Batch Distillation of Water-Methanol System

i

Batch distillation of methanol-water system and modeling

and simulation of distillation column using ARTIFICIAL

NEURAL NETWORK and ChemCAD

Satadru Chakrabarty, Sayantan Roy Choudhury, Tapas Saha

DEPARTMENT OF CHEMICAL ENGINEERING

NATIONAL INSTITUTE OF TECHNOLOGY

AGARTALA-799055, INDIA

MAY - 2013

ii

BATCH DISTILLATION OF METHANOL-WATER SYSTEM

AND MODELING AND SIMULATION OF DISTILLATION

COLUMN USING ARTIFICIAL NEURAL NETWORK AND

CHEMCAD

Report submitted to

National Institute of Technology, Agartala

For the award of the degree

Of

Bachelor of Technology

By

Satadru Chakrabarty (09UCH013)

Sayantan Roy Choudhury (09UCH014)

Tapas Saha (09UCH016)

Supervisor

Mr. Bibhab Kumar Lodh

Assistant Proffessor Chemical Engg Department

Chemical Engineering Department

National Institute of Technology, Agartala

May- 2013

2013 Satadru Chakrabarty, Sayantan Roy Choudhury, Tapas Saha. All rights reserved

iii

Dedicated:

To all the teachers of the department

of chemical engineering, who have

inspired us to make this project

successfully

iv

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: ____________

v

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: __________

vi

CERTIFICATE

This is certified that the work contained in the project titled Batch distillation of methanol-

water 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

vii

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 batch 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 Chakrabarty

Sayantan Roy Choudhury

Tapas Saha

viii

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.

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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 28C 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 softwares data entry sheet 43

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

input parameters 44

Fig 6.3: the importance given to the different inputs 45

Fig. 6.3: the error graph, showing the target error, maximum,

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

x

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

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LIST OF SYMBOLS AND ABBREVIATIONS

MB =liquid holdup in still pot (kmol)

MD =liquid holdup in reflux drum (kmol)

Mn =liquid holdup in the nth tray (kmol)

nT =total number of trays

QR =Heat input to the still pot (kJ/min)

R=Reflux flow rate (kmol/min)

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)

VnT =vapor flow rate of vapor leaving top tray (kmol/min)

D=distillate flow rate (kmol/min)

Ln=liquid flow rate of liquid leaving the nth tray (kmol/min)

xB,i=composition of component i in the still

xD,i=composition of component i in the Distillate

xn,i=composition of component i in Liquid stream leaving the nth tray

r,i=rate of reaction of component (kmol/lit.min)

=volume of catalyst (lit)

k1, k2 =rate constants

K1, K2= GMC controller tuning parameters

vb =volume of reboiler (lit)

vd=volume of reflux drum (lit)

v=volume of tray (lit)

RF=multiplication factor (RF=1 for reactive section, RF=0 for non-reactive section)

Him=stoichiometric coefficient of i the component of m the reaction.

1, 2, 3=estimator tuning parameters.

Radii=rate of reaction of component i in distillate.

Rabbi= rate of reaction of component i in bottom.

Roni = rate of reaction of component i in nth tray.

xii

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 output.

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.

xiii

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- H2O system 27

4.1 Introduction 27

4.2 Theory 28

4.3 Calibration Curve 29

xiv

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

APPENDIX 77

LITERATURE REVIEW

17

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 system having

more than two components, but this does not preclude it being used for a binary system. Batch

distillation is actually a semi batch process where the system is charged once 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.

LITERATURE REVIEW

18

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 stills concentration can be determined via Rayleighs 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 start-

up periods. In S. Elguea, L. Prata, M. Cabassuda,, J.M. Le Lanna, J. Cezeracb, two simple

mathematical models are proposed for the simulation of the dynamic behavior during start-up

LITERATURE REVIEW

19

operations from an empty cold state. These mathematical models are based on a rigorous tray-

by-tray description of the column described by conservation laws, liquidvapour 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 capture 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

LITERATURE REVIEW

20

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 firms innovation capability, (2) the firms 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/.

MODELLING OF THE DISTILLATION COLUMN

21

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


22

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 vaporliquid

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

vaporliquid 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


23

The operation of batch distillation described here corresponds to a ternary system of

cyclohexanetoluenechlorobenzene. 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


24


25


26

EXPERIMENTAL DISTILLATION OF MeOH-H2O SYSTEM

27

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


28

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 condenser, 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+

+XD (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 xb and xd based on the

relative volatility of the components , which is defined in terms of equilibrium concentrations:

= ( / )

(/ ) (4.2)

An ideal mixture follows Raoults law and the relative volatility is the ratio of vapor pressure.

Thus:

p1 = P1x1


29

p2 = P2x2

y1 = p1/P (3.3)

y2 = p2/P

Therefore = P1/P2 (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 28C. 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 25C

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


30

Fig.4.3.1: the calibration curve attained from the calibration data at 28C

The calibration curve follows the following curve fitting equation:

y = -4 + 8e-11x5 2e-09x4 3e-07x3 + 1e-05x2 + 0.000x + 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 of the column. The bottom product is

collected in the tank. The vapours form the top of column are condensed in the shell and tube

1.322

1.324

1.326

1.328

1.33

1.332

1.334

1.336

1.338

1.34

1.342

1.344

0 20 40 60 80 100 120

refr

acti

ve in

de

x

molefractuion

ri

Poly. (ri)


31

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.


32

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 digital temperature controller. The

temperature should be in range of 85-95C

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.


33

4.5 EXPERIMENT OBSERVATIONS

DAY

S

MeOH

IN

WATE

R IN

TEMP. REFLUX

RATIO

MeOH

TOP

MeOH

BOTTOM

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


34

SIMULATION IN CHEMCAD

35

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.


36

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.


37


38

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, youll 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.


39

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)


40

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.


41

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.


42

ARTIFICIAL NEURAL NETWORK

43

CHAPTER 6


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


44

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


45

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


46

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.

The last two steps are repeated till we have the required amount of data for our

simulation.

Then the data is recorded up in a spreadsheet.

6.5 PREDICTION RESULTS:

DAYS MeOH

IN

WATER

IN

TEMP. REFLUX

RATIO

MeOH

TOP

MeOH

BOTTOM

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 0.7499 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

Table 6.1: A.N.N prediction result

RESULTS AND DISCUSSIONS

47

CHAPTER 7


7.1 Comparison between experimental result and ChemCAD results:

EXPERIMENT ChemCAD

MeOH TOP MeOH BOTTOM MeOH TOP MeOH BOTTOM

0.7

0.1 0.366693 0.0301

0.75 0.15 0.6067237 0.07327176

0.6 0.4 Could not converge Could not converge

0.65 0.5 0.5395392 0.011748

0.58 0.4 0.7081614 0.02940297

0.75 0.65 0.9921251 0.03692383




Table 7.1: experiment vs. ChemCAD


48

7.2 ChemCAD generated results

The ChemCAD data/calculation for the following sample:

day MeOH in Water in temperature Reflux ratio

10 0.55 0.45 107 1.5

CHEMCAD 6.1.3

Page 1

Job Name: srsctsbldist Date: 05/14/2013 Time: 12:53:57

FLOWSHEET SUMMARY

Equipment Label Stream Numbers

1 TOWR 4 -5 -6

2 MIXE 2 1 -3

3 HTXR 3 -4

Stream Connections

Stream Equipment Stream Equipment Stream

Equipment

From To From To From

To

1 2 3 2 3 5 1

2 2 4 3 1 6 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


Overall Mass Balance lbmol/h lb/h

Input Output Input Output

Methanol 0.000 0.000 0.000 0.000


49

Water 0.000 0.000 0.000 0.000

Total 0.000 0.000 0.001 0.001

CHEMCAD 6.1.3

Page 3


COMPONENTS

ID # Name Formula

1 117 Methanol CH4O

2 62 Water H2O

THERMODYNAMICS

K-value model : UNIFAC

No correction for vapor fugacity

Enthalpy model : Latent Heat

Liquid density : Library

Std vapor rate reference temperature is 0 C.

Atmospheric pressure is 1.0332 kg/cm2.

CHEMCAD 6.1.3

Page 4


EQUIPMENT SUMMARIES

Towr Rigorous Distillation Summary

Equip. No. 1

Name

No. of stages 8

1st feed stage 8

Condenser mode 1

Condenser spec. 1.5000

Cond. comp i 1

Reboiler mode 3

Reboiler spec. 93.4000

Reboiler comp i 1

Initial flag 6

Calc cond duty -8.2924e-007

(MMBtu/h)

Calc rebr duty 6.7044e-007

(MMBtu/h)

Est. Dist. rate 1.0679e-005

(lbmol/h)

Est. Reflux rate 1.6019e-005

(lbmol/h)

Est. T top C 63.8918


50

Est. T bottom C 93.4000

Est. T 2 C 63.9418

Column type 1

No of sections 1

Calc Reflux ratio 1.5000

Calc Reflux mole 2.9996e-005

(lbmol/h)

Calc Reflux mass lb/h 0.0008

Mixer Summary

Equip. No. 2

Name

Output Pressure 0.0500

(kg/cm2-G)

Heat Exchanger Summary

Equip. No. 3

Name

1st Stream dp kg/cm2 0.0750

1st Stream T Out C 93.4000

Calc Ht Duty MMBtu/h 3.6360e-007

LMTD Corr Factor 1.0000

1st Stream Pout -0.0250

(kg/cm2-G)

CHEMCAD 6.1.3

Page 5


STREAM PROPERTIES

Stream 1 Methanol+wat properties:

Overall Vapor Liquid

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 -1.115E-006

0.000E+000

Molar flow lbmol/h 0.000 0.000 0.000

0.000


51

Mass flow lb/h 0.000 0.000 0.000

0.000

Avg. mol. wt. 25.730 0.000 25.730

0.000

Actual dens lb/ft3 52.386 0.000 52.386

0.000

Actual vol ft3/hr 0.000 0.000 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.004 0.000 0.004

0.000

Cp Btu/lbmol-F 0.000 18.938

0.000

Z factor 0.000 1.616E-003

Viscosity cP 0.00000 0.6104

Thermal cond. Btu/hr-ft-F 0.0000 0.1543

Surface tension dyne/cm 30.7655

Component mole fractions

Methanol 0.5500 0.0000 0.5500

0.0000

Water 0.4500 0.0000 0.4500

0.0000

CHEMCAD 6.1.3

Page 6


STREAM PROPERTIES

Stream 2 Water+methan properties:


Solid






Std sp. gr. * wtr = 1 0.854

Std sp. gr. * air = 1 0.888

Deg API 34.14


0.000E+000


0.000

Mass flow lb/h 0.000 0.000 0.000

0.000

Avg. mol. wt. 25.730 0.000 25.730

0.000

Actual dens lb/ft3 52.386 0.000 52.386

0.000


52

Actual vol ft3/hr 0.000 0.000 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.004 0.000 0.004

0.000


0.000

Z factor 0.000 1.616E-003





Methanol 0.5500 0.0000 0.5500

0.0000

Water 0.4500 0.0000 0.4500

0.0000

CHEMCAD 6.1.3

Page 7


STREAM PROPERTIES

Stream 3 properties:


Solid






Std sp. gr. * wtr = 1 0.854

Std sp. gr. * air = 1 0.888

Deg API 34.14


0.000E+000


0.000

Mass flow lb/h 0.001 0.000 0.001

0.000

Avg. mol. wt. 25.730 0.000 25.730

0.000

Actual dens lb/ft3 52.386 0.000 52.386

0.000

Actual vol ft3/hr 0.000 0.000 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.007 0.000 0.007

0.000


53


0.000

Z factor 0.000 1.694E-003





Methanol 0.5500 0.0000 0.5500

0.0000

Water 0.4500 0.0000 0.4500

0.0000

CHEMCAD 6.1.3

Page 8


STREAM PROPERTIES

Stream 4 Methanol+wat properties:


Solid


Pressure kg/cm2-G -0.025




Std sp. gr. * wtr = 1 0.854

Std sp. gr. * air = 1 0.888

Deg API 34.14

Enthalpy MMBtu/h -1.867E-006 -1.867E-006 0.000E+000

0.000E+000


0.000

Mass flow lb/h 0.001 0.001 0.000

0.000

Avg. mol. wt. 25.730 25.730 0.000

0.000

Actual dens lb/ft3 0.053 0.053 0.000

0.000

Actual vol ft3/hr 0.010 0.010 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.007 0.007 0.000

0.000


0.000

Z factor 0.989 0.000E+000





54


Methanol 0.5500 0.5500 0.0000

0.0000

Water 0.4500 0.4500 0.0000

0.0000

CHEMCAD 6.1.3

Page 9


STREAM PROPERTIES



Solid






Std sp. gr. * wtr = 1 0.854

Std sp. gr. * air = 1 0.888

Deg API 34.14


0.000E+000


0.000

Mass flow lb/h 0.001 0.000 0.001

0.000

Avg. mol. wt. 25.731 0.000 25.731

0.000

Actual dens lb/ft3 50.127 0.000 50.127

0.000

Actual vol ft3/hr 0.000 0.000 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.007 0.000 0.007

0.000


0.000

Z factor 0.000 1.462E-003





Methanol 0.5501 0.0000 0.5501

0.0000


55

Water 0.4499 0.0000 0.4499

0.0000

CHEMCAD 6.1.3

Page 10


STREAM PROPERTIES



Solid






Std sp. gr. * wtr = 1 0.984

Std sp. gr. * air = 1 0.640

Deg API 12.26


0.000E+000


0.000

Mass flow lb/h 0.000 0.000 0.000

0.000

Avg. mol. wt. 18.534 0.000 18.534

0.000

Actual dens lb/ft3 58.803 0.000 58.803

0.000

Actual vol ft3/hr 0.000 0.000 0.000

0.000

Std liq vol ft3/hr 0.000 0.000 0.000

0.000

Std vapor scfh 0.000 0.000 0.000

0.000


0.000

Z factor 0.000 8.549E-004





Methanol 0.0370 0.0000 0.0370

0.0000

Water 0.9630 0.0000 0.9630

0.0000

CHEMCAD 6.1.3

Page 11


56


FLOW SUMMARIES

Stream No. 1 2 3

4

Stream Name Methanol+wat Water+methan

Methanol+wat

Temp C 32.0000 32.0000 32.0001

93.4000

Pres kg/cm2-G 0.0000 0.0000 0.0500 -

0.0250

Enth MMBtu/h -1.1154E-006 -1.1154E-006 -2.2307E-006 -

1.8671E-006

Vapor mole fraction 0.00000 0.00000 0.00000

1.0000

Total lbmol/h 0.0000 0.0000 0.0000

0.0000

Flowrates in lbmol/h

Methanol 0.0000 0.0000 0.0000

0.0000

Water 0.0000 0.0000 0.0000

0.0000

Stream No. 5 6

Stream Name

Temp C 71.4974 93.4072

Pres kg/cm2-G -0.0250 -0.0250

Enth MMBtu/h -2.2026E-006 -3.2673E-010

Vapor mole fraction 0.00000 0.00000

Total lbmol/h 0.0000 0.0000

Flowrates in lbmol/h

Methanol 0.0000 0.0000

Water 0.0000 0.0000

CHEMCAD 6.1.3

Page 12


DISTILLATION PROFILE

Unit type : TOWR Unit name: Eqp # 1

* Net Flows *

Temp Pres Liquid Vapor Feeds Product

Duties

Stg C kg/cm2-G lbmol/h lbmol/h lbmol/h lbmol/h

MMBtu/h

1 71.5 -0.02 0.00 0.00 -

8.292E-007

2 82.4 -0.02 0.00 0.00

3 90.1 -0.02 0.00 0.00

4 92.2 -0.02 0.00 0.00

5 92.9 -0.02 0.00 0.00


57

6 93.2 -0.02 0.00 0.00

7 91.7 -0.02 0.00 0.00

8 93.4 -0.02 0.00 0.00 0.00

6.704E-007

Mole Reflux ratio 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 Page

1


XY data for Methanol / Water

K value model: UNIF

Mole Fractions

T Deg C P kg/cm2-G X1 Y1 Gamma1 Gamma2 Phi1

Phi2

120.161 1.000 0.00000 0.00000 2.296 1.000 1.000

1.000

112.308 1.000 0.05000 0.26083 2.027 1.003 1.000

1.000

107.285 1.000 0.10000 0.40333 1.819 1.012 1.000

1.000

103.744 1.000 0.15000 0.49456 1.656 1.025 1.000

1.000

101.065 1.000 0.20000 0.55956 1.527 1.043 1.000

1.000

98.925 1.000 0.25000 0.60962 1.424 1.064 1.000

1.000

97.138 1.000 0.30000 0.65051 1.340 1.089 1.000

1.000

95.595 1.000 0.35000 0.68549 1.272 1.116 1.000

1.000

94.224 1.000 0.40000 0.71649 1.216 1.147 1.000

1.000

92.980 1.000 0.45000 0.74475 1.170 1.179 1.000

1.000

91.830 1.000 0.50000 0.77108 1.132 1.214 1.000

1.000

90.752 1.000 0.55000 0.79606 1.101 1.252 1.000

1.000

89.732 1.000 0.60000 0.82006 1.076 1.292 1.000

1.000


58

88.758 1.000 0.65000 0.84339 1.055 1.333 1.000

1.000

87.821 1.000 0.70000 0.86623 1.039 1.377 1.000

1.000

86.916 1.000 0.75000 0.88876 1.026 1.423 1.000

1.000

86.037 1.000 0.80000 0.91110 1.016 1.471 1.000

1.000

85.182 1.000 0.85000 0.93332 1.009 1.521 1.000

1.000

84.347 1.000 0.90000 0.95551 1.004 1.573 1.000

1.000

83.530 1.000 0.95000 0.97772 1.001 1.627 1.000

1.000

82.730 1.000 1.00000 1.00000 1.000 1.682 1.000

1.000


59

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


60

fig 7.2: x-y graph

7.2.1.2 Temperature vs. Liquid heat capacity graphs


Stream 5 Pressure =-0.025 kg/cm2-G

Liquid Heat Capacity

Temperature C Btu/lbmol-F

28.000 1.88302e+001

32.158 1.89384e+001

36.316 1.90552e+001

40.474 1.91755e+001

44.632 1.93002e+001

48.789 1.94291e+001

52.947 1.95666e+001

57.105 1.97076e+001


61

61.263 1.98530e+001

65.421 1.99983e+001

69.579 2.01600e+001

73.737 2.03183e+001

77.895 2.04843e+001

82.053 2.06581e+001

86.211 2.08406e+001

90.368 2.10160e+001

94.526 2.12063e+001

98.684 2.13973e+001

102.842 2.15926e+001

107.000 2.17880e+001

Fig 7.3: liquid heat capacity curve


62

7.2.1.3 Temperature vs. vapour heat capacity graphs

CHEMCAD 6.1.3


Stream 5 Pressure = -0.025 kg/cm2-G

Vapor Heat Capacity

Temperature C Btu/lbmol-F

28.000 9.41930e+000

32.158 9.45749e+000

36.316 9.49649e+000

40.474 9.53626e+000

44.632 9.57677e+000

48.789 9.61798e+000

52.947 9.65985e+000

57.105 9.70235e+000

61.263 9.74545e+000

65.421 9.78910e+000

69.579 9.83328e+000

73.737 9.87794e+000

77.895 9.92306e+000

82.053 9.96860e+000

86.211 1.00145e+001

90.368 1.00608e+001

94.526 1.01075e+001

98.684 1.01544e+001

102.842 1.02016e+001

107.000 1.02491e+001


63

Fig 7.4: : liquid heat capacity curve

7.2.1.3 Temperature vs. vapour thermal conductivity graphs

CHEMCAD 6.1.3 Page

1



Vapor Thermal Conductivity

Temperature C Btu/hr-ft-F

28.000 1.00217e-002

32.158 1.01995e-002

36.316 1.03803e-002

40.474 1.05640e-002

44.632 1.07507e-002

48.789 1.09402e-002

52.947 1.11326e-002

57.105 1.13276e-002

61.263 1.15254e-002

65.421 1.17258e-002

69.579 1.19288e-002

73.737 1.21344e-002

77.895 1.23426e-002

82.053 1.25533e-002

86.211 1.27665e-002

90.368 1.29822e-002

94.526 1.32003e-002

98.684 1.34208e-002

102.842 1.36437e-002

107.000 1.38690e-002


64

Fig 7.5: Temperature vs. vapour thermal conductivity graphs

7.2.1.4 Temperature vs. liquid thermal conductivity graphs

CHEMCAD 6.1.3



Liquid Thermal Conductivity

Temperature C Btu/hr-ft-F

28.000 1.54744e-001

32.158 1.54317e-001

36.316 1.53870e-001

40.474 1.53403e-001

44.632 1.52916e-001

48.789 1.52409e-001

52.947 1.51883e-001

57.105 1.51339e-001

61.263 1.50777e-001

65.421 1.50196e-001

69.579 1.49597e-001

73.737 1.48981e-001

77.895 1.48347e-001

82.053 1.47695e-001

86.211 1.47026e-001

90.368 1.46341e-001

94.526 1.45638e-001

98.684 1.44918e-001

102.842 1.44181e-001

107.000 1.43427e-001


65

Fig 7.5: Temperature vs. liquid thermal conductivity graphs

7.2.1.5 Composite Curve

CHEMCAD 6.1.3 Page

1


Column 1 Condenser

NP Temp Pres Del H Vapor Liquid Vap mole Vap mass

C kg/cm2-G MMBtu/h lb/h lb/h frac. frac.

1 82.4 -0.0 8.29E-007 0 0 1.0000 1.0000

2 81.3 -0.0 7.65E-007 0 0 0.9271 0.9410

3 80.2 -0.0 7.04E-007 0 0 0.8562 0.8818

4 79.1 -0.0 6.42E-007 0 0 0.7853 0.8202

5 78.0 -0.0 5.79E-007 0 0 0.7120 0.7538

6 76.9 -0.0 5.13E-007 0 0 0.6337 0.6797

7 75.8 -0.0 4.41E-007 0 0 0.5469 0.5943

8 74.8 -0.0 3.59E-007 0 0 0.4475 0.4925

9 73.7 -0.0 2.63E-007 0 0 0.3296 0.3673

10 72.6 -0.0 1.47E-007 0 0 0.1849 0.2086

11 71.5 -0.0 3.25E-010 0 0 0.0006 0.0007

Column 1 Reboiler



66


1 86.5 -0.0 9.54E-013 0 0 0.3463 0.3961

2 87.2 -0.0 4.06E-008 0 0 0.3862 0.4367

3 87.9 -0.0 8.37E-008 0 0 0.4283 0.4786

4 88.6 -0.0 1.30E-007 0 0 0.4736 0.5226

5 89.3 -0.0 1.80E-007 0 0 0.5227 0.5696

6 89.9 -0.0 2.36E-007 0 0 0.5770 0.6205

7 90.6 -0.0 2.99E-007 0 0 0.6379 0.6768

8 91.3 -0.0 3.70E-007 0 0 0.7074 0.7400

9 92.0 -0.0 4.53E-007 0 0 0.7880 0.8125

10 92.7 -0.0 5.51E-007 0 0 0.8837 0.8975

11 93.4 -0.0 6.70E-007 0 0 0.9998 0.9998

Stream 3



1 32.0 0.1 0.000 0 0 0.0000 0.0000

2 38.1 0.0 4.21E-009 0 0 0.0000 0.0000

3 44.3 0.0 8.45E-009 0 0 0.0000 0.0000

4 50.4 0.0 1.27E-008 0 0 0.0000 0.0000

5 56.6 0.0 1.71E-008 0 0 0.0000 0.0000

6 62.7 0.0 2.14E-008 0 0 0.0000 0.0000

7 68.8 0.0 2.59E-008 0 0 0.0000 0.0000

8 75.0 -0.0 1.60E-007 0 0 0.4119 0.4551

9 81.1 -0.0 3.22E-007 0 0 0.8916 0.9117

10 87.3 -0.0 3.61E-007 0 0 1.0000 1.0000

11 93.4 -0.0 3.64E-007 0 0 1.0000 1.0000

Hot Composite

Temp C DH MMBtu/h

71.497 0.000e+000

71.606 1.647e-008

71.715 3.216e-008

71.823 4.755e-008

CHEMCAD 6.1.3 Page

2


71.932 6.263e-008

72.041 7.741e-008

72.150 9.190e-008

72.258 1.061e-007

72.367 1.200e-007

72.476 1.335e-007

72.584 1.466e-007

72.693 1.591e-007

72.802 1.715e-007

72.911 1.836e-007

73.019 1.955e-007

73.128 2.073e-007

73.237 2.188e-007

73.345 2.301e-007

73.454 2.412e-007


67

73.563 2.521e-007

73.671 2.629e-007

73.780 2.732e-007

73.889 2.833e-007

73.998 2.932e-007

74.106 3.030e-007

74.215 3.127e-007

74.324 3.222e-007

74.432 3.316e-007

74.541 3.408e-007

74.650 3.499e-007

74.759 3.589e-007

74.867 3.674e-007

74.976 3.759e-007

75.085 3.844e-007

75.193 3.927e-007

75.302 4.009e-007

75.411 4.091e-007

75.519 4.171e-007

75.628 4.251e-007

75.737 4.329e-007

75.846 4.407e-007

75.954 4.482e-007

76.063 4.557e-007

76.172 4.631e-007

76.280 4.704e-007

76.389 4.777e-007

76.498 4.849e-007

76.607 4.921e-007

76.715 4.992e-007

76.824 5.062e-007

76.933 5.132e-007

77.041 5.200e-007

77.150 5.267e-007

77.259 5.334e-007

77.367 5.401e-007

77.476 5.468e-007

77.585 5.534e-007

77.694 5.599e-007

77.802 5.665e-007

77.911 5.730e-007

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78.020 5.795e-007

78.128 5.858e-007

78.237 5.921e-007

78.346 5.984e-007

78.454 6.047e-007

78.563 6.110e-007

78.672 6.173e-007

78.781 6.235e-007

78.889 6.298e-007

78.998 6.360e-007


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79.107 6.422e-007

79.215 6.483e-007

79.324 6.544e-007

79.433 6.605e-007

79.542 6.667e-007

79.650 6.728e-007

79.759 6.789e-007

79.868 6.851e-007

79.976 6.912e-007

80.085 6.974e-007

80.194 7.035e-007

80.302 7.096e-007

80.411 7.157e-007

80.520 7.218e-007

80.629 7.279e-007

80.737 7.341e-007

80.846 7.403e-007

80.955 7.465e-007

81.063 7.527e-007

81.172 7.590e-007

81.281 7.653e-007

81.390 7.716e-007

81.498 7.779e-007

81.607 7.842e-007

81.716 7.906e-007

81.824 7.970e-007

81.933 8.034e-007

82.042 8.098e-007

82.150 8.163e-007

82.259 8.227e-007

82.368 8.292e-007

Cold Composite

Temp C DH MMBtu/h

32.000 0.000e+000

32.614 4.189e-010

33.228 8.382e-010

33.842 1.258e-009

34.456 1.678e-009

35.070 2.099e-009

35.685 2.519e-009

36.299 2.941e-009

36.913 3.362e-009

37.527 3.784e-009

38.141 4.207e-009

38.755 4.629e-009

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39.369 5.052e-009

39.983 5.476e-009

40.597 5.900e-009

41.211 6.324e-009

41.825 6.749e-009


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42.439 7.174e-009

43.053 7.599e-009

43.667 8.025e-009

44.282 8.452e-009

44.896 8.878e-009

45.510 9.306e-009

46.124 9.733e-009

46.738 1.016e-008

47.352 1.059e-008

47.966 1.102e-008

48.580 1.145e-008

49.194 1.188e-008

49.808 1.231e-008

50.422 1.274e-008

51.036 1.317e-008

51.650 1.360e-008

52.264 1.403e-008

52.879 1.447e-008

53.493 1.490e-008

54.107 1.533e-008

54.721 1.577e-008

55.335 1.620e-008

55.949 1.663e-008

56.563 1.707e-008

57.177 1.751e-008

57.791 1.794e-008

58.405 1.838e-008

59.019 1.881e-008

59.633 1.925e-008

60.247 1.969e-008

60.861 2.013e-008

61.476 2.057e-008

62.090 2.101e-008

62.704 2.145e-008

63.318 2.189e-008

63.932 2.233e-008

64.546 2.278e-008

65.160 2.322e-008

65.774 2.366e-008

66.388 2.410e-008

67.002 2.455e-008

67.616 2.499e-008

68.230 2.543e-008

68.844 2.596e-008

69.458 3.815e-008

70.073 5.061e-008

70.687 6.334e-008

71.301 7.635e-008

71.915 8.963e-008

72.529 1.032e-007

73.143 1.170e-007

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70

73.757 1.311e-007

74.371 1.455e-007

74.985 1.602e-007

75.599 1.764e-007

76.213 1.925e-007

76.827 2.087e-007

77.441 2.249e-007

78.055 2.410e-007

78.670 2.572e-007

79.284 2.734e-007

79.898 2.895e-007

80.512 3.057e-007

81.126 3.218e-007

81.740 3.257e-007

82.354 3.297e-007

82.968 3.337e-007

83.582 3.376e-007

84.196 3.416e-007

84.810 3.455e-007

85.424 3.495e-007

86.038 3.535e-007

86.652 3.669e-007

87.267 4.072e-007

87.881 4.459e-007

88.495 4.870e-007

89.109 5.314e-007

89.723 5.796e-007

90.337 6.326e-007

90.951 6.921e-007

91.565 7.593e-007

92.179 8.361e-007

92.793 9.266e-007

93.407 1.034e-006


71

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


72

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.


73


74

CONCLUSION AND FUTURE SCOPE OF STUDY

73

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.

REFERENCES

75

CHAPTER 9

REFERENCES:

1. ^ Laurence M. Harwood, Christopher J. Moody (1989). Experimental organic chemistry:

Principles and Practice (Illustrated ed.). Oxford: Blackwell Scientific Publications.

pp. 141143. ISBN 978-0-632-02017-1.

2. ^ a b Forbes, Robert James (1970). A short history of the art of distillation: from the

beginnings up to the death of Cellier Blumenthal. BRILL. pp. 57, 89. ISBN 978-90-04-

00617-1. Retrieved 29 June 2010.

3. ^ Taylor, F. (1945). "The evolution of the still". Annals of Science 5 (3):

185.doi:10.1080/00033794500201451.

4. ^ a b Stephen G. Haw (10 September 2012). "Wine, women and poison". Marco Polo in

China. Routledge. pp. 147148. ISBN 978-1-134-27542-7. "The earliest possible period

REFERENCES

76

seems to be the Eastern Han dynasty... the most likely period for the beginning of true

distillation of spirits for drinking in China is during the Jin and Southern Song dynasties"

5. ^ Sarton, George (1975). Introduction to the history of science. R. E. Krieger Pub. Co.

p. 145. ISBN 0-88275-172-7.

6. ^ Holmyard, Eric John (1990). Alchemy. Courier Dover Publications. p. 53. ISBN 0-486-

26298-7.

7. ^ Magnum Opus Hermetic Sourceworks Series

8. ^ Industrial Engineering Chemistry (1936) page 677

9. ^ Sealing Technique, accessed 16 November 2006.

10. ^ Traditional Alembic Pot Still, accessed 16 November 2006.

11. ^ a b D. F. Othmer (1982) Distillation Some Steps in its Development, in W. F. Furter

(ed) A Century of Chemical Engineering ISBN 0-306-40895-3

12. ^ A. Coffey British Patent 5974, 5 August 1830

13. ^ U.S. Patent 198,699 Improvement in the Ammonia-Soda Manufacture

14. ^ ST07 Separation of liquidliquid mixtures (solutions), DIDAC by IUPAC

15. ^ a b c d Perry, Robert H. and Green, Don W. (1984). Perry's Chemical Engineers'

Handbook (6th ed.). McGraw-Hill. ISBN 0-07-049479-7.

16. ^ Fractional Distillation

17. ^ Spinning Band Distillation at B/R Instrument Corporation (accessed 8 September 2006)

18. ^ Laurence M. Harwood, Christopher J. Moody (1989). Experimental organic chemistry:

Principles and Practice (Illustrated ed.). Wiley, Blackwell. pp. 151153.ISBN 978-0-

632-02017-1.

19. ^ Vogel's 5th ed.

20. ^ Laurence M. Harwood, Christopher J. Moody (13 June 1989). Experimental organic

chemistry: Principles and Practice (of) (Illustrated ed.). Wiley, Blackwell.

p. 150.ISBN 978-0-632-02017-1.

21. ^ Kravchenko, A.I. (2011), "Zone distillation: a new method of refining", Problems of

atomic science and technology (in Russian) (19): 2426 More than one

of |number=and |issue= specified (help)

22. ^ http://124.205.222.100/Jwk_spkx/EN/abstract/abstract15544.shtml

REFERENCES

77

23. ^ Kister, Henry Z. (1992). Distillation Design (1st ed.). McGraw-Hill. ISBN 0-07-

034909-6.

24. ^ Seader, J. D., and Henley, Ernest J. (1998). Separation Process Principles. New York:

Wiley. ISBN 0-471-58626-9.

25. ^ Energy Institute website page

26. ^ Random Packing, Vapor and Liquid Distribution: Liquid and gas distribution in

commercial packed towers, Moore, F., Rukovena, F., Chemical Plants & Processing,

Edition Europe, August 1987, p. 11-15

27. ^ Spiegel, L (2006). "A new method to assess liquid distributor quality". Chemical

Engineering and Processing 45 (11): 1011. doi:10.1016/j.cep.2006.05.003.

28. ^ Kunesh, John G.; Lahm, Lawrence; Yanagi, Takashi (1987). "Commercial scale

experiments that provide insight on packed tow

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