Florida

Department

Of

Chemical

Engineering

Unit

Operations

Laboratory[2011] was created to demonstrate the separation of water and isopropanol (i-Pr) and to evaluate a parallel applied multi-correlation approach to creating a high accuracy process model based on correlations with known margins of error. Only few rigorous models for distillation columns start-up are available in literature and generally required a lot of parameters related to tray or pack geometry. On an industrial viewpoint, such a complexity penalizes the achievement of a fast and reliable estimate of startup periods. In S. Elguea, L. Prata, M. Cabassuda,∗, J.M. Le Lanna, J. C´ezerac b, two “simple” mathematical models are proposed for the simulation of the dynamic behavior during start-up 18

LITERATURE REVIEW

operations from an empty cold state. These mathematical models are b ased on a rigorous tray by-tray description of the column described by conservation laws, liquid – vapour equilibrium relationships and equations representative of hydrodynamics.

2.2 Artificial Neural Network

Because the pattern of the relationships between the independent (input) factors and the dependent (output) factor in our model will be learned from the data by the Artificial Neural Network (ANN) algorithm, the selection of input to the neural networks is an important decision. It is crucial to select factors that fully ca pture the domain of feed and product relation in the distillation process. In this session we focuses on a literature review of the factors to provide an understanding of how they affect the successful data prediction from limited known factors. Also, as our resources to study the effectiveness of A.N.N in distillation process is somewhat limited; therefore, instead of adopting a micro approach to understand the specific effects of a few factors, we use a macro approach that examines a broad variety of factors in an effort to capture the complexities of the process. This macro approach is warranted because we are trying to subsume the intricacies of the process into our model to improve the accuracy of its predictions (Calantone, di Benedetto, and Bojanic 1988) [6]. Furthermore, all the measures were IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 2, March 2012 ISSN (Online): 1694-0814 www.IJCSI.org 114 Copyright (c) 2012 International Journal of Computer Science Issues. All Rights Reserved. Well-validated and accepted measures in the new product literature (see Song and Parry 1997 [7]). In choosing the input for our models, we rely on the resource-based theory of the firm (Wernerfelt 1984, Barney 1991; Conner 1991) [8]-[10]. Resource-based theory provides a unique insight into the situation that faces managers who make project selection and resource allocation decisions. This theory is relatively new in relation to industrial organization theory. Traditional industrial organization theory posits that a firm's strategy and ultimately its ability to create and sustain a competitive advantage are dependent on environmental factors. Resource-based theory takes a different position by viewing firm resources as heterogeneous and immobile. Thus, each firm has a limited, heterogeneous endowment of resources, and its task is to combine the endowment to form capabilities which are the basis for creating a unique, valuable market offering that is not easily imitated or substituted. The central tenant of 19

LITERATURE REVIEW

resource-based theory is that this offering is the mechanism for creating a sustainable competitive advantage for the firm. A review of literature in the study of factors influence the successful product innovation has shown numerous factors which can be grouped into three main factors: (1) the firm’s innovation capability, (2) the firm’s new product development capability, (3) the external competitive environment.

2.3 ChemCAD

The software CHEMCAD 6.1.3 is a very important tool for this study of packed bed distillation column and its simulation based on laboratory experiments. The main parts of this software include the setting up of the units, thermodynamic parameters, setting up of the process flowsheet. The problems and techniques of ChemCAD have been used from the Help menu of the software. More detailed information about the software has been got from the website http://www.chemstations.com/ . More information about the working of ChemCAD is available at, http://www.chemstations.com/Why_ChemCAD/

Detailed demos of the software are available at http://www.chemstations.com/.

20

MODELLING OF THE DISTILLATION COLUMN

CHAPTER 3


3.1.

Process Description

In batch distillation, a liquid mixture is charged into a vessel and heat is added to produce vapor that is fed into a rectifying column. The liquid mixture can be a fresh feed and also with any recycled slop cuts. During the initial startup period, the column operates under total reflux condition in which vapor from the top of the column is condensed and returned to the column. The operation of batch distillation described here corresponds to a ternary system. During the column operation under total reflux condition, the concentration of the lightest component buildup on the upper trays in the column and the concentrations of the intermediate component and heaviest component decreases in the top of the column but increases in the still pot. When the concentration of the lightest component in the distillate reaches its specified purity level, then the distillate product withdrawal is begun. During the withdrawal of the first product, there is a composition front located in the lower part of the column that separates the lightest and intermediate components. This front moves up the column as light product is removed.

When this front nears the top of the column, the distillate stream is diverted to another tank as the 1rst slop cut. When the concentration of the intermediate component in the distillate reaches its speci1ed purity level, the distillate is diverted to another tank in which second product is 21


collected. When the purity of the material in this tank drops to the speci1ed purity level, the distillate stream is diverted into another tank, and the second slop cut is collected until the average composition of the material remaining in the still pot and on the trays in the column meets the purity speci1cation of the heavy product.

In order to represent realistic operation of actual batch distillation column, a rigorous nonlinear model that considers simultaneous effect of heat and mass transfer operations and fluid flow on the plates is needed. Such batch distillation model is derived from first principles involving dynamic material and component, and algebraic energy equations supported by vapor – liquid equilibrium and physical properties. The multicomponent batch distillation dynamics simulator has major computation functions like vapor flow, liquid flow and tray holdup calculations, enthalpy calculations, average molecular weight and density calculations, and vapor – liquid equilibrium calculations.

As assumed, the production phase the reflux drum holdup is kept constant employing Proportional

controller.

Fig. 3.1.1. Schematic representation of the multicomponent Batch Distillation Process

22


The operation of batch distillation described here corresponds to a ternary system of cyclohexane – toluene – chlorobenzene. Among these constituent feed components, cyclohexane is the lightest component, toluene is the intermediate component, and chlorobenzene is the heaviest component. The model structure of the ternary distillation.

3.2.

Modeling Equations

Material balance, component balance and enthalpy balance equations can be written accordingly, The change in the heat energy for a very small amount of time can be considered negligible i.e. the change is very less. So d(M Hl)/dt is very small, d(M Hl)/dt = 0; on rearrangement, we get

23


24


25


26

EXPERIMENTAL DISTILLATION OF MeOH-H2O SYSTEM

CHAPTER 4

EXPERIMENTAL DISTILLATION OF MEOH- H2O SYSTEM

4.1

Introduction

Batch Distillation is often preferred to continuous distillation in cases where relatively small quantities of material are to be handled at irregularly scheduled periods. The simplest case of batch distillation is one in which the material to be separated is charged to a heated kettle fitted with a total condenser and product receiver. The material is distilled without reflux until a definite quantity of one of the components of the mixture has been recovered or until a definite change in composition of the still contents has been effected. In all types of batch distillation, a quantity of feed is charged to a still pot, or kettle, and heat is applied to it. The vapor which is usually passed through a fractionating column is then condensed giving the overhead product while a less volatile residue remains in the kettle at the end of the distillation. Continuous distillation is a steady state process because once equilibrium has been attained, conditions at any given point remains constant whereas batch distillation is an unsteady state process the concentration of the more volatile component

27


decreasing continually so that the temperature and composition of the mixture at a point in the system must alter as the distillation proceeds. 4

4.2

Theory

Batch distillation with only a single still does not give a good separation unless the relative volatility is very high. To obtain product with a narrow composition range, a rectifying batch still is used that consists of a reboiler, a rectifying column, a condens er, some means of splitting of a portion of condensed vapor or distillate as reflux and the receiver. The operation of a batch still and column can be analysed using the same operating line equation as for the rectifying section of the continuous distillation.

Yn+1=



n +



X D +

+

(4.1)

For the binary system: y2= 1- y1x2= 1- x1

Since the slope of the operating line is R/(R+1), the slope increases as the reflux increases, until when reflux is infinite. Under total reflux slope is 1. The operating line then coincides with the diagonal. The number of plate is minimum at the total reflux. Minimum number of plates required can be calculated from the terminal concentration of x b and xd based on the relative volatility of the components α, which is defined in terms of equilibrium concentrations:

α=

( / ) (/ )

(4.2)

An ideal mixture follows Raoult’s law and the relative volatility is the ratio of vapor pressure. Thus: p1 = P’1x1

28


p2 = P’2x2 y1 = p1/P

(3.3)

y2 = p2/P

Therefore ᾳ = P’1/P’2

(3.4)

The final ratio does not change much over the range of temperature encountered in a typical column, so the relative volatility is taken as constant.

4.3

Calibration Curve

Prepare a calibration curve for Me OH- Water by plotting RI as a function of mole fraction Me OH at the current room temperature 28°C. Prepare different mixtures of Me OH + Water by volume and measure the RI of each mixture. Convert your volume fractions and plot mole fraction Me OH vs. RI on a simple graph.

Mole% Me OH

R.I. at 25 C

0

1.332

10

1.335

20

1.339

30

1.341

40

1.342

50

1.34

60

1.337

70

1.334

80

1.331

90

1.329

100

1.324

Table 4.3.1: calibration curve data for different mole fraction 29


1.344 1.342 1.34 1.338 1.336 x e d n i e v i t c a r f

1.334 ri

1.332

Poly. (ri)

e r

1.33 1.328 1.326 1.324 1.322 0

20

40

60

80

100

120

molefractuion

Fig.4.3.1: the calibration curve attained from the calibration data at 28⁰ C

The calibration curve follows the following curve fitting equation: -11 5 -09 4 -07 3 -05 2 x – 2e x – 3e x + 1e x + 0.000x y = -4−  + 8e + 1.332

(4.5)

The above equation has been developed using MS-Excel So, if we know the refractive index, it becomes easy to find the required mole fraction, using this equation.

4.4

Description Of Apparatus

The column is made of stainless steel material packed with borosilicate glass rasching rings. An electrically heated reboiler is installed at the bottom o f the column. The bottom product is collected in the tank. The vapours form the top of column are condensed in the shell and tube 30


Fig 4.4.1 : the laboratory packed bed distillation column

type condenser by circulating cooling water, supplied by laboratory overhead tank. The condensate is divided into reflux and distillate by automatic reflux divider and R/D ratio can be varied. Reflux is fed back to the column and distillate is received in a receiving tank. The complete column is insulated for minimizing the heat loss.

31


4.4.1

Utilities

1. 50 l of methanol 2. Electricity Supply: single phase, 220V AC, 6KW with earth connection. 3

Water supply: 2LPM at 5m head

4

Floor drain required

5

Required chemicals

6

Refractometer for analysis.

4.4.2 Experimental Procedure 1. Connect the cooling water supply to setup. 2. Fill the reboiler with Methanol-water solution. The total amount of solution should not be less than 15lts. The composition of should be in range of 15-25% of methanol by volume. 3. Set a process temperature for the process using the d igital temperature controller. The temperature should be in range of 85-95°C 4. Start the heaters and cooling supply. 5. Adjust the cooling water flow rate to a moderate value. 6. Set the cyclic timer for total reflux. 7. Wait for 25-30 min for the system to achieve steady state. 8. Now take the samples from the bottom and distillate stream. 9. Cool down the samples to room temperature and measure RI 10. Now adjust the cyclic timer to a desired reflux ratio and wait for 5min 11. Now take out the samples from both distillate and bottom product. 12. Cool down the samples and measure RI. 13. The experiment can be repeated with different set point temperatures and reflux ratios.

32


4.5

EXPERIMENT OBSERVATIONS

DAY S

MeOH IN

WATE R IN

TEMP.

REFLUX RATIO

1

0.4

0.6

95

1

0.7

0.1

2

0.5

0.5

90

1

0.75

0.15

3

0.3

0.7

104

2.33333

0.6

0.4

4

0.3

0.7

105

1.5

0.65

0.5

5

0.6

0.4

103

0.66667

0.58

0.4

6

0.8

0.2

104

2.33333

0.75

0.65

7

0.2

0.8

106

0.4285714 2

0.75

0.4

8

0.9

0.1

105

4

0.56

0.1

9

0.85

0.15

107.1

4

0.72

0.65

10

0.55

0.45

107

1.5

0.7

0.65

Table 4.1: daily experiment log

33

MeOH TOP

MeOH BOTTOM


34

SIMULATION IN CHEMCAD

CHAPTER 5


5.1

Overview of the Batch Distillation Process

The process of building the flowsheet and simulating the batch distillation involves the following steps:

o

Create a new simulation.

o

Select engineering units.

o

Draw the flowsheet.

o

Select the components.

o

Select thermodynamic options.

o

Specify pot charge.

o

Specify the distillation column.

o

Define operating steps.

o

Run the simulation.

o

Review the results and print as needed.

35


5.2

Creating a New Simulation

Start by creating a new simulation and giving it a name. To do this, launch CHEMCAD and then Select File >Save to open the Save As dialog box. Navigate to the directory where you want to store the simulation (try MySimulations, located under My Documents) and give your simulation a name, leaving the type as CHEMCAD 6 (*.cc6) . Then click Save to create the file and return to the main CHEMCAD window.

5.3

Selecting Engineering Units

Select Format >Engineering Units to open the Engineering Unit Selection dialog box. The English units option is the default and is currently highlighted. To change the engineering units system, you would click the Alt SI, SI, or Metric button; you could then change any of the individual units as well. For our project we will be using the S.I. unit. So we will select it.

5.4

Drawing the Flowsheet

Creating a flowsheet is a matter of placing UnitOp icons on the screen, connecting them with streams, and then adding various graphical objects to enhance the drawing.

36


37


5.5

Selecting Components

Now you need to identify the components to be used in this simulation. Start by selecting Thermophysical >Select Components . For this example, you’ll choose components from the standard CHEMCAD database. In the Select Components dialog box, find and add each needed component. 

From the available component section select the following components: a) Methanol b) Water



Then add the components to the selected components section.



Press ok

5.5.1 Thermodynamic Options As soon as we have finished component selection, the Thermodynamics Wizard appears. This tool can suggest thermodynamics options to use with this simulation. Here we keep all the other constraints unchanged, just the global K option is changed to Peng-Robinson

5.5.1.1

Specifying Pot Charge

Now that we have thermodynamics and components defined for this simulation and we have a batch column in the flowsheet, we can define the pot charge for the column. We will specify the pot charge according to the following rules: The Temp ( C), Pressure (psia) , Vapor Fraction , and Enthalpy (MMBtu/h) fields are the thermodynamic properties of the charge.

38


We specify the temperature , pressure , total flow and mole fraction of methanol and water entering into the system. And press on the FLASH button. This will automatically calculate the enthalpy. In this case the mole fraction is kept 0.5 for both.

5.5.3

Specify the distillation column.

We will specify the distillation column details according to our condition. There the only fixed constraint will be the number of stages which we will take as 3. The other constraints like pressure etc will be fixed after we have the initial conditions from the experimental setup.

Fig 5.5.1(a) : distillation column specification (general model)

39


Fig 5.5.1(b): : distillation column specification (heat and material balance)

5.5.4

Defining the Operating Steps

When you have completed the initial column specification, the Batch Operation Parameters dialog box appears. Here we will set the reflux ratio for the process. During our simulation we will take up different values of reflux ratio and simulate the column operation.

5.6

Run the simulation.

In this step we run the simulation for the column that we have designed. To run the simulation, click the Run All toolbar button. If there are errors that will be shown. So steps would be taken to correct those accordingly.

40


5.7

Review the results and print as needed

Once the simulation is complete, we can review the results interactively before Printing a hard copy. The commands needed to do this are located in the Report and Plot menus. From this menu we can get all the required outputs. Which has been displayed in chapter 7 (results and discussion), section 7.2.

41


42

ARTIFICIAL NEURAL NETWORK

CHAPTER 6

ARTIFICIAL NEURAL NETWORK The software that we are going to use for this project is known as justNN. This is a freeware and very effective ANN software. The process of using the software is described as under: 6.1.1 DATA ENTRY AND TRAINING:

First of all tables are created in the software and the input and output rows are designated. Then the data (got from experiments) are fed into the system. Then the system is trained to understand and find a correlation between the input and output.

Fig 6.1: the data entry and prediction sheet generated in ChemCAD 43


6.1.2 DATA PREDICTION AND FURTHER LEARNING: In this step we insert a query row and for different conditions of input, the ANN gives us suitable outputs. The ANN is then made to learn the new values that we have predicted. It must be kept in mind that the error range changes with every new stage of data prediction and learning. The software validates whether the given input in sync with the correlations that it had created and based on that gives the output.

Fig 6.2: the internal network of ANN , correlating the output and input parameters

44


6.1.3 INPUT IMPORTANCE

Fig 6.3: the importance given to the different inputs

6.1.3 ERROR ANALYSIS: The error analysis function of the software allows us to check the amount of error that has occurred during the data prediction and further validation. The software allows us to set a certain range of error so that the predicted output falls within that range of approximation. After the learning process is complete, the software gives us a graph which shows us the amount of error that is present in the prediction. It must be noted that with increments in the number of prediction and validation, the amount of average error is reduced.

Fig 6.3 : the error graph , showing the target error, maximum, minimum and average error values 45


6.4

PROCEDURE



The input and output rows are defined in the software in the data entry page.



The input and output data, from the experiment, are entered in their respective rows. Then the software is made to learn these values. After series of iterations, the software creates a correlation between the input and output.





Now query rows are inserted in the table.



The input variables are inserted and the software predicts the output.



Then the values predicted are learnt.





6.5

The last two steps are repeated till we have th e required amount of data for our simulation. Then the data is recorded up in a spreadsheet.

PREDICTION RESULTS:

DAYS MeOH WATER TEMP. IN IN

REFLUX MeOH RATIO TOP

MeOH BOTTOM

95

1

0.7

0.1

0.5

90

1

0.75

0.15

0.3

0.7

104

2.33333

0.7499

0.5122

4

0.3

0.7

105

1.5

0.7499

0.4805

5

0.6

0.4

103

0.66667

0.7499

0.5173

6

0.8

0.2

104

2.33333

0.7373

0.501

7

0.2

0.8

106

0.4285714

0.75

0.4

8

0.9

0.1

105

4

0.56

0.1

9

0.85

0.15

107.1

4

0.72

0.65

10

0.55

0.45

107

1.5

0.7498

0.5922

1

0.4

2

0.5

3

0.6

Table 6.1: A.N.N prediction result

46

RESULTS AND DISCUSSIONS

CHAPTER 7

RESULTS AND DISCUSSIONS 7.1

Comparison between experimental result and ChemCAD results:

EXPERIMENT

ChemCAD

MeOH TOP

MeOH TOP

MeOH BOTTOM

MeOH BOTTOM

0.7

0.1

0.366693

0.0301

0.75

0.15

0.6067237

0.07327176

0.6

0.4

0.65

0.5

0.5395392

0.011748

0.58

0.4

0.7081614

0.02940297

0.75

0.65

0.9921251

0.03692383

0.75

0.4

Could not converge

Could not converge

0.72

0.65

Could not converge

Could not converge

0.7

0.65

Could not converge

Could not converge

Could not converge

Table 7.1: experiment vs. ChemCAD 47

Could not converge


7.2

ChemCAD generated results

The ChemCAD data/calculation for the following sample: day 10

MeOH in 0.55

Water in 0.45

temperature 107

Reflux ratio 1.5

CHEMCAD 6.1.3 Page 1 Job Name: srsctsbldist

Date: 05/14/2013

Time: 12:53:57

FLOWSHEET SUMMARY Equipment 1 2 3

Label

Stream Numbers

TOWR MIXE HTXR

4 2 3

-5 1 -4

-6 -3

Stream Connections Stream Equipment

Equipment From

Stream

To

Equipment From

To

2 3

3 1

Stream From

To 1 2

2 2

3 4

5 6

1 1

Calculation mode : Sequential Flash algorithm : Normal Equipment Calculation Sequence 2 3 1 No recycle loops in the flowsheet.

CHEMCAD 6.1.3 Page 2 Job Name: srsctsbldist Overall Mass Balance Methanol

Date: 05/14/2013 lbmol/h Input 0.000

Output 0.000

48

Time: 12:53:57 lb/h Input 0.000

Output 0.000


Water

0.000

0.000

0.000

0.000

Total

0.000

0.000

0.001

0.001

CHEMCAD 6.1.3 Page 3 Job Name: srsctsbldist COMPONENTS ID # 1 117 2 62

Date: 05/14/2013

Name Methanol Water

Time: 12:53:57

Formula CH4O H2O

THERMODYNAMICS K-value model

:

Enthalpy model Liquid density

: :

UNIFAC No correction for vapor fugacity Latent Heat Library

Std vapor rate reference temperature is 0 C. Atmospheric pressure is 1.0332 kg/cm2. CHEMCAD 6.1.3 Page 4 Job Name: srsctsbldist EQUIPMENT SUMMARIES

Date: 05/14/2013

Time: 12:53:57

Towr Rigorous Distillation Summary Equip. No. Name No. of stages 1st feed stage Condenser mode Condenser spec. Cond. comp i Reboiler mode Reboiler spec. Reboiler comp i Initial flag Calc cond duty (MMBtu/h) Calc rebr duty (MMBtu/h) Est. Dist. rate (lbmol/h) Est. Reflux rate (lbmol/h) Est. T top C

1 8 8 1 1.5000 1 3 93.4000 1 6 -8.2924e-007 6.7044e-007 1.0679e-005 1.6019e-005 63.8918 49


Est. T bottom C Est. T 2 C Column type No of sections Calc Reflux ratio Calc Reflux mole (lbmol/h) Calc Reflux mass lb/h

93.4000 63.9418 1 1 1.5000 2.9996e-005 0.0008

Mixer Summary Equip. No. Name Output Pressure (kg/cm2-G)

2 0.0500

Heat Exchanger Summary Equip. No. Name 1st Stream dp kg/cm2 1st Stream T Out C Calc Ht Duty MMBtu/h LMTD Corr Factor 1st Stream Pout (kg/cm2-G)

3 0.0750 93.4000 3.6360e-007 1.0000 -0.0250

CHEMCAD 6.1.3 Page 5 Job Name: srsctsbldist STREAM PROPERTIES Stream

Date: 05/14/2013

1 Methanol+wat properties: Overall

Solid Temperature deg C 32.000 Pressure kg/cm2-G 0.000 Vapor fraction 0.000E+000 Critical T deg C 280.67 Critical P kg/cm2-G 105.47 Std sp. gr. * wtr = 1 0.854 Std sp. gr. * air = 1 0.888 Deg API 34.14 Enthalpy MMBtu/h -1.115E-006 0.000E+000 Molar flow lbmol/h 0.000 0.000

Time: 12:53:57

Vapor

Liquid

0.000E+000

-1.115E-006

0.000

0.000

50


Mass flow lb/h 0.000 0.000 Avg. mol. wt. 25.730 0.000 Actual dens lb/ft3 52.386 0.000 Actual vol ft3/hr 0.000 0.000 Std liq vol ft3/hr 0.000 0.000 Std vapor scfh 0.004 0.000 Cp Btu/lbmol-F 0.000 Z factor Viscosity cP Thermal cond. Btu/hr-ft-F Surface tension dyne/cm

0.000

0.000

0.000

25.730

0.000

52.386

0.000

0.000

0.000

0.000

0.000

0.004

0.000

18.938

0.000 0.00000 0.0000

1.616E-003 0.6104 0.1543 30.7655

Component mole fractions Methanol

0.5500

0.0000

0.5500

Water

0.4500

0.0000

0.4500

0.0000 0.0000 CHEMCAD 6.1.3 Page 6 Job Name: srsctsbldist STREAM PROPERTIES Stream

Date: 05/14/2013

2 Water+methan properties: Overall

Solid Temperature deg C 32.000 Pressure kg/cm2-G 0.000 Vapor fraction 0.000E+000 Critical T deg C 280.67 Critical P kg/cm2-G 105.47 Std sp. gr. * wtr = 1 0.854 Std sp. gr. * air = 1 0.888 Deg API 34.14 Enthalpy MMBtu/h -1.115E-006 0.000E+000 Molar flow lbmol/h 0.000 0.000 Mass flow lb/h 0.000 0.000 Avg. mol. wt. 25.730 0.000 Actual dens lb/ft3 52.386 0.000

Time: 12:53:57

Vapor

Liquid

0.000E+000

-1.115E-006

0.000

0.000

0.000

0.000

0.000

25.730

0.000

52.386

51


Actual vol ft3/hr 0.000 Std liq vol ft3/hr 0.000 Std vapor scfh 0.000 Cp Btu/lbmol-F 0.000 Z factor Viscosity cP Thermal cond. Btu/hr-ft-F Surface tension dyne/cm

0.000

0.000

0.000

0.000

0.000

0.000

0.004

0.000

0.004

0.000

18.938

0.000 0.00000 0.0000

1.616E-003 0.6104 0.1543 30.7655


0.5500

0.0000

0.5500

Water

0.4500

0.0000

0.4500

0.0000 0.0000 CHEMCAD 6.1.3 Page 7 Job Name: srsctsbldist STREAM PROPERTIES

Date: 05/14/2013

Stream

properties: Overall

3

Solid Temperature deg C 32.000 Pressure kg/cm2-G 0.050 Vapor fraction 0.000E+000 Critical T deg C 280.67 Critical P kg/cm2-G 105.47 Std sp. gr. * wtr = 1 0.854 Std sp. gr. * air = 1 0.888 Deg API 34.14 Enthalpy MMBtu/h -2.231E-006 0.000E+000 Molar flow lbmol/h 0.000 0.000 Mass flow lb/h 0.001 0.000 Avg. mol. wt. 25.730 0.000 Actual dens lb/ft3 52.386 0.000 Actual vol ft3/hr 0.000 0.000 Std liq vol ft3/hr 0.000 0.000 Std vapor scfh 0.007 0.000

Time: 12:53:57

Vapor

Liquid

0.000E+000

-2.231E-006

0.000

0.000

0.000

0.001

0.000

25.730

0.000

52.386

0.000

0.000

0.000

0.000

0.000

0.007

52


Cp Btu/lbmol-F 0.000 Z factor Viscosity cP Thermal cond. Btu/hr-ft-F Surface tension dyne/cm

0.000

18.934

0.000 0.00000 0.0000

1.694E-003 0.6104 0.1543 30.7655


0.5500

0.0000

0.5500

Water

0.4500

0.0000

0.4500

0.0000 0.0000 CHEMCAD 6.1.3 Page 8 Job Name: srsctsbldist STREAM PROPERTIES Stream

Date: 05/14/2013

4 Methanol+wat properties: Overall

Solid Temperature deg C 93.400 Pressure kg/cm2-G -0.025 Vapor fraction 1.000E+000 Critical T deg C 280.67 Critical P kg/cm2-G 105.47 Std sp. gr. * wtr = 1 0.854 Std sp. gr. * air = 1 0.888 Deg API 34.14 Enthalpy MMBtu/h -1.867E-006 0.000E+000 Molar flow lbmol/h 0.000 0.000 Mass flow lb/h 0.001 0.000 Avg. mol. wt. 25.730 0.000 Actual dens lb/ft3 0.053 0.000 Actual vol ft3/hr 0.010 0.000 Std liq vol ft3/hr 0.000 0.000 Std vapor scfh 0.007 0.000 Cp Btu/lbmol-F 0.000 Z factor Viscosity cP Thermal cond. Btu/hr-ft-F Surface tension dyne/cm

Time: 12:53:57

Vapor

Liquid

-1.867E-006

0.000E+000

0.000

0.000

0.001

0.000

25.730

0.000

0.053

0.000

0.010

0.000

0.000

0.000

0.007

0.000

10.095

0.000

0.989 0.01206 0.0131

0.000E+000 0.0000 0.0000 0.0000

53



0.5500

0.5500

0.0000

Water

0.4500

0.4500

0.0000

0.0000 0.0000 CHEMCAD 6.1.3 Page 9 Job Name: srsctsbldist STREAM PROPERTIES

Date: 05/14/2013

Stream

properties: Overall

5


Time: 12:53:57

Vapor

Liquid

0.000E+000

-2.203E-006

0.000

0.000

0.000

0.001

0.000

25.731

0.000

50.127

0.000

0.000

0.000

0.000

0.000

0.007

0.000

20.233

0.000 0.00000 0.0000

1.462E-003 0.3565 0.1493 26.2743

0.0000

0.5501


0.5501

0.0000

54


Water

0.4499

0.0000

0.4499

0.0000 CHEMCAD 6.1.3 Page 10 Job Name: srsctsbldist STREAM PROPERTIES

Date: 05/14/2013

Stream

properties: Overall

6


Time: 12:53:57

Vapor

Liquid

0.000E+000

-3.267E-010

0.000

0.000

0.000

0.000

0.000

18.534

0.000

58.803

0.000

0.000

0.000

0.000

0.000

0.000

0.000

18.322

0.000 0.00000 0.0000

8.549E-004 0.3012 0.3522 54.4451


0.0370

0.0000

0.0370

Water

0.9630

0.0000

0.9630

0.0000 0.0000 CHEMCAD 6.1.3 Page 11

55


Job Name: srsctsbldist FLOW SUMMARIES

Date: 05/14/2013

Stream No. 1 4 Stream Name Methanol+wat Methanol+wat Temp C 32.0000 93.4000 Pres kg/cm2-G 0.0000 0.0250 Enth MMBtu/h -1.1154E-006 1.8671E-006 Vapor mole fraction 0.00000 1.0000 Total lbmol/h 0.0000 0.0000 Flowrates in lbmol/h Methanol 0.0000 0.0000 Water 0.0000 0.0000 Stream No. 5 Stream Name Temp C 71.4974 Pres kg/cm2-G -0.0250 Enth MMBtu/h -2.2026E-006 Vapor mole fraction 0.00000 Total lbmol/h 0.0000 Flowrates in lbmol/h Methanol 0.0000 Water 0.0000

Time: 12:53:57

2

3

Water+methan 32.0000

32.0001

0.0000

0.0500

-1.1154E-006

-2.2307E-006

0.00000

0.00000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

-

6 93.4072 -0.0250 -3.2673E-010 0.00000 0.0000 0.0000 0.0000

CHEMCAD 6.1.3 Page 12 Job Name: srsctsbldist DISTILLATION PROFILE Unit type : TOWR

Temp Duties Stg C MMBtu/h 1 71.5 8.292E-007 2 82.4 3 90.1 4 92.2 5 92.9

Pres

Date: 05/14/2013

Unit name:

Eqp #

* Net Flows * Liquid Vapor

kg/cm2-G lbmol/h -0.02

0.00

-0.02 -0.02 -0.02 -0.02

0.00 0.00 0.00 0.00

lbmol/h

Time: 12:53:57

1

Feeds

Product

lbmol/h

lbmol/h 0.00 -

0.00 0.00 0.00 0.00 56


6 93.2 7 91.7 8 93.4 6.704E-007

-0.02 -0.02 -0.02

0.00 0.00

Mole Reflux ratio

0.00 0.00 0.00

0.00

0.00

1.500

Total liquid entering stage

8 at

93.407 C,

0.000 lbmol/h.

7.2.1 ChemCAD GENERATED GRAPHS AND RESULTS 7.2.1.1

TPXY GRAPH OF MeOH- H2O SYSTEM

CHEMCAD 6.1.3 1

Page

Job Name: srsctsbldist

Date: 05/14/2013

Time: 13:26:07

XY data for Methanol / Water K value model: UNIF

T Deg C Phi2 120.161 1.000 112.308 1.000 107.285 1.000 103.744 1.000 101.065 1.000 98.925 1.000 97.138 1.000 95.595 1.000 94.224 1.000 92.980 1.000 91.830 1.000 90.752 1.000 89.732 1.000

P kg/cm2-G

Mole Fractions X1 Y1

Gamma1

Gamma2

Phi1

1.000

0.00000

0.00000

2.296

1.000

1.000

1.000

0.05000

0.26083

2.027

1.003

1.000

1.000

0.10000

0.40333

1.819

1.012

1.000

1.000

0.15000

0.49456

1.656

1.025

1.000

1.000

0.20000

0.55956

1.527

1.043

1.000

1.000

0.25000

0.60962

1.424

1.064

1.000

1.000

0.30000

0.65051

1.340

1.089

1.000

1.000

0.35000

0.68549

1.272

1.116

1.000

1.000

0.40000

0.71649

1.216

1.147

1.000

1.000

0.45000

0.74475

1.170

1.179

1.000

1.000

0.50000

0.77108

1.132

1.214

1.000

1.000

0.55000

0.79606

1.101

1.252

1.000

1.000

0.60000

0.82006

1.076

1.292

1.000

57


88.758 1.000 87.821 1.000 86.916 1.000 86.037 1.000 85.182 1.000 84.347 1.000 83.530 1.000 82.730 1.000

1.000

0.65000

0.84339

1.055

1.333

1.000

1.000

0.70000

0.86623

1.039

1.377

1.000

1.000

0.75000

0.88876

1.026

1.423

1.000

1.000

0.80000

0.91110

1.016

1.471

1.000

1.000

0.85000

0.93332

1.009

1.521

1.000

1.000

0.90000

0.95551

1.004

1.573

1.000

1.000

0.95000

0.97772

1.001

1.627

1.000

1.000

1.00000

1.00000

1.000

1.682

1.000

58


fig 7.1: T-x-y graph of MeOH-H2O system

59


fig 7.2: x-y graph

7.2.1.2

Temperature vs. Liquid heat capacity graphs

Job Name: srsctsbldist Stream 5

Temperature C 28.000 32.158 36.316 40.474 44.632 48.789 52.947 57.105

Date: 05/14/2013

Time: 13:11:00

Pressure =-0.025 kg/cm2-G Liquid Heat Capacity Btu/lbmol-F 1.88302e+001 1.89384e+001 1.90552e+001 1.91755e+001 1.93002e+001 1.94291e+001 1.95666e+001 1.97076e+001 60


61.263 65.421 69.579 73.737 77.895 82.053 86.211 90.368 94.526 98.684 102.842 107.000

1.98530e+001 1.99983e+001 2.01600e+001 2.03183e+001 2.04843e+001 2.06581e+001 2.08406e+001 2.10160e+001 2.12063e+001 2.13973e+001 2.15926e+001 2.17880e+001

Fig 7.3: liquid heat capacity curve

61


7.2.1.3

Temperature vs. vapour heat capacity graphs

CHEMCAD 6.1.3 Job Name: srsctsbldist Stream 5

Pressure =

Temperature C 28.000 32.158 36.316 40.474 44.632 48.789 52.947 57.105 61.263 65.421 69.579 73.737 77.895 82.053 86.211 90.368 94.526 98.684 102.842 107.000

Date: 05/14/2013 -0.025 kg/cm2-G Vapor Heat Capacity Btu/lbmol-F 9.41930e+000 9.45749e+000 9.49649e+000 9.53626e+000 9.57677e+000 9.61798e+000 9.65985e+000 9.70235e+000 9.74545e+000 9.78910e+000 9.83328e+000 9.87794e+000 9.92306e+000 9.96860e+000 1.00145e+001 1.00608e+001 1.01075e+001 1.01544e+001 1.02016e+001 1.02491e+001

62

Time: 14:05:29


Fig 7.4: : liquid heat capacity curve

7.2.1.3

Temperature vs. vapour thermal conductivity graphs

CHEMCAD 6.1.3 1 Job Name: srsctsbldist Stream 5

Pressure =

Temperature C 28.000 32.158 36.316 40.474 44.632 48.789 52.947 57.105 61.263 65.421 69.579 73.737 77.895 82.053 86.211 90.368 94.526 98.684 102.842 107.000

Page

Date: 05/14/2013

Time: 13:13:44

-0.025 kg/cm2-G Vapor Thermal Conductivity Btu/hr-ft-F 1.00217e-002 1.01995e-002 1.03803e-002 1.05640e-002 1.07507e-002 1.09402e-002 1.11326e-002 1.13276e-002 1.15254e-002 1.17258e-002 1.19288e-002 1.21344e-002 1.23426e-002 1.25533e-002 1.27665e-002 1.29822e-002 1.32003e-002 1.34208e-002 1.36437e-002 1.38690e-002 63


Fig 7.5: Temperature vs. vapour thermal conductivity graphs

7.2.1.4

Temperature vs. liquid thermal conductivity graphs

CHEMCAD 6.1.3 Job Name: srsctsbldist Stream 5 Pressure =

Temperature C 28.000 32.158 36.316 40.474 44.632 48.789 52.947 57.105 61.263 65.421 69.579 73.737 77.895 82.053 86.211 90.368 94.526 98.684 102.842 107.000

Date: 05/14/2013 Time: 13:22:30 -0.025 kg/cm2-G Liquid Thermal Conductivity Btu/hr-ft-F 1.54744e-001 1.54317e-001 1.53870e-001 1.53403e-001 1.52916e-001 1.52409e-001 1.51883e-001 1.51339e-001 1.50777e-001 1.50196e-001 1.49597e-001 1.48981e-001 1.48347e-001 1.47695e-001 1.47026e-001 1.46341e-001 1.45638e-001 1.44918e-001 1.44181e-001 1.43427e-001

64


Fig 7.5: Temperature vs. liquid thermal conductivity graphs

7.2.1.5

Composite Curve

CHEMCAD 6.1.3 1

Page


Date: 05/14/2013

Time: 13:20:45

Column 1 Condenser NP 1 2 3 4 5 6 7 8 9 10 11

Temp C 82.4 81.3 80.2 79.1 78.0 76.9 75.8 74.8 73.7 72.6 71.5

Pres Del H kg/cm2-G MMBtu/h -0.0 8.29E-007 -0.0 7.65E-007 -0.0 7.04E-007 -0.0 6.42E-007 -0.0 5.79E-007 -0.0 5.13E-007 -0.0 4.41E-007 -0.0 3.59E-007 -0.0 2.63E-007 -0.0 1.47E-007 -0.0 3.25E-010

Vapor lb/h 0 0 0 0 0 0 0 0 0 0 0

Liquid lb/h 0 0 0 0 0 0 0 0 0 0 0

Vap mole frac. 1.0000 0.9271 0.8562 0.7853 0.7120 0.6337 0.5469 0.4475 0.3296 0.1849 0.0006

Vap mass frac. 1.0000 0.9410 0.8818 0.8202 0.7538 0.6797 0.5943 0.4925 0.3673 0.2086 0.0007

Vapor

Liquid

Vap mole

Vap mass

Column 1 Reboiler NP

Temp

Pres

Del H

65


1 2 3 4 5 6 7 8 9 10 11

C 86.5 87.2 87.9 88.6 89.3 89.9 90.6 91.3 92.0 92.7 93.4

kg/cm2-G MMBtu/h -0.0 9.54E-013 -0.0 4.06E-008 -0.0 8.37E-008 -0.0 1.30E-007 -0.0 1.80E-007 -0.0 2.36E-007 -0.0 2.99E-007 -0.0 3.70E-007 -0.0 4.53E-007 -0.0 5.51E-007 -0.0 6.70E-007

lb/h

lb/h 0 0 0 0 0 0 0 0 0 0 0

Pres Del H kg/cm2-G MMBtu/h 0.1 0.000 0.0 4.21E-009 0.0 8.45E-009 0.0 1.27E-008 0.0 1.71E-008 0.0 2.14E-008 0.0 2.59E-008 -0.0 1.60E-007 -0.0 3.22E-007 -0.0 3.61E-007 -0.0 3.64E-007

Vapor lb/h 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0

frac. 0.3463 0.3862 0.4283 0.4736 0.5227 0.5770 0.6379 0.7074 0.7880 0.8837 0.9998

frac. 0.3961 0.4367 0.4786 0.5226 0.5696 0.6205 0.6768 0.7400 0.8125 0.8975 0.9998

Liquid lb/h 0 0 0 0 0 0 0 0 0 0 0

Vap mole frac. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.4119 0.8916 1.0000 1.0000

Vap mass frac. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.4551 0.9117 1.0000 1.0000

Stream 3 NP 1 2 3 4 5 6 7 8 9 10 11

Temp C 32.0 38.1 44.3 50.4 56.6 62.7 68.8 75.0 81.1 87.3 93.4

Hot Composite Temp C 71.497 71.606 71.715 71.823 CHEMCAD 6.1.3 2

DH MMBtu/h 0.000e+000 1.647e-008 3.216e-008 4.755e-008 Page

Job Name: srsctsbldist 71.932 72.041 72.150 72.258 72.367 72.476 72.584 72.693 72.802 72.911 73.019 73.128 73.237 73.345 73.454

Date: 05/14/2013

6.263e-008 7.741e-008 9.190e-008 1.061e-007 1.200e-007 1.335e-007 1.466e-007 1.591e-007 1.715e-007 1.836e-007 1.955e-007 2.073e-007 2.188e-007 2.301e-007 2.412e-007 66

Time: 13:20:45


73.563 73.671 73.780 73.889 73.998 74.106 74.215 74.324 74.432 74.541 74.650 74.759 74.867 74.976 75.085 75.193 75.302 75.411 75.519 75.628 75.737 75.846 75.954 76.063 76.172 76.280 76.389 76.498 76.607 76.715 76.824 76.933 77.041 77.150 77.259 77.367 77.476 77.585 77.694 77.802 77.911 CHEMCAD 6.1.3 3

2.521e-007 2.629e-007 2.732e-007 2.833e-007 2.932e-007 3.030e-007 3.127e-007 3.222e-007 3.316e-007 3.408e-007 3.499e-007 3.589e-007 3.674e-007 3.759e-007 3.844e-007 3.927e-007 4.009e-007 4.091e-007 4.171e-007 4.251e-007 4.329e-007 4.407e-007 4.482e-007 4.557e-007 4.631e-007 4.704e-007 4.777e-007 4.849e-007 4.921e-007 4.992e-007 5.062e-007 5.132e-007 5.200e-007 5.267e-007 5.334e-007 5.401e-007 5.468e-007 5.534e-007 5.599e-007 5.665e-007 5.730e-007 Page

Job Name: srsctsbldist 78.020 78.128 78.237 78.346 78.454 78.563 78.672 78.781 78.889 78.998

Date: 05/14/2013

5.795e-007 5.858e-007 5.921e-007 5.984e-007 6.047e-007 6.110e-007 6.173e-007 6.235e-007 6.298e-007 6.360e-007 67

Time: 13:20:45


79.107 79.215 79.324 79.433 79.542 79.650 79.759 79.868 79.976 80.085 80.194 80.302 80.411 80.520 80.629 80.737 80.846 80.955 81.063 81.172 81.281 81.390 81.498 81.607 81.716 81.824 81.933 82.042 82.150 82.259 82.368 Cold Composite Temp C 32.000 32.614 33.228 33.842 34.456 35.070 35.685 36.299 36.913 37.527 38.141 38.755 CHEMCAD 6.1.3 4

6.422e-007 6.483e-007 6.544e-007 6.605e-007 6.667e-007 6.728e-007 6.789e-007 6.851e-007 6.912e-007 6.974e-007 7.035e-007 7.096e-007 7.157e-007 7.218e-007 7.279e-007 7.341e-007 7.403e-007 7.465e-007 7.527e-007 7.590e-007 7.653e-007 7.716e-007 7.779e-007 7.842e-007 7.906e-007 7.970e-007 8.034e-007 8.098e-007 8.163e-007 8.227e-007 8.292e-007

DH MMBtu/h 0.000e+000 4.189e-010 8.382e-010 1.258e-009 1.678e-009 2.099e-009 2.519e-009 2.941e-009 3.362e-009 3.784e-009 4.207e-009 4.629e-009 Page

Job Name: srsctsbldist 39.369 39.983 40.597 41.211 41.825

Date: 05/14/2013

5.052e-009 5.476e-009 5.900e-009 6.324e-009 6.749e-009 68

Time: 13:20:45


42.439 43.053 43.667 44.282 44.896 45.510 46.124 46.738 47.352 47.966 48.580 49.194 49.808 50.422 51.036 51.650 52.264 52.879 53.493 54.107 54.721 55.335 55.949 56.563 57.177 57.791 58.405 59.019 59.633 60.247 60.861 61.476 62.090 62.704 63.318 63.932 64.546 65.160 65.774 66.388 67.002 67.616 68.230 68.844 69.458 70.073 70.687 71.301 71.915 72.529 73.143 CHEMCAD 6.1.3 5

7.174e-009 7.599e-009 8.025e-009 8.452e-009 8.878e-009 9.306e-009 9.733e-009 1.016e-008 1.059e-008 1.102e-008 1.145e-008 1.188e-008 1.231e-008 1.274e-008 1.317e-008 1.360e-008 1.403e-008 1.447e-008 1.490e-008 1.533e-008 1.577e-008 1.620e-008 1.663e-008 1.707e-008 1.751e-008 1.794e-008 1.838e-008 1.881e-008 1.925e-008 1.969e-008 2.013e-008 2.057e-008 2.101e-008 2.145e-008 2.189e-008 2.233e-008 2.278e-008 2.322e-008 2.366e-008 2.410e-008 2.455e-008 2.499e-008 2.543e-008 2.596e-008 3.815e-008 5.061e-008 6.334e-008 7.635e-008 8.963e-008 1.032e-007 1.170e-007


Page

Date: 05/14/2013

69

Time: 13:20:45


73.757 74.371 74.985 75.599 76.213 76.827 77.441 78.055 78.670 79.284 79.898 80.512 81.126 81.740 82.354 82.968 83.582 84.196 84.810 85.424 86.038 86.652 87.267 87.881 88.495 89.109 89.723 90.337 90.951 91.565 92.179 92.793 93.407

1.311e-007 1.455e-007 1.602e-007 1.764e-007 1.925e-007 2.087e-007 2.249e-007 2.410e-007 2.572e-007 2.734e-007 2.895e-007 3.057e-007 3.218e-007 3.257e-007 3.297e-007 3.337e-007 3.376e-007 3.416e-007 3.455e-007 3.495e-007 3.535e-007 3.669e-007 4.072e-007 4.459e-007 4.870e-007 5.314e-007 5.796e-007 6.326e-007 6.921e-007 7.593e-007 8.361e-007 9.266e-007 1.034e-006

70


Fig 7.6: composite curve

7.2.2

Discussion:



We can see that there is lots of errors in the ChemCAD simulation results. It is because of the fact that ChemCAD works for ideal situations, since we do not have those ideal situations in our current lab, so for many conditions we get these error messages.



From the comparison study we see that the results do not match and there is huge amount of difference. This problem arises, because our equipments are not module to serve the purpose of high accuracy distillation operations

71


7.3

Comparison between experimental result and A.N.N results:

EXPERIMENT

ANN

MeOH TOP

MeOH BOTTOM

MeOH TOP

MeOH BOTTOM

0.7

0.1

0.75

0.2897

0.75

0.15

0.75

0.15

0.6

0.4

0.7499

0.5151

0.65

0.5

0.7499

0.4805

0.58

0.4

0.7499

0.5173

0.75

0.65

0.7373

0.501

0.75

0.4

0.75

0.4

0.56

0.1

0.56

0.1

0.72

0.65

0.72

0.65

0.7

0.65

0.7498

0.5922

Table 7.2: experiment vs. ANN

7.3.1 

Discussions: We see that A.N.N results are quite similar to the experimental results. Here we were limited by the equipment in the implementation of the many other controlling factors. But in real life industrial situation, where we will have the liberty of finding many other factors, the use of A.N.N, would be good.



The accuracy of A.N.N depends on the number of input factors. The differences in our results with the A.N.N values is due to the lack of chances to monitor controlling factors.

72


73


74

CONCLUSION AND FUTURE SCOPE OF STUDY

CHAPTER 8

CONCLUSIONS AND FUTURE SCOPE OF STUDY

In the past A.N.N has never been used to study and predict the outcome of the products from the distillation column. This has been a first attempt to do so. And the results from the A.N.N predictions are really promising.

With only four changeable factors, namely methanol in, water in, temperature and reflux ratio, we have seen that the results from the prediction are quite close to the experimental results.

So it can be hoped that, in the future, if these studies are conducted on real life industrial scale columns, then A.N.N might be really successful.

As for the case of the ChemCad software simulation. We have used it to actually see the variations that our equipments suffer from that of the original data. So there is a lot of scope for the upcoming students to investigate the sectors where the laboratory equipments are falling short of the real/ industrial distillation columns.

73

REFERENCES

CHAPTER 9

REFERENCES:

1.

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^ Taylor,

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Batch Distillation of Water-Methanol System

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