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
ii
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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
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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
xii
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
Table 46 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-heptane (target
solute: n-octane). ......................................................................................................................... 91
Table 47 - List of compounds obtained from ProCAMD for the azeotrope: ethanol-n-heptane (target
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.
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 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
𝑵𝒔𝒕𝒂𝒈𝒆 7
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.
The output information of this step are the temperature and the composition of the azeotrope,
which are presented in Table 16.
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
Step 1.2. – Selection of the target solute
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.
Step 1.3. – Boiling point of the mixture components
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
As the solvent must present a boiling point 30 − 40℃ higher than the highest boiling of the
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. – Solvent selection
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.
Table 18 – Input information introduced in ProCAMD.
Parameter Value
Molar composition of ethanol in the azeotrope 0,341
Molar composition of n-hexane in the azeotrope 0,659
Target solute Ethanol
𝑻𝒎𝒊𝒏 ,𝒔𝒐𝒍𝒗𝒆𝒏𝒕(𝑲) 381
Minimum value of Selectivity 0,1
Minimum value of Solvent Power 0,1
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)
Step 4.1. Adjust the separation process design
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
𝑵𝒔𝒕𝒂𝒈𝒆 12
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.
Target solute: Ethanol
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.
Target solute: Ethanol
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 – Problem Definition
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).
The output information of this step are the temperature and the composition of the azeotrope,
which are presented in Table 25.
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
Step 1.2. – Selection of the target solute
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
Step 1.3. – Boiling point of the mixture components
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
As the solvent must present a boiling point 30 − 40℃ higher than the highest boiling of the
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. – Solvent selection
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.
Table 27 – Input information introduced in ProCAMD.
Parameter Value
Molar composition of ethanol in the azeotrope 0,631
Molar composition of n-heptane in the azeotrope 0,369
Target solute n-Heptane
𝑻𝒎𝒊𝒏 ,𝒔𝒐𝒍𝒗𝒆𝒏𝒕(𝑲) 391
Minimum value of Selectivity 0,1
Minimum value of Solvent Power 0,1
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.
Step 3. Design & Analysis
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.
Step 1 – Problem Definition
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
𝑵𝒔𝒕𝒂𝒈𝒆 30
𝑵𝑭,𝑨𝒛𝒆𝒐 27
𝑵𝑭,𝑺𝒐𝒍𝒗𝒆𝒏𝒕 3
𝑹𝑹 0,41
Solvent flowrate (kmol/h) 68
Recovery Column
Parameter Value
𝑵𝒔𝒕𝒂𝒈𝒆 10
𝑵𝑭 5
𝑹𝑹 0,977
68
The output information of this step are the temperature and the composition of the azeotrope,
which are presented in Table 30.
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
Step 1.2. – Selection of the target solute
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.
Step 1.3. – Boiling point of the mixture components
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
As the solvent must present a boiling point 30 − 40℃ higher than the highest boiling of the
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 ethanol in the azeotrope 0,8406
Molar composition of n-octane in the azeotrope 0,1594
Target solute n-octane
𝑻𝒎𝒊𝒏 ,𝒔𝒐𝒍𝒗𝒆𝒏𝒕(𝑲) 418
Minimum value of Selectivity 0,1
Minimum value of Solvent Power 0,1
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)
Step 4.1. Adjust the separation process design
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.
Target solute: Paraffins
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.
Target solute: Paraffins
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
Target solute: Ethanol
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
Extractive distillation column
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
Extractive distillation column
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
Extractive distillation column
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).
Compounds Selectivity Solvent Power
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).
Compounds Selectivity Solvent Power
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).
Compounds Selectivity Solvent Power
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 ketone 2,08 0,614 15,31 2,96 2,81 15,89
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
2-ethylhexyl acetate 2,42 0,668 15,43 3,36 5,68 17,11
n-octyl acetate 2,42 0,668 15,58 3,8 5,72 17,79
methyl decanoate 3,53 0,813 15,59 4,4 5,7 17,51
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
diisobutyl ketone 1,95 0,577 15,31 2,96 2,81 15,89
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
2-ethylhexyl acetate 2,26 0,624 15,43 3,36 5,68 17,11
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
methyl decanoate 3,34 0,769 15,59 4,4 5,7 17,51
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 ethanol in the azeotrope 0,9797
Molar composition of n-nonane in the azeotrope 0,0203
Target solute n-nonane
𝑻𝒎𝒊𝒏 ,𝒔𝒐𝒍𝒗𝒆𝒏𝒕(𝑲) 444
Minimum value of Selectivity 0,1
1 The process flowsheet was the same for all the case studies (the only modification are the mixture components ).