Development of a systematic methodology for the separation of binary azeotropic mixtures · Development of a systematic methodology for the separation of binary azeotropic mixtures

Development of a systematic methodology for the separation of binary azeotropic mixtures

Estelle Sónia Rosa Garanhão

Thesis to obtain the Master of Science Degree in

Chemical Engineering

Supervisors: Prof. Dr.ª Ana Isabel Cerqueira de Sousa Gouveia Carvalho

Prof. Dr. Rafiqul Gani

Examination Committee

Supervisor: Prof. Dr.ª Ana Isabel Cerqueira de Sousa Gouveia Carvalho

Vogal: Prof. Dr. Henrique Aníbal Santos de Matos

President: Prof. Dr.ª Maria Filipa Gomes Ribeiro

July 2015

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Acknowledgments

I would like to thank my supervisors, Prof. Ana Carvalho and Prof. Rafiqul Gani, for giving me the

opportunity to work in this interesting project, and for all the support during the internship and thesis

development.

For all the CAPEC-PROCESS team I want to say thank you for the support and kindness during

my internship. But especially, I would like to thank the PhD students (Seyed, Catarina, Carolina,

Felipe, Dasha and Stefano) and the Master students (Tannaz, Teresa, Mafalda and Maria) who were

like a family to me during the 6 months of internship in Denmark, making me feel at home, in a

foreigner country.

I also want to thank my amazing friends, Thayná, Matias, Diogo Marçal and Raquel for their

support and friendship over the past 6 years.

Finally, I want to thank my parents, brother and my boyfriend, the most important persons in my

life, for their huge support and love given to me for being far from home.

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Abstract

Since it is not feasible to separate azeotropic mixtures through simple distillation, choosing the

right distillation process is a challenge. The extractive distillation is recognized as an appropriate

technique to separate azeotropic mixtures, since it involves the addition of an extractive agent,

solvent, that promotes an effective separation of the azeotropic mixture. The selection of the extractive

agent is the key for an efficient extractive distillation process.

The objective of this dissertation is to present a systematic methodology for the selection of the

most suitable solvent to use in the extractive distillation column, and based on the azeotropic mixture

and solvent, design the separation process.

For a given azeotropic mixture, the selection of the target solute is the first criteria defined in

order to choose solvents with high affinity to the target solute and no affinity at all to the other

compound. The design of the candidate solvents is performed using a computer aided tool called

ProCAMD (Harper and Gani (2000)) and after obtaining the solvents candidates, those are analysed

through a step-by-step procedure in order to select the most suitable solvent for the separation of the

azeotropic mixture. With the solvent selected, the separation process design is made using the

simulator AspenPlus.

The proposed methodology will allow the user to access to a database of extractive distillation

column designs, where the user can collect data, doing only some minor modifications to the designed

process, when required.

The suggested methodology was highlighted through the use of the case study: ethanol-

paraffins.

Keywords: azeotropic mixture, target solute, solvent selection, extractive distillation, ProCAMD.

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Resumo

Uma vez que a separação de misturas azeotrópicas através de destilação simples não é

possível, o desafio passa por escolher o processo de destilação mais adequado. A destilação

extractiva é um processo adequado para a separação de misturas azeotrópicas, uma vez que utiliza

um agente extractivo (solvente) que promove eficazmente a separação do azeótropo. A escolha do

agente extractivo adequado é a chave para uma separação eficiente.

O objectivo da dissertação é apresentar uma metodologia que visa a selecção do solvente mais

adequado para a separação do azeótropo, assim como o dimensionamento do processo para a

separação da mistura azeotrópica utilizando o solvente seleccionado.

Neste trabalho, para uma determinada mistura azeotrópica, a selecção do soluto alvo é o

primeiro passo, de forma a escolher os solventes com maior afinidade com o soluto alvo e mínima ou

nula com o outro composto. A selecção dos solventes candidatos é feita através de ProCAMD

(Harper and Gani (2000)), sendo estes analisados através de um procedimento passo-a-passo que

visa filtrar os solventes até chegar ao solvente mais adequado para separar a mistura em causa.

Finalmente, usando AspenPlus, é dimensionado o processo de separação utilizando o agente de

extracção seleccionado.

No caso de o utilizador pretender estudar um par solvente/soluto que esteja desenvolvido na

base de dados, é possível adaptar os resultados da metodologia desenvolvida neste trabalho para

dimensionar o processo, fazendo pequenas modificações quando necessário.

A metodologia foi aplicada a um caso de estudo, etanol-parafinas.

Palavras-chave: mistura azeotrópica, soluto alvo, selecção de solvente, ProCAMD.

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Table of Contents

ABSTRACT ................................................................................................................................. IV

RESUMO ..................................................................................................................................... V

TABLE OF CONTENTS ............................................................................................................... VI

LIST OF FIGURES...................................................................................................................... VIII

LIST OF TABLES.......................................................................................................................... XI

NOMENCLATURE ......................................................................................................................XIV

1. INTRODUCTION ...................................................................................................................1

1.1. CONTEXT........................................................................................................................1

1.2. OBJECTIVES ....................................................................................................................2

1.3. STRUCTURE OF THE THESIS ...............................................................................................2

2.1. AZEOTROPIC MIXTURES.....................................................................................................3

2.1.1. Vapor-liquid phase equilibrium phenomenon............................................................3

2.1.2. Nonideality and Separation by distillation .................................................................4

2.1.3. Azeotropic Mixtures ................................................................................................5

2.1.3.1. Positive Deviation from Raoult’s Law................................................................................................ 5

2.1.3.2. Negative Deviation from Raoult’s Law .............................................................................................. 6

2.2. AZEOTROPIC SEPARATION TECHNIQUES ...............................................................................7

2.2.2. Azeotropic Distillation Process ................................................................................9

2.2.3. Extractive Distillation ............................................................................................ 10

2.2.3.1. Types of entrainers used in extractive distillation ......................................................................... 11

2.2.4. Conclusions ......................................................................................................... 14

2.2.5. Extractive distillation with liquid entrainers ............................................................. 14

2.2.5.1. Approach to solvent selection .......................................................................................................... 15

2.2.5.2. Conclusions ......................................................................................................................................... 17

2.3. DISTILLATION COLUMNS DESIGN ....................................................................................... 18

2.3.1. Driving force method ............................................................................................ 18

2.3.2. Sensitivity Analysis............................................................................................... 20

2.3.3. Conclusions ......................................................................................................... 21

2.4. COMPUTATIONAL TOOLS.................................................................................................. 21

2.4.1. ICAS ................................................................................................................... 22

2.4.2. AzeoPro .............................................................................................................. 22

2.4.3. ProCAMD ............................................................................................................ 22

2.4.4. ProPed ................................................................................................................ 22

2.4.5. PDS .................................................................................................................... 23

2.5. CONCLUSIONS ............................................................................................................... 23

vii

3. METHODOLOGY................................................................................................................. 24

3.1. METHODOLOGY OVERVIEW .............................................................................................. 24

3.2. CONCLUSIONS ............................................................................................................... 42

4. APPLICATION OF THE PROPOSED METHODOLOGY TO THE CASE STUDY: ETHANOL-

PARAFFINS ................................................................................................................................ 43

4.1. CASE STUDY DESCRIPTION .............................................................................................. 43

4.2. ETHANOL-N-PENTANE ..................................................................................................... 43

4.3. ETHANOL-N-HEXANE....................................................................................................... 57

4.4. ETHANOL-N-HEPTANE ..................................................................................................... 64

4.5. ETHANOL-N-OCTANE AND ETHANOL-N-NONANE ................................................................... 67

4.6. CONCLUSIONS ABOUT THE SELECTION OF THE TARGET SOLUTE AND ITS EFFECT IN THE SEPARATION

OF AZEOTROPIC MIXTURES. .............................................................................................................. 73

4.7. CONCLUSIONS ............................................................................................................... 75

5. CONCLUSIONS AND FUTURE WORK................................................................................. 79

REFERENCES ............................................................................................................................ 81

APPENDIXES.............................................................................................................................. 85

APPENDIX 1 – DRIVING-FORCE TABLE ............................................................................................... 85

APPENDIX 2 – WORK-FLOW DIAGRAM ................................................................................................ 86

APPENDIX 3 – DATA OBTAINED FROM PROCAMD................................................................................ 87

A. Ethanol-n-pentane ....................................................................................................... 87

B. Ethanol-n-hexane......................................................................................................... 89

C. Ethanol-n-heptane ....................................................................................................... 90

D. Ethanol-n-octane ......................................................................................................... 91

E. Ethanol-n-nonane ........................................................................................................ 92

APPENDIX 4 – DATA OBTAINED FROM PROCAMD ............................................................................... 92

APPENDIX 5 – DATA OBTAINED FROM DSTWU ................................................................................... 93

A. Ethanol-n-pentane-neopentyl glycol .............................................................................. 93

APPENDIX 6 – DATA OBTAINED FROM STEP 2.2.B. –SELECTION FROM SOLVENT POWER VS. HILDEBRAND

SOLUBILITY PARAMETER PLOT; ......................................................................................................... 93

A. Ethanol-n-heptane ....................................................................................................... 93

A. Ethanol-n-heptane ....................................................................................................... 94

APPENDIX 8 – FLOWSHEET OF EXTRACTIVE DISTILLATION PROCESS. ....................................................... 95

APPENDIX 9 – STREAM TABLE RESULTS. ............................................................................................ 95

APPENDIX 9 – INFORMATION INTRODUCED IN PROCAMD. .................................................................... 95

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List of Figures

Figure 1 - Vapor vs. liquid mole fractions at 1 atm for the system ethanol-n-hexane (ICAS). ................4

Figure 2 – Temperature-composition phase diagram showing a positive deviation from Raoult’s law

(Reger et al., 2010). .......................................................................................................................5

Figure 3 – Temperature-composition phase diagram for a Nonideal solution showing a negative

deviations from Raoult’s law (Reger et al., 2010). .............................................................................6

Figure 4 - Schematic diagram of various techniques for the separation of azeotropic mixtures (Mahdi

et al., 2015). ..................................................................................................................................8

Figure 5 – Schematic diagram for pressure-swing distillation: (a)T-x diagram for a minimum-boiling

binary azeotrope sensitive to changes in pressure; (b) Pressure -swing distillation column sequence. ..8

Figure 6 - Schematic diagram of an azeotropic distillation, where A and B are light and heavy

components of the feed mixture, respectively, S is the solvent component; a) homogeneous process

and b) heterogeneous process (Mahdi et al, 2014). ........................................................................ 10

Figure 7 - Schematic diagram of an extractive distillation double column process where A and B are

light and heavy components of the feed mixture, respectively; S is a solvent c omponent (Lei et al.,

2005). ......................................................................................................................................... 11

Figure 8 - Scheme of a single column process with salt: 1 - feed stream, 2 - extractive distillation

column, 3 - equipment for salt recovery, 4 - bottom product, 5 - the salt recovered, 6 - reflux tank, and

7 - overhead product (Lei et al, 2005) ............................................................................................ 12

Figure 9 – Extractive distillation using ionic liquid as non-volatile entrainer (A: main column, B: flash

drum, C: Stripping column) (Seiler et al., 2004). ............................................................................. 12

Figure 10 – x-y-VLE plot of the binary mixture etanol-n-hexane where i tis confirmed that etanol is the

target solute (Peng-noo et al, 2015). ............................................................................................. 16

Figure 11 - Driving force diagram for constant relative volatility (zeotropic mixtures) (Bek-Pedersen

and Gani, 2004). .......................................................................................................................... 19

Figure 12 - Conditions of distillation column feed and products that require a scaling factor to be

included in the design procedure (Bek-Pedersen and Gani, 2000). .................................................. 20

Figure 13 – Effect of solvent flowrate on the distillate and bottom composition using sensitivity analysis

(Figueirêdo et al, 2010). ............................................................................................................... 21

Figure 14 – Starting window in ProPed. ......................................................................................... 23

Figure 15 - Overview of the proposed methodology for the separation of azeotropic mixtures using

extractive distillation ..................................................................................................................... 25

Figure 16 - Tasks to follow in Step 1.1. - Mixture selection. ............................................................. 26

Figure 17 - Compound selection screen of AzeoPro – Selection of compound 1 (orange rectangle);

selection of compound 2 (red rectangle) and selection of the pressure. ........................................... 27

Figure 18 - Tasks follow in Step 1.2. - Selection of the target solute................................................. 28

Figure 19 - Tasks to follow in step 2.1. - Solvent screening. ............................................................ 29

Figure 20 – Selection of solvents regarding solvent power (blue) vs. Selectivity (green). ................... 31

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Figure 21 - Selection of solvents for a generic mixture of compound A and compound B where

compound B is the target solute. ................................................................................................... 32

Figure 22 - Selection of solvents for a generic mixture of component A and component B where

component B is the target solute. .................................................................................................. 34

Figure 23 – Translation of the circle created in turn of the target solute, for the generic mixture of

component A and component B where component B is the target solute. ........................................ 34

Figure 24 - Tasks to follow in Task 2.2.D. – Solvent to Feed (S/F) ratio............................................ 35

Figure 25 - VLE plot of a generic mixture of component 1 and component 2, when S/F ratio is fixed for

the three solvents: solvent A, solvent B and solvent C, when the solvents present different curves. ... 36

Figure 26 - VLE plot of a generic mixture of component 1 and component 2, when S/F ratio is fixed for

three solvents: solvent A, solvent B and solvent C and the solvent curves present the same behaviour.

................................................................................................................................................... 36

Figure 27 –Sketch of the extractive distillation process (Luo, H. et al., 2014). ................................... 37

Figure 28 - Generic flowsheet of the process simulation in AspenPlus. ............................................ 39

Figure 29 - Tasks to follow in step 3.1. – Simulation & Sensitivity analysis. ...................................... 39

Figure 30 - Driving force diagram of mixture A (red line) and mixture B (blue l ine). ........................... 41

Figure 31 – VLE screen showing two different VLE charts: (a) x-y VLE plot; (b) T-x-y VLE plot

(AzeoPro). ................................................................................................................................... 44

Figure 32 - Selection of solvents regarding task 2.2.A. Selection from solvent power vs. selectivity. .. 46

Figure 33 – The solvents obtained as output data of task 2.2.A. ...................................................... 46

Figure 34 - Selection of solvents for the mixture components ethanol-n-pentane, where ethanol is the

target solute, regarding task 2.2.B. ................................................................................................ 47

Figure 35 - The plot of 𝛿𝐷 𝑣𝑠 𝛿𝐻 for the solvents obtained in task 2.2.B. and of the mixture

components (ethanol and n-pentane). ........................................................................................... 49

Figure 36 - The plot of 𝛿𝑃 𝑣𝑠 𝛿𝐻 for the solvents obtained in task 2.2.B. and of the mixture

components (ethanol and n-pentane). ........................................................................................... 49

Figure 37 - The plot of 𝛿𝐷 𝑣𝑠 𝛿𝑃 for the solvents obtained in task 2.2.B. and for the mixture

components (ethanol and n-pentane) when the circle has his centre in ethanol with a diameter equal

than 𝛿𝑃 = 5𝑀𝑃𝑎12 (a); and when the circle has his centre in ethanol with a diameter equal than

𝛿𝑃 = 3 𝑀𝑃𝑎12 (b). ........................................................................................................................ 50

Figure 38 - VLE plot of ethanol-n-pentane (blue line); VLE plot of ethanol-n-pentane with the solvent

hexylene glycol (HG) with S/F ratio equal than 0,2 (green line); VLE plot of ethanol-n-pentane with the

solvent hexylene glycol (HG) with S/F ratio equal than 0,3 (purple line)............................................ 51

Figure 39 - VLE plot of ethanol-n-pentane (blue line); VLE plot of ethanol-n-pentane with the solvent

neopentyl glycol (NG) with S/F ratio equal than 0,2 (green line); VLE plot of the ethanol-n-pentane with

the solvent neopentyl glycol (NG) with S/F ratio equal than 0,3 (purple line). .................................... 51

Figure 40 – VLE plot of the system ethanol-n-pentane with a fixed value of S/F ratio equal than 0,2 for

hexylene glycol (HG) and neopentyl glycol (NG)............................................................................. 51

Figure 41 – Diagram that represents the number of solvents selected in each task of the solvent

analysis step, for the separation of ethanol-n-pentane. ................................................................... 52

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Figure 42 – Process flow diagram for the extractive distillation column with the parameters to design.

................................................................................................................................................... 53

Figure 43 – Information about the streams of the extractive distillation column (EDC). ...................... 54

Figure 44 – Influence of number of stages (N) and reflux ratio (RR) on molar purity of n-pentane at the

top of the extractive distillation column. .......................................................................................... 55

Figure 45 – Output streams results obtained when introduced the new design variables: N=7 and

RR=0,3........................................................................................................................................ 56

Figure 46 – Driving force diagram for the system ethanol-n-pentane (blue) and ethanol-n-hexane (red)

at 101,32kPa (ICAS). ................................................................................................................... 60

Figure 47 – Effect of solvent mole flowrate on the distillate (a) and bottom composition (b) of n-

hexane. ....................................................................................................................................... 61

Figure 48 - Behaviour of solvent flowrate according to the carbon number when Nstage equal than 12

(a); Effect on the Nstage when the carbon number increase with solvent flowrate equal than 30 kmol/h

(b). .............................................................................................................................................. 64

Figure 49 - Diagram that represents the number of solvents selected in each task of the solvent

analysis step, for the separation of ethanol-n-heptane. ................................................................... 66

Figure 50 - Driving force diagram for the system ethanol-n-heptane (blue), ethanol-n-octane (red) and

ethanol-n-nonane at 101,32kPa (ICAS ). ........................................................................................ 70

Figure 51 - Behaviour of solvent flowrate according to the carbon number when Nstage is fixed and

equal than 30 (a); Effect on the Nstage when the carbon number increase with solvent flowrate fixed

and equal than 60 kmol/h (b). ....................................................................................................... 73

Figure 52 - Composition of ethanol in the azeotrope according to the carbon number of the paraffin

(a); Boiling point of ethanol and the paraffins (b)............................................................................. 74

Figure 53 – Corresponding values of reflux ratio, minimum reflux ratio, number of stages, product

purities and driving force (Bek-Pedersen and Gani, 2004). .............................................................. 85

Figure 54 - Work-flow diagram of the proposed methodology. ......................................................... 86

Figure 55 – A solvent candidate obtained for the separation of ethanol-n-pentane, given by ProCAMD

after the generation of the solvents................................................................................................ 92

Figure 56 - Variables introduced in the DSTWU (a); Stream results obtained from the simulation of the

DSTWU with the variables introduced (b) ...................................................................................... 93

Figure 57 - Selection of solvents regarding the Hildebrand solubility parameter and solvent power, for

the system: ethanol-n-heptane when n-heptane is the target solute. ................................................ 93

Figure 58 – HSP δH vs δP of the solvents and the solutes. .............................................................. 94

Figure 59 - HSP δH vs δD of the solvents and the solutes................................................................ 94

Figure 60 - HSP δP vs δD of the solvents and the solutes. ............................................................... 94

Figure 61 - Proposed extractive distillation separation process of ethanol-n-pentane using neopentyl

glycol as the best solvent. ............................................................................................................. 95

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List of Tables

Table 1 - Examples of the different types of binary azeotropes (Gmehling et al., 2004). ......................6

Table 2 - Examples of homogenous minimum boiling azeotropes. Compound 1 forms an azeotrope

with several compound 2 that belong to the same functional group: paraffins (Gmehling et al., 2004). .7

Table 3 - The mixture in study: Methanol-n-hexane when the target solute is methanol using solvent A,

for the separation process. ........................................................................................................... 41

Table 4 - The mixture in study: Methanol-n-octane when the target solute is n-octane ...................... 41

Table 5 – Temperature and composition of the binary azeotrope: ethanol-n-pentane (AzeoPro). ....... 44

Table 6 – Boiling point of ethanol, and n-pentane obtained from ProPed. ......................................... 44

Table 7 – Input information introduced in ProCAMD. ...................................................................... 45

Table 8 – Information about the values of HSP of the solvents obtained in task 2.2.B. ...................... 48

Table 9 – information about the values of HSP of ethanol and n -pentane. ........................................ 48

Table 10 - Results obtained from PDS for the extractive distillation preliminary design. ..................... 53

Table 11 – Extractive distillation column (EDC) pre-design parameters. ........................................... 54

Table 12 – Extractive distillation column design variables obtained through the rigorous simulation. .. 56

Table 13 –Stream results obtained from the rigorous simulation for EDC. ........................................ 56

Table 14 – Recovery column design variables obtained through rigorous simulations. ...................... 57

Table 15 – Stream results obtained through the rigorous simulations (Recovery Column). ................ 57

Table 16 – Temperature and composition of the binary azeotrope: ethanol -n-hexane (AzeoPro). ...... 58

Table 17 – Boiling point of ethanol, and n-hexane obtained from ProPed. ........................................ 58

Table 18 – Input information introduced in ProCAMD. .................................................................... 58

Table 19 – Extractive distillation column design variables obtained through rigorous simulations for

ethanol-n-hexane using Neopentyl glycol....................................................................................... 60

Table 20 – Design variables obtained for the azeotropes: ethanol-n-pentane and ethanol-n-hexane

when the number of stages of the EDC is fixed and equal to N=12 using neopentyl glycol. ............... 62

Table 21 – Recovery column design variables obtained for the azeotropes: ethanol-n-pentane and

ethanol-n-hexane when the number of stages of the EDC is fixed and equal to N=12. ...................... 62

Table 22 - Stream results obtained for the separation of ethanol-n-pentane for the design variables

obtained for the extractive distillation column and the stream results obtained for the recovery column.

................................................................................................................................................... 62

Table 23 – Design variables obtained for the EDC when the azeotropes to be separated are: ethanol-

n-pentane and ethanol-n-hexane using solvent flowrate equal than 30kmol/h. .................................. 63

Table 24 – Summary table of the stream results of EDC obtained from the rigorous simulation using

the design variables obtained in Table 25 for the system: ethanol-n-pentane-NG (a) and ethanol-n-

hexane-NG (b). ............................................................................................................................ 63

Table 25 – Temperature and composition of the binary azeotrope: ethanol -n-heptane (AzeoPro). ..... 64

Table 26 – Boiling point of ethanol, and n-hexane obtained from ProPed. ........................................ 65

Table 27 – Input information introduced in ProCAMD. .................................................................... 65

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Table 28 – Extractive distillation column and recovery column design. ............................................. 67

Table 29 – Stream results obtained for the separation of ethanol -n-heptane using neopentyl glycol... 67

Table 30 – Temperature and composition of the binary azeotropes: ethanol-n-octane and ethanol-n-

nonane (AzeoPro). ....................................................................................................................... 68

Table 31 – Boiling point of ethanol, and n-octane and n-nonane obtained from ProPed. ................... 68

Table 32 – Input information introduced in ProCAMD for ethanol-n-octane. ...................................... 69

Table 33 - Design variables obtained for the azeotropes: ethanol-n-octane, ethanol-nonane and

ethanol-n-heptane (database) when the number of stages of the extractive distillation column is fixed

and equal than N=30 using di-n-pentyl-ether. ................................................................................. 70

Table 34 – Recovery column design variables obtained for the azeotropes: ethanol-n-octane, ethanol-

nonane and ethanol-n-heptane (database) when the number of stages of the extractive distillation

column is fixed and equal than N=30 using di-n-pentyl-ether. .......................................................... 71

Table 35 - Summary table of the stream results of extractive distillation column obtained from the

rigorous simulation using the design variables obtained in Table 36 for the system: ethanol-n-heptane-

di-n-pentyl-ether (a); ethanol-n-octane-di-n-pentyl-ether (b) and ethanol-n-nonane-di-n-pentyl-ether

(c). .............................................................................................................................................. 71

Table 36 - Summary table of the stream results of recovery column obtained from the rigorous

simulation using the design variables obtained in Table 37 for the system: ethanol -n-heptane-di-n-

pentyl-ether (a); ethanol-n-octane-di-n-pentyl-ether (b) and ethanol-n-nonane-di-n-pentyl-ether (c). .. 72

Table 37 - Design variables obtained for the extractive distillation column when the azeotropes to be

separated are: ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane using di-n-pentyl-ether with

a solvent flowrate equal than 60kmol/h. ......................................................................................... 72

Table 38 – Design variables of the extractive distillation column in order to obtain a molar product

purity in the distillate of 99,5%. ...................................................................................................... 74

Table 39 – Summary table with the information about the process design variables for the separation

of the azeotropic mixtures of the case study................................................................................... 75

Table 40 - Extractive distillation and recovery column design variables obtained for the azeotropes:

ethanol-n-pentane and ethanol-n-hexane when the number of stages of the extractive distillation

column is fixed and equal than N=12 using neopentyl glycol. .......................................................... 76

Table 41 – Extractive distillation and recovery column design variables obtained for the azeotropes:

ethanol-n-octane, ethanol-nonane and ethanol-n-heptane when the number of stages of the extractive

distillation column is fixed and equal than N=30 using di -n-pentyl-ether. .......................................... 77

Table 42 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-pentane (target

solute: ethanol). ........................................................................................................................... 87

Table 43 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-pentane (target

solute: ethanol) (Continued). ......................................................................................................... 88

Table 44 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-hexane (target

solute: ethanol). ........................................................................................................................... 89

Table 45 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-heptane (target

solute: n-heptane). ....................................................................................................................... 90

xiii


solute: n-octane). ......................................................................................................................... 91


solute: n-nonane). ........................................................................................................................ 92

Table 48 – Stream results obtained for the separation of ethanol -n-pentane using neopentyl glycol... 95

Table 49 - Input information introduced in ProCAMD for ethanol-n-nonane. ..................................... 95

xiv

Nomenclature

Abbreviations

ASOG Analytical Solution Of Groups

CAMD Computer Aided Molecular Design

DF Driving Force

EDC Extractive distillation column

HSP Hansen Solubility Parameter

ICAS Integrated Computer Aided System

N Number of stages

NC Number of carbons

NRTL Non Random Two Liquids

PDS Process Design Studio

RR Reflux ratio

RC Recovery column

S/F Solvent-to-feed

UNIFAC Universal Functional Activity Coefficient

UNIQUAC Universal Quasi-Chemical Activity Coefficient

VLE Vapor-Liquid-Equilibrium

List of symbols

𝐶 Capacity

𝑔 Molar Gibbs energy

P Pressure

𝑃𝑖𝑠𝑎𝑡 Vapor pressure of component i

R Universal gas constant

𝑆 Selectivity

𝑆𝑃 Solvent Power

T Temperature

𝑣 Molar volume

𝑥 Liquid molar fraction

𝑦 Vapor molar fraction

xv

Greek Symbols

𝛼𝑖𝑗 Relative volatility of compound 𝑖 to compound 𝑗

𝛾𝑖 Activity coefficient of compound 𝑖

𝛾𝑖∞ Activity coefficient of compound 𝑖 at infinite dilution

∆𝐻𝑣𝑎𝑝 Enthalpy of vaporization

𝛿 Solubility Parameter

𝛿𝑇 Hildebrand Solubility parameter

Subscripts/Superscripts

𝐷 Dispersive interaction

𝐸 Excess

𝐹 Feed

𝐻𝐾 Heavy key

𝐻 Hydrogen bonding interaction

𝑖 Component i

𝑗 Component j

𝐿𝐾 Light key

𝑀𝐴𝑋 Maximum

𝑀𝑖𝑛 Minimum

𝑃 polar interaction

𝑆𝑎𝑡 Saturation

𝑉𝑎𝑝 Vaporization

∞ Infinite

𝐴𝑍 Azeotrope

1

1. Introduction

1.1. Context

Azeotropic mixtures are widely present in the chemical, petrochemical and pharmaceutical

industry (Yuan et al, 2013), and are a challenging problem in terms of separation process, as the

compositions of the vapor and liquid phase at the azeotropic point are identical, these mixtures

cannot be separated by using a conventional distillation (Figueirêdo et al, 2010). In order to

outline the situation, several enhanced distillation-based separation are used for azeotropic

separation, namely, azeotropic distillation, pressure-swing distillation and extractive distillation.

The application of the extractive distillation for the separation of azeotropic mixtures has been

widely used at industrial scale (Gutiérrez et al., 2015).

The design of extractive distillation is more complex compared with the conventional

distillation schemes, and additional degrees of freedom must be considered during the design

stage, such as the solvent flowrate and the solvent feed location. These design parameters can

be obtained through any conventional simulation approach. However, the selection of a solvent

to be used is still another challenging issue which further makes the design more difficult. The

effectiveness of an extractive distillation process relies on the choice of the extractive agent, so,

the search of the most suitable solvent required in the separation of azeotropic mixture is a very

demanding task. Several criteria have been considered such as, the selectivity, the boiling point

of the solvent, the solvent must not form additional azeotropes with the components to be

separated, and environmental concerns. Regarding the several criteria to take into account for

the evaluation of the solvent performance, it can be affirmed that if this procedure is carried out

manually, a very time-consuming is expected. To effectively select the best solvent with the

desired properties, all possible solvents, including non-existing compounds must be preliminary

screened in a systematic way. The solvent candidates can be generated systematically through

Computer Aided Molecular Design (ProCAMD, Harper and Gani (2000)), which is an effective

approach under continuing development for the design of solvent candidates. This tool is very

useful for the screening of solvents; however the selection of the most suitable solvent must be

made through rigorous analysis apart from ProCAMD (Harper and Gani (2000)).

In terms of process design, several methods for the estimation of the design parameters are

proposed, such as: graphical (McCabe et al., 1925), empirical methods (Anderson et al, 1984),

driving force based methods (Pedersen et al, 2000). The driving force method presents a

framework based on thermodynamic insights that relies on chemical/physical properties of the

mixture. This method showed to be very effective (Pedersen et al., 2000), since it can predict the

near optimal solutions to separation design, only with the VLE data of the azeotropic mixture to

be separate, making this method a very appreciate tool for since it can be applied in a fast and

reliable way. After obtained the pre-design of the parameters, the separation process design

2

must be verified through rigorous simulation. Any process simulator can be used for the rigorous

simulations; however AspenPlus was the simulator used.

1.2. Objectives

The aim of this project was to develop a systematic methodology for the separation of azeotropic

mixtures using extractive distillation, in order to guide the user through a step-by-step procedure for

the selection of the most suitable solvent, and to design the separation process of an extractive

distillation column.

The secondo main objective was to validate the proposed methodology applying it to the case

study ethanol-n-pentane, ethanol-n-hexane, ethanol-n-heptane, ethanol-n-octane, and ethanol-n-

nonane (ethanol-paraffins - homologous series).

As the azeotropic mixtures studied in this case study, belong to a homologous series, when the

target solute is ethanol, it is expected to use the same solvent for those cases. And the same behavior

should happen, for the case where the target solute are the paraffins the same solvent can be used for

the separation of those azeotropic mixtures.

1.3. Structure of the thesis

This dissertation is divided into five chapters:

Chapter 1 includes a brief introduction about what is going to be done in the thesis and

the objectives to achieve during the work.

Chapter 2 gives an overview of the theoretical background related to the proposed

methodology. This includes an overview of the known distillation techniques applied for

the separation of azeotropic mixture, the most adequate solvent to use in terms of

solvent-based distillation processes, and a brief description about the tools used over the

thesis is presented.

Chapter 3 shows the proposed methodology, and a detailed explanation is made.

Chapter 4 presents the case study application to illustrate the performance to the

methodology.

Chapter 5 presents conclusions and directions for future work.

3

2. State of the art

This chapter covers the theoretical background of the thesis. In Section 2.1 the main concepts

related to azeotropic mixtures are presented. Section 2.2., presents the literature review of some of

the techniques applied for the separation of azeotropic mixtures, but giving more attention to the

technique used in this dissertation for the separation of the azeotropic series: extractive distillation.

Section 2.3., reviews the distillation column design techniques. Finally, the main conclusions are

presented (Section 2.4.).

2.1. Azeotropic mixtures

The term azeotrope means “to boil unchanged” (Doherty, M.F. et al, 2004) and denotes a mixture

of two or more components where the liquid and vapor are in equilibrium and the compositions are

identical at a given pressure and temperature (Hilmen, 2000). Since azeotropes boil at a constant

temperature, sometimes they can be compared to single components, however for azeotropes a

difference in pressure, can change not only the boiling temperature, but also the composition of the

azeotropic mixture (Hilmen, 2000).

The term azeotropy was introduced (Wade and Merriman, (1911)) to describe mixtures

characterized by a minimum or a maximum in the vapor pressure under constant temperature

conditions, or, equivalently, with a maximum or minimum point in the boiling temperature at constant

pressure (Swietoslawski,1963; Malesinkski, 1965). A mixture which composition corresponds to an

extremal point is designated by azeotrope. If at the equilibrium temperature the liquid mixture is

homogeneous, the azeotrope is a homoazeotrope. If the vapor phase coexists with two liquid phases,

it is a heteroazeotrope. Systems which do not form azeotropes are named zeotropic (Swietoslawski,

1963).

Summarizing, at the azeotropic point, for homogenous systems the mole fractions in the liquid

phase are identical with the mole fractions in the vapour phase. This feature makes azeotropes

problematic mixtures to separate; and the separation by simple distillation is not possible (Figueirêdo,

et al., 2010).

2.1.1. Vapor-liquid phase equilibrium phenomenon

At low to moderate pressure ranges, the fundamental composition relationship between the

vapor and liquid phases in equilibrium can be expressed as a function of the total system pressure,

the vapor pressure of each pure component, and the liquid-phase activity coefficient of each

component, 𝑖, in the mixture is expressed by equation (1) (Doherty et al., 2002):

4

𝑦𝑖 𝑃 = 𝑥 𝑖𝛾𝑖𝑃𝑖𝑠𝑎𝑡 , 𝑖 = 1,2, … , 𝑛 (1)

where 𝑦𝑖 and 𝑥 𝑖 , are the vapor and liquid compositions of component 𝑖, respectively, 𝛾𝑖 is the

activity coefficient of component 𝑖 in the liquid phase, 𝑃 is the system pressure, and 𝑃𝑖𝑠𝑎𝑡 is the vapor

pressure of component 𝑖. Since by definition, the activity coefficient, 𝛾𝑖 is a measure of the deviation

from the ideality of a solution, when 𝛾𝑖 = 1, the mixture is ideal and equation (1) simplifies to Raoult’s

law (equation 2) (Doherty et al.,2002):

𝑦𝑖 𝑃 = 𝑥 𝑖𝑃𝑖𝑠𝑎𝑡 , 𝑖 = 1,2, … , 𝑛 (2)

At azeotropic points, a single liquid phase is in equilibrium with the vapour phase 𝑥 = 𝑦, as can

be observed in Figure 1.

Figure 1 - Vapor vs. liquid mole fractions at 1 atm for the system ethanol-n-hexane (ICAS).

2.1.2. Nonideality and Separation by distillation

The relative volatility of the key components 𝑖 and 𝑗, in a given mixture with ideal vapor phase is

given by Equation 3 (Kossack et al., 2008):

𝛼𝑖 ,𝑗 =𝑦𝑖 𝑥𝑖⁄

𝑦𝑗 𝑥𝑗⁄=

𝛾𝑖 𝑃𝑖𝑠𝑎𝑡

𝛾𝑗 𝑃𝑗𝑠𝑎𝑡 (3)

where 𝑥 and 𝑦 are the molar fractions in the liquid and vapor fraction, respect ively, 𝛾𝑖 is the

activity coefficient and 𝑝𝑠𝑎𝑡 it the vapor pressure. This parameter is a measure of the degree of

enrichment, or ease of separation, since the more 𝛼𝑖 ,𝑗 deviates from unity, the easier it is to separate

component 𝑖 from component 𝑗 (Abildskov et al., 2015).

For azeotropic mixtures, at the azeotropic point, the relative volatility of equals one (𝛼𝑖 ,𝑗 = 1),

meaning that those azeotropes can never be separated into pure components by ordinary distillation.

Typically, conventional distillation becomes uneconomical when 0,95 < 𝛼𝑖,𝑗 < 1,05, since a high reflux

ratio and a high number of stages are required (Van Winkle et al., 1967).

5

Following Equation 3, azeotropic behaviour will always occur in homogenous binary systems

when the vapour pressure ratio 𝑃𝑖𝑠𝑎𝑡 𝑃𝑗

𝑠𝑎𝑡⁄ is equal to the ratio of the activity coefficients 𝛾𝑗 𝛾𝑖⁄

(Gmehling et al., 2001).

Various thermodynamic methods based on 𝑔𝐸 − 𝑚𝑜𝑑𝑒𝑙𝑠 (Wilson, NRTL, UNIQUAC) or group

contribution methods (UNIFAC, modified UNIFAC, ASOG) can be used for either calculating or

predicting the required activity coefficients for the components under given conditions of temperature

and composition (Gmehling et al., 1992).

2.1.3. Azeotropic Mixtures

Azeotropes are formed due to differences in intermolecular forces of attraction among the mixture

components (hydrogen bounding and others). Considering two-component mixture of compounds A

and B the following statements are presented (Reger et al., 2010):

Positive deviation from Raoult’s law occurs when the A-B interactions are weaker

than the interactions between identical molecules. This may cause the formation of a minimum

boiling azeotrope.

Negative deviation from Raoult’s law occurs when the intermolecular forces between

A-B are stronger than the interaction between identical molecules. This may cause the

formation of a maximum-boiling azeotrope.

2.1.3.1. Positive Deviation from Raoult’s Law

Azeotropes showing positive deviations from Raoult’s law - that is, maxima in P- are more

common than those exhibiting negative deviations.

We can also see azeotropic behaviour on Txy phase diagrams at constant pressure observing

the system represented in Figure 2. A system that exhibits a maximum in pressure (positive deviations

from Raoult’s law) will exhibit a minimum in temperature (see Figure 2). These are termed minimum

boiling azeotrope.

Figure 2 – Temperature-composition phase diagram showing a positive deviation from Raoult’s law (Reger et al.,

2010).

6

2.1.3.2. Negative Deviation from Raoult’s Law

Systems which exhibit negative deviations from Raoult’s law, 𝛾𝑖 < 1, occur when the unlike

intermolecular interactions are more attractive than the like interactions of the pure species. The Px

and Py curves exhibit minima at exactly the same composition, and consequently the azeotrope point

has a higher boiling point than the pure components (see Figure 3). These are termed the maximum-

boiling azeotropes.

Figure 3 – Temperature-composition phase diagram for a Nonideal solution showing a negative deviations from

Raoult’s law (Reger et al., 2010).

If only one liquid phase exists, the mixture forms a homogenous azeotrope; if more than one

liquid phase exists, the azeotrope is heterogeneous (Seader et al., 2005).Another type of azeotrope is

the double azeotrope which has two azeotropic points (Gmehling et al., 2004)

Because of the importance of azeotropic data for the design of distillation processes,

compilations have been available in book form for quite some time (Horsley et al.,1973) . The most

recent data collection was published in 1994 (Gmehling et al., 1994); and a revised and extended

version appeared in 2004 (Gmehling et al., 2004).

From this collection, a few examples of the different azeotropic systems are given in Table 1.

Table 1 - Examples of the different types of binary azeotropes (Gmehling et al., 2004).

Type of azeotrope System

Homogenous minimum-boiling azeotrope

Acetone – methanol

Water – Acetonitrile

2-Methyl-2-propanol – Cyclohexene

Heterogeneous minimum-boiling azeotrope Water – 1-Bromopropane

N-Butyl-n-butyrate – Ethylene glycol

Homogenous maximum-boiling azeotrope Acetone – Chloroform

Water – Formic acid

Homogenous minimum-boiling azeotrope can also belong to a homologous series, where

component 1 is fixed and the second component of the azeotropic mixture belongs to the same

functional group. An homologous series is a series of compounds with the same general formula,

usually varying by a single parameter – such as the increasing of the carbon number (Gourley et al,

1964):.

7

The compounds of a homologous series:

Have the same general formula ( paraffins - 𝐶𝑛𝐻2𝑛+2);

If neighbors differ by one 𝐶𝐻2 group (e.g. methane (𝐶𝐻4) and ethane (𝐶2𝐻6);

Have similar chemical properties;

Gradually changing physical properties (e.g. the boiling point of paraffins increases

with the number of carbons).

A few examples are given in Table 2 (Gmehling et al., 2004).

Table 2 - Examples of homogenous minimum boiling azeotropes. Compound 1 forms an azeotrope with several

compound 2 that belong to the same functional group: paraffins (Gmehling et al., 2004).

Component 1 Component 2 (Group) Component 2

Acetic Acid Paraffins n-Hexane; n-Heptane; n-Octane; n-Nonane; n-

Decane; n- Undecane

Methanol Paraffins n-Butane; n-Pentane; n-Hexane; n-Heptane; n-

Octane; n- Nonane

2.2. Azeotropic separation techniques

Separation of azeotropic mixtures is a challenging task in various petrochemical and/or

biochemical processes. As presented previously, an azeotrope can be either homogeneous, or

heterogeneous. Approximately 90% of all azeotropic mixtures are homogenous (Lide et al., 2000).

In the history of chemical separation processes, conventional distillation has been applied to

more commercial processes than all other techniques combined (Song et al., 2008). This well-known

operation takes advantage of the difference in boiling points of chemical compounds, and it is suitable

for separating a variety of mixtures. However, not all liquid mixtures are possible to separate by

conventional distillation. For instance, low relative volatility mixtures (including azeotropic mixtures)

are difficult or economically unfeasible to separate by ordinary distillation.

As normal distillation has limitations for azeotropic mixtures, enhancements have been proposed

that either apply a pressure swing distillation system or introduce a third component as an extractive in

extractive and azeotropic distillation processes(Mahdi et al., 2015).

Figure 4 shows some of the currently available technologies for the separation of azeotropic

mixtures. Conventional separation processes such as azeotropic and extractive distillations are

observed to be the main technologies used at present and in the near future, with opportunities for

improvements such as, by introducing new extractive agents with desirable properties. (Lide et al.,

2001).

8

Figure 4 - Schematic diagram of various techniques for the separation of azeotropic mixtures (Mahdi et al., 2015). In the following sections the separation processes are going to be briefly described.

2.2.1. Pressure-swing distillation

Pressure-swing distillation is used for separate mixtures that form a pressure sensitive azeotrope

by utilizing two columns in sequence at different pressures. Pressure changes can have a large effect

on the vapor-liquid equilibrium compositions of azeotropic mixtures and thereby affect the possibilities

to separate the mixture by ordinary distillation (Luo et al., 2014). The azeotrope composition can

change or even make azeotropes appear or disappear, by increasing or decreasing the operating

pressure (see Figure 5a) (Seader et al, 2005).

Figure 5 – Schematic diagram for pressure-swing distillation: (a)T-x diagram for a minimum-boiling binary

azeotrope sensitive to changes in pressure; (b) Pressure-swing distillation column sequence.

From Figure 5a, it is observed that the bottom product from the first column, (𝑃1 column) is

relatively pure A, whereas the overhead is an azeotrope with 𝑥𝐷1. This azeotrope is fed to the high-

pressure column (𝑃2 column), which produces relatively pure 𝐵2 in the bottom and an azeotrope with

composition 𝑥𝐷2 in the overhead.

This azeotrope is recycled into the feed of the low-pressure column (𝑃1 column). The smaller the

change in azeotropic composition with pressure, the larger is the recycle (Seader et al, 2005).

9

However, the pressure-swing distillation cannot be used with all azeotropic mixture, since the

distance between the two azeotropic compositions has to be large enough to make the distillation

work. It can be easily understood since similar compositions at different pressures will mean that the

feed composition of the second column will be very close to the azeotropic composition thus

separation of both pure compounds is not feasible (Fernández, 2012). To consider pressure-swing

distillation a feasible technique for the separation of azeotropic mixtures, the azeotropic composition

must vary at least 5% over a moderate pressure range (not more than 10 atmospheres within the two

pressures) (Perry et al, 2008). For the case of the separation of isopropyl alcohol/ diisopropyl ether,

Luo et al. 2014, showed that it was more advantageous to use pressure-swing distillation instead of

extractive distillation; since the azeotropic compositions changed significantly with pressure.

Even though pressure-swing distillation method seems very attractive and easy to use, however,

temperature problems in the two columns appear and refrigeration will be needed, if the difference

between the two pressures is too large and this technique will not be feasible from an economic point

of view. Another key design factor is the recycle ratio, which depends on the variation in azeotropic

composition with column pressure, since the cost of gas compressor can become very high. Thus , in

spite of the fact that pressure-swing distillation can be operated in theory, in practice the operational

cost devalues all its advantages, making this technique generally not an option (VanWinkle, 1967).

2.2.2. Azeotropic Distillation Process

Azeotropic distillation can be defined as a distillation in which a relatively small amount of the

extractive agent (solvent) added forms an azeotrope with one or more of the components in the feed

based on differences in polarity (Kumar et al, 2010).

Most of the solvents are highly volatile compared to the components to be separated so that the

solvent is taken off from the overhead of the column. Azeotropic distillation processes basically utilize

two columns. The first column serves as the main column, and the second column is used for solvent

recovery. In this process, the solvent leaves the first column from the column overhead with the lighter

component, while the heavies are collected as a bottom product. The solvent and the lighter

component are then fed to the second column to produce a high purity product at the bottom while the

recovered solvent is recycled back to the first column.

Azeotropic distillation is usually classified into two classes based on the type of mixtures to be

separated (Li et al, 2005):

i) Homogeneous azeotropic distillation;

ii) Heterogeneous azeotropic distillation;

These two techniques are illustrated in Figure 6. In the case of homogeneous process, phase

split does not appear in the liquid along the whole column, unlike the heterogeneous counterpart, in

which the two liquid phases exist in some regions of a composition space. A decanter is used in

heterogeneous azeotropic distillation to collect the condensed vapor from the condenser and permits

the separation of the two liquid phases. Commonly, these two liquids are the entrainer and the lighter

10

component where the entrainer phase is refluxed back to the column. The other phase is fed to the

second column where it is fractionated to remove the dissolved solvent.

Figure 6 - Schematic diagram of an azeotropic distillation, where A and B are light and heavy components of the feed mixture, respectively, S is the solvent component; a) homogeneous process and b) heterogeneous process

(Mahdi et al, 2014).

Heterogeneous azeotropic distillation is often preferred industrially over homogeneous azeotropic

distillation due to the ease of recovery of the entrainer and the transition across a distillation boundary

in the decanter (Meirelles et al, 1992). However, heterogeneous azeotropic distillation suffers from

some disadvantages associated with the high degree of nonlinearity, distillation boundaries, and

heterogeneous liquid-liquid equilibrium, limiting the operating range of the system under different feed

disturbances (Gomis et al, 2007). For both types of azeotropic distillation, the solvent must be

vaporized through the top of the column, thus consuming much energy.

2.2.3. Extractive Distillation

Extractive distillation involves a relatively non-volatile entrainer compared to the components to

be separated (Luo et al., 2014). Therefore, the entrainer is charged continuously near the top of the

fractionation column, so an appreciably high amount of entrainer is maintained on all plates in the

extractive distillation column below its entry, and the solvent is removed from the bottom of the

extractive distillation column. An extractive distillation process is more commonly applied in the

chemical and petrochemical industries than the azeotropic distillation (Hilal et al, 2002). In Figure 7 is

it presented the principle of this technology, where components A and B are fed to the first column that

acts as an extractive column where the solvent (S) is introduced at the top stage. In this process, the

component (A) is withdrawn at the top of the first column; while the solvent with the other component

(B) are withdraw at the bottom. The bottom products of the first column are then fed to the second

column, in which component (B) is withdrawn at the top and the entrainer is separated from the bottom

and recycled back to the first column. The separation in the second column is often easier because of

the larger boiling point difference between the high-boiling entrainer and the existing second

component, and because the solvent does not form an azeotrope with the second component (Perry

et al, 2008).

11

Figure 7 - Schematic diagram of an extractive distillation double column process where A and B are light and heavy components of the feed mixture, respectively; S is a solvent component (Lei et al., 2005).

Extractive distillation is more commonly used due to lower energy requirement and wider

selection of entrainers (Sucksmith et al., 1982). However, extractive distillation cannot produce highly

pure product compared to azeotropic distillation because the solvent coming from the bottom of the

solvent recovery column most likely contains impurities that may affect the separation process (Gang

et al., 1999). Another drawback of the extractive distillation is the number of degrees of freedom when

compared with a simple distillation setup.

In a simple distillation setup, the degrees of freedom are the reflux ratios and the number of

stages of the distillation columns; while in extractive distillation, the entrainer type its flow rate and the

entrainer feed location comprise additional degrees of freedom (Kossack et al., 2008).

2.2.3.1. Types of entrainers used in extractive distillation

The choice of a separating agent influences the economics of the extractive distillation process

(Kossack et al., 2008). This separating agent can be a liquid solvent, dissolved salt, ionic liquids and

hyperbranched polymers. Based on the type of separating agent, the extractive distillation process can

be further divided into four categories that will be discussed in the following subsections.

i) Extractive distillation with a liquid solvent

In respect to technical parameters, the variable that has the most significant impact on the

economics of an extractive distillation is the solvent-to-feed (S/F) ratio. Operating solvent to feed (S/F)

ratios for economic acceptable solvents is between 2 and 5 (Perry et al, 2008), but sometimes higher

solvent to feed ratio are required, making the solvent-based distillation uneconomic technique.

However, as the solvent can be recovered effectively under normal operating conditions, this

technique remains a preferred choice in industry rather than schemes using other types of extractive

agents and attracts the interest of many researchers (Andrea et al., 2011; Nieuwoudt et al., 2002;

Yang et al., 2009).

ii) Extractive distillation with solid salt

A separating agent in a form of a solid salt is fed at the top of the column, dissolved into the liquid

phase, and recovered from the column by evaporation (Barba et al., 1985). A schematic diagram of

12

this process is presented in Figure 8. The solid salt must be soluble in the feed components, non-

volatile and able to flow all the way down the column. The salt extracted from the bottom of the column

is then recycled to the column.

Figure 8 - Scheme of a s ingle column process with salt: 1 - feed stream, 2 - extractive distillation column, 3 - equipment for salt recovery, 4 - bottom product, 5 - the salt recovered, 6 - reflux tank, and 7 - overhead product

(Lei et al, 2005)

Solid salt is a more effective separating agent when compared to the liquid agent, and requires a

much smaller salt ratio, thus leading to a high production capacity and low energy consumption (Gil et

al., 2008). Furthermore, the product at the top of the column is free from salt impurities, since solid salt

is not volatile, being more environmentally friendly. However, when solid salt is used in industrial

operation, it causes corrosion of equipment, limiting the application of salt in the process industry (Lei

et al, 2005).

iii) Extractive distillation with ionic liquid

The use of ionic liquids (ILs) as separating agents in the extractive distillation process is a recent

strategy that has been adopted and is often used in processes involving chemical reactions (Owens et

al., 2002). This separation process has a similar configuration to the configuration of extractive

distillation but where the entrainer added is a solid salt as can be observed in Figure 9. The features of

this process include salts consisting completely of ions, which are in the liquid state at room

temperature. Those ionic liquids have properties of interest such as the negligible vapor pressure at

room temperature (Earle, et al, 2006), leading to a lower risk of worker exposure and minimal loss of

solvent to the atmosphere. The application of ionic liquids can be made for a specific application by

accurate selection of the cations and anions (Huddleston et al., 2001). The salts of ionic liquids

therefore do not need to be melted by an external heat source (Murugesan et al,. 2005).

Figure 9 – Extractive distillation using ionic liquid as non-volatile entrainer (A: main column, B: flash drum, C: Stripping column) (Seiler et al., 2004).

13

In addition, extractive distillation with the ionic liquid technique has the following advantages

(Earle et al., 2000):

Absence of product impurities at the top of the column, because ionic liquids are non-volatile;

Due to the non-volatility of ILs, they can be used over a wide temperature range from room

temperature to above 300℃, which corresponds to the typical operating conditions of extractive

distillation.

Easy recovery and reuse of ionic liquids.

High stability of ionic liquids under the operating conditions of extractive distillation in terms of

thermal and chemical conditions.

Taking all of these features into account, the ionic liquids are considered good candidates for

application as extracting solvents in the separation of azeotropic mixtures, and have demonstrated

capabilities to separate many mixtures (Dhanalakshmi et al., 2013; Werner et al., 2010). However,

despite of the increase in publications addressing azeotropic separations with ionic liquids, these

studies are limited due to the lack of information of ionic liquids to analysis the liquid-liquid equilibria

(Meindersma et al., 2008) and vapor-liquid equilibria (Zhao et al., 2006) or simulation of the extractive

distillation process with ionic liquids (Pereiro et al, 2012).

Extractive distillation with ionic liquids also suffers from some disadvantages such as the long

time required preparing the ionic liquids and the high cost of synthesis of such components (Lei et al.,

2005). The separation of viscous solutions using this technique is very difficult to manage (Seiler et al.,

2002) and the ionic liquids demonstrate moisture sensitivity (Earle et al., 2000). The application of this

process in industry has slowed down because of the disadvantages presented (Lei et al., 2005).

iv) Extractive distillation with hyperbranched polymers

Hyperbranched polymers are highly branched, polydisperse, three-dimensional macromolecules

which, due to their unique structures and properties, have attracted increasing attention in the yield of

chemical engineering (Seiler et al., 2002).

Most of the applications are related to the presence of a large number of functional groups within

a molecule. Furthermore, the functional groups of hyperbranched polymers allow modifying their

thermal, and solution properties. This modification provides the opportunity to design entrainers for a

wide variety of applications (Voit et al, 2002; Gao et al., 1004)).

Unlike the conventional linear polymers, hyperbranched polymers not only show a remarkable

selectivity and capacity, but because of a lack of chain entanglements, also show a comparatively low

solution and melt viscosity but also present a high thermal stability (Seiler et al., 2004).

Experimental results illustrated the potential of such entrainers in breaking the azeotropic mixture

(Seiler et al., 2004) and concluded that the use of hyperbranched polyesters provides cost saving

compared to conventional separation processes (Sunder et al., 2000).

14

However, like ionic liquids, hyperbranched polymers are also new separating agents used in

extractive distillation, and it is necessary to investigate more about these entrainers. The phase

behavior of polymer solutions must be better understanding (Seiler et al. 2002).

2.2.4. Conclusions

Due to the importance of the chemical and petrochemical industry to the world economy, studies

on even old technologies such as chemical separation continue to be relevant. Considering the

separation of azeotropic mixtures, various studies taking different approaches have been reported.

However, more studies are needed to improve the economic efficiency and ease of operation whi le

ensuring safety to personnel and the environment.

Because conventional processes are well-understood and established, azeotropic and extractive

distillations would still be the main technologies used for large scale applications in the near future.

The search for “perfect” entrainers should therefore be continued by examining existing options or

synthesizing new ones aiming at entrainers that are effective in separation, highly selective, energy

efficient, and environmentally friendly with minimal safety and health hazards. The use of ionic liquids

and hyperbranched polymers has shown promising potential (Mahdi et al., 2014). Regarding the

previous statements, as extractive distillation is observed to be the main technology used at the

present and the near future (Mahdi et al, 2014), it was the technique chosen to separate the

azeotropic mixtures studied in this thesis. Relatively to the entrainer, liquid solvents were chosen once

they are the most common class of solvents used in extractive distillation processes (Gutiérrez et al.,

2013).

2.2.5. Extractive distillation with liquid entrainers

In the ordinary distillation of ideal or nonazeotropic mixtures, the component with the lowest pure-

component boiling point is always recovered primarily in the distillate, while the highest boiler is

recovered primarily in the bottoms.

The situation is not as straightforward for an extractive distillation operation. With some solvents,

the key component with the lower pure-component boiling point in the original mixture will be

recovered in the distillate as in ordinary distillation. For another solvent, the expected order is

reversed, and the component with the higher pure-component boiling point will be recovered in the

distillate. The possibility that the expected relat ive volatility may be reversed by the addition of solvent

is entirely a function of the way that the solvent interacts with the components and modifies the activity

coefficients and, thus, the volatility of the components in the mixture (Perry et al., 2008).

In normal applications of extractive distillation (i.e., close-boiling, or azeotropic systems), the

relative volatilities between the light and heavy key components will be unity or close to unity (See

equation 3).

Since activity coefficients have a strong dependence on composition, the effect of the solvent on

the activity coefficients is generally more pronounced. However, the magnitude and direction of

15

change are highly dependent on the solvent concentration as well as on the liquid-phase interactions

between the solvent and the key components.

The natural relative volatility of the system is enhanced when the activity coefficient of the lower-

boiling pure component is increased by the solvent addition (𝛾𝐿 𝛾𝐻⁄ 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒𝑠 𝑎𝑛𝑑 𝑃𝐿

𝑠𝑎𝑡 𝑃𝐻𝑠𝑎𝑡⁄ > 1). In

this case, the lower-boiling pure component will be recovered in the distillate as expected. It is

normally better to select a solvent that forces the lower-boiling component overhead (Perry et al.,

2008).

2.2.5.1. Approach to solvent selection

Solvents are widely used in chemical and processing industries to aid in many separation

processes. For instance, extractive distillation separates azeotropic mixtures into high purity products

by the addition of a solvent. This technique involves the addition of a solvent to extract one of the

components in the mixtures (target component) causing the change in the relative volatilities of the

mixture (Pereiro, et al., 2012).

The search for candidate solvents for a given separation is a major task in process design, and

can be performed following different criteria. The criteria for solvent selection follow the below

statements (Perry et al., 2008):

The solvent must be chosen to affect the liquid-phase behaviour of the key components

differently; otherwise, no enhancement in separability will occur.

The solvent must be higher-boiling than the key components of the separation and must be

relatively non-volatile in the extractive column, in order to remain largely in the liquid phase.

The solvent should not form additional azeotropes with the components in the mixture to be

separated.

The solvent should be nonreactive with the materials of construction of the equipment.

The solvent should force the lower-boiling component overhead.

The solvents to be selected must extract one of the components in the binary mixture (target

component) (Pereiro et al., 2012). The target component (solute) is the solute that leaves the bottom

of the extractive column with the entrainer. The choice of the target solute determines the nature of the

solvents to be generated (Achenie et al, 2010).

According to Peng-noo et al, (2015) the selection of the target solute is made plotting the x-y vapor

liquid equilibrium (VLE) plot of the binary mixture wherein the component that features the smaller

composition in the azeotrope is selected to be the target solute. For the binary mixture ethanol -n-

hexane, ethanol is confirmed to be the target solute as shown in Figure 10 (Peng-noo et. al, 2015).

16

Figure 10 – x-y-VLE plot of the binary mixture etanol-n-hexane where i tis confirmed that etanol is the target

solute (Peng-noo et al, 2015).

For other authors the solvent should be selected according to the following thermodynamic

considerations.

The relative volatility of the key components i and j in a given mixture with ideal vapor phase is

defined by Equation 3 (Kossack et al., 2008). For small temperature changes the ratio 𝑝𝑖𝑠𝑎𝑡 𝑝𝑗

𝑠𝑎𝑡⁄ is

almost constant and the relative volatility can only be affected by introducing a solvent that alters the

ratio 𝛾𝑖 𝛾𝑗⁄ . This ratio, in the presence of the solvent, is called selectivity, 𝑆𝑖𝑗, as presented in Equation

4 (Kossack et al., 2008).

𝑆𝑖𝑗 = (𝛾𝑖

𝛾𝑗)

𝑆

(4)

The activity coefficients, 𝛾 depend on the liquid phase composition. Since the effect of the

entrainer tends to increase with concentration in the mixture, it is common pract ice to evaluate the

selectivity at infinite dilution (Lei et al, 2003), which is represented by equation 5.

𝑆𝑖𝑗∞ = (

𝛾𝑖∞

𝛾𝑗∞) (5)

Another proposed measure to assess the suitability of an entrainer is the capacity (Horsley et al,

1973) which is determined using equation 6.

Cj ,Entrainer∞ =

1

γj∞ (6)

where 𝑗 denotes the solute. The smaller the value of the activity coefficient 𝛾𝑗∞, the stronger are the

interactions between component 𝑗 and the entrainer, which results in a large capacity, 𝐶𝑗,𝐸𝑛𝑡𝑟𝑎𝑖𝑛𝑒𝑟∞ .

Another criteria for the solvent selection was presented, where the selectivity at infinite dilution

(𝑺𝒊𝒋∞ ) is the primary criterion chosen for select the suitable solvent, due to its concentration-

independent (Lek-utaiwan et al., 2011). However, the selection of the best solvent on the basis of only

𝑺𝒊𝒋∞ is inadequate because it does not directly relate to the distillation design. The suitable criterion

should return to how significant the solvent could alter the driving force (DF) of the key components.

The value of driving force is defined as the difference in light key compositions between vapor and

liquid phases when considering only two key components in the binary system (Bek-Pedersen et al,

2004). This diagram can verify the separation-enhancement potential of each solvent recommended

17

by ProCAMD (Harper and Gani, 2000). The combination of these two steps has shown to be very

crucial since the reliable solvent ranking must be achieved before proceeding into the design step

(Lek-utaiwan et al., 2010),. Regarding this, another test is made to observe the solvent performance.

At various solvent to feed ratio (S/F ratio equal than 1:1 to 5:1) are tested to see the effectiveness of

the solvent to break the azeotrope (Lek-utaiwan et al., 2010).

Another parameter that showed to be one of the criteria required to determine the best candidate

for use as a solvent is the Hildebrand solubility parameter (Roughton et al, 2012).

The Hildebrand solubility parameter is used to predict whether compounds will be miscible

(Barton et al, 1991). Compounds with similar solubility parameter values are more likely to form a

miscible solution (Barton et al, 1991). Solubility is a key parameter for selecting an extractive agent,

allowing the solubility parameter to be used as a tool for designing or selecting possible candidates.

The cohesive energy density of a compound 𝑖 (𝑐𝑖𝑖)is determined by its molar volume (𝑣𝑖 ) and

enthalpy of vaporization (∆ℎ𝑣𝑎𝑝 ). The Hildebrand solubility parameter (𝛿𝑖)is defined as the square root

of the cohesive energy density, as shown in equation (7) (Roughton et al, 2012).

𝛿𝑖 = 𝑐𝑖𝑖1 2⁄

= (∆ℎ𝑣𝑎𝑝−𝑅𝑇

𝑣𝑖)

1 2⁄

(7)

The entrainer selection should also consider safety, environmental effects, safety and availability

(Mahdi, T. et al.). A solvent with a low boiling point and a high vapor pressure would tend to require

more emission control technology and would be more likely to volatize in the wastewater treatment

plant than another solvent of higher boiling point and vapor pressure (Curzons et al., 1999). Regarding

this fact, solvents that present low vapor pressure and high boiling points are preferable than solvents

with high vapor pressure and low boiling points (Curzons et al., 1999). As longer as the vapor

pressure is low enough, the compound never actually show up in the vapor phase meaning that a

solvent with a low vapor pressure is preferred to minimize loss of solvent (Curzons et al., 1999).

The candidate solvents can also be selected according to the Hansen solubility parameter.

According to Hansen’s approach (Hansen, et al, 1969), the total solubility parameter, 𝛿𝐻𝑆𝑃 , can be

expressed as the sum of three contributions: the dispersive 𝛿𝐷, the polar 𝛿𝑃 , and the hydrogen-

bonding 𝛿𝐻 interactions as observed In Equation 8 (Hansen et al, 2007).

𝛿𝐻𝑆𝑃 = √𝛿𝐷2 + 𝛿𝑃

2 + 𝛿𝐻2 (8)

The solvents that present the closest values of Hansen solubility parameter to the solute the

better the solubility between these solvents and the solute is (Benazzouz et al, 2013).

2.2.5.2. Conclusions

The selection of the target solute in a binary mixture is the first step to take before the selection of

a solvent (Peng-noo et al, 2015); once the solvent must only affect the target solute.

18

The selectivity gives a first estimate for a relative ranking of entrainers alternatives (Kossack et al

2007), however, the selectivity alone, is not a very good screening tool and should not be used alone

to predict entrainer performance (Kossack et al 2007). The use of the capacity and selectivity already

improves the screening accuracy (Kossack et al 2007). However, the selection of the best solvent on

the basis of only selectivity is inadequate because it does not directly relate to the distillation design

(Lek-utaiwan et al, 2011).

ProCAMD (Harper and Gani, 2000) approach has been proven to be very efficient for the solvent

screening (Lek-utaiwan et al. 2011), and complemented with the solvent performance test at various

S/F ratios the best solvents are selected (Lek-utaiwan et al. 2011). Another parameter that revealed to

be a key to determine a suitable entrainer was the Hildebrand Solubility parameter (Kulajanpeng, et al.

2014) where the solvent to be selected must have a Hildebrand solubility parameter close to the target

solute (Peng-noo et al, 2015.).

The Hildebrand solubility parameter along with the capacity and selectivity are the key

parameters for selecting the suitable entrainer (Peng-noo, et al., 2015).

The environmental effects and the solvent loss showed to be also an important criteria on the

solvent selection and parameters as vapor pressure and enthalpy of vaporization must be taken into

account.

2.3. Distillation columns design

The design of the distillation columns involves the specification of the number of trays (𝑁), feed

location (𝑁𝐹) and reflux ratio (𝑅). In this section a brief discussion about the state-of-art regarding the

driving-force method for the estimation of these parameters is presented.

2.3.1. Driving force method

The driving force has been defined by Equation (9) (Bek-Pedersen and Gani, 2000):

𝐷𝐹𝑖 = 𝑦𝑖 − 𝑥 𝑖 =𝑥𝑖 𝛼𝑖𝑗

1+𝑥𝑖(𝛼𝑖𝑗−1)− 𝑥 𝑖 (9)

As seen in the model equation above, the driving force is defined as the difference in

composition. The terms 𝑥 𝑖 and 𝑦𝑖 denote liquid and vapour phase composition of component 𝑖, where

𝐷𝐹𝑖 is the driving force for component 𝑖.The relative volatility 𝛼𝑖𝑗 , provides a measure of the driving

force (Bek-Pedersen and Gani, 2000).

From the driving force model (Equation 9) it can be noticed that at fixed 𝑃 (𝑜𝑟 𝑇), two-

dimensional plots of |𝐷𝐹𝑖| versus 𝑥𝑖 (𝑜𝑟 𝑦𝑖) can be made where each data point may also indicate a

different 𝑇 (𝑜𝑟 𝑃). Therefore, these diagrams can be used to design and configure separation

schemes, including conditions of operation (Bek-Pedersen and Gani, 2000).

It can also be observed that when the driving force decreases, the separation becomes difficult.

As the size of the driving force for a given separation approaches zero, separation of the species

involved becomes infeasible. On the contrary situation, when the driving force approaches its

19

maximum value, the separation becomes easier. This happens because, in separation processes

where energy is required, the driving force is inversely proportional to the energy added to the system

to create and maintain the two-phase system. From an operational point of view, a process should be

designed to operate at the highest possible driving force (Bek-Pedersen and Gani, 2000).

Six algorithms related to separation synthesis and retrofit design have been developed (Bek-

Pedersen and Gani, 2000).

The first algorithm “Single-distillation column design‟ calculates the optimal (with respect to

operational cost) feed plate location given a number of stages. This algorithm is illustrated in Figure

11.

Figure 11 - Driving force diagram for constant relative volatility (zeotropic mixtures) (Bek-Pedersen and Gani,

2004).

Given a mixture to be separated into two products in a distillation column given a number of

stages, 𝑁, the optimal (with respect to cost of operation) feed plate location (𝑁𝐹) and the

corresponding reflux ratio for different product purity specifications are given following this algorithm

(Bek-Pedersen and Gani, 2004).

The algorithm used to calculate the optimal 𝑁𝐹 is presented below:

1. Generate or retrieve from a database the vapor-liquid-equilibrium (VLE) data for the binary

system in the column.

2. Calculate the driving force between the two components at the actual operating pressure.

Plot the calculated driving force as a function of the light component composition (In Figure

11, 𝑥𝑖 refers to the light compound).

3. Locate the point 𝐷𝑥 as the point on the x-axis that corresponds to the largest driving force.

4. Specify the desired specifications.

5. Determine whether rescaling needs to be applied. If condition 1 or 2 (Figure 12) is satisfied,

scaling is needed, go to 6. Otherwise, go to 7.

6. If condition 1 (see Figure 12) is satisfied and go to 6.2.

6.1. If condition 1a is satisfied, then relocate 𝑁𝐹 between 5 and 10% up in the column. Else

condition 1b is satisfied, then relocate 𝑁𝐹 between 5 and 10% down in the column.

6.2. If condition 2a is satisfied, then relocate 𝑁𝐹 10% down. Else, if condition 2b is satisfied, then

relocate 𝑁𝐹 5% down.

7. Apply equation 10 (taking the scaling factors determined in step 4 into consideration) to

determine 𝑁𝐹 for a given value of N.

𝑁𝐹 = 𝑁(1 − 𝐷𝑥) (10)

20

Figure 12 - Conditions of distillation column feed and products that require a scaling factor to be included in the

design procedure (Bek-Pedersen and Gani, 2000).

The “Retrofit design of distillation columns‟ algorithm (Algorithm R1), solves retrofit problems

where the design of any existing distillation column is known and it is necessary to determine if it can

be used to separate a specific mixture (and the corresponding condition of operation) (Bek Pedersen

and Gani ,2004). This algorithm can be applied to estimate the minimum reflux ration, 𝑅𝑀𝐼𝑁 and the

minimum number of stages, 𝑁𝑀𝐼𝑁 using Figure 55 in Appendix 1. The only requirements needed to

calculate these variables are the desired molar product purity and the maximum driving force.

With this approach, the only requirements for application of the integrated framework are

compositions of the mixture to be separate. (Bek Pedersen and Gani, 2004) confirmed the theory, with

a rigorous number of case studies, where the separation at the highest driving force is the easiest

separation and, therefore, should require a minimum of energy since energy is needed to create the

driving force.

2.3.2. Sensitivity Analysis

AspenPlus was the simulator used to perform the simulations for the separation of the azeotropic

mixtures analysed in this thesis. The sensitivity analysis is applied in order to identify the variables that

can be changes in order to perform the separation process and the achievement of the target

specification. Target specification scan be: product purity at the top of a column, product recovery,

energy consumption, number of stages, and others.

In order to show how the sensitivity analysis is important during the simulation process, Figure 13

shows the influence of the mass fraction of a component at the top and at the bottom of the extractive

distillation column with the solvent flowrate, when the number of stages and the reflux ratio are fixed

variables. In this example, the mass fraction of the component at the top of the extractive distillation

column is the target specification.

21

Figure 13 – Effect of solvent flowrate on the distillate and bottom composition using sensitivity analysis

(Figueirêdo et al, 2010).

It is observed in Figure 13, that when the solvent flowrate reaches the value of 80 kmol/h, after

that value, increasing the solvent flowrate, the mass fraction of the component at the top and at the

bottom of the extractive distillation is constant. That means that it is not necessary to use a higher

solvent flowrate than 80 kmol/h. Regarding this, sensitivity analysis makes a significant improvement

in the process, since it defined which is the minimum solvent flowrate necessary to reach the target

specification (mole faction of ethanol at the top).

2.3.3. Conclusions

The driving force method can, efficiently, not only predict near optimal solutions to separation

design, but also the solutions can be found very easily, via a systematic approach using VLE data.

Since the method is based on actual thermodynamic behaviour and it does not make any assumption

as to phase behaviour, the solutions would also reflect real systems. In conclusion, the driving force

method is a method to apply on the estimation of the design variables for the distillation sequence.

This model was used for the pre-design of the extractive distillation columns. The design was then

verified by rigorous simulation using AspenPlus, through rigorous simulations and sensitivity analysis.

Sensitivity analysis showed to be an important step, since it allows performing the separation process

design, identifying variables that affect the target specification and giving the minimum value to reach

this target specification as presented in Figure 13.

2.4. Computational tools

Several tools were required for the development of the methodology of this thesis, such as

azeotropic database, (AzeoPro v.1.0. (CAPEC_DTU_2013), the design/selection of solvents

(ProCAMD, (Harper and Gani, 2000), property prediction tools (ProPed, (Marrero and Gani, 2001),

integrated computer aided system for designing, analysing and simulating chemical processes (ICAS

v.17.0, Gani et al. 2014), design, synthesis and analysis of distillation based separation schemes

(PDS, Hostrup and Gani, 1999), and the simulators such as AspenPlus and PRO/II. The tools

previously enounced, are briefly described here.

22

2.4.1. ICAS

ICAS combines computer aided tools for modelling, simulation (inc luding property prediction),

synthesis/design, control and analysis into a single integrated system. These tools are present in ICAS

as toolboxes of ProCAMD, ProPed, PDS and others. ICAS is used in Step 2 and in Step 4 of the

methodology (See Figure 15) in order to retrieve the driving force plots of the azeotropic mixtures.

2.4.2. AzeoPro

AzeoPro is a model-based tool which is used to design azeotropic separation processes in a fast

way (Rodrigues,2013). It is based on Gmehling’s Azeotrope database (Gmehling et al., 2004) which is

a compendium of azeotrope information. For a given compound, the application checks the

compounds which form azeotrope with, and give as output the azeotrope information. This tool applied

in Step 1 of the methodology (See Figure 15).

2.4.3. ProCAMD

ProCAMD is based on a multi-level computer-aided molecular design technique developed by

(Harper and Gani, 2000). ProCAMD is a tool integrated within ICAS. This tool is divided into six

categories such as, general problem control, non-temperature dependent properties, temperature

dependent properties, mixture properties, biodegradation calculations, azeotrope/miscibility

calculations. For the generation and screening of suitable solvents the six categories must be fulfilled.

To use ProCAMD the user must: 1) Define the problem (identify the goals on the design operation); 2)

specify the design criteria based on the problem; 3) identify the compounds having the desired

properties; and finally 4) Analyze the suggested compounds using external tools). This tool shows to

be very effective in the solvent ranking (Lek-utaiwan et al, 2011).

2.4.4. ProPed

ProPed is a toolbox integrated within ICAS, for the estimation of pure compound properties of

organic compounds (Marrero and Gani, 2013). ProPed might be used to estimate missing data when

data is not available. The estimation of these compound properties is based on the Marrero and Gani

method (2001), the Constantinou and Gani method (2001), the Joback and Reid method (1987) and

the Wilson’s method (1964). The starting window in ProPed is presented in Figure 14. ProPed is

divided in two windows. One window (left side) is for the display of properties of molecules and the

other window (right side) provides the molecular structure (either by using drawing tools or by

importing SMILES of the molecule). ProPed was applied in step 2 of the proposed methodology (See

Figure 15).

23

Figure 14 – Starting window in ProPed.

2.4.5. PDS

Process Design Studio, PDS is a toolbox integrated in ICAS. This tool is used to obtain the

separation process design of distillation columns. The input of this step is to select the compounds

that the user wants to separate through distillation. After that, the user must select the thermodynamic

model, and finally the user must define which of the mixture components are the light component and

the heavy component. The importance of defining the light and heavy key comes from the fact that the

separation process design is made regarding the driving force approach. The output information of this

tool are the design variables obtained for the separation mixtures defined as input. PDS is used in

step 3 of the methodology (See Figure 15).

2.5. Conclusions

In this chapter, a review of the literature regarding the VLE phenomenon was presented, in order

to introduce the nonideal behaviour of azeotropic mixtures. The different techniques applied for the

separation of such mixtures was also described.

As extractive distillation showed to be the more effective technique for the separation of

azeotropic mixtures, an overview about the different types of entrainers that can be used to break the

azeotropes is presented.

The selection of the best solvent for the separation of azeotropic mixtures is still a challenge, so,

a selection of methods reported in the relevant literature for the selection of the most suitable solvent

have been discussed.

Regarding to the design of the separation process, the driving force method presents a

framework based on thermodynamic insights that relies on chemical/physical properties of the mixture.

This method can, efficiently, not only predict near optimal solutions to separation design, but also the

solutions can be found very easily, via a systematic approach using VLE data. The final design is

made using a process simulator and applying sensitivity analysis which showed to be very useful

since it present the minimum value of variables required in order to get the target specifications.

24

3. Methodology

This chapter covers the methodology developed for the solvent selection and the design of binary

azeotropic mixtures separation, using extractive distillation. The methodology is described in detail. In

Section 3.1., the generic methodology is presented. In Section 3.2., the steps used for the selection of

the most suitable solvent are presented. Section 3.3., presents the process design and analysis.

Section 3.4., shows the design flexibility for an azeotropic serie. Finally, the main conclusions are

presented (Section 3.5.).

3.1. Methodology overview

The focus of this work is on the development of a systematic methodology for the separation of

binary azeotropic mixtures, through extractive distillation. The methodology will guide the user through

a step-by-step procedure for the selection of the most suitable solvent, since the extractive distillation

is a solvent-based distillation.

The overall methodology is presented in Figure 1. Several supporting tools are used in each step

of the methodology, such as azeotropic database, (AzeoPro v.1.0. (CAPEC_DTU_2013), the

design/selection of solvents (ProCAMD, (Harper and Gani, 2000), property prediction tools (ProPed,

(Marrero and Gani, 2001), integrated computer aided system for designing, analysing and simulating

chemical processes (ICAS v.17.0, Gani et al. 2014), design, synthesis and analysis of distillation

based separation schemes (PDS, Hostrup and Gani, 1999), and the simulators such as AspenPlus

and PRO/II, as can be seen in Figure 15.

25

1.1. Mixture Selection

1.2. Selection of the target solute

1.3. Boiling point of the mixture components

STEP 1

Problem definition

3.1. Pre-Design EDC & RC

3.2. Simulation & Sensitivity analysis

STEP 3

Design & Analysis

4.1. Adjust the separation process design

STEP 4

Fine tune the design available at the database

Q3. Is it the first time that the

selected solvent is used for the

target solute/ or for an

homologous serie ?

Yes

Q4. Does the mixture

belong to an azetropic

mixture of the database?

No

Yes

Q5. Do you want

to analyse another

mixture?

Yes

No

No

F2. Design of Extractive

distillation separation – Save in

database

STEP 2

Solvent Selection

2.1. Solvent screening

Q1. Is the first time that the

screening of solvent is made

according to this target solute/ or

an homologous serie?

Yes

No

S0

Q2. Do you want to use a

solvent previously selected? No

Yes

2.2. Solvent analysis

2.2.A. Selection from

solvent power vs.

Selectivity plot

2.2.B. Selection from

solvent power vs.

Hildebrand solubility

parameter plot

2.2.C. Selection from

Plot Hansen Solubility

Parameter plot

2.2.D. Selection from

Solvent to feed (S/F)

ratio plot

Check solvent power vs.

Selectivity to make final

decision

Solvent selected

S1

S2

S3

S4

F1. Design of Extractive distillation

separation

STOP

START

Figure 15 - Overview of the proposed methodology for the separation of azeotropic mixtures using extractive

distillation

26

Step 1 – Problem Definition

The purpose of this step is to select and classify the azeotropic mixture, which is going to be

analyzed through the methodology. This step is divided into 3 sub-steps, which are: Mixture selection

(Step 1.1.), Selection of the target solute (Step 1.2.) and the Characterization of the target solute (Step

1.3.).

Step 1.1. Mixture selection

The aim of this step is to select the mixture to be analyzed. The selection of the mixture is made

using AzeoPro. Figure 16 presents the steps required to obtain the binary azeotropic mixture.

Mixture

Selected

List of compounds present in AzeoPro

Database

Select Compound 1

List of azeotropes for compound 1

(AzeoPro)

Select Compound 2

Type of azeotrope

(homPmax or homPmin)

Select the pressure of the

azeotrope

Composition and

temperature of the azeotrope

Output data obtained from Step 1.1. Input data Selection block

Figure 16 - Tasks to follow in Step 1.1. - Mixture selection.

When the user opens the software AzeoPro the selection of the azeotrope is made as presented

in Figure 16. A list of several compounds is presented in AzeoPro database. From that list, the user

selects compound 1, which is the main compound for the separation. When compound 1 is selected,

the software database shows a list of all the compounds that form an azeotrope with compound 1. The

selection of compound 2 is made from this second list, and therefore the mixture is selected. When the

mixture is selected, the user must select from AzeoPro the azeotrope characterization in terms of

pressure. The type of azeotrope, which is going to be separated, is an output data obtained from this

step. The azeotrope can be homogenous pressure-maximum azeotrope (homPmax) or homogenous

27

pressure-minimum azeotrope (homPmin). The composition and the temperature of the azeotrope are

also obtained as output data of this step.

Figure 17 shows the main screen of AzeoPro, in order to illustrate how the mixture can be

selected.

Figure 17 - Compound selection screen of AzeoPro – Selection of compound 1 (orange rectangle); selection of

compound 2 (red rectangle) and selection of the pressure.

Step 1.2. Selection of the target solute

The objective of this step is to select the target solute. The target solute is the solute, which will

be dragged into the bottom of the extractive distillation column (EDC) with the solvent. The selection of

the target solute determines the nature of the solvents to be generated.

The selection of the target solute is made considering the composition of the azeotrope, being

this information obtained as an output information from step 1.1. – Mixture selection.

Based on the molar composition of the azeotrope, the target solute, will be the component with a

molar composition in the azeotrope (𝑥𝐴𝑍 ) lower than 0,5. This criteria was established because the

aim of the extractive distillation column design is to obtain the target solute and the solvent at the

bottom of the EDC and the other compound to be purified in the top of the EDC.

After this step the user has information on the target solute and its properties (for example the

boiling point). The flow of information conducted in this step can be summarized in Figure 18.

28

Composition of the azeotropic mixture

(output obtained from step 1.1.)

Composition of the azeotropic mixture

(output obtained from step 1.1.)

Select the component that

present xAZ < 0,5

Select the component that

present xAZ < 0,5

Target solute

selected

Target solute

properties

information

Output data obtained from Step 1.2. Output data obtained from Step 1.2. Input dataInput data Selection blockSelection block

Figure 18 - Tasks follow in Step 1.2. - Selection of the target solute.

Step 1.3. Boiling point of the azeotropic mixture

The aim of this step is to know the boiling point of the azeotropic mixture, because this property is

critical for the solvents’ choice. The solvent must present a boiling point 30 − 40℃ higher than the

highest boiling of the mixture component to be separated, in order to recovery the solvent in the liquid

phase, and therefore be dragged into the bottom of the extractive distillation column.

Step 2. Solvent selection

The main objective of this step is to select the most suitable solvent for the separation of the

azeotropic mixture selected in step 1.1.. This step is divided into two sub-steps which are: Screening

of solvents (Step 2.1.) and the analysis of solvents (Step 2.2.). A detailed explanation about this step

is described in detail over this section.

Step 2.1. Solvent Screening

The main objective of this step is to obtain a list of solvents that can be used in the extraction of

the target solute defined in step 1.2. ProCAMD is a suitable tool to indicate possible solvents, which

can be used in the extraction. To reach this objective the input data for the solvent generation and

screening step is:

1) The output data obtained from step 1.2.;

2) Additional input information about solvent/mixture constraints: the solvent should not form

additional azeotropes with any of the mixture components; the boiling point should be higher

29

than the highest boiling component of the mixture to be separated; select the functional

groups allowed for the generation of the solvent through group contribution methods

(ProCAMD);

3) Input the properties constraints as selectivity and solvent power to be obtained as output

information of each solvent.

The output data is the list of solvents generated by ProCAMD. The concept of the solvent

screening concept can be summarized as shown in Figure 19.

Step 2.1. - Solvent

Screening

List of candidate

solvents.

Mixture Properties:

- Mixture components

- Mole fractions of the

mixture components

(output data obtained from

step 1.1.)

Mixture Properties:

- Mixture components

- Mole fractions of the

mixture components

(output data obtained from

step 1.1.)

Target solute (output

data obtained from

step 1.2.)

Target solute (output

data obtained from

step 1.2.)

Solvent functional groups;

Solvent target properties:

selectivity, solvent power,

boiling point, no azeotrope …

(additional data)

Solvent functional groups;

Solvent target properties:

selectivity, solvent power,

boiling point, no azeotrope …

(additional data)

ProCAMD ProCAMD

Output data obtained from Step 2.1.Output data obtained from Step 2.1.Input dataInput data Supporting Tool Supporting Tool

Figure 19 - Tasks to follow in step 2.1. - Solvent screening.

The list of solvents generated by ProCAMD is submitted to a detailed analysis in order to obtain

the most suitable solvent.

After obtaining the list of possible solvents to extract the target solute, the user must answer the

following question 1 (Q1. See Figure 15)

Q1. Is it the first time that the solvents are screened for this target solute/ or homologous serie?

This question aims to save time to the user, when the solvents have been already analysed and

data already exists. Two possible answers arise from the previous question.

In affirmative case, the user must go to the step 2.2. – Solvent analysis, once it is the first

time that solvents are being screened for this target solute/or target solute of the

homologous serie;

In negative case, the user has the possibility to use a solvent previously selected for the

target solute.

After answering to question 1 (Q1.- Is it the first time that the solvents are screened for this target

solute/or homologous serie?), the user must answer to question 2 (Q2. – See Figure 15) in order to

decide which direction is going to take.

30

Q2. Do you want to use a solvent previously selected?

This question needs to be answered when a negative answer was given to question 1 (Q1.).

Regarding this question, the user may choose between using a solvent previously selected (according

to the same target solute) or the user can analyse the solvents obtained from the step 2.1. in order to

obtain the most suitable solvent.

In affirmative case, the user has the solvent selected. Information from the database

should be used;

In negative case, the user must go to the step 2.2. – solvent analysis, to select the most

suitable solvent. This option it only makes sense if new solvents were introduced to the

solvents proposal list.

Step 2.2. Solvent Analysis

This step aims to select the best solvent (entrainer) for the separation of an azeotropic mixture.

The input data of this step is the list of solvents obtained as output data in step 2.1. - Solvent

Screening. The analysis of those solvents is made in a step-by-step procedure (see Figure 15). This

selection procedure will filter the number of solvents (Si) over the steps (i). At the end, the most

suitable solvent for the separation will be achieved. The procedure for step 2.2. comprises four tasks:

2.2.A. Selection from solvent power vs. selectivity plot;

2.2.B. Selection from solvent power vs. Hildebrand solubility parameter plot;

2.2.C. Selection from Hansen solubility parameter plot;

2.2.D. Selection from Solvent to Feed (S/F) ratio plot.

To plot the above mentioned tasks information was required. The solvent power and the

selectivity of the solvents are properties obtained as output information of step 2.1. Solvent Screening.

The Hildebrand solubility parameter and the Hansen solubility parameter of the solvents and the

mixture components are obtained using the property prediction tool ProPed. ICAS was the supporting

tool used to generate the results for the solvent to feed ratio step.

Task 2.2.A. - Selection from solvent power vs. selectivity plot

The selectivity and solvent power at infinite dilution, 𝑆𝑖𝑗∞ and 𝑆𝑝

∞, respectively, are the primary

criterion chosen to filter the solvents. The 𝑆𝑖𝑗∞ is a measure of the degree of the separation and the 𝑆𝑝

∞

is a measure of solubility. Equation 5 and 6 (see chapter 2) represent the 𝑆𝑖𝑗∞ and 𝑆𝑝

∞, respectively.

Those parameters have been selected to ensure the increase of the relative volatility , 𝛼𝑖 ,𝑗,

(Equation 3 presented in chapter 2) of the azeotropic mixture. For that reason, the selection of a

suitable solvent must reflect on solvents that present a high 𝑆𝑖𝑗∞ and a high 𝑆𝑝

∞, causing higher value

31

of 𝛼𝑖,𝑗 . The higher the value of 𝛼𝑖 ,𝑗, the easier the separation of the azeotropic mixture will be. The data

about 𝑆𝑖𝑗∞and 𝑆𝑝

∞ were obtained as output data of step 2.1. – Solvent screening.

This task has been selected as a first criteria because the screening of solvents is largely

reduced compared with the initial solvents, and the selection through the matrix is easier that the

following tasks.

The screening of solvents is made plotting the 𝑆𝑖𝑗∞ in x-axis and plotting 𝑆𝑝

∞ in y-axis. The best

solvents are those that present high values of 𝑆𝑖𝑗∞ and high values of 𝑆𝑝

∞and therefore from Figure 20,

it is observed that the best solvents are the ones that are present in the first quadrant (Red circle,

Figure 20).

Figure 20 – Selection of solvents regarding solvent power (blue) vs. Selectivity (green).

Task 2.2.B. - Selection from solvent power (𝑆𝑝∞) vs. Hildebrand solubility parameter (𝛿𝑇 ) plot

The objective of this step is to select solvents that present a value of Hildebrand solubility

parameter, 𝛿𝑇 , close to the 𝛿𝑇 of the target solute. In this step the 𝑆𝑝∞was selected to be plotted

against the 𝛿𝑇 because it is also a measure of solubility. However, the selection of the solvents through

this plot will be only reflecting the 𝛿𝑇 information, since the selection through the 𝑆𝑝∞has already been

previously done in task 2.2.A.

Since the effectiveness of a solvent depends on its ability to adequately dissolve the target

solute, while leaving the other compound unaffected, compounds with similar solubility parameter

values are more likely to form miscible solution. The Hildebrand solubility parameter is a numerical

value that indicates the relative solvency behavior of a specific solvent, that is why this parameter has

been selected as a second selection criteria. Consequently, solvents presenting a 𝛿𝑇 close to the 𝛿𝑇 of

the target solute and far from the other compound will be selected, creating only a miscible solution

between the target solute and the selected solvents.

Figure 21 shows an example of how the selection of the solvents is made when solvent power vs.

Hildebrand solubility parameter is plotted for a generic mixture of compound A and compound B,

where the target solute is compound B.

32

The solvents that are inside the green rectangle are the solvents selected (green triangles),

because they are close to the target solute (similar value of 𝛿𝑇 ) and far from compound A (do not mix

with this compound). The solvent that is represented by a purple triangle (Figure 21) is not a suitable

solvent to be selected since the 𝛿𝑇 is far from the 𝛿𝑇 of the target solute and therefore the might not be

miscible. Finally, the solvent that is represented by the red triangle (Figure 21) it is also not a suitable

solvent to be selected, because despite 𝛿𝑇 is close to the 𝛿𝑇 of the target solute, is may be miscible

with compound A. Solvents which are in the area between 𝛿𝑇 − compound A and 𝛿𝑇 − compound B,

will be an undesirable choice and therefore they will be excluded.

The range applied for the selection of solvents should start in the value of the 𝛿𝑇 of the target

solute and the maximum value of the 𝛿𝑇 of the solvent should have 𝛿𝑇 = ±4 MPa1 2⁄ according to the

target solute position (Figure 21).

Figure 21 - Selection of solvents for a generic mixture of compound A and compound B where compound B is the

target solute.

For the case where compound A is the target solute, the solvents to be selected are in the

maximum range of values: 𝛿𝑇 = 6 − 10𝑀𝑃𝑎 1 2⁄ .

Task 2.2.C. - Selection from Hansen solubility parameter plot;

The aim of this step is to select the solvents that present values of Hansen solubility parameter

(HSP) similar to the HSP of the target solute. According to Hansen, the total solubility parameter 𝛿 is

expressed by the sum of the three contributions representing the dispersive (𝛿𝐷), the polar (𝛿𝑃) and

the hydrogen-bonding (𝛿𝑃) interactions (see equation 8, Chapter 2).

The use of the Hansen’s solubility parameter is suggested to be a criteria in the screening of

solvents, because it is composed by three contributions interactions, which allow to have deeper

knowledge about the solvents. Compounds with similar HSP have high affinity for each other. The

extent of similarity in a given situation determines the extent of the interaction. Regarding this, the

closer the Hansen solubility for the target solute and the solvents are, the better the solubility is

between both.

This parameters were established as a third criteria for the solvent selection, since in this step it

is important to have a smaller number of solvents, because the HSP analysis requires the plot of three

33

graphs, taking more time to analyze the solvents. Another fact is that this step must be applied after

the Hildebrand solubility parameter step analysis, because firstly it is necessary to analyze from a

general point of view in terms of solubility of the solvents (Task 2.2.B. - Selection from solvent power

(𝑆𝑝∞) vs. Hildebrand solubility parameter plot 𝛿𝑇 ) and then go to a more specific approach (Task

2.2.C.).

The screening of the solvents is performed through the analysis of three plots which are:

𝛿𝐷 𝑣𝑠 𝛿𝐻

𝛿𝑃 𝑣𝑠 𝛿𝐻

𝛿𝐷 𝑣𝑠 𝛿𝑃

The solvents, which present values of HSP close to the values of HSP of the target solute, will be

selected. In order to determine the suitable solvents, a circle centred in the HSP of the target solute

value is created. The solvents that are inside that circle are selected. For the two plots correspondent

to: 𝛿𝐷 𝑣𝑠 𝛿𝐻 and 𝛿𝑃 𝑣𝑠 𝛿𝐻 it is observed that for both plots the 𝛿𝐻 is plotted in the x-axis, and for that

reason that parameter will define the diameter of the circle. The circle centered in the target solute

should have a diameter between 2 − 8𝑀𝑃𝑎1 2⁄ . For the third plot, correspondent to 𝛿𝐷 𝑣𝑠 𝛿𝑃 , it is

observed that as the 𝛿𝑃 is plotted in the x-axis, this parameter will define the diameter of the circle for

this plot. For this case, the circle centred in the target solute should have a diameter between 2 −

4𝑀𝑃𝑎 1 2⁄ .

An important feature is that the circle cannot be close to the other component; otherwise the

solvents will present HSP similar to the undesirable component and therefore they can mix. For that

reason the most important criteria is that the border line of the circle created around the target solute

(the side closer to the other component) must be far from the other component at least 4𝑀𝑃𝑎 1 2⁄ . This

should happen for the three plots and if it is not possible to reach this distance, the circle must be

relocated to the opposite side of the solute until the border of the circle match the HSP value of the

target solute. In this last case, the circle is not anymore centred at the target solute. An example is

presented to better understand these criteria.

As the screening of solvents is made in the same way for the three plots, Figure 22 shows an

example of how the selection of the solvents is made when the 𝛿𝑃 𝑣𝑠 𝛿𝐻 is plotted for a generic

mixture of component A and component B, where the target solute is component B.

For the example presented in Figure 22, two circles are created in turn of the target solute

(component B). The green circle with 𝐷𝑔𝑟𝑒𝑒𝑛 𝑐𝑖𝑟𝑐𝑙𝑒 = 4𝑀𝑃𝑎1 2⁄ and a purple circle 𝐷𝑝𝑢𝑟𝑝𝑙𝑒 𝑐𝑖𝑟𝑐𝑙𝑒 =

8𝑀𝑃𝑎 1 2⁄ . Component A presents a 𝛿𝐻,𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝐴 = 14𝑀𝑃𝑎 1 2⁄ and component B has 𝛿𝐻,𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝐵 =

8𝑀𝑃𝑎 1 2⁄ . From the information given by Figure 24, it is observed that the distance between the

component A and the lower limit of the green circle is 𝛿𝐻,𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝐴 −𝑔𝑟𝑒𝑒𝑛 𝑐𝑖𝑟𝑐𝑙𝑒 = 4𝑀𝑃𝑎 1 2⁄ and the

distance between component A and the lower limit of the purple circle is 𝛿𝐻,𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝐴−𝑝𝑢𝑟𝑝𝑙𝑒 𝑐𝑖𝑟𝑐𝑙𝑒 =

34

2𝑀𝑃𝑎 1 2⁄ . In this example it is shown that the 𝐷𝑝𝑢𝑟𝑝𝑙𝑒 𝑐𝑖𝑟𝑐𝑙𝑒 = 8𝑀𝑃𝑎1 2⁄ for the screening of solvents

cannot be chosen, because the solvents that are intended to mix with the target solute will also be

miscible with component A. Therefore, the user has to be critical in order to decrease the diameter of

the circle to a value equal or lower to 𝐷 𝑐𝑖𝑟𝑐𝑙𝑒 = 4 𝑀𝑃𝑎 1 2⁄ to only select solvents that present HSP

values close to the HSP values of the target solute and far from the component A HSP values. So the

distance between the 𝛿𝐻,𝑐𝑜𝑚𝑝𝑜𝑛𝑒𝑛𝑡 𝐴 and the lower limit of the circle should be equal or higher to

𝛿𝐻 = 4 𝑀𝑃𝑎 1 2⁄ .

Figure 22 - Selection of solvents for a generic mixture of component A and component B where component B is

the target solute.

The solvents to be selected are those that are inside the circle and must be within at least two of

the three HSP plots. As this criteria can be hard to reach for several mixtures another solution is

created. To select solvent candidates that have HSP close to the HSP of the target solute, and far

from the other compound and must be present at least in two of the three HSP plots, the circle might

need to suffer a translation. Figure 23 shows how the translation of the circle can be made, for a

generic mixture of component A and component B, where component B is the target solute.

Figure 23 – Translation of the circle created in turn of the target solute, for the generic mixture of component A

and component B where component B is the target solute.

Regarding Figure 23, the green circle created in turn of the target solute need to move towards

the right side, in the opposite side of component A, since it is intended to select solvents whose HSP

values are close to the HSP values of the target solute (component B) and far from the HSP values of

35

component A. The circle will move to the further position that it can get from component A (See Figure

23, red circle).

In other situations where component A would be the target solute, the circle created in turn of the

component A should move to the left side, in the opposite side of component B.

The solvents to be selected are inside the circle created and must be present at least in two of

the three HSP plots.

Task 2.2.D. – Solvent to Feed (S/F) ratio

This task is the last task of the solvent selection step. This last step requires more calculations

because the solvents must be analyzed one by one and therefore this criteria should be the last to be

analyzed.

The main objective of this step is to select the most suitable solvent taking into account the

required quantity of solvent to break the azeotrope mixture. This variable has a great impact on the

economics of an extractive distillation, and for economic acceptable solvents operating solvent to feed

(S/F) ratio should be between two and five.

The input data of this step is the list of solvents obtained from task 2.2.C. - Selection from

Hansen solubility parameter plot. The concept of task 2.2.D. - Solvent to Feed (S/F) ratio can be

summarized as shown in Figure 24.

Task 2.2.D. – Solvent to

Feed (S/F) ratio

Mixture components

(output data obtained

from step 1.1.)

Mixture components

(output data obtained

from step 1.1.)

List of solvents obtained

from task 2.2.C.

List of solvents obtained

from task 2.2.C.

ICAS ICAS

VLE graphs for

differen solvents at

different S/F ratio

Select the solvent that present

the lower value of S/F ratio;

Select the solvent that present

the lower value of S/F ratio;

The most suitable

solvent is selected

Output data obtained from Step 2.2.D.Output data obtained from Step 2.2.D.Input dataInput data Supporting Tool Supporting Tool Selection blockSelection block

Figure 24 - Tasks to follow in Task 2.2.D. – Solvent to Feed (S/F) ratio.

With the mixture components (output data obtained from step 1.1.) and the list of solvents

obtained from task 2.2.C., those variables are introduced in the supporting tool ICAS it will generate

the VLE graphs of the mixture components with different solvents at different values of S/F ratio.

The selection of the most suitable solvent is made to the solvent that presents the lower value of

S/F ratio that means the solvent that requires a lower quantity to break the mixture components.

For solvents that present the same S/F ratio, the user must select from the VLE plot the solvent

that presents the highest curve (the higher value of the molar composition in the vapor phase),

because that solvent will be more effective in the separation of the mixture components. However it

may happen in rare cases, that for fixed values of S/F ratio the solvents present the same behaviour,

and in those cases the user must check the task 2.2.A. - Selection from solvent power vs. selectivity

plot, in order to select the solvent that present the highest value of selectivity and the highest value of

solvent power.

36

The VLE plot is presented for a generic mixture of component 1 and component 2 in Figure 25,

when solvent A, solvent B and solvent C present the same value of S/F ratio but different curves.

Figure 25 - VLE plot of a generic mixture of component 1 and component 2, when S/F ratio is fixed for the three

solvents: solvent A, solvent B and solvent C, when the solvents present different curves.

From Figure 25, solvent A must be selected, because it is the solvent exhibiting the highest

curvature (better performance in the mixture component separation).

In Figure 26, the VLE plot is presented to a generic mixture of component 1 and component 2

when solvent A, solvent B and solvent C present the same value of S/F ratio and the same curves

behaviour (the solvents present the same effectiveness for the separation of the generic mixture of

component 1 and component 2).

Figure 26 - VLE plot of a generic mixture of component 1 and component 2, when S/F ratio is fixed for three

solvents: solvent A, solvent B and solvent C and the solvent curves present the s ame behaviour.

For the case presented in Figure 26, for a fixed value of S/F the solvents present the same

behavior. In this case, an auxiliary step is needed, and the user must check Task 2.2.A. – Plot of

𝑺𝒑∞ 𝒗𝒔. 𝑺𝒊𝒋

∞ to make the final decision. The most suitable solvent must present the highest value of

selectivity and the highest value of solvent power. If it is not possible to achieve this criteria, a solvent,

which presents a high value of selectivity and a low value of solvent power is preferable than a solvent

that presets a low value of selectivity and a high value of solvent power. The reason to give priority to

the selectivity is explained by observing Equation 5 and 6, presented in Chapter 2. If the solvents

present the same value of selectivity and solvent power, the user can selected the user can feel free

to choose the solvent.

37

When the solvent is selected, the user is faced with question 3 (Q3. See Figure 15).

Q3. Is it the first time that the selected solvent is used for the target solute?

With the solvent selected, this question is made in order to allow the user to design the

separation process with the solvent obtained from step 2. However, for solvents already analysed and

included in the database the separation process design does not need to be performed from the

scratch, being only necessary some small adjustments.

In affirmative case, the user must go to step 3. – Design & Analysis; to get the full design

of the extractive separation process using the solvent selected.

In negative case, the user may go to step 4. – Fine tune the design available at the

database;

If the answer from Q3. Is negative, than the user must answer to question 4 (Q4. See Figure 15).

This question comes from the negative answer to question 3 (Q3.). This question is applied when

the solvent selected was already used for the target solute. With the same solvent (according to the

same target solute) the user must know if the mixture belongs to an azeotropic mixture of the

database.

Step 3. Design & Analysis

The objective of this step is to design the separation process of an extractive distillation column

(EDC) and a solvent recovery column (RC) using the solvent selected in task 2.2.D.

In the extractive distillation process (see Figure 27), the first column is an extractive distillation

column and the second column is the solvent recovery distillation column. The solvent is fed into the

extractive distillation column, above the azeotrope mixture feed. One of the components of the

azeotrope mixture is withdraw at the top of the extractive distillation column, while the other

component, together with the solvent, forms the bottom product of the extractive column. In the

recovery column, the solvent is separated from the second feed component of the azeotrope mixture

and recycled to the first column.

Figure 27 –Sketch of the extractive distillation process (Luo, H. et al., 2014).

38

Step 3.1. – Pre-Design of extractive distillation column (EDC) and recovery column (RC)

The aim of this step is to obtain the pre-design of the extractive distillation column and the

recovery column before applying the rigorous simulation, because it is easier to have an

approximation of the design variables before starting the rigorous simulation, without any idea of the

design of the separation process. This initial design doesn’t require calculations, since the design

variables of the separation process are predicted using the supporting tool such as Process Design

Studio, PDS, (Section 2.4 in Chapter 2).

For the extractive distillation column, the standard approach for process design of such

separation processes usually involves the detailed specification of all relevant design parameters: the

number of stages, 𝑁, the reflux ratio, 𝑅𝑅, the feed stage of the mixture to be separated, 𝑁𝐹, and the

solvent feed stage, 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 . For the recovery column, the design parameters are the same as

described for the extractive distillation column, with the exception of the 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 , since the recovery

distillation column only has one feed.

The input data necessary to generate the design variables are: the mixture components to be

separated (output data obtained from step 1.1.), and the identification of the heavy component (higher

boiling) and the light component (lower boiling) in the mixture. With the input data, the user runs the

PDS software, and the pre-design of the extractive column and the recovery column are obtained as

output data. The output data given by PDS consists on the following variables: Minimum reflux ratio

(RRMIN), the reflux ratio (RR), the minimum number of stages (NMIN) and the feed stage of the mixture

to be separated (NF).

The solvent feed stage, 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 , is not given by PDS, because this tool only gives the design of

systems that present only one feed (in this case, the feed of the mixture to be separated). Usually, the

the 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 is introduced in the EDC one or two stages bellow the condenser, therefore this

information will be applied in the pre-design for this variable.

Step 3.2. – Simulation & Sensitivity Analysis

The aim of this step is to obtain the design of the extractive distillation process of the mixture

components to be separated regarding the achievement of target specifications. Target specifications

are parameters such as: stream purity; component recovery; condenser duty; reboiler duty; reflux

ratio; among others, which the user must specify before running the simulation, in order to get those

target specifications in the final process design.

The input data of this step is composed by:

The pre-design of the extractive distillation column and the recovery column obtained as

output data in step 3.1.;

The mixture components obtained as output data in step 1.1.

The selected solvent and properties obtained as output in step 2.

39

In this simulation, the RadFrac model of AspenPlus was selected as the distillation column

applied to simulate the EDC and the RC. This model has been selected, because it is the most

rigorous for distillation columns. A generic flowsheet of the process simulation in AspenPlus is

presented in Figure 28.

Figure 28 - Generic flowsheet of the process simulation in AspenPlus.

The final simulation & sensitivity analysis step can be summarized as shown in Figure 29.

Step 3.1. – Simulation &

Sensitivity Analysis

Design of extractive

distillation separation

Pre-Desig of the EDC and

RC (output data obtained

from step 3.1.)

Pre-Desig of the EDC and

RC (output data obtained

from step 3.1.)

AspenPlus AspenPlus

Output data obtained from Step 2.1.Output data obtained from Step 2.1.Input dataInput data Supporting Tool Supporting Tool

Composition and Temperature of

the mixture components to be

separated (output information

obtained from step 1.1.)

Composition and Temperature of

the mixture components to be

separated (output information

obtained from step 1.1.)

The S/F ratio (output information

obtained from task 2.2.D)

The solvent selected (output data

obtained from step 2.)

The S/F ratio (output information

obtained from task 2.2.D)

The solvent selected (output data

obtained from step 2.)

Figure 29 - Tasks to follow in step 3.1. – Simulation & Sensitivity analysis.

During the simulation, several sensitivity analysis must be done for both extractive distillation and

recovery column, in order to fine tune the output variable, getting them closer to the target

specifications at a maximum performance.

For the extractive distillation column and recovery column the variables analysed were as follows:

RR, N, NF, and S/F ratio. The sensitivity analysis can be carried out using option 1 or option 2:

Option 1) Fix the variable N: vary the S/F ratio and the RR in order to see what happens to those

variables for a fixed target specification;

Option 2) Fix the variable S/F ratio: vary the N and the RR in order to see what happens to those

variables for a fixed target specification;

The user obtains the separation process design after getting the design of the EDC and RC

reaching the target specification.

RC

EDC

MIXERD2

RECSOLV

SOLVMIX

AZEO

D1

D2+SOLV

MAKEUP COOLER

SOLVENT

40

Step 4. Fine tune the design available at the database

The aim of this step is to perform minor modifications of the separation process design of the

mixture components being previously analysed, (mixture belonging to an azeotropic mixture of the

database). To reach this step it is necessary to collect azeotropic mixtures to a database. So, at each

time that the methodology runs, the mixtures are saved in the database. As many times the

methodology runs, more mixtures will be added to the database and will allow the user to save time,

coming directly to step 4, avoiding step 3.

Step 4.1. Adjust the separation process design

The user will reach this step whenever:

The target solute and the solvent are in the database;

The mixture present in the database: Component 1 is fixed and the second component is part

of a homologous series.

An homologous series is a series of compounds with the same general formula, usually varying

by a single parameter – such as the increasing of the carbon number (Gourley, H.R. et al, 1964):.

The compounds of a homologous series:

Have the same general formula ( paraffins - 𝐶𝑛𝐻2𝑛+2);

Neighbors differ by one 𝐶𝐻2 group (e.g. methane (𝐶𝐻4) and ethane (𝐶2𝐻6);

Have similar chemical properties;

Gradually changing physical properties (e.g. the boiling point of paraffins increases

with the number of carbons).

The aim of this step is to fine tune the design variables of the separation process design of the

current azeotropic mixture, when the user reaches the two cases above mentioned.

The input data of this step are: the component mixture obtained as output data in step 1.1. and

the solvent selected in step 2., when Q3. (See Figure 15) is answered negatively and Q4. (See Figure

15) is answered positively.

An example is presented when the user reaches step 4, and the target solute and the solvent are

in the database; Example: the mixture methanol-n-pentane is in the database, the target solute is

methanol, and the solvent is solvent A. From Table 3, it is observed that for the mixture in study

(methanol-n-hexane) the target solute and the solvent are the same as presented in the database, so

the separation process design of methanol-n-pentane can be applied to the separation of methanol-n-

hexane, and only minor modifications are going to be observed.

41

Table 3 - The mixture in study: Methanol-n-hexane when the target solute is methanol using solvent A, for the separation process.

Component 1 Component 2 Target solute Solvent

Methanol n-Hexane Methanol A

An example is now presented when step 4 is reached and the mixture in the database is:

component 1 fixed and the second component is part of a homologous series ; Example; the mixture of

the database is methanol-n-heptane, the target solute is n-heptane, and the solvent used is solvent B.

The mixture presented in Table 4 present the same component 1 (methanol), but the target solute is

different compared with the target solute of the database; however, as on both case the target solute

is a component that belongs to the same homologous series, same functional group, (n-heptane in the

mixture of the database and n-octane in the mixture in study), they present similar chemical and

physical properties, and the target solute can be assumed to be the “same”, in order to be able (if the

user wants) to use the same solvent (Solvent B) for the mixture in study, and therefore the process

design of methanol-n-heptane can be used for the separation of methanol-n-octane, and only minor

modifications are going to be observed.

Table 4 - The mixture in study: Methanol-n-octane when the target solute is n-octane

Component 1 Component 2 Target solute

Methanol n-Octane n-Octane

When the target solute and the solvent are together in the database, the user should at first, plot

the driving-force (DF) diagram for both mixtures (the mixture in study and the mixture in the database)

in order to see the efficiency of the separation process (Section 2.3.1. in Chapter 2). If they present

similar DF plots, the user will only need to make small modifications in the process design; if they

present different DF, the user will need to make more modifications to the process design.

An example is presented to illustrate the explanation given about the efficiency of the separation

of mixture components according to the DF method, for two generic mix tures: Mixture A, is the mixture

in the database – design is known and Mixture B is the mixture in study (See Figure 30).

Figure 30 - Driving force diagram of mixture A (red line) and mixture B (blue line).

42

Figure 30 illustrates the DF plot of mixture A. (red line) and mixture B. (blue line). The driving

force is plotted in the y-axis. From Figure 30, it is noticed that the DF of mixture A is lower than the DF

of mixture B, meaning that it is easier to separate the components of mixture B, when compared with

the separation of the components that belong to mixture A, and the separation process design will

require major modifications, since the DF value is different for the two mixtures.

Knowing the process design for the mixture of the database and knowing its driving force value,

the user should use the data available in Figure 55 (Appendix 1), to know the parameter changes that

should be performed in order to obtain the required target specifications. (The same procedure is

made for the case whenever: Component 1 is fixed and the second component is part of a

homologous series in the database).

With the design variables obtained using Figure 55 (Appendix 1), the user must introduce those

variables in AspenPlus (RadFrac routine is the model used for the simulation of the columns) in order

to run the simulation and apply the sensitivity analysis and obtain the final process design.

3.2. Conclusions

A generic methodology for the separation of binary azeotropic mixtures, through extractive

distillation has been presented. The proposed methodology is able to deal with any type of binary

homogenous azeotropes.

The methodology can be summarized in 4 main steps: Step 1. – Problem definition, step 2.-

solvent selection, step 3. – Design & Analysis and step 4. – Fine tune the design available in the

database. This methodology allows the determination of the most suitable solvent for a specific target

solute. The generation of solvents according to the target solute allows to obtain better results in terms

of efficiency of the solvent and energy consumption of the separation process.

Finally, the output of this methodology can be incorporate into a database, saving time in

process synthesis design of azeotropic mixtures.

This systematic methodology complemented with the supporting tools presented over the project

shows that it is possible to obtain in a fast and reliable way the most suitable solvent for the separation

of an azeotropic mixture. Another factor that makes this methodology so exclusive is due to the fact

that with a separation process design in the database a mixtures being analysed that have similar

properties than the one present in the database will allow the user to use the same process design or

only fine tune variables are required.

43

4. Application of the proposed methodology to the case study: ethanol-paraffins

In this chapter, the detailed procedures of the proposed methodology will be applied to a case

study. This chapter is divided in six sections. In Section 4.1. an overview about the selection of this

case study is presented. Section 4.2. presents the main results obtained for the application of the

methodology to ethanol-n-pentane. In the third section (Section 4.3.), the main results obtained for the

application of the methodology to ethanol-n-hexane are presented. In Section 4.4., the results

obtained for the application of the methodology to ethanol-n-heptane are shown. The fifth section

(Section 4.5.) presents the main results obtained for the application of the methodology for both

ethanol-n-octane and ethanol-n-nonane systems. In section 4.6., the conclusions about the selection

of the target solute are presented. In the end of the chapter general conclusion are presented

(Section 4.7.).

4.1. Case study description

In order to highlight the proposed methodology the case study: ethanol-paraffins is going to be

analysed. In this case study compound 1 will be fixed and it will be ethanol. Compound 2 will change,

however compound 2 will belong to the same series of organic compound. This means that the first

compound 2 will be the n-pentane and then in each iteration the compound will keep the same

functional groups, increasing only the number of carbons. Therefore, the following azeotropic

mixtures: ethanol-n-pentane, ethanol-n-hexane, ethanol-n-heptane, ethanol-n-octane and ethanol-n-

nonane are going to be analysed in an iterative process. This case study has been selected in order to

show the potential of this methodology. This case study will allow to compare the proc ess design of a

list of azeotropes, which belong to an organic series. Therefore it would be possible to analyse the

influence of the carbon increase in the selection of the solvent and in the process design.

The first binary azeotropic mixture being analysed is ethanol-n-pentane since n-pentane is the

paraffin with the lower carbon number from the ethanol paraffins series of azeotropes.

4.2. Ethanol-n-pentane

Step 1 – Problem Definition (Step 1.1. – Mixture Selection)

As the azeotropic mixture is known (ethanol-n-pentane), the input data of step 1.1., is ethanol

(Compound 1) that is selected from the list of compounds present in AzeoPro Database, and the

selection of n-pentane to be compound 2 is made from the list of compounds that form azeotropes

with Compound 1.

44

The pressure of the azeotrope is 101,32 kPa.

The output information of this step are the temperature and the composition of the azeotrope,

which are presented in Table 5.

Table 5 – Temperature and composition of the binary azeotrope: ethanol-n-pentane (AzeoPro).

Azeotrope information 𝑻𝑨𝒛(𝑲) 𝑷 (𝒌𝑷𝒂) 𝒙𝒊 (𝒆𝒕𝒉𝒂𝒏𝒐𝒍) 𝒙𝒋(𝒏 − 𝒑𝒆𝒏𝒕𝒂𝒏𝒆)

307,15 101,32 0,089 0,911

The azeotrope should also be classified in homogenous pressure-maximum (homPmax) or

homogenous pressure-minimum (homPmin), because this information is an output of this step.

From the VLE chart presented in Figure 31(b), it is observed that experimental points are plotted

together with the model-based VLE and it can be seen a good agreement between the experimental

points and the model-based applied in AzeoPro. This information validates the data obtained in

AzeoPro. Also from Figure 31(b) it is observed that ethanol-n-pentane is a homogenous pressure-

maximum (minimum boiling azeotrope).

Figure 31 – VLE screen showing two different VLE charts: (a) x-y VLE plot; (b) T-x-y VLE plot (AzeoPro).

Step 1.2. – Selection of the target solute

The target solute is the component that leaves the bottom of the extractive distillation column

(EDC). Observing the composition of ethanol and n-pentane mixture in the azeotrope, presented in

Table 5, it is observed that ethanol has a composition n the azeotrope lower than 0,5 (𝑥𝐴𝑍𝑒𝑡ℎ𝑎𝑛𝑜𝑙 =

0,089) and therefore ethanol is selected as the target solute.

Step 1.3. – Boiling point of the mixture components

Table 6 shows the boiling point of ethanol and n-pentane.

Table 6 – Boiling point of ethanol, and n-pentane obtained from ProPed.

Compound Tb (K)

Ethanol 351,52

N-pentane 309,25

45

As the solvent must present a boiling point 30 − 40℃ higher than the highest boiling of the

mixture component to be separated, in Table 6, it is observed that ethanol has the higher boiling

component. So it can be concluded that the solvent should present a higher boiling point than ethanol.

Step 2. – Solvent selection

The objective of this step is to select the most suitable solvent for the separation of the mixture

components ethanol-n-pentane when the target solute is ethanol. As the solvent must extract ethanol,

the nature of the solvent to be generated must have particular properties in order to only affect ethanol

and not n-pentane. The selection of the most suitable solvent is made following the sub-steps of step

2., as presented in Figure 15 in Chapter 2.

Step 2.1. – Solvent screening

The objective of this step is to obtain a list of solvents that can be used in the extraction of

ethanol and ProCAMD was applied to generate the list of candidate solvents.

The input information introduced in ProCAMD for the generation of the list of solvents is:

The type of solvents that we want to generate: acyclic compounds are generated with the

following functional groups: alcohols, ketones, aldehydes, acids, esters and ethers;

The solvent should not form additional azeotropes with any of the mixture components ;

The boiling point must be higher than the boiling point of ethanol.

Table 7 shows the input data introduced in ProCAMD.

Table 7 – Input information introduced in ProCAMD.

Parameter Value

Molar composition of ethanol in the azeotrope 0,0611

Molar composition of n-pentane in the azeotrope 0,9389

Target solute Ethanol

𝑻𝒎𝒊𝒏 ,𝒔𝒐𝒍𝒗𝒆𝒏𝒕(𝑲) 381

Minimum value of Selectivity 0,1

Minimum value of Solvent Power 0,1

The UNIFAC (VLE) was the model selected to calculate the thermodynamic properties of the

solvents. ProCAMD has generated a list of solvents in which fulfil the desired properties enounced in

this step.

A total of 458 solvents were generated. This number includes all the compounds present in

ProCAMD (the ones that are present in the ProCAMD database, and the ones that are not in the

database). For this project, only the compounds, which properties are in the ProCAMD database were

taking into account so, just 54 compounds were obtained as candidate solvents. The 54 solvents are

46

listed in Table 43 (Appendix 3.A.). For each compound obtained as a solvent candidate, a list of their

properties is presented as can be seen in Figure 57 (Appendix 4).

With the list of solvents generated by ProCAMD, Q1. must be answered. As it is the first time that

the screening of solvents is made according to the target solute, because it is the first time that a

mixture is being analysed, the list of solvents obtained in step 2.1. must be analysed in step 2.2..

Step 2.2. – Solvent Analysis

The list of 54 solvents obtained as output in step 2.1., will be screened through the following

steps.

Task 2.2.A. – Selection from solvent power 𝑆𝑝∞ vs. selectivity 𝑆𝑖𝑗

∞ plot;

Figure 32 plots the 𝑆𝑖𝑗∞ in x-axis and the 𝑆𝑝

∞ in y-axis, in order to determine the solvents, which

are on the right and up side of the plot. From Figure 37, it is observed that the best solvents are the

ones that are present in the first quadrant (blue rectangle). The range used for the selection was:

selectivity higher than 4; solvent power higher than 0,9. The selection of this range comes from the

fact that a high concentration of solvents presents selectivity between 1 and 4 and the solvent power

is almost constant, meaning that solvents that present higher value are more disperse in the plot , and

a lower number of solvent is screened.

Figure 32 - Selection of solvents regarding task 2.2.A. Selection from solvent power vs. selectivity.

From this step, a total of 16 solvent candidates were obtained as presented in Figure 33.

Figure 33 – The solvents obtained as output data of task 2.2.A.

47

Task 2.2.B. - Selection from solvent power (𝑆𝑝∞) vs. Hildebrand solubility parameter plot 𝛿𝑇

The objective of this step is to select solvents that present a value of Hildebrand solubility

parameter, 𝛿𝑇 , close to the 𝛿𝑇 of ethanol. The input data of this step are the 16 solvent candidates

obtained as output data in task 2.2.A.

The 16 solvents were plotted jointly with ethanol (𝛿𝑇,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 = 21,87 𝑀𝑃𝑎1 2⁄ ) and n-pentane

(𝛿𝑇 ,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 = 17 𝑀𝑃𝑎1 2⁄ ) as can be observed in Figure 33. The selection of the solvents is made

when the 𝛿𝑇 of the solvents are close to the 𝛿𝑇 of ethanol and far from the 𝛿𝑇 of n-pentane.

A vertical line is drawn to represent the value of 𝛿𝑇,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 = 21,87 𝑀𝑃𝑎 1 2⁄ in order to define that

the solvents that are in the left side of that line are not selected. The range of values of the Hildebrand

solubility parameter for the selection of the solvents was between 𝛿𝑇 = 21,87 − 24,87 𝑀𝑃𝑎1 2⁄ .

The solvents that are in the right side of the orange line and inside the blue rectangle were

selected, as it can be observed in Figure 34. The reason of not selecting all the solvents in the right

side of the orange line is due to the fact that the solvents must present close values to 𝛿𝑇,𝑒𝑡ℎ𝑎𝑛𝑜𝑙, and

for this case study it was considered that the solvents that present a Hildebrand solubility parameter

higher than 𝛿𝑇 = 24,87 𝑀𝑃𝑎1 2⁄ were not considered solvent candidates.

Figure 34 - Selection of solvents for the mixture components ethanol-n-pentane, where ethanol is the target

solute, regarding task 2.2.B.

A total of 6 solvents were the output data obtained in this step, as can be observed in Figure 34.

Task 2.2.C. - Selection from Hansen solubility parameter plot;

The aim of this step is to select the solvents that present values of Hansen solubility parameter

(HSP) similar to the HSP of ethanol. The 6 solvents obtained as output data in step 2.2.B. are

analysed in three graphs which are:

𝛿𝐷 𝑣𝑠 𝛿𝐻

𝛿𝑃 𝑣𝑠 𝛿𝐻

𝛿𝐷 𝑣𝑠 𝛿𝑃

48

The HSP of the solvents are presented in Table 8 and the HSP of ethanol and n-pentane can be

observed in Table 8.

Table 8 – Information about the values of HSP of the solvents obtained in task 2.2.B.

Compounds δD

(MPa1/2

) δP

(MPa1/2

) δH

(MPa1/2

)

acetic acid 16,04 5,51 9,8

propionic acid 16,04 5,36 9,59

hexylene glycol 15,62 7,95 20,59

neopentyl glycol 15,73 8,9 21,11

2,3-butanediol 16,23 7,11 19,34

acetaldol 15,7 12,43 17,11

Table 9 – information about the values of HSP of ethanol and n-pentane.

δD

(MPa1/2

) δP

(MPa1/2

) δH

(MPa1/2

) δT

(MPa1/2

)

Ethanol 15,59 6,81 13,8 21,87

N-pentane 15,13 3,55 3,78 17

As explained in Section 2.2.5.1. in Chapter 2, the creation of a circle for the three plots is made to

select only the solvents that are inside that circle (HSP of solvents similar than HSP of ethanol). For

the plot relative to 𝛿𝐷 𝑣𝑠 𝛿𝐻, the circle has its centre in ethanol (orange circle in Figure 38), and the

diameter of that circle is 𝛿𝐻 = 8𝑀𝑃𝑎1 2⁄ . The reason of choosing the maximum value of diameter

comes from the fact that the 𝛿𝐻,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 is quite far compared with the 𝛿𝐻,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 (distance between

both compounds 𝛿𝐻,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 − 𝛿𝐻,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 ≈ 10 𝑀𝑃𝑎1 2⁄ . Regarding this fact, it is possible to create a

circle with a diameter equal to 𝛿𝐻 = 8𝑀𝑃𝑎 1 2⁄ once the distance between the 𝛿𝐻,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 and the

lower limit of the circle is higher to 𝛿𝐻 = 4𝑀𝑃𝑎1 2⁄ (that distance was stablished as the security

distance between the lower boarder line of the circle created and n-pentane in order not to select

solvents that may be miscible with n-pentane). That distance between the lower limit of the circle and

n-pentane is represented by a D in the 𝛿𝐷 𝑣𝑠 𝛿𝐻 plot as can be observed in Figure 38. However it will

be necessary to displace the orange circle to the right side (blue circ le) because:

1) In 𝛿𝐷 𝑣𝑠 𝛿𝐻 plot acetaldol is the only that is inside the orange circle (Figure 35);

2) In 𝛿𝑃 𝑣𝑠 𝛿𝐻 there are no solvents inside the orange circle (Figure 36);

3) In 𝛿𝐷 𝑣𝑠 𝛿𝑃 there are no solvents inside the circle once the circle is not far enough from n-

pentane (Figure 37).

49

Figure 35 - The plot of 𝛿𝐷 𝑣𝑠 𝛿𝐻 for the solvents obtained in task 2.2.B. and of the mixture components (ethanol and n-pentane).

For the plot 𝛿𝑃 𝑣𝑠 𝛿𝐻, the circle has its centre in ethanol (orange circle in Figure 36), and the

diameter of that circle is 𝛿𝐻 = 8𝑀𝑃𝑎1 2⁄ . The reason of choosing the maximum value of diameter

comes from the same fact explained for the plot of 𝛿𝐷 𝑣𝑠 𝛿𝐻. Regarding this fact, it is possible to

create a circle with a diameter equal than 𝛿𝐻 = 8𝑀𝑃𝑎 1 2⁄ once the distance between the 𝛿𝐻,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒

and the lower limit of the circle is higher than 𝛿𝐻 = 4𝑀𝑃𝑎 1 2⁄ . The plot of 𝛿𝑃 𝑣𝑠 𝛿𝐻 is presented in Figure

36.

Figure 36 - The plot of 𝛿𝑃 𝑣𝑠 𝛿𝐻 for the solvents obtained in task 2.2.B. and of the mixture components (ethanol and n-pentane).

For the plot δD vs δP, the circle has is centre in ethanol and the diameter of that circle is δP =

5 MPa1 2⁄ (Figure 37(a)).

This plot was just made to show that for a circle with a diameter equal than δP = 5 MPa1 2⁄ the

distance between δP,n−pentaneand the lower boarder of the circle is around δP ≈ 1 MPa1 2⁄ , meaning that

this circle is not adequate once the distance between them should be δP > 4 MPa1 2⁄ . Regarding this,

another circle was made, with a diameter equal to δP = 3 MPa1 2⁄ as can be observed in Figure 37(b).

However, even with the decrease of the diameter of the circle the distance D, is around δP ≈ 3 MPa1 2⁄ .

50

Figure 37 - The plot of 𝛿𝐷 𝑣𝑠 𝛿𝑃 for the solvents obtained in task 2.2.B. and for the mixture components (ethanol

and n-pentane) when the circle has his centre in ethanol with a diameter equal than 𝛿𝑃 = 5𝑀𝑃𝑎1 2⁄ (a); and when

the circle has his centre in ethanol with a diameter equal than 𝛿𝑃 = 3 𝑀𝑃𝑎1 2⁄ (b).

The translation is made for the three plots in the same way represented as a blue circle in Figure

35, Figure 36 and Figure 37(b). With the circles translated, the solvents to be selected must be inside

the blue circle and should be present at least in two of the three HSP plots.

As a conclusion of this step, the solvents that are inside the blue circle are:

Figure 35: hexylene glycol, neopentyl glycol and acetaldol;

Figure 36: hexylene glycol and 2,3-butanediol;

Figure 37(b): hexylene glycol, neopentyl glycol.

Regarding the criteria used for the selection of solvents mentioned upward a total of 2 solvents

were selected: neopentyl glycol and hexylene glycol.

Task 2.2.D. – Solvent to Feed (S/F) ratio

This task is the last task of the solvent selection step. The main objective of this task is to select

the most suitable solvent taking into account the required quantity of solvent to break the azeotrope

mixture ethanol-n-pentane the lowest value of solvent to feed (S/F) ratio.

The input data of this step are the solvents: hexylene glycol (HG) and neopentyl glycol (NG)

obtained as output data in task 2.2.D.

Operating S/F ratios for economic acceptable solvents are between 2 and 5 (Perry et al, 2008).

The VLE plots of the systems ethanol-n-pentane-HG and ethanol-n-pentane-NG represented in figure

38, and 39, respectively, were obtained using the supporting tool ICAS, where the thermodynamic

model chosen for the liquid phase was the Original UNIFAC, and the thermodynamic model for the

vapor phase was Ideal Gas. The feed of the azeotropic mixture was always considered to be equal

than 100kmol/h.

51

Figure 38 - VLE plot of ethanol-n-pentane (blue line); VLE plot of ethanol-n-pentane with the solvent hexylene glycol (HG) with S/F ratio equal than 0,2 (green line); VLE plot of ethanol-n-pentane with the solvent hexylene

glycol (HG) with S/F ratio equal than 0,3 (purple line).

Figure 39 - VLE plot of ethanol-n-pentane (blue line); VLE plot of ethanol-n-pentane with the solvent neopentyl

glycol (NG) with S/F ratio equal than 0,2 (green line); VLE plot of the ethanol-n-pentane with the solvent neopentyl

glycol (NG) with S/F ratio equal than 0,3 (purple line).

Analyzing Figure 38 and 39 it is observed that on both case (using hexylene glycol or neopentyl

glycol) the azeotrope mixture is broken with a S/F ratio equal or higher than 0, 2. Since it is known the

minimum quantity of solvent necessary to break ethanol-n-pentane (theoretically), NG and HG were

plotted in the VLE plot with a fixed value of S/F ratio equal to 0,2, in order to observe which of both

solvents is the most effective in the separation of ethanol-n-pentane for the same value of S/F ratio.

The VLE plot of both solvents is presented in Figure 40.

Figure 40 – VLE plot of the system ethanol-n-pentane with a fixed value of S/F ratio equal than 0,2 for hexylene

glycol (HG) and neopentyl glycol (NG).

52

In Figure 40, it is observed that the behavior of both solvents is similar for the same value of S/F

ratio. However, it can be seen a very small difference where neopentyl glycol seems to have a better

effect on the separation of the azeotrope than HG regarding the orange circle in Figure 40. But, as this

difference is so small, the choice of the solvent is not conclusive. For that reason, it is necessary to

analyse the plot of selectivity vs. solvent power to take the final decision. From Figure 33, it is

observed that hexylene glycol presents a 𝑆𝑝 = 1,28, a 𝑆𝑖,𝑗 = 5,61 and neopentyl glycol presents

𝑆𝑝 = 1,23 and a 𝑆𝑖,𝑗 = 7,03. With these values it is concluded that the solvent selected was neopentyl

glycol, because it presents a higher value of selectivity and the solvent power is almost the same for

both solvents.

A summary of the solvents obtained in each tasks of the solvent analysis step, in order to obtain

the most suitable solvent can be seen in Figure 41.

Figure 41 – Diagram that represents the number of solvents selected in each task of the solvent analysis step, for

the separation of ethanol-n-pentane.

With the best solvent selected for the separation of ethanol-n-pentane, question 3 (Q3. See

Figure 15, Chapter 2) is answered. As it is the first time that the selected solvent is used for the target

solute, step 3 – Design & Analysis is the next step to follow.


The objective of this step is to design the separation process of an extractive distillation column

(EDC) and a solvent recovery column (RC) using neopentyl glycol (NG) for ethanol-n-pentane.

Step 3.1. – Pre-Design of extractive distillation column (EDC) and recovery column (RC)

The pre-design of the extractive distillation column (EDC) is firstly performed, since the design of

the recovery column is necessary to obtain the bottom product of the extractive column, because this

stream is the input stream of the Recovery column (RC).

Extractive distillation column (EDC):

The process flow diagram and the variables to design for the extractive distillation column for the

separation of ethanol-n-pentane using neopentyl glycol is presented in Figure 42.

53

Figure 42 – Process flow diagram for the extractive distillation column with the parameters to design.

In figure 42, it is observed that for the extractive distillation column, the relevant design

parameters are: the number of stages, 𝑁, the reflux ratio, 𝑅𝑅, the feed stage of the mixture to be

separated, 𝑁𝐹,𝐴𝑧𝑒𝑜, and the solvent feed stage, 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛 𝑡.To obtained a preliminary design of the

variables of the EDC, the software PDS was used. Ethanol-n-pentane is added to the software, and

the thermodynamic model selected for the liquid phase was the Original UNIFAC and for the vapor

phase the thermodynamic model applied was the Ideal Gas. After the selection of the thermodynamic

model, the calculation method selected for the calculation of the design variables was the driving force

method, which only takes into account the binary mixture (ethanol-n-pentane) and the composition of

in the distillate, 𝑥𝐷, and bottom 𝑥𝐵 of n-pentane. The 𝑥𝐷,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 was set to a value of 0,995 and

𝑥𝐵,𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 was set to a value of 0,005. The selection of n-pentane as the light component (lowest

boiling component) and ethanol being the heavy component (highest boiling component) was made

before starting the calculation. The results obtained from PDS about the design variables of the EDC

for the separation of ethanol-n-pentane are presented in Table 10.

Table 10 - Results obtained from PDS for the extractive distillation preliminary design.

Parameter Value

Minimum reflux ratio, 𝑹𝒎𝒊𝒏 0,1742

Reflux ratio, 𝑹 0,2090

Minimum number of stages, 𝑵𝒎𝒊𝒏 20

Feed stage location 𝑵𝑭 16

As this software only gives an approximation about the design variables for the distillation

columns that only have on feed, the parameters obtained from PDS were submitted to rigorous

simulations using AspenPlus as process simulator. The target specification for this separation process

is the product purity in the top of both column reaching a value of 99,5%. The reflux ratio 𝑅, minimum

number of stages, 𝑁𝑚𝑖𝑛and feed stage location 𝑁𝐹 presented in Table 8 were the variable selected

from the pre-design, to be submitted to rigorous simulation in AspenPlus.

The solvent feed stage, 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 , is not a variable given by PDS because this tool only gives the

design of systems that present only one feed (in this case, the azeotrope feed). As generally the

54

𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 is introduced in the EDC one or two stages bellow the condenser, it is not necessary a pre-

design for this variable.

Step 3.2. – Simulation & Sensitivity Analysis

At this point, the simulation and the sensitivity analysis is only made for the extractive distillation

column since the pre-design was firstly made for this column.

The extractive distillation design parameters that are introduced in the RadFrac column in

AspenPlus are presented in Table 11.

Table 11 – Extractive distillation column (EDC) pre-design parameters.

Parameter Value

Solvent flowrate (kmol/h) 20

Azeotrope flow (kmol/h) 100

- Ethanol mole flow (kmol/h) 9,25

- N-pentane mole flow (kmol/h) 90,75

Number of stages (N) 20

Solvent feed stage, 𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 3

Azeotrope feed stage, 𝑵𝑭,𝑨𝒛𝒆𝒐 16

Reflux ratio (RR) 0,21

Distillate flowrate (kmol/h) 91

Running the simulation with the design variables specified in Table 11, the simulation results are

presented in Figure 43. From Figure 43, the stream ID correspondent to AZEO is the azeotrope feed

stream of the EDC; the SOLVENT stream corresponds to the solvent flowrate that enters into the

EDC; the stream ETOH+SOL is the bottom stream results of the EDC; the stream N-PENTAN is the

top stream results of the EDC.

Figure 43 – Information about the streams of the extractive distillation column (EDC).

Heat and Material Balance Table

Stream ID AZEOT ETOH+SOL N-PENTAN SOLVENT

From EDC EDC

To EDC EDC

Phase LIQUID LIQUID LIQUID LIQUID

Substream: MIXED

Mole Flow km ol/hr

ETHANOL 9,250000 9,004075 ,2459248 0,0

PENTANE 90,75000 9,61613E-5 90,74990 0,0

NEOPE-01 0,0 19,99583 4,17137E-3 20,00000

Total Flow km ol/hr 100,0000 29,00000 91,00000 20,00000

Total Flow kg/hr 6973,777 2497,363 6559,395 2082,982

Total Flow l/min 186,1934 46,27996 178,6878 33,76584

Temperature C 34,00000 115,3463 35,45778 38,00000

Pressure bar 1,000000 1,000000 1,000000 1,000000

Vapor Frac 0,0 0,0 0,0 0,0

Liquid Frac 1,000000 1,000000 1,000000 1,000000

Solid Frac 0,0 0,0 0,0 0,0

Enthalpy cal/m ol -43225,03 -1,0545E+5 -41029,63 -1,2936E+5

Enthalpy cal/gm -619,8224 -1224,489 -569,2135 -1242,085

Enthalpy cal/s ec -1,2007E+6 -8,4944E+5 -1,0371E+6 -7,1868E+5

Entropy cal/m ol-K -124,8000 -134,4127 -129,3738 -178,4249

Entropy cal/gm-K -1,789561 -1,560833 -1,794833 -1,713169

Dens ity mol/c c 8,95127E-3 ,0104436 8,48780E-3 9,87191E-3

Dens ity gm /cc ,6242412 ,8993681 ,6118114 1,028150

Average MW 69,73777 86,11597 72,08126 104,1491

Liq Vol 60F l/min 182,4067 42,57798 173,6837 33,85500

55

From Figure 43, the molar recovery of n-pentane at the top of the EDC is approximately 100%,

and the molar purity of n-pentane in the stream is around 99,7%. Ethanol has a molar recovery of

97,3% on the bottom stream. The results given by the simulator using the variables designed from

PDS for the extractive distillation column are good however sensitivity analysis is made in order to

identify the variables that have the potential to make significant improvements in the process.

For this azeotropic mixture, the sensitivity analysis was carried out using option 2 (see Chapter 2,

step 3.2).

Option 2: Fix the variable S/F ratio: vary the N and the RR in order to see what happens to those

variables for a molar product purity of n-pentane at the top of the EDC equal than 0,995.

With the S/F ratio fixed and equal than 2, with the distillate flowrate fixed and equal than 90,75

kmol/h, and with NF,Neopentyl Glycol = 3, in order to see if the separation process can be made using a

lower number of stages reaching the target specification, the range applied for the sensitivity analysis

regarding the number of stages was between 7 and 20, and the range of the reflux ratio was between

0,2 and 1. Regarding the range applied for the number of stages, the feed location of the azeotrope

has changed to 𝑁𝐹,𝐴𝑧𝑒𝑜 = 5.

The sensitivity analysis was made to the target specification (99,5% of n-pentane purity at the top

of the extractive distillation column) in order to perform the two variables, the reflux ratio and number

of stages since the solvent to feed ratio is the variable fixed.

From Figure 44, it is observed that the decrease on the value of RR, makes an increase in the

molar composition of n-pentane at the top of the EDC. It is also observed that regarding the number of

stages, when N>10 the molar composition of n-pentane at the top of the EDC is always constant.

Figure 44 – Influence of number of stages (N) and reflux ratio (RR) on molar purity of n-pentane at the top of the

extractive distillation column.

From Figure 44, it can be observed that at a certain point, for a number of stages equal than 10,

for different values of reflux ratio, the composition of n-pentane on the distillate (Figure 44) is constant.

In terms of reflux ratio, the composition of n-pentane on distillate increases with the decrease of the

RR. So, as the better results are obtained with a lower value of RR, the RR=0,3 was selected.

Regarding the number of stages, as it is pretended to obtain n-pentane with a molar purity equal than

0,995 at the top of the column, it is enough to choose a column with a number of stages equal than 7.

The sensitivity analysis allowed to perform the separation process design, since, instead of using a

56

column with a 20 number of stages, it is possible to obtain the target specification with only 7 stages

for a reflux ratio equal than 0,3.

With the new values of N and RR obtained from the sensitivity analysis, the simulation is run with

N=7, RR=0,3, and the simulation results (output streams) are obtained and can be observed in Figure

45.

Figure 45 – Output streams results obtained when introduced the new design variables: N=7 and RR=0,3.

In order to obtain the target specification (molar purity of n-pentane at the top of the EDC equal

than 0,995) the design spec (AspenPlus) was used to specify the purity of n-pentane at the top of the

EDC with the target of 0,995, varying the RR between 0,25 and 0,35. The final design variables

obtained for the extractive distillation column through rigorous simulation, taking into account the

target specification are shown in Table 12 and the stream results of the EDC are shown in Table 13.

Table 12 – Extractive distillation column design variables obtained through the rigorous simulation.

Parameters Values

𝑵𝒔𝒕𝒂𝒈𝒆 7

𝑵𝑭,𝒂𝒛𝒆𝒐 5

𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 3

𝑹𝑹 0,271

Solvent flowrate

(kmol/h) 20

𝑸𝒓(𝒄𝒂𝒍/𝒔) 231100

𝑸𝒄(𝒄𝒂𝒍/𝒔) -198969

Table 13 –Stream results obtained from the rigorous simulation for EDC.

STREAM ID AZEO SOLVENT N-PENTAN ETOH+SOL

ETHANOL 9,250 0,000 0,491 8,759

PENTANE 90,750 0,000 90,708 0,042

NEOPE-01 0,000 20,000 0,005 19,995

Total Flow (kmol/hr) 100,000 20,000 91,204 28,796

Temperature (°C) 34,000 38,000 35,282 114,308

57

The outlet stream ETOH+SOL (bottom of EDC) is the input data necessary to determine the pre-

design of the recovery column using the DSTWU model in AspenPlus to calculate the preliminary

design in order to obtain, after rigorous simulations, the extractive distillation process design. The pre-

design obtained for the recovery column can be seen in Figure 58 in Appendix 5.A. After rigorous

simulations, the final design variables for the recovery column are presented in Table 14 and the

stream results are presented in Table 15.It is observed that at the top of both EDC and RC, the target

specification was achieved (molar product purity at the top of both columns equal than 0,995).

Table 14 – Recovery column design variables obtained through rigorous simulations.

Column Recovery Column


𝑵𝑭 4

𝑹𝑹 0,3

In Table 15, the stream ID ETOH+SOL represents the bottom stream of the EDC (input stream in

the RC); the ETOH stream is the top stream of the RC; and the RECSOL stream is the bottom stream

of the RC that is majorly composed by neopentyl glycol that is recycled to the EDC.

Table 15 – Stream results obtained through the rigorous simulations (Recovery Column).

STREAM ID ETOH+SOL ETOH RECSOL

ETHANOL 8,759 8,701 0,058

PENTANE 0,042 0,042406 1,56E-07

NEOPE-01 19,995 9,43E-05 19,995

Total Flow kmol/hr 28,796 8,743 20,053

In Figure 63 in Appendix 7, it can be observed the process flowsheet.

At this point, the design of the extractive distillation separation of ethanol-n-pentane using

neopentyl glycol is obtained reaching the target specification. Observing the methodology (See Figure

15 Chapter 2) question 5 (Q5.) must be answered and as the ethanol-n-pentane was the first

azeotrope analyse, now the next mixture will be analysed (ethanol-n-hexane), so regarding this we

must go to step 1.

4.3. Ethanol-n-hexane

Step 1 – Problem Definition (Step 1.1. – Mixture Selection)

As the azeotropic mixture is known (ethanol-n-hexane), the input data of step 1.1., is ethanol

(Compound 1) that is selected from the list of compounds present in AzeoPro Database, and the

58

selection of n-hexane to be compound 2 is made from the list of compounds that form azeotropes with

Compound 1. The pressure of the azeotrope is 101,32 kPa.



Table 16 – Temperature and composition of the binary azeotrope: ethanol -n-hexane (AzeoPro).

Azeotrope information 𝑻𝑨𝒛(𝑲) 𝑷 (𝒌𝑷𝒂) 𝒙𝒊 (𝒆𝒕𝒉𝒂𝒏𝒐𝒍) 𝒙𝒋(𝒏 − 𝒉𝒆𝒙𝒂𝒏𝒆)

331,65 101,32 0,341 0,659


Observing the composition of ethanol and n-hexane mixture in the azeotrope, presented in Table

16, it is observed that ethanol has a composition in the azeotrope lower than 0,5 (𝑥𝐴𝑍𝑒𝑡ℎ𝑎𝑛𝑜𝑙 = 0,341)

and therefore ethanol is selected as the target solute.


Table 17 shows the boiling point of ethanol and n-hexane.

Table 17 – Boiling point of ethanol, and n-hexane obtained from ProPed.

Compound Tb (K)

Ethanol 351,52

N-hexane 331,65


mixture component to be separated, in Table 17, it is observed that ethanol has the higher boiling

component. So it can be concluded that the solvent should present a higher boiling point than ethanol.


Step 2.1. Solvent screening

The solvent screening step for the azeotrope ethanol-n-hexane was made in the same manner

as done for ethanol-n-pentane using ProCAMD.

Table 18 shows the input data introduced in ProCAMD for the system ethanol-n-hexane-

neopentyl glycol.


Parameter Value


Molar composition of n-hexane in the azeotrope 0,659

Target solute Ethanol




59

The solvents generated by ProCAMD can be observed in Table 45 in the Appendix 3.B.

After obtaining the list of solvent candidates to extract ethanol, question 1 (Q1. See Figure 15 in

Chapter 2) must be answered. As it is not the first time that the solvents are screened for this target

solute, question 2, (Q2. See Figure 15 in Chapter 2) must be answered. Since neopentyl glycol is

presented in the list of solvents generated for the separation of ethanol-n-hexane in Table 45,

Appendix 3.B., and was already used for the separation of ethanol-n-pentane, neopentyl glycol was

the solvent selected for the separation of the azeotrope ethanol-n-hexane. With the solvent selected,

question 3 (Q3. See Figure 15 in Chapter 2) must be answered. As it is not the first time that the

selected solvent is used for the same target solute, Step 4. is reached and the process design does

not need to be performed from the scratch, being only necessary some small adjustments.

Step 4. - Fine tune the design available at the database

The objective of this step is use the separation process design of the mixture ethanol -n-pentane

(present in the database) and fine tune the design in order to obtain the separation process design for

the mixture ethanol-n-hexane.

As the case study is where all the mixtures have as component 1 ethanol, and component 2

belong to an homologous series (same functional group) it was interesting to investigate the design of

the extractive distillation column with respect to the change in the size of the carbon (paraffin)


As ethanol-n-pentane and ethanol-n-hexane present the same target solute, ethanol, and on both

mixtures the solvent applied is the same, neopentyl glycol, it is intended to show for ethanol-n-hexane:

1) The process design for the separation of ethanol-n-hexane;

2) That the same number of stages can be used to separate both mixtures (ethanol-n-pentane

and ethanol-n-hexane) in order to see what happen to the quantity of solvent required for the

separation of the azeotropes.

3) That the same solvent to feed ratio can be used for both mixtures (ethanol-n-pentane and

ethanol-n-hexane) in order to see what happens to the number of stages required for the

separation of the azeotropes;

Starting with the modification of the process design, the driving force (DF) diagram is plotted for

ethanol-n-pentane and ethanol-n-hexane as can be observed in Figure 46.

According to Gani. R, et al., 2004 , the separation process takes place at the highest driving

force where the operation is the easiest and requires the least energy.

60

From figure 46, it is observed that for both azeotropes the DF is maximum (DFMAX

) when n-

pentane and n-hexane are removed at the top of the column, showing that the selection of ethanol

being the target solute resulted in the best choice taking into account the easier separation process.

Figure 46 – Driving force diagram for the system ethanol-n-pentane (blue) and ethanol-n-hexane (red) at

101,32kPa (ICAS).

It can be also verified that the DFMAX

is higher for ethanol-n-pentane meaning that it will be more

difficult to extract ethanol from n-hexane than ethanol from n-pentane.

Regarding this statement the decision on the design variables were made taking into account the

design variables of the EDC used for the separation ethanol-n-pentane and analysing Figure 55 in

Appendix 1. In Figure 55 presented in Appendix 1, it is observed that for a 𝐷𝐹𝑀𝐴𝑋 = 0,478 to obtain a

molar composition equal than 0,995 in the distillate the minimum number of stages required for the

separation is N=10 and the RR=0,54. Regarding this fact with the azeotrope ethanol-n-hexane which

present a 𝐷𝐹𝑀𝐴 𝑋 = 0,518 the value of driving force is quite similar, so that means that those variables

are used to be verified through rigorous simulations. Another reason that proves that the design

variables present a good approximation for the separation of ethanol-n-hexane is because for the case

of ethanol-n-pentane 𝐷𝐹𝑀 𝐴𝑋𝑒𝑡ℎ𝑎𝑛𝑜𝑙−𝑛−𝑝𝑒𝑛𝑡𝑎𝑛𝑒 = 0,715 and the number of stages required to obtain a

molar product purity at the distillate equal to 0,995 was N=7.

As the minimum number of stages required for the separation of ethanol-n-hexane (regarding the

DF value in Figure 55 in appendix 1) is N=10, the number of stages selected to be verified through

rigorous simulations for the separation of ethanol-n-hexane was N=12. The reflux ratio used was

RR=0,54, the 𝑁𝐹,𝑎𝑧𝑒𝑜 = 9 and 𝑁𝐹,𝑠𝑜𝑙𝑣𝑒𝑛𝑡 = 9. The results obtained for the design variables of the EDC

for the separation of ethanol-n-hexane using neopentyl glycol are presented in Table 19.

Table 19 – Extractive distillation column design variables obtained through rigorous simulations for ethanol-n-hexane using Neopentyl glycol.

Parameters Values


𝑵𝑭,𝒂𝒛𝒆𝒐 9

𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 3

𝑹𝑹 0,56

𝑺𝒐𝒍𝒗𝒆𝒏𝒕 𝒇𝒍𝒐𝒘𝒓𝒂𝒕𝒆

(kmol/h) 50

61

It is presented in Figure 47, the sensitivity analysis applied to the solvent flowrate in order to see

the influence of this variable on the distillate and bottom composition of n-hexane and to select the

minimum quantity of solvent flowrate necessary for the separation of ethanol-n-hexane. The number of

stages was fixed (N=12), and the design variables presented in Table 19 were fixed with the exception

of the solvent flowrate since it is the variable analysed. It is observed in Figure 47a that the solvent

flowrate has a linear effect on the mole composition of n-hexane on the distillate until a certain value of

flowrate (50 kmol/h) after that value, the composition of n-hexane at the top is constant even with the

increase of the solvent flowrate. In Figure 47b it is observed that the composition of n-hexane in the

bottom is always approximately zero until the solvent flowrate reached a value of 50kmol/h, after that

value the composition of n-hexane at the bottom increases with the increase of the solvent flowrate,

and that fact is not desirable, since it is intended to have a purity of n-hexane at the top of the EDC

equal than 0,995 it is not intended that n-hexane is withdraw into the bottom, and must be recovered

at the top. So, from the sensitivity analysis it could be concluded that the solvent flowrate selected in

order to improve the separation process was 50kmol/h.

Figure 47 – Effect of solvent mole flowrate on the distillate (a) and bottom composition (b) of n -hexane.

As ethanol-n-pentane and ethanol-n-hexane present the same target solute: ethanol, and the

same solvent (neopentyl glycol) an analysis was made in order to see a linear relationship in terms of

quantity of solvent necessary to break the azeotrope between the separation of ethanol-n-pentane and

ethanol-n-hexane when the number of stages was fixed to be equal than 12 (number of stages of the

EDC). Since it was observed (from the driving force plot, in Figure 47) that for the azeotrope ethanol-

n-hexane the minimum number of stages required for the separation process was N=12, that was the

number of stages fixed for the separation of both azeotropes: ethanol-n-pentane and ethanol-n-

hexane.

Number of stages fixed and equal than N=12

After rigorous simulations, the process design variables obtained for the separation of ethanol -n-

pentane and ethanol-n-hexane in order to reach the target specification, are presented in Table 20

and Table 21. In Table 20, it is observed that for a fixed value of number of stages of the EDC, the

quantity of solvent required for the separation of ethanol-n-pentane is lower when compared with the

quantity of solvent required for the separation of ethanol-n-hexane. That result comes from the fact

that, as presented in the driving force plots (Figure 47), the azeotrope ethanol-n-hexane presents a

62

lower value of DFMAX

, meaning that it is more difficult to separate this mixture compared with ethanol-

n-pentane, so it is expected to see an increase in the solvent quantity for the separation of ethanol-n-

hexane.

Table 20 – Design variables obtained for the azeotropes: ethanol-n-pentane and ethanol-n-hexane when the number of stages of the EDC is fixed and equal to N=12 using neopentyl glycol.

Target solute: Ethanol

𝑺𝒐𝒍𝒗𝒆𝒏𝒕 𝒇𝒍𝒐𝒘𝒓𝒂𝒕𝒆 (𝒌𝒎𝒐𝒍/𝒉)

𝑻𝒔𝒐𝒍𝒗𝒆𝒏𝒕 (℃) 𝑹𝑹 𝑵𝑭,𝒂𝒛𝒆𝒐 𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕

Ethanol-n-pentane 6 40 0,297 10 4

Ethanol-n-hexane 50 50 0,56 9 3

The design variables obtained for the recovery column for both ethanol-n-pentane and ethanol-n-

hexane are presented in Table 21.

Table 21 – Recovery column design variables obtained for the azeotropes: ethanol-n-pentane and ethanol-n-

hexane when the number of stages of the EDC is fixed and equal to N=12.


𝑹𝑹 𝑵 𝑵𝑭

Ethanol-n-pentane 1 10 5

Ethanol-n-hexane 0,644 7 4

The information about the stream results obtained for the extractive distillation column and the

recovery column are presented in Table 22 for the separation of ethanol-n-pentane. It is observed that

the n-pentane presents a product purity in the distillate of 99,5% and ethanol is obtained at the top of

the recovery column with a purity of 99,5%, meaning that the target specifications were achieved for

the process design obtained.

Table 22 - Stream results obtained for the separation of ethanol-n-pentane for the design variables obtained for the extractive distillation column and the stream results obtained for the recovery column.

STREAM ID AZEO PENTANE

(TOP) ETOH+SOLV

(BOTTOM ETOH (TOP)

RECSOL (BOTTOM)

PENTANE 90,750 90,709 0,041 4,134E-02 3,316E-09

ETHANOL 9,250 0,461 8,789 8,788 5,970E-04

NEOPE-01 0,000 0,006 5,994 5,632E-05 5,994

Total Flow (kmol/h)

100,000 91,175 14,825 8,830 5,995

It can be concluded that neopentyl glycol was an efficient solvent in the separation of both

azeotropes, since the target specification was achieved, and in both cases the solvent recovery was

around 100%, meaning that only a very small amount of solvent was lost during the s eparation

process.

Solvent to feed (S/F) ratio fixed and equal than S/F ratio = 0,3

63

This analysis was only realized for the extractive distillation column because is where the

azeotrope is separated and where it is interesting to observe what happens to the number of stages

for the separation of ethanol-n-pentane and ethanol-n-hexane when the S/F ratio is fixed.

The solvent was fed into the EDC with a flowrate equal than 30kmol/h for both azeotropic

systems. After rigorous simulations, the design parameters obtained for the EDC for the separation of

ethanol-n-pentane and ethanol-n-hexane can be observed in Table 23.

It is observed that, when the solvent flowrate used for the separation of both azeotropes is fixed

and equal than 30kmol/h, the number of stages required to separate ethanol-n-hexane is increasingly

higher compared to number of stages required for ethanol-n-pentane. The results obtained were

expected since the same behaviour happened when the number of stages was fixed.

Table 23 – Design variables obtained for the EDC when the azeotropes to be separated are: ethanol -n-pentane and ethanol-n-hexane using solvent flowrate equal than 30kmol/h.


𝑵𝒔𝒕𝒂𝒈𝒆 𝑻𝒔𝒐𝒍𝒗𝒆𝒏𝒕 (℃) 𝑹𝑹 𝑵𝑭,𝒂𝒛𝒆𝒐 𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕

n-Pentane 7 40 0,2435 5 3

n- Hexane 20 50 0,548 10 3

The information about the stream results obtained for the extractive distillation column for the

separation of ethanol-n-pentane and ethanol-n-hexane are presented in Table 24.

Table 24 – Summary table of the stream results of EDC obtained from the rigorous simulation using the design

variables obtained in Table 25 for the system: ethanol-n-pentane-NG (a) and ethanol-n-hexane-NG (b).

STREAM ID PENTANE

(TOP) ETOH+SOLV

(TOP) STREAM ID

HEXANE (TOP)

ETOH+SOLV (BOTTOM

PENTANE 90,71 0,04 HEXANE 0,24 33,37

ETHANOL 0,41 8,84 ETHANOL 0,06 29,92

NEOPE-01 0,01 29,99 NEOPE-01 66,23 0,16

Total Flow (kmol/h)

91,12 38,88 Total Flow (kmol/h)

66,55 63,45

Conclusions about the analysis made for ethanol-n-pentane and ethanol-n-hexane with

respect to the change in the size of hydrocarbon (paraffin).

Two plots were created to summarize the information obtained: one plot represents the solvent

flowrate required for the separation of ethanol-n-pentane and ethanol-n-hexane when the number of

stages was fixed and equal to 12 and the other plot shows the number of stages required for the

separation of the same azeotropes when the solvent flowrate was fixed an equal than 30 kmol/h.

64

Figure 48 - Behaviour of solvent flowrate according to the carbon number when Nstage equal than 12 (a); Effect

on the Nstage when the carbon number increase with solvent flowrate equal than 30 kmol/h (b).

In Figure 48b, it is observed that increasing the carbon number of the paraffin group, a higher

number of stages are required to separate the azeotrope, when the same quantity of solvent is used to

break the azeotrope. The same can be concluded from Figure 48a, where, fixing the number of

stages, it can be observed that a higher quantity of solvent will be required to break the azeotrope

when the carbon number of the paraffin increases. That information can be useful for the prediction of

the number of stages and solvent flowrate required when the carbon number of the paraffin that forms

the azeotrope with ethanol increases.

With the process design obtained for ethanol-n-hexane, the next step is to analyse the remaining

azeotropes.

4.4. Ethanol-n-heptane


Step 1.1. – Mixture Selection

This step is made in the same way as done for the previous mixtures (ethanol -n-pentane and

ethanol-n-hexane).



Table 25 – Temperature and composition of the binary azeotrope: ethanol -n-heptane (AzeoPro).

Azeotrope information 𝑻𝑨𝒛(𝑲) 𝑷 (𝒌𝑷𝒂) 𝒙𝒊 (𝒆𝒕𝒉𝒂𝒏𝒐𝒍) 𝒙𝒋(𝒏 − 𝒉𝒆𝒑𝒕𝒂𝒏𝒆)

344,35 101,32 0,631 0,369


Observing the composition of ethanol and n-heptane mixture in the azeotrope, presented in

Table 25, it is observed that ethanol has a composition in the azeotrope higher than 0,5 (𝑥𝐴𝑍𝑒𝑡ℎ𝑎𝑛𝑜𝑙 =

0,631) and therefore n-heptane is selected as the target solute.

65


Table 26 shows the boiling point of ethanol and n-heptane.

Table 26 – Boiling point of ethanol, and n-hexane obtained from ProPed.

Compound Tb (K)

Ethanol 351,52

N-heptane 371,57


mixture component to be separated, from Table 26, it is observed that n-heptane has the higher

boiling component. So it can be concluded that the solvent should present a higher boiling point than

n-heptane.


Step 2.1. Solvent screening

The solvent screening step for the azeotrope ethanol-n-heptane was made in the same manner

as done for ethanol-n-hexane using ProCAMD. Table 27 shows the input data introduced in ProCAMD

for the system ethanol-n-heptane.


Parameter Value


Molar composition of n-heptane in the azeotrope 0,369

Target solute n-Heptane




The solvents generated by ProCAMD can be observed in Table 46 in the Appendix 3.C.

With the list of possible solvents to extract n-heptane, question 1 (Q1. See Figure 15, Chapter 2)

must be answered. As it is the first time that the solvents are screened for this target solute, it is

necessary to go to step 2.2. in order analyse the solvents obtained in step 2.1.

Step 2.2. Solvent Analysis

This step is made in the same way as done for the system ethanol-n-pentane, however in task

2.2.B - Selection from solvent power vs. Hildebrand solubility parameter, the selection of the solvent

is made in the left side of n-heptane, meaning that the solvents present a solubility close to n-heptane,

and far from ethanol (see Figure 57 in the Appendix 6.A.). For the task 2.2.C. - Selection from Hansen

66

solubility parameter plot, the solvents selected from that task are presented in Figure 58, Figure 59

and Figure 60, in Appendix 7.A..

After concluding all the tasks of step 2.2., the most suitable solvent for the separation of ethanol-

n-heptane is di-n-pentyl-ether.

A summary of the solvents obtained in each tasks of the solvent analysis step, in order to obtain

the most suitable solvent can be seen in Figure 49.

Figure 49 - Diagram that represents the number of solvents selected in each task of the solvent analysis step, for

the separation of ethanol-n-heptane.

With the most suitable solvent selected for the separation of ethanol-n-heptane, question 3 (Q3.

See Figure 15, Chapter 2) is affirmative since it is the first time that the selected solvent is used for the

target solute, and for that reason it is necessary to go to step 3.


The Step 3.1. Pre-design of extractive distillation column (EDC) and recovery column (RC) and

the Step 3.2. – Simulation & sensitivity analysis were made in the same way as performed for the

separation of ethanol-n-pentane. Regarding this, for this step only the results obtained for the design

variables of the EDC and RC are presented and the respective stream results. It is important to refer

that the target specification product purity at the top of both extractive distillation and recovery column

equal than 99,5%.

The results obtained of the design variables for the extractive distillation column and recovery

column for the separation of ethanol-n-heptane using di-n-pentyl-ether are presented in Table 28.

67

Table 28 – Extractive distillation column and recovery column design.

The process flowsheet for the separation of ethanol-n-heptane is the same as used for ethanol-n-

pentane, only the components are different. The simulation results (output streams) are obtained and

can be observed in Table 29.

Table 29 – Stream results obtained for the separation of ethanol-n-heptane using neopentyl glycol.

STREAM ID AZEO SOLVENT ETHANOL

(TOP) HEPT-SOLV (BOTTOM)

HEPTANE (TOP)

RECSOL (BOTTOM)

ETHAN-01 62,09 0 61,905 0,185 0,185 1,995E-11

N-HEP-01 37,91 0 0,169 37,741 37,706 0,035

DI-N—01 0 68 0,139 67,861 1,430E-02 67,847

Total Flow kmol/hr 100,00 68 62,213 105,787 37,906 67,881

The separation process design for the system ethanol-n-heptane-di-n-pentyl-ether is obtained

achieving the target specification (product purity on the top of both column equal than 99,5%).

Question 5 (Q5. See Figure 15) is answered positively since there are still two azeotropic mixtures to

analyse (ethanol-n-octane and ethanol-n-nonane) so regarding this we must go to step 1.

4.5. Ethanol-n-octane and Ethanol-n-nonane

To save time the mixtures are presented in the same step, since the analysis is made in the

same way.


This step is made in the same way as done for the previous mixtures (ethanol -n-pentane,

ethanol-n-hexane and ethanol-n-heptane).

Extractive distillation column

Parameter Value


𝑵𝑭,𝑨𝒛𝒆𝒐 27

𝑵𝑭,𝑺𝒐𝒍𝒗𝒆𝒏𝒕 3

𝑹𝑹 0,41

Solvent flowrate (kmol/h) 68

Recovery Column

Parameter Value


𝑵𝑭 5

𝑹𝑹 0,977

68



Table 30 – Temperature and composition of the binary azeotropes: ethanol-n-octane and ethanol-n-nonane (AzeoPro).

Azeotrope information

𝑻𝑨𝒛(𝑲) 𝑷 (𝒌𝑷𝒂) 𝒙𝒊 (𝒆𝒕𝒉𝒂𝒏𝒐𝒍) 𝒙𝒋(𝒏 − 𝒐𝒄𝒕𝒂𝒏𝒆)

349,85 101,32 0,825 0,175

𝑻𝑨𝒛(𝑲) 𝑷 (𝒌𝑷𝒂) 𝒙𝒊 (𝒆𝒕𝒉𝒂𝒏𝒐𝒍) 𝒙𝒋(𝒏 − 𝒏𝒐𝒏𝒂𝒏𝒆)

351,35 101,32 0,941 0,059


Observing the composition of ethanol and octane in the azeotrope and ethanol and n-nonane in

the azeotrope, presented in Table 30, it is observed that ethanol has a composition in the azeotrope

higher than 0,5 for both cases and therefore n-octane and n-nonane are selected as the target solute.


Table 31 shows the boiling point of ethanol, n-octane and n-nonane.

Table 31 – Boiling point of ethanol, and n-octane and n-nonane obtained from ProPed.

Compound Tb (K)

Ethanol 351,52

N-octane 398,25

N-nonane 424,3


mixture component to be separated, from Table 31, it is observed that for the azeotropic mixture

ethanol-n-octane the solvent should present a higher boiling point than n-octane. For the system

ethanol-n-nonane, as n-nonane is the component that presents the highest boiling point, the solvent

should have a higher boiling point than n-nonane.

Step 2. Solvent Selection

Step 2.1. Solvent Screening

The solvent screening step for the azeotropes ethanol-n-octane and ethanol-n-nonane were

made in the same manner as done for the previous azeotropic mixtures using ProCAMD.

Table 32 shows the input data introduced in ProCAMD for the system ethanol-n-octane and

Table 49 show the input data introduced in ProCAMD for the azeotrope ethanol-n-nonane in Appendix

9.

69

Table 32 – Input information introduced in ProCAMD for ethanol-n-octane.

Parameter Value


Molar composition of n-octane in the azeotrope 0,1594

Target solute n-octane




The solvents generated by ProCAMD can be observed in Table 46 in the Appendix 3.D., and

Table 47 in Appendix 3.E.

After obtaining the list of solvent candidates to extract ethanol, question 1 (Q1. See Figure 15 in

Chapter 2) must be answered. Despite of being the first time that the solvents are screened for the

target solutes (n-octane and n-nonane), it is observed that in both azeotropic mixtures ethanol is the

component 1 (fixed) and component 2 belong to the same homologous series (same functional

group), regarding this with the fact that there is a mixture with the same behaviour in the database

(ethanol-n-heptane). Since di-n-pentyl-ether is presented in the list of solvents generated for the

separation of ethanol-n-octane and ethanol-n-nonane (Table 46 and Table 47, in Appendix 3.D. and

Appendix 3.E., respectively) and was already used for the separation of ethanol-n-heptane, di-n-

pentyl-ether was the solvent selected for the separation of the azeotrope ethanol-n-octane and

ethanol-n-nonane. With the solvent selected, question 3 (Q3. See Figure 15 in Chapter 2) must be

answered. As it is not the first time that the selected solvent is used for the same homologous serie,

Step 4. is reached and the process design does not need to be performed from the scratch, being only

necessary some small adjustments.

Step 4. Fine tune the design available at the database

The objective of this step is use the separation process design of the mixture ethanol-n-heptane

(present in the database) and fine tune the design in order to obtain the separation process design for

the mixture ethanol-n-octane and ethanol-n-nonane.

As the case study: ethanol-paraffins have as component 1 ethanol, and component 2 belong to

an homologous series (same functional group) it was of interest to investigate the design of the

extractive distillation column with respect to the change in the size of the carbon (paraffin)


Ethanol-n-heptane (mixture of the database) ethanol-n-octane and ethanol-n-nonane present

“similar” target solute, because they belong to a homologous series (n-heptane, n-octane and n-

nonane, respectively); di-n-pentyl ether was applied for the three systems.

70

Starting with the modification of the process design, the driving force (DF) diagram is plotted for

ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane as can be observed in Figure 51.

Figure 50 - Driving force diagram for the system ethanol-n-heptane (blue), ethanol-n-octane (red) and ethanol-n-

nonane at 101,32kPa (ICAS).

In Figure 50, it can be noticed that ethanol-n-heptane present a value of 𝐷𝐹𝑀𝐴 𝑋lower than the

other two azeotropic mixtures, meaning that it will be more difficult to separate this mixture compared

with the other two azeotropic mixtures. The number of stages necessary to break the azeotropic

mixture ethanol-n-heptane showed to be equal than 30. Regarding this, to separate ethanol-n-octane

and ethanol-n-nonane, regarding the driving force values in Table 13, Chapter 2 a lower number of

stages can be used to break the azeotropes in study. However, to save time, the number of stages

was fixed and equal than 30 for both ethanol-n-octane and ethanol-n-nonane, in order to see the effect

on the solvent flowrate necessary to break the azeotrope with the increasing of the carbon number

(paraffin).

Number of stages fixed and equal than N=30

After rigorous simulations, in order to reach the target specification (obtain a molar product purity

in the distillate of both extractive distillation and recovery column equal than 0,995) the extractive

distillation and recovery column design variables obtained for the separation of ethanol-n-octane and

ethanol-n-nonane are presented in Table 33 and Table 34, respectively.

Table 33 - Design variables obtained for the azeotropes: ethanol-n-octane, ethanol-nonane and ethanol-n-heptane (database) when the number of stages of the extractive distillation column is fixed and equal than N=30

using di-n-pentyl-ether.

Target solute: Paraffins

𝑺𝒐𝒍𝒗𝒆𝒏𝒕 𝒇𝒍𝒐𝒘 (𝒌𝒎𝒐𝒍/𝒉)

𝑻𝒔𝒐𝒍𝒗𝒆𝒏𝒕 (℃) 𝑹𝑹 𝑸𝒓 (𝒄𝒂𝒍/𝒔) 𝑵𝑭,𝒂𝒛𝒆𝒐 𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕

n-Heptane 68 65,5 0,41 370088 27 3

n- Octane 27 83 0,561 408000 26 5

n-Nonane 10 85 0,495 406150 22 4

71

Table 34 – Recovery column design variables obtained for the azeotropes: ethanol-n-octane, ethanol-nonane and ethanol-n-heptane (database) when the number of stages of the extractive distillation column is fixed and equal

than N=30 using di-n-pentyl-ether.


𝑹𝑹 𝑵 𝑵𝑭

n-Heptane 0.977 10 5

n- Octane 0.2 13 7

n-Nonane 5,715 28 15

In Table 33, it is observed that for a fixed value of number of stages of the EDC, the solvent

flowrate required to break the azeotrope decreases when the number of carbon increases. That result

comes from the fact that as presented in the driving force plots (Figure 51), as the azeotrope ethanol-

n-heptane presents a lower value of DFMAX

, it is more difficult to separate this mixture compared with

ethanol-n-octane and ethanol-n-nonane, so it is expected to see a decrease in the solvent quantity for

azeotropic mixtures that present higher values of driving force.

The information about the stream results obtained for the of extractive distillation column are

presented in Table 35: a) for ethanol-n-heptane b) ethanol-n-octane and c) ethanol-n-nonane; and the

information about the stream results obtained for the recovery column are presented in Table 36: a)

for ethanol-n-heptane b) ethanol-n-octane and c) ethanol-n-nonane.

It is observed in Table 35, that the target specification was achieved. Ethanol present in the three

separations a molar purity equal than 99,5% at the top of the extractive distillation column. Those

results show that di-n-pentyl ether proven to be a very good entrainer.

Table 35 - Summary table of the stream results of extractive distillation column obtained from the rigorous simulation using the design variables obtained in Table 36 for the system: ethanol-n-heptane-di-n-pentyl-ether (a);

ethanol-n-octane-di-n-pentyl-ether (b) and ethanol-n-nonane-di-n-pentyl-ether (c).

In Table 36, it is shown that the target specification was achieved. The three paraffins were

obtained at the top of the recovery column with a molar purity of 99,5%. For the separation of n-

heptane-di-n-pentyl ether and n-octane-di-n-pentyl ether the solvent recovery presents a value around

100% in both cases. For the separation of n-nonane-di-n-pentyl ether, the solvent recovery was

around 98%. A reason that can explains the decrease in the solvent recovery can be the fact that the

quantity of n-nonane to separate from di-n-pentyl ether is so small that makes the separation more

72

difficult. The difference between the boiling temperature of both components is quite different so this is

not the reason (𝑇𝑒𝑏 ,𝑛𝑜𝑛𝑎𝑛𝑒 = 423 ,5𝐾 and 𝑇𝑒𝑏,𝑑𝑖−𝑛−𝑝𝑒𝑛𝑡𝑦𝑙 𝑒𝑡ℎ𝑒𝑟 = 463 ,4𝐾).

Table 36 - Summary table of the stream results of recovery column obtained from the rigorous simulation using the design variables obtained in Table 37 for the system: ethanol-n-heptane-di-n-pentyl-ether (a); ethanol-n-

octane-di-n-pentyl-ether (b) and ethanol-n-nonane-di-n-pentyl-ether (c).

Solvent to feed (S/F) ratio fixed and equal than S/F ratio = 0,6

This analysis was only realized for the extractive distillation column (EDC) because is where the

azeotrope is separated and where it is interesting to observe what happens to the number of stages

for the separation of ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane when the S/F ratio is

fixed and equal than 0,6.

The solvent was fed into the EDC with a flowrate equal than 60kmol/h for the three azeotropic

systems. After rigorous simulations, the design parameters obtained for the EDC for the separation of

ethanol-n-heptane, ethanol-n-heptane and ethanol-n-nonane can be observed in Table 37.

It is observed that, when the solvent flowrate is fixed for the three azeotropic mixtures is fixed and

equal than 60kmol/h, the number of stages required to separate ethanol-n-octane is increasingly lower

than compared to number of stages required for the separation of ethanol-n-heptane, and even lower

when the mixture analysed is ethanol-n-nonane. The results obtained were expected since the same

behaviour happened when the number of stages was fixed. Concluding, when the solvent flowrate is

fixed, it is expected to see the decreasing of the number of stages with the increasing in the number of

carbon number (paraffin).

Table 37 - Design variables obtained for the extractive distillation column when the azeotropes to be separated are: ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane using di-n-pentyl-ether with a solvent flowrate

equal than 60kmol/h.


𝑵𝒔𝒕𝒂𝒈𝒆 𝑻𝒔𝒐𝒍𝒗𝒆𝒏𝒕 (℃) 𝑹𝑹 𝑸𝒓 (𝒄𝒂𝒍/𝒔) 𝑵𝑭,𝒂𝒛𝒆𝒐 𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕

n-Heptane 38 70,6 0,44 350000 33 6

n- Octane 12 78 0,55 488500 9 4

n-Nonane 10 87 0,337 489140 7 4

73

Conclusions about the analysis made for ethanol-n-heptane, ethanol-n-octane and ethanol-n-

nonane with respect to the change in the size of the carbon number (paraffin).

Two plots were created to summarize the information obtained: one plot represents the solvent

flowrate required for the separation of ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane

when the number of stages was fixed and equal to 30 and the other plot shows the number of stages

required for the separation of the same azeotropes when the solvent flowrate was fixed an equal than

60 kmol/h (See Figure 51a and Figure 51b).

Figure 51 - Behaviour of solvent flowrate according to the carbon number when Nstage is fixed and equal than 30 (a); Effect on the Nstage when the carbon number increase with solvent flowrate fixed and equal than 60 kmol/h

(b).

From Figure 51a, it can be observed that a lower quantity of solvent will be required to break the

azeotropes when the carbon number of the paraffin increases. That information can be useful for the

prediction of the number of stages and solvent flowrate required when the carbon number of the

paraffin that forms the azeotrope with ethanol decreases. The same behaviour can be observed in

Figure 51b, where it is shown that increasing the carbon number of the paraffin group, a lower number

of stages are required to separate the azeotropes, when the same quantity of solvent is used to break

the azeotrope.

With the conclusions obtained from the analysis made for ethanol-n-heptane, ethanol-n-octane

and ethanol-n-nonane with respect to the change in the size of the carbon number (paraffin), the

methodology overs, since there are no mixtures to be analysed.

4.6. Conclusions about the selection of the target solute and its effect in the separation of azeotropic mixtures.

In this step, the conclusions about the effect of the selection of the target solute on the solvent

selection and on the separation process design are observed for the proposed case study.

In Figure 52a, the plot of the composition of ethanol in the azeotrope for the five paraffins is

presented (orange points). The composition of ethanol equal than 𝑥𝐸𝑡ℎ𝑎𝑛𝑜𝑙𝐴𝑍 = 0,5 is represented by a

blue line (Figure 52a) and was defined as the criteria for selecting the target solute. For an azeotropic

mixture that present a composition of ethanol lower than 𝑥𝐸𝑡ℎ𝑎𝑛𝑜𝑙𝐴𝑍 < 0,5, the target solute to be

selected is ethanol; if the composition of ethanol in the azeotrope is higher than 𝑥𝐸𝑡ℎ𝑎𝑛𝑜𝑙𝐴𝑍 > 0,5, the

target solute be selected are the paraffins. In Figure 52b, the plot of the boiling point of ethanol (blue

74

line) and the boiling point of the parafiins according to the increase in the carbon number are

presented (orange point). It is observed that the paraffins n-pentane and n-hexane present a lower

boling temperature than ethanol, and they correspond to the azeotropic mixtures where the target

solute is ethanol (Figure 52a). It can also be seen, in Figure 52b, that n-heptane, n-octane and n-

nonane present higher boiling temperature than ethanol, and observing Figure 52a, those paraffins

correspond to the azeotropic mixtures where the target solute are the paraffins.

Figure 52 - Composition of ethanol in the azeotrope according to the carbon number of the paraffin (a); Boiling

point of ethanol and the paraffins (b).

As the composition of ethanol over the series increases (Figure 52a), it is expected that along the

series the separation process will be more difficult (in the case that ethanol was selected as the target

solute for the entire series), and a higher number of stages and/or solvent quantity would be required

to break the azeotrope. This statement can be confirmed with the results obtained for the azeotrope

ethanol-n-heptane. When the target solute of this azeotropic mixture is ethanol, ethanol is dragged at

the bottom of the EDC; when n-heptane is selected as the target solute, this component is extracted at

the bottom of the extractive distillation column. To achieve a molar product purity at the top of the

column equal than 99,5%, the extractive distillation column design variables obtained for the

separation of ethanol-n-heptane, when the target solute is ethanol and for the case where the target

solute is n-heptane are presented in Table 38. When the target solute was ethanol, the solvent used

was neopentyl glycol, and when the target solute was n-heptane, the solvent used was di-n-pentyl

ether.

Table 38 – Design variables of the extractive distillation column in order to obtain a molar product purity in the distillate of 99,5%.

Azeotrope: Ethanol-n-Heptane

𝐍𝐬𝐭𝐚𝐠𝐞 𝐍𝐅,𝐚𝐳𝐞𝐨 𝐍𝐅,𝐬𝐨𝐥𝐯𝐞𝐧𝐭 𝐒𝐨𝐥𝐯𝐞𝐧𝐭 𝐟𝐥𝐨𝐰

(𝐤𝐦𝐨𝐥/𝐡) 𝐓𝐬𝐨𝐥𝐯𝐞𝐧𝐭 (℃) 𝐑𝐑


40 33 3 110 (Neopentyl-

Glycol) 70 1,045

Target solute: N-Heptane

30 27 3 68 (Di-n-pentyl ether) 65,5 0,41

From Table 38, it is observed that when the target solute is ethanol, a higher number of stages

and a higher solvent flowrate are necessary to break the azeotrope in order to achieve the target

specification, compared with the case where the target solute is n-heptane. Those results show that it

is important to select the right target solute since the nature of the solvent depends on the component

that will be withdraw at the bottom of the column. The effectiveness of the solvent (selected based on

the target solute) is an important variable in terms of economic of the separation process, since a

lower number of stages, and a lower solvent flowrate will be required if the selection of the right target

75

solute is made. Summarizing, the selection of the target solute is a crucial step, in order to select the

most suitable solvent, minimizing the number of stages and the solvent flowrate and globally to reduce

the cost of the process. It was presented over the case study that, when the target solute was ethanol,

the solvent used was neopentyl glycol, and for the case where the target solute was the paraffins, the

solvent used for the separation process was di-n-pentyl-ether. This feature shows that for the

separation of the 5 azeotropes only two solvents (neopentyl glycol and di-n-pentyl ether) were used.

Observing Table 20, with the number of stages fixed (𝑁 = 12), the separation of ethanol-n-pentane

and ethanol-n-hexane with neopentyl glycol is possible. The same can be observed in Table 34, when

with the number of stages fixed (𝑁 = 30), the separation of ethanol-n-heptane, ethanol-n-octane and

ethanol-n-nonane using the same solvent di-n-pentyl ether is possible. With this information it can be

concluded that, analysing globally the series, the separation of the five azeotropes can be made only

using two solvents (neopentyl glycol and di-n-pentyl ether) and only two extractive distillation columns

(one when the target solute is ethanol, and the other when the target solute are the paraffins).

4.7. Conclusions

Five case studies have been presented to validate the proposed methodology, and proved to be

effective in the solvent selection and the design of extractive distillation separation.

A summary table is presented (Table 39) in order to show the general information obtained of the

separation process design of the azeotropic mixtures studied in this project. The systems ethanol-n-

octane and ethanol-n-nonane do not present results for the separation process design since it was

defined that their design would be made from the design of the ethanol-n-heptane.

Table 39 – Summary table with the information about the process design variables for the separation of the

azeotropic mixtures of the case study.

Ethanol-

n-pentane

Ethanol-

n-hexane

Ethanol-

n-heptane

Ethanol-n-

octane

Ethanol-n-

nonane

Target solute Ethanol Ethanol n-heptane n-octane n-nonane

Solvent Neopentyl

glycol

Neopentyl

glycol

Di-n-pentyl

ether

Di-n-pentyl

ether Di-n-pentyl ether


𝑵𝒔𝒕𝒂𝒈𝒆 7 12 30 - -

𝑵𝑭,𝒂𝒛𝒆𝒐 5 9 27 - -

𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 3 3 3 - -

𝑹𝑹 0,271 0,56 0,41 - -

Solvent flowrate

(kmol/h) 20 50 68 - -

Recovery column

𝑵𝒔𝒕𝒂𝒈𝒆 7 7 10 - -

𝑵𝑭 4 4 5 - -

𝑹𝑹 0,3 0,644 0,977 - -

76

It is observed from Table 39, that neopentyl glycol is the solvent used for the separation of both

ethanol-n-pentane and ethanol-n-hexane azeotropes, when the target solute is ethanol. And di-n-

pentyl ether was the solvent used for the azeotropes where the target solute were the paraffins. It can

also be observed that when ethanol is the target solute, with the increase of the carbon numbers of

the paraffins, the extractive distillation design changes, and the number of stages and the solvent

flowrate increases. That result was expected, since it was concluded from the DF plots (See Figure

30) that ethanol-n-hexane presented the lower value of driving force, meaning that the separation was

more difficult to achieve compared with ethanol-n-hexane.

In Table 40, it is observed the final results obtained for the azeotropic mixtures where the target

solute was ethanol, when the number of stages of the extractive distillation column was fixed and

equal than N=12.

Table 40 - Extractive distillation and recovery column design variables obtained for the azeotropes: ethanol -n-pentane and ethanol-n-hexane when the number of stages of the extractive distillation column is fixed and equal

than N=12 using neopentyl glycol.

Ethanol-n-pentane Ethanol-n-hexane

Target solute Ethanol Ethanol

Solvent Neopentyl glycol Neopentyl glycol


𝑵𝒔𝒕𝒂𝒈𝒆 12 12

𝑵𝑭,𝒂𝒛𝒆𝒐 10 9

𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 4 3

𝑹𝑹 0,297 0,56

Solvent flowrate

(kmol/h) 6 50

Recovery column

𝑵𝒔𝒕𝒂𝒈𝒆 7 10

𝑵𝑭 4 5

𝑹𝑹 0,644 1

In Table 40, it is observed that when the number of stages is fixed and equal than 12, and the

solvent used is the same (neopentyl glycol) the solvent flowrate necessary to break the azeotropes

increases, with the increase of the carbon number of the paraffins. The same behavior was observed

for a fixed value of solvent flowrate, where the number of stages increased with the increase of the

carbon number (See Table 23). Two reasons can be behind this scenario:

1) The composition of the paraffin in the azeotrope decreases over the series, so for ethanol-n-

hexane the composition of both components are quite similar (Table 17), and it is more

difficult to extract ethanol from n-hexane compared with ethanol from n-pentane where the

composition of n-pentane in the azeotrope is almost 1 (Table 5), making the separation

easier.

77

2) The boiling point of the paraffins increases with the increase of the carbon number. As

ethanol and n-hexane present a close value of boiling point

(𝑇𝑒𝑏,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 = 351,52 𝑎𝑛𝑑 𝑇𝑒𝑏,𝑛−ℎ𝑒𝑥𝑎𝑛𝑒 = 331,65) it will be more difficult to separate this

azeotrope compared with the other, since n-pentane present a lower boiling points than n-

hexane.

In Table 41, it is observed the final results obtained for the azeotropic mixtures where the target

solute was the paraffins, when the number of stages of the extractive distillation column was fixed and

equal than N=30.

Table 41 – Extractive distillation and recovery column design variables obtained for the azeotropes: ethanol-n-octane, ethanol-nonane and ethanol-n-heptane when the number of stages of the extractive distillation column is

fixed and equal than N=30 using di-n-pentyl-ether.

Ethanol-n-heptane Ethanol-n-octane Ethanol-n-nonane

Target solute n-heptane n-octane n-nonane

Solvent Di-n-pentyl ether Di-n-pentyl ether Di-n-pentyl ether


𝑵𝒔𝒕𝒂𝒈𝒆 30 30 30

𝑵𝑭,𝒂𝒛𝒆𝒐 27 26 22

𝑵𝑭,𝒔𝒐𝒍𝒗𝒆𝒏𝒕 3 5 4

𝑹𝑹 0,41 0,561 0,495

Solvent flowrate

(kmol/h) 68 27 10

Recovery Column

𝑵𝒔𝒕𝒂𝒈𝒆 10 13 28

𝑵𝑭 5 7 15

𝑹𝑹 0,977 0,2 5,175

In Table 41, it is observed that when the number of stages is fixed and equal to 30, and the

solvent used is the same (di-n-pentyl ether) the solvent flowrate necessary to break the azeotropes

decreases, with the increase of the carbon number of the paraffins. The same behavior was observed

for a fixed value of solvent flowrate, the number of stages decreased with the increase of the carbon

number (Table 37). Two reasons can be behind this scenario:

1) The composition of ethanol in the azeotrope increases over the series, so for ethanol -n-

heptane the composition of both components are quite similar (Figure 52), and it is more

difficult to extract n-heptane from ethanol compared with ethanol-n-nonane where the

composition of ethanol in the azeotrope is almost 1 (Figure 52), making the separation

easier.

78

2) The boiling point of the paraffins increases with the increase of the carbon number. As

ethanol and n-heptane present a close value of boiling point

(𝑇𝑒𝑏,𝑒𝑡ℎ𝑎𝑛𝑜𝑙 = 351,52 𝑎𝑛𝑑 𝑇𝑒𝑏,𝑛−ℎ𝑒𝑝𝑡𝑎𝑛𝑒 = 371,57) it will be more difficult to separate this

azeotrope compared with the other two, since n-octane and n-nonane present higher boiling

points than n-heptane.

It can also be observed in Table 41, that the number of stages of the recovery column increases

with the increase of the carbon number of the paraffin. That can be explained by the fact that as the

boiling point of the paraffins increase with the carbon number, it will be more difficult to separate the n-

nonane from di-n-pentyl ether compared with the other two paraffins. However, the main reason

should come from the fact that as the composition of the paraffin in the azeotrope decreases with the

increase of the carbon number, at a certain point the quantity of paraffin to remove in the recovery

column is so small (as the case of n-nonane-di-n-pentyl ether), that the separation is difficult to be

performed; being necessary to use a higher number of stages to achieve the target specifications.

Analyzing the results obtained for the entire series it is observed an opposite behavior in terms of

process design with the increasing of the carbon number, because for the ethanol -n-pentane and

ethanol-n-hexane case studies, ethanol is the target solute, and the separation is more difficult

increasing the carbon number. For ethanol-n-heptane, ethanol-n-octane and ethanol-n-nonane the

target solute is the “paraffin” and the separation process becomes easier with the increase of the

carbon number. Regarding this, it is concluded that the selection of the target solute is a determinant

step which will define the solute that will be dragged at the bottom of the extractive distillation column

and the solvent should be selected to only affect the target solute.

Since the verification of the results with the methodology developed in this project against

rigorous simulations has been carried out successfully, it can be concluded that the scope of the

methodology, which was to develop a systematic approach for the separation of azeotropic mixtures

through extractive distillation was achieved.

79

5. Conclusions and future work

Even though different techniques and numerous studies have been developed for solving

problems related to the separation of azeotropic mixtures through distillation, it wasn’t found any

solution in the literature about the development of a methodology consisting in the steps presented:

i. The separation of azeotropic mixtures is made according to the target solute;

ii. A detailed step in the solvent screening step is made according to the target solute;

iii. The same process design can be used and only fine tune is required in terms of

extractive distillation column design, when:

the target solute and solvent are present at the database;

a homologous series is being analysed (component 1 fixed and component 2

belongs to an homologous series) and is present at the database.

For that reason, the creation of a systematic methodology was developed; this method has to be

fast, easy to calculate and reliable as confirmed through the application of the case study.

The methodology focused its attention on extractive distillation. Since for this technique the

process is effective only if we are able to find a suitable solvent; thus, this task was cons idered

carefully in a systematic way. For the solvent selection the ProCAMD approach has been proven to be

very efficient for the solvent screening, since from a large number of organic compounds in the

ProCAMD database, regarding the parameters specified as target properties as selectivity and solvent

power, a small list of solvents was given by the tool. The steps used for the solvent analysis presented

in the methodology proved to be well applied since the solvents selected for the case where ethanol

was the target solute (neopentyl glycol) and for the case where the paraffins where the target solute

(di-n-pentyl ether), showed to be effective in the separation of the azeotropic mixtures through

simulation, reaching a molar product purity of 0,995 at the top of both extractive distillation and

recovery column.

The advantage of the integrated approach lies in:

The selection of the target solute, since it provides which of the compounds of the binary

azeotropic mixture will be dragged at the bottom of the column;

The solvent selection, since the solvent is selected taking into account the target solute,

a first solvent screening through ProCAMD and the solvent analysis step: properties

such as selectivity, solvent power, Hildebrand solubility parameter solubility, Hansen

solubility parameter and the solvent to feed ratio required to break the azeotrope;

80

The driving force approach, which is based on the VLE data that can predict the

distillation configuration, since the separation takes place at the highes t driving force

where the operation is the easiest and requires the least energy.

Fine tune the design available at the database.

Since the methodology was applied to only one case study, ethanol-paraffins, for the cases

where the first component of the azeotrope is an alcohol and the second compounds that form the

azeotrope are paraffins, the methodology would be valid for those systems, since they present similar

properties comparing with the case study analysed. For different azeotropic systems, such as:

ketones-paraffins, esters-paraffins, carboxylic acids-paraffins, the verification and validation of the

methodology must be done, because it is not known if the behaviour between those two components

are similar to the case study analysed, leading to probably the need to change/perform some steps of

the methodology.

As the extractive distillation process was the technique used for the separation of the azeotropes,

it could be also interesting to instead of using organic solvents as entrainer, apply ionic liquids since

they have become increasingly attractive options in solvent selection due to their negligible vapour

pressure, environmental concerns that are reduced in comparison to many conventional solvents

(Marsh et al., 2004).

The development of the methodology was only focus on the UNIFAC thermodynamic model, so it

could be important to test more thermodynamic models, since different results can be obtained. In

order to verify the results obtained using the proposed methodology, experimental verification can be

made in order to see if there is a good agreement between the theoretical and real results.

Regarding the separation process design, another feature that can be integrated into the

systematic methodology is energy integration, which might offer significant cost savings and can also

affect the selection of the most suitable solvent.

81

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85

Appendixes

Appendix 1 – Driving-Force table

Figure 53 – Corresponding values of reflux ratio, minimum reflux ratio, number of stages, product purities and

driving force (Bek-Pedersen and Gani, 2004).

86

Appendix 2 – Work-flow diagram

2.1. Solvent Screening

2.2. Solvent Analysis

1.1. Mixture Selection

1.2. Selection of the target solute

1.3. Boiling point of the azeotropic mixture

STEP 1

Problem definition

STEP 2

Solvent Selection

3.1. Pre-Design EDC & RC

3.2. Simulation & Sensitivity analysis

STEP 3

Design & Analysis

4.1. Adjust the separation process design

STEP 4

Fine tune the design available in the

database

Mixture selection

Solvent selection

Design Simulators SimulatorsDesign

Task

Tools

QAzeotrope

ICAS 17

ICAS - ProCAMD

ICAS ProPed

ICAS PDS

AspenPlus v8.4

Pro/II

Figure 54 - Work-flow diagram of the proposed methodology.

87

Appendix 3 – Data obtained from ProCAMD

A. Ethanol-n-pentane

Table 42 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-pentane (target solute:

ethanol).

Compounds Selectivity Solvent Power

δD (MPa

1/2)

δP (MPa

1/2)

δH (MPa

1/2)

δT (MPa

1/2)

ethylene glycol monopropyl ether

3,42 1 15,91 7,52 11,74 21,08

2,4 – pentanediol 7,01 1,21 15,86 9,03 23,68 24,46

hexylene glycol 5,61 1,28 15,62 7,95 20,59 23,2

methoxyacetic acid 10,16 0,695 16,12 6,45 10,72 22,98

propionic acid 5,31 1,03 16,04 5,36 9,59 22,58

isobutyric acid 3,57 1,01 15,95 5,98 11,57 21,74

n-butyric acid 3,57 1,01 16,03 5,21 9,37 22,42

neopentyl glycol 7,03 1,23 15,73 8,9 21,11 24,03

1,4-BUTANEDIOL 9,19 1,14 16,32 11,67 20,29 25,98

2-heptanol 1,66 0,885 15,49 5,99 13,3 20,42

1-octanol 1,43 0,865 15,58 5,92 12,51 20,95

1-heptanol 1,66 0,886 15,58 6,06 12,72 21,1

1-PENTANOL 2,39 0,934 15,59 6,36 13,15 21,41

1,2-butanediol 9,19 1,13 16,08 8,09 19,48 25,3

dipropylene glycol monomethyl ether

3,19 1 15,37 4,86 10,34 20,32

Acetaldol 10,73 0,99 15,7 12,43 17,11 23,95

propylene glycol monoethyl ether

5,52 0,957 15,44 4,64 9,89 21,06

2-butanol 3,04 0,96 15,5 6,44 13,95 20,88

2-methylbutyric-acid 2,64 0,974 15,99 5,95 11,54 21,59

2-ethoxyethanol 4,51 0,99 15,91 7,67 11,95 21,24

1-butanol 3,04 0,961 15,59 6,51 13,37 21,56

2-pentanol 2,39 0,933 15,5 6,29 13,73 20,73

Diacetone alcohol 3,79 1,04 15,84 8,63 11,73 21,57

2-octanol 1,43 0,865 15,49 5,84 13,09 20,27

acetic acid 9,5 0,989 16,04 5,51 9,8 22,73

88

Table 43 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-pentane (target solute: ethanol) (Continued).


δD (MPa

1/2)

δP (MPa

1/2)

δH (MPa

1/2)

δT (MPa

1/2)

methacrylic acid 9,5 0,989 16,29 6,02 11,99 21,4

2-(2-methoxyethoxy)ethanol

3,74 1,01 16 8,6 12,87 21,49

2-ethyl butyric acid 1,63 0,896 15,99 5,8 11,33 21,43

2-butoxyethanol 2,29 1,03 15,91 7,37 11,52 20,93

2,2- dimethyl-1-propanol 2,07 0,956 15,31 6,45 14,1 19,62

3-methyl-2-butanol 2,05 0,946 15,6 5,2 13,4 20,05

ethyl lactate 4,35 0,916 15,85 6,8 11,47 21,49

2-methyl-1-propanol 3,04 0,96 15,4 5,98 13,14 20,88

isovaleric acid 2,65 0,974 15,84 4,54 8,93 21,59

1,3-propylene glycol 12,87 1,02 16,32 11,82 20,5 26,13

n pentanoic acid 2,64 0,975 16,03 5,06 9,16 22,27

2-methyl-2-butanol 2,41 0,942 15,26 5,36 10,85 19,62

2,3-butanediol 9,2 1,13 16,23 7,11 19,34 24,62

1,3-butanediol 9,19 1,13 15,95 9,25 23,32 25,3

2-methyl-1-butanol 2,39 0,933 15,44 5,95 13,11 20,73

3-methyl-1-butanol 2,39 0,933 15,4 5,83 12,92 20,73

3-pentanol 2,39 0,933 15,5 6,29 13,73 20,73

diethylene glycol 12,34 1,08 16,36 10,48 21,33 25,65

4-methyl-2-pentanol 1,96 0,908 15,31 5,61 13,28 19,89

2-ethyl-1-butanol 1,96 0,908 15,44 5,8 12,89 20,57

2-hexanol 1,96 0,908 15,5 6,14 13,52 20,57

2-methyl-1-pentanol 1,96 0,908 15,44 5,8 12,89 20,57

1-hexanol 1,96 0,909 15,58 6,21 12,94 21,26

Propylene glycol-tert-butyl ether-1

2,24 0,998 15,15 4,28 10,19 18,81

5-methyl-1-hexanol 1,66 0,885 15,39 5,54 12,49 20,42

2-ethyl-1-hexanol 1,43 0,865 15,43 5,5 12,46 20,27

2,6-dimethyl-4-heptanol 1,25 0,846 15,11 4,64 12,41 18,75

ethylene glycol diacetate 2,11 0,545 16,03 5,96 10 19,98

2-methyl-1,3-propanediol 9,19 1,13 15,89 8,91 22,7 25,3

89

B. Ethanol-n-hexane

Table 44 - List of compounds obtained from ProCAMD for the azeotrope: ethanol -n-hexane (target solute: ethanol).


δD (MPa

1/2)

δP (MPa

1/2)

δH (MPa

1/2)

δT (MPa

1/2)

propionic acid 6,53 1,03 16,04 5,36 9,59 22,58

isobutyric acid 4,27 1,01 15,95 5,98 11,57 21,74

n-butyric acid 4,27 1,01 16,03 5,21 9,37 22,42

ethylene glycol monopropyl ether 4,35 1 15,91 7,52 11,74 21,08

1,4-butanediol 13,51 1,14 16,32 11,67 20,29 25,98

neopentyl glycol 10,04 1,23 15,73 8,9 21,11 24,03

2,4-pentanediol 9,98 1,21 15,86 9,03 23,68 24,46

hexylene glycol 7,79 1,28 15,62 7,95 20,59 23,2

2-heptanol 1,97 0,885 15,49 5,99 13,3 20,42

1-heptanol 1,97 0,886 15,58 6,06 12,72 21,1

1-octanol 1,69 0,865 15,58 5,92 12,51 20,95

methoxyacetic acid 15,16 0,695 16,12 6,45 10,72 22,98

1-pentanol 2,88 0,934 15,59 6,36 13,15 21,41

2-methyl-1-butanol 2,88 0,933 15,44 5,95 13,11 24,69

2-ethyl-1-butanol 2,34 0,908 15,44 5,8 12,89 20,57

2-ethyl-1-hexanol 1,69 0,865 15,43 5,5 12,46 20,27

2-nonanol 1,48 0,847 15,49 5,7 12,87 20,11

dipropylene glycol monomethyl ether 4,63 1,01 15,37 4,86 10,34 20,32

diethylene glycol monobutyl ether 3,2 1,03 16,1 7,86 12,13 20,75

dipropylene glycol 10,36 1,27 15,8 7,08 19,42 24,49

1,6 – hexanediol 7,75 1,27 16,31 11,37 19,86 25,67

2,6-dimethyl-4-heptanol 1,48 0,846 15,11 4,64 12,41 18,75

1-hexanol 2,34 0,909 15,58 6,21 12,94 21,26

triethylene glycol 13,85 1,22 16,68 11,33 19,48 25,02

2-(2-ethoxythoxy)etanol 4,96 1 16,11 8,16 12,56 21,05

1,5-pentanediol 9,98 1,21 16,32 11,52 20,07 25,82

4-methyl-2-pentanol 2,34 0,908 15,31 5,61 13,28 19,89

2-(2-methoxyethoxy)etanol 8,16 0,928 16 8,6 12,87 21,49

3-methyl-2-butanol 2,88 0,933 15,6 5,2 13,4 20,05

2-butoxyethanol 3,4 0,997 15,91 7,37 11,52 20,93

2-ethyl butyric acid 2,44 0,94 15,99 5,8 11,33 21,43

2,2dimethyl-1-propanol 2,92 0,942 15,31 6,45 14,1 19,62

neopentanoic acid 3,13 0,997 15,86 6,45 12,53 20,48

2-methyl butyric acid 3,13 0,974 15,99 5,95 11,54 21,59

isovaleric acid 3,13 0,974 15,84 4,54 8,93 21,59

n-pentanoic acid 3,13 0,975 16,03 5,06 9,16 22,27

diacetone alcohol 6,54 0,963 15,84 8,63 11,73 21,57

2-methyl-2-butanol 2,92 0,942 15,26 5,36 10,85 19,62

3-methyl-1-butanol 2,88 0,933 15,4 5,83 12,92 20,73

2-pentanol 2,88 0,933 15,5 6,29 13,73 20,73

3-pentanol 2,88 0,933 15,5 6,29 13,73 20,73

diethylene glycol 19,46 1,08 16,36 10,48 21,33 25,65

2-hexanol 2,34 0,908 15,5 6,14 13,52 20,57

2-methyl-1-petanol 2,34 0,908 15,44 5,8 12,89 20,57

Propylene glycol-tert-butyl ether-1 2,78 0,998 15,15 4,28 10,19 18,81

5-methyl-1-hexanol 1,97 0,885 15,39 5,54 12,49 20,42

2-octanol 1,68 0,865 15,49 5,84 13,09 20,27

90

C. Ethanol-n-heptane

Table 45 - List of compounds obtained from ProCAMD for the azeotrope: ethanol -n-heptane (target solute: n-heptane).


δD (MPa

1/2)

δP (MPa

1/2)

δH (MPa

1/2)

δT (MPa

1/2)

diisobutyl ketone 2,23 0,663 15,31 2,96 2,81 15,89

diisobutyl ether 5,31 0,888 14,95 2,84 3,71 15,3

5-nonanone 2,23 0,664 15,69 4,02 3,27 17,25

n-butyl valerate 3,12 0,757 15,59 4,7 6,13 17,82

di-n-butyl ether 5,3 0,888 15,33 3,9 4,17 16,66

n-heptyl acetate 2,32 0,661 15,58 3,95 5,93 17,95

isopentyl isovalerate 3,44 0,815 15,21 3,5 5,45 16,3

2-ethylhexyl acetate 2,6 0,725 15,43 3,36 5,68 17,11

1-nonanal 2 0,616 15,33 8,94 6,08 19,45

n-octyl acetate 2,6 0,726 15,58 3,8 5,72 17,79

n-octyl formate 2,26 0,618 15,49 6,06 7,55 18,04

methyl decanoate 3,74 0,871 15,59 4,4 5,7 17,51

di-n-pentyl ether 6,1 0,966 15,32 3,6 3,74 16,35

1-decanal 2,25 0,674 15,33 8,8 5,87 19,29

n-nonyl acetate 2,88 0,788 15,58 3,65 5,5 17,64

isopentyl acetate 1,73 0,519 15,4 3,72 6,13 17,57

2-ethylhexanal 1,75 0,556 14,85 6,19 7,1 18,92

ethyl-n-hexyl ether 5,3 0,888 15,33 3,9 4,17 16,66

1-octanal 1,75 0,556 15,33 9,09 6,3 19,6

n-heptyl formate 1,98 0,553 15,49 6,21 7,77 18,19

2-nonanone 1,53 0,551 15,71 6,38 4,01 18,48

1,2 – diethoxyethane 1,56 0,523 15,53 4,99 5,64 17,09

3-heptanone 1,71 0,542 15,69 4,32 3,7 17,56

4-heptanone 1,71 0,542 15,69 4,32 3,7 17,56

n-pentyl acetate 1,74 0,517 15,58 4,25 6,36 18,26

n-hexyl acetate 2,04 0,59 15,58 4,1 6,15 18,1

91

D. Ethanol-n-octane

Table 46 - List of compounds obtained from ProCAMD for the azeotrope: ethanol -n-heptane (target solute: n-

octane).

Compounds Selectivity Solvent Power δD

(MPa1/2

) δP

(MPa1/2

) δH

(MPa1/2

) δT

(MPa1/2

)


diisobutyl ether 5,21 0,862 14,95 2,84 3,71 15,3

5-nonane 2,08 0,615 15,69 4,02 3,27 17,25

n-butyl valerate 2,95 0,709 15,59 4,7 6,13 17,82

5-nonanone 2,08 0,615 15,69 4,02 3,27 17,25

di-n-butyl ether 5,21 0,862 15,33 3,9 4,17 16,66

n-heptyl acetate 2,16 0,608 15,58 3,95 5,93 17,95

isopentyl isovalerate 3,25 0,762 15,21 3,5 5,45 16,3


n-octyl acetate 2,42 0,668 15,58 3,8 5,72 17,79


di-n-pentyl ether 5,91 0,925 15,32 3,6 3,74 16,35

1-decanal 2,07 0,618 15,33 8,8 5,87 19,29

n-nonyl acetate 2,68 0,725 15,58 3,65 5,5 17,64

3-heptanone 1,59 0,505 15,69 4,32 3,7 17,56

4-heptanone 1,59 0,505 15,69 4,32 3,7 17,56

n-hexyl acetate 1,88 0,544 15,58 4,1 6,15 18,1

2-ethylhexanal 1,62 0,51 14,85 6,19 7,1 18,92

ethyl-n-hexyl ether 5,21 0,862 15,33 3,9 4,17 16,66

1-octanal 1,62 0,51 15,33 9,09 6,3 19,6

n-heptyl formate 1,82 0,504 15,49 6,21 7,77 18,19

1-nonanal 1,85 0,565 15,33 8,94 6,08 19,45

n-octyl formate 2,08 0,564 15,49 6,06 7,55 18,04

92

E. Ethanol-n-nonane

Table 47 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-heptane (target solute: n-nonane).

Compounds Selectivity Solvent Power δD

(MPa1/2

) δP

(MPa1/2

) δH

(MPa1/2

) δT

(MPa1/2

)

n-hexyl acetate 1,76 0,508 15,58 4,1 6,15 18,1


5-nonanone 1,95 0,578 15,69 4,02 3,27 17,25

n-heptyl acetate 2,02 0,568 15,58 3,95 5,93 17,95


1-nonanal 1,72 0,527 15,33 8,94 6,08 19,45

n-octyl acetate 2,26 0,625 15,58 3,8 5,72 17,79

n-octyl formate 1,93 0,522 15,49 6,06 7,55 18,04


di-n-pentyl ether 5,74 0,898 15,32 3,6 3,74 16,35

1-decanal 1,93 0,576 15,33 8,8 5,87 19,29

n-nonyl acetate 2,5 0,678 15,58 3,65 5,5 17,64

Appendix 4 – Data obtained from ProCAMD

Figure 55 – A solvent candidate obtained for the separation of ethanol -n-pentane, given by ProCAMD after the

generation of the solvents.

93

Appendix 5 – Data obtained from DSTWU

A. Ethanol-n-pentane-neopentyl glycol

Figure 56 - Variables introduced in the DSTWU (a); Stream results obtained from the simulation of the DSTWU

with the variables introduced (b)

Appendix 6 – Data Obtained from Step 2.2.B. –Selection from solvent power vs. Hildebrand solubility parameter plot;

A. Ethanol-n-heptane

Figure 57 - Selection of solvents regarding the Hildebrand solubility parameter and solvent power, for the system:

ethanol-n-heptane when n-heptane is the target solute.

94

Appendix 7 –Data Obtained from Step 2.2.C. - Selection from Hansen solubility parameter plot

A. Ethanol-n-heptane

Figure 58 – HSP 𝛅𝐇 𝐯𝐬 𝛅𝐏 of the solvents and the solutes.

Figure 59 - HSP 𝛅𝐇 𝐯𝐬 𝛅𝐃 of the solvents and the solutes.

Figure 60 - HSP 𝛅𝐏 𝐯𝐬 𝛅𝐃 of the solvents and the solutes.

95

Appendix 8 – Flowsheet of extractive distillation process.

Figure 61 - Proposed extractive distillation separation process of ethanol-n-pentane using neopentyl glycol as the

best solvent1.

Appendix 9 – Stream table results.

Table 48 – Stream results obtained for the separation of ethanol-n-pentane using neopentyl glycol.

Mole Flow kmol/hr

AZEOT SOLVMIX MAKEUP N-PENTAN ETOH+SOL ETOH RECSOLV SOLVCOLD

ETHANOL 9,25 0,017 0,00 0,618 8,649 8,632 0,017 0,017

PENTANE 90,75 0,00 0,00 90,717 0,033 0,033 0,00 0,00

NEOPE-01 0,00 20,00 0,006 0,005 19,995 0,00 19,995 19,995

Total Flow kmol/hr

100,00 20,017 0,006 91,34 28,68 8,665 20,01 20,01

Total Flow

kg/hr 6973,77 2083,78 0,584 6574,28 2483,27 400,06 2083,21 2083,21

Total Flow

l/min 186,19 33,85 0,009 178,87 45,94 9,07 41,03 33,84

Temperature C

34,00 39,99 25,00 35,20 114,99 76,37 208,94 40,00

Pressure bar

1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00

Vapor Frac 0,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Liquid Frac 1,00 1,00 1,00 1,00 1,00 1,00 1,00 1,00

Enthalpy

cal/mol -43225,03

-

129190,00 -130140,00 -41134,24 -105910,00 -64591,90 -117950,00 -129190,00

Enthalpy

cal/sec -1200700,00

-

718320,00 -202,65

-

1043700,00 -843670,00

-

155470,00 -655670,00 -718130,00

Appendix 10 – Information introduced in ProCAMD.

Table 49 - Input information introduced in ProCAMD for ethanol-n-nonane.

Parameter Value


Molar composition of n-nonane in the azeotrope 0,0203

Target solute n-nonane



1 The process flowsheet was the same for all the case studies (the only modification are the mixture components ).

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Development of a systematic methodology for the separation of binary azeotropic mixtures · Development of a systematic methodology for the separation of binary azeotropic mixtures