A Molecular Design Method Based on the COSMO‐SAC Model...

THERMODYNAMICS AND MOLECULAR-SCALE PHENOMENA

A Molecular Design Method Based on the COSMO-SAC Modelfor Solvent Selection in Ionic Liquid Extractive Distillation

Jing Fang, Rui Zhao, Weiyi Su, and Chunli LiDept. of Chemical Engineering, Hebei University of Technology, Tianjin, China

Jing Liu and Bo LiDept. of Chemical Engineering, Hebei University of Technology, Tianjin, China

DOI 10.1002/aic.15247Published online April 7, 2016 in Wiley Online Library (wileyonlinelibrary.com)

In this study, a molecular design method was used to select solvents for extractive distillation. A COSMO-SAC model wasused to screen for prospective solvents from a wide variety of ionic liquids for extractive distillation. Based on theCOSMO-SAC model, the r-profile database of ILs was established. Selectivity and solubility were used as the indexes forsolvent screening. According to the molecular design method, three suitable extractive distillation solvents were determinedfor acetonitrile-water and ethanol-cyclohexane systems. Vapor - liquid equilibrium experiment were used to test chosenILs. This study showed that the experimental and design results were consistent with each other. Therefore, this method iseffective and applicable to pick ILs solvents for extractive distillation, and the results could provide a theoretical founda-tion for industrial production. VC 2016 American Institute of Chemical Engineers AIChE J, 62: 2853–2869, 2016

Keywords: phase equilibrium, ionic liquids, thermodynamics/classical, extraction, separation techniques

Introduction

Extractive distillation is widely used because of its superior-ity in application and control, especially for azeotropic systems.The selection of efficient and suitable solvent with high selec-tivity is paramount to ensure an economic operation and lowerthe total annual cost.1 In Comparison with traditional organic

solvents and electrolytes, ionic liquids (ILs) as entrainer offer aseries of outstanding advantages,2–9 especially the adjustablesolubility in several substances via the design of cations andanions. Some studies indicate10 that the ratio of solvent to feedusing ILs could be 20% less than using conventional extractionsolvents, thereby reducing column size and operating cost. Itwas determined that overall heat duty on reboilers in extractivedistillation process decreases about 17% when ILs are used as

entrainer. Because ILs are ionized, they have salt-like effects onthe solute similar to those of inorganic salts, which indicatesthat the addition of ILs into the solvent can influence the rela-tive volatility of the separated systems. Therefore, IL distillationsolvents provide enhanced separations.11

ILs are composed of anions and cations, and more than 1018

types of ILs exist. However, commonly used ILs are syntheticchemicals and not as green as desired, especially due to theirpotential toxicity and relatively high make-up cost. Thus muchattention has been focused to avoid toxic synthesis material and

environmentally unfriendly synthetic method for further studiesin chemistry.12 With the improvement of manufacturing techni-ques, the ILs will have broader range of applications. Therefore,the selection of appropriate ILs for azeotropic systems is thebasis of extractive distillation using ILs as solvents. However,

current experimental screening methods are time-consuming,labor intensive, and onerous. If a proper thermodynamic modelcould be found to accurately fit the limited data of a related sys-

tem, this model would greatly aid in the screening of ILs. With astrong predictive model and accurate experimental data, reliablethermodynamic data could be obtained to be used as guidance

for practical applications.The computer-aided molecular design technique has produced

notable results in extractive distillation and other process sincethe UNIFAC model was first applied in molecular design of sol-vents in extractive distillation processes by Gani and Brignole in1983.13–20 The basic concept of the UNIFAC model is the groupcontribution method (GCM), which is not suitable for ILs asmost group interaction parameters in ILs are absent. In 1995,Klamt proposed a new “conductor-like screening model for realsolvents” (COSMO-RS), which was based on the solvent molec-ular orbital continuum model.21–23 Although the COSMO-RSmethod has provided promising results,24–27 the equation forchemical potential does not converge for certain boundary condi-tions, and the final expression for activity coefficient fails to sat-isfy the thermodynamic consistency relationships.

Based on the COSMO-RS framework, Lin and Sandler28–30

introduced the Staverman-Guggenheim combinatorial term31,32

and proposed a new activity coefficient model known as theCOSMO-segment activity coefficient (SAC) model. Theseparameters are significantly advanced for phase equilibrium cal-

culations compared with those of GCM. In addition, an obviousadvantage of the COSMO-SAC model36,37 is that the interaction

parameters are too limited to perform the UNIFAC calculation.In this article, a molecular design was applied based on des-

ignable ILs to determine the appropriate ILs for specificextractive distillations. Specifically, a r-profile database wasestablished and calculated via COSMO-SAC, and three appro-priated extractive distillation solvents were selected for

Correspondence concerning this article should be addressed to Chunli Li [email protected].

VC 2016 American Institute of Chemical Engineers

AIChE Journal 2853August 2016 Vol. 62, No. 8

acetonitrile–water and ethanol–cyclohexane systems, respec-tively. It was found that 1-ethyl-3-methyl imidazole acetate([EMIM]OAc), 1-butyl-4-methyl pyridinium acetate([4MePy]OAc), and 1-hexyl-2,3-dimethyl imidazolium acetate([HDMIM]OAc) were better fits for ethanol–cyclohexane sys-tems. For the separation of acetonitrile from water, the ILs 1-ethyl-3-methyl imidazole acetate ([EMIM]OAc), 1-butyl-4-methyl pyridinium acetate ([4MePy]OAc), and 1-hexyl-2,3-dimethyl imidazolium acetate ([HDMIM]OAc) were moresuitable.

Models and Methods

COSMO-SAC model

In the models based on COSMO, the most time-consumingprocess was the quantum chemistry calculation. The time cost

grew exponentially with the number of atoms in the mole-cules. To reduce the calculation workload, the ILs weredivided into cations and anions, and then the quantum chemis-try calculations were performed separately to obtain the r-

profile. By combining the r-profiles of the cations and anions,the overall r-profile of the ILs could be obtained. Generally,24 types of anions (Appendix A) and 176 types of cations(Appendix B) would produce 4224 combinations of ILs.

First, the molecular structure of ethylimidazole (EMIM)was drawn in the DMol3 module of Materials Studio based on

the principles of energy minimization, as shown in Figure 1.Next, the screening charge density (r*) was calculated by

COSMO. The surface charge density of standard segment rm

was obtained using Eq. 1.

rm5

Pnr�n

r2n r2

eff

r2n1r2

eff

exp 2d2

mn

r2n1r2

effPn

r2n r2

eff

r2n1r2

eff

exp 2d2

mn

r2n1r2

eff

; (1)

where rn* 5 qn/an (an is the surface area of the segment n) is the

charge density of segment n, rn5ffiffiffiffiffiffiffiffiffiffian=p

pis the radius of segment

n, reff 5ffiffiffiffiffiffiffiffiffiffiffiffiaeff =p

p(aeff 57.50 A2)28 is the radius of a standard sur-

face segment, reff is an adjustable parameter, and dmn is the dis-tance between segments m and n. The r-profile pi(r) of pure

matter denotes the probability of finding a surface segment with

screening charge density r, which is defined as the following:

piðrÞ5AiðrÞ

Ai; (2)

where Ai(r) is the surface area with a charge density of value

r, and Ai is the total surface area of species i. Figure 2 showsthe r-profile of cation [EMIM]1.

For a mixture, the r-profile is determined using Eq. 3:

psðrÞ5P

ixinipiðrÞPixini

: (3)

The chemical potential of a surface segment with charge den-sity rm in a solution S can be calculated using Eq. 4:

lsðrmÞ52kTlnnX

rm

exph2Epairðrm; rnÞ1lsðrnÞ

kT

io1kTln psðrmÞ:

(4)

where Epairðrm;rnÞ is the energy of a segment pair m and n,

which reflects the contributions of the electrostatic interac-tions, including the misfit energy Emf, the hydrogen-bonding

interactions Ehb, and non-electrostatic and mostly dispersioninteractions Ene, as shown below:

Epairðrm; rnÞ5Emf ðrm; rnÞ1Ehbðrm; rnÞ1Eneðrm; rnÞ

5ða0=2Þðrm1rnÞ21chbmax ½0; racc2rhb�min ½0; rdon1rhb�1cne;

(5)

a05ð0:6430:33a2=3eff Þ=18; (6)

1851:468310235ðC2s2molÞ=ðkg�m2Þ; (7)

chb51:395653106ðkg�mÞ=ðmol�C2�s2Þ; (8)

Figure 1. Chemical structure of EMIM in materialsstudio.

[Color figure can be viewed in the online issue, which is

available at wileyonlinelibrary.com.]

Figure 2. The r-profile of [EMIM]1.

Table 1. Physical Properties of Acetonitrile and Water

Compound FormulaBoiling point (K)

(101.3 kPa)Mole mass

(g/mol)Relative density

(20/48C)Azeotropypoint (K)

(101.3 kPa)Composition of

azeotrope

Acetonitrile CH3CN 354.75 41.05 0.7822 349.15 0.8500Water H2O 373.15 18.02 0.9982 0.1500

Table 2. Screening Indexes of the Extractive Solvents for

Acetonitrile–Water System

Screening indexes Index

Temperature (K)SelectivitySolubility

349.15S1A;B� 50Sp� 20

2854 DOI 10.1002/aic Published on behalf of the AIChE August 2016 Vol. 62, No. 8 AIChE Journal

http://wileyonlinelibrary.com

rhb50:134568C=m2; (9)

where a0 is a constant for the misfit energy, 18 is the permittiv-ity of free space, chb is a constant for the hydrogen-bondinginteraction, rhb is a cutoff value for hydrogen-bonding interac-tions, racc and rdon are the largest and smallest values of rm

and rn, respectively, and the non-electrostatic contribution Ene

is assumed as a constant cne.The SAC is shown in Eq. 10:

ln CsðrmÞ52lnnX

rn

psðrnÞCsðrnÞexph2DWðrm; rnÞ

kT

io;

(10)

where DW(rm, rn) 5 Epair(rm, rn) 2 Epair(0, 0), known as theexchange energy, is the energy required to obtain one (rm, rn)pair from a neutral pair.

By standardizing the molecular volume and area ascalculated by COSMO, the normalized volume parameter ri andsurface area parameter qi for i can be obtained. These valuesare subsequently used to calculate the combination of activitycoefficient using the Staverman-Guggenheim model equation:

ln cSGi=s5ln

ui

xi1

z

2qiln

hi

ui

1li2ui

xi

Xj

xjlj; (11)

hi5ðxiqiÞ=�X

j

xjqj

�; (12)

ui5ðxiriÞ=�X

j

xjrj

�; (13)

Table 3. Part Results of Molecular Design

No. Anion No. Cation No. r1A;S r1B;S S1A;B Sp

74 9 4 0.84508 0.00064 1317.176 1558.64075 9 111 0.96382 0.00059 1639.969 1701.52581 9 65 0.73197 0.00038 1935.920 2644.826

Note: for the anion number, see Appendix A; for the cation number, see Appendix B.

Figure 3. StructureChart of ILs (a) [EMIM]OAc, (b) [4MePy]OAc, and (c) [HDMIM]OAc.

[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 4. VLE diagram for the acetonitrile (1)-water (2)-[EMIM]OAc (3) system.

Figure 5. VLE diagram for the acetonitrile (1)-water (2)-[4MePy]OAc (3) system.

Figure 6. VLE diagram for the acetonitrile (1)-water (2)-[HDMIM]OAc (3) system.

AIChE Journal August 2016 Vol. 62, No. 8 Published on behalf of the AIChE DOI 10.1002/aic 2855


li5z

2½ðri2qiÞ2 ðri21Þ�; (14)

where xi is the mole fraction of component i, and z is the coor-

dination number, usually taken as 10.Thus, the final equation for the activity coefficient is written

as follows:

ln ci=s5ni

Xrm

piðrmÞ½ln CsðrmÞ2ln CiðrmÞ�1ln rSGi=s ; (15)

If the content of solvent S is close to 1 (e.g., the number of

xi is set as 1 3 1028), the infinite dilution activity coefficient

of molecule i in solvent S can be calculated. Based on the infi-

nite dilution activity coefficient of each component in the ILs,

a selection of extractive distillation solvents can be performed.

Molecular design method

Selectivity and Solubility. Solvents can change the rela-

tive volatility of the original components according to the sep-

aration requirements. Generally, the solvent added into the

system should have better selectivity for one of the compo-

nents. It is difficult to calculate the activity coefficient of each

component because it is related to the solution composition;

however, the infinite dilution activity coefficient of each com-

ponent in the solvent is relatively easy to obtain. Therefore,

the selectivity can be expressed by the infinite dilution activitycoefficient, as shown in Eq. 16:

S1A;B5r1A;Sr1B;S

; (16)

Where r1A;S and r1B;S are the infinite dilution activity coefficientsof A and B in S, respectively. The value of S1A;B represents theresult of separating A and B, and the larger the value, the bet-ter the separation results. In other words, high S1A;B indicates ahigh selectivity of the solvent.

Table 4. Calculated Results of Wilson Model Parameters (Kij)

i

j Acetonitrile Water [EMIM]OAc [4MePy]OAc [HDMIM]OAc

Acetonitrile 0 0.1619 0.000015 0.003245 0.01078Water 0.6482 0 4.1419 5.1766 5.5869[EMIM]OAc 1.7874 21.8172 0 – –[4MePy]OAc 2.8112 26.1194 – 0 –[HDMIM]OAc 3.6729 26.9359 – – 0

Table 5. Antoine Equation Parameters

Component A B C

Acetonitrile 7.07350 1279.200 224.010Water 7.96681 1668.210 228.0

Figure 7. Comparison of VLE diagrams of experimentand calculated data ([EMIM]OAc).



Figure 8. Comparison of VLE diagrams of experimentand calculated data ([4MePy]OAc).



Figure 9. Comparison of VLE diagrams of experimentand calculated data ([HDMIM]OAc).







The solubility of component B can be calculated using Eq. 17:

Sp51

r1B;S; (17)

where the value of Sp represents the solubility of a non-

volatile solute. Generally, a high solubility of the component

in the solvent can prevent the liquid immiscibility phenom-

enon. Thus, a proper solvent with both good selectivity and

high solubility should be selected.

Model Regression and Prediction of Vapor Composition.. Based on the concept of local composition and a thermal

solution theory, Wilson38 presented a relationship between

activity coefficient and composition in 1964, which was

expressed as the following:

GE

RT52

Xi

xiln�X

j

xjKij

�: (18)

The activity coefficient equation is correspondingly noted as:

ln ci512ln�X

j

xjKij

�2X

k

� xkKkiPjxjKkj

�: (19)

The most prominent advantage of the Wilson equation is that

it can express the component activity coefficients of both polar

and nonpolar systems. In addition, the binary data can be

directly applied to multiple vapor equilibrium. Thus, in this

article, the Wilson equation was used to predict the vapor

composition for molecular design.

Results and Discussion

The acetonitrile–water system is a typical polar binary azeo-

tropic system that is completely miscible. Although acetoni-

trile is a common solvent widely used in pharmaceutical and

other industries, it remains difficult to improve the purity of

acetonitrile to greater than 99% due to azeotropy. In addition,

the ethanol–cyclohexane system is a binary azeotropic system

composed of one polar component and one nonpolar compo-

nent. Therefore, this article uses these two typical azeotropic

systems to perform an experimental verification of the molecu-

lar design of ILs as extractive distillation solvents. The spe-

cific experimental method can be found in the Refs. 39, 40.

The molecular design of acetonitrile–water system

Table 1 lists the physical properties of acetonitrile and

water. The screening indexes of the extractive solvents for the

system are presented in Table 2.The results of the molecular design of solvents used to sepa-

rate acetonitrile–water mixture are shown in Appendix C with

the screening indexes of selectivity S1A;B and solubility Sp.

Table 3 presents the partial results of the molecular design.According to the design results of Appendix C, No. 74,

1-ethyl-3-methyl imidazole acetate ([EMIM]OAc), No. 75,

1-butyl-4-methyl pyridinium acetate ([4MePy]OAc), and No.

81, 1-hexyl-2,3-dimethyl imidazolium acetate ([HDMIM]OAc),

were more appropriate as solvents for extractive distillations

because of their higher selectivities and solubilities. Acetonitrile

and water are polar solvents and the polarity of water is stronger

than that of acetonitrile. It can be found from Table 3 that the

activity coefficients for water dissolved in the ILs solution are

much less than unity, which indicates the attractive interaction

between water and ILs is higher than that between acetonitrile

and ILs. The low activity coefficients mean the ILs and water

can form hydrogen bond relatively easily. And the ILs can be

good extractive distillation solvents compared to conventional

solvents for acetonitrile–water system. Thus, [EMIM]OAc,

[4MePy]OAc, and [HDMIM]OAc were selected for the VLE

verification of the acetonitrile (1)-water (2) system. The struc-

tures of these three ILs are shown in Figure 3.The VLE data for an acetonitrile (1)-water (2) system with

different ILs at various weight fractions (15 and 30%) were

measured at 101.3 kPa, and the vapor–liquid equilibrium dia-

grams are plotted in Figures 4–6, respectively.As shown in Figures 4–6, [EMIM]OAc, [4MePy]OAc, and

[HDMIM]OAc increased the relative volatility of acetonitrile

to water. In addition, the concentrations of acetonitrile were

augmented as the mass fractions of the ILs increased. The aze-

otropic point vanished when the mass fractions of the ILs

reached 30%, and the relative volatility of acetonitrile to water

Table 6. Analysis of the Calculated Results of Vapor Phase Composition

System Maximum relative deviation, % Minimum relative deviation, % Average relative deviation, %

Acetonitrile–water–[EMIM]OAc 19.63 1.05 5.81Acetonitrile–water–[4MePy]OAc 11.15 0.98 4.41Acetonitrile–water– [HDMIM]OAc 18.58 0.03 4.54

Figure 10. Experimental and calculated Relative volatil-ities with different ILs.

[Color figure can be viewed in the online issue, which

is available at wileyonlinelibrary.com.]

Table 7. Physical Properties of Ethanol and Cyclohexane

Compound FormulaBoiling point (K)

(101.3 kPa) Mole mass (g/mol)Relative density

(20/48C)Azeotropypoint (K)

(101.3 kPa)Composition of

azeotrope

Ethanol CH3CH2OH 351.47 46.07 0.7893 338.05 0.3000Cyclohexane C6H12 353.87 84.16 0.7785 0.7000



was greater than 1 over the entire concentration range. Gener-ally, in the acetonitrile–water system, the ILs enhanced the rel-ative volatility of acetonitrile to water by improving themiscibility of the two via chemical affinity and hydrogenbonds. Because the polarity of water is far stronger than thatof acetonitrile and the ILs are strong polar solvents, the ILshave much stronger attraction toward water than acetonitrileso that the volatility of acetonitrile can be enhanced.

It is clear that at low acetonitrile content, the acetonitrile mol-ecules were isolated by water molecules. When the anions werethe same, the effect order of cations on the relative volatilitywas [HDMIM]1> [4MePy]1> [EMIM]1. This order is relatedto the length of the alkyl chains of the different cations, i.e., thelonger the alkyl chain, the stronger the interaction between ILsand water molecules would be. At the same time, it was easierfor acetonitrile to volatilize. At low water content, the watermolecules were isolated by acetonitrile molecules. When theanions were the same, the effect order of cations on the relativevolatility was [HDMIM]1> [EMIM]1> [4MePy]1. This orderis related to the species of cations, i.e., when water moleculeswere surrounded by acetonitrile molecules, the hydrogen bondsbetween the water molecules and the imidazolium cations werestronger than that with the pyridinium cations, and thus, the rel-ative volatility of acetonitrile with [EMIM]1 was higher thanthat with [4MePy]1. Similarly, the relative volatility of acetoni-trile with [HDMIM]1 was also higher than that with [EMIM]1.

Vapor composition prediction is based on the activity coef-ficient method, as described in Section “Model Regression andPrediction of Vapor Composition”. Tables 4 and 5 present thecalculated results of the Wilson model parameters (Kij) andthe Antoine equation parameters, respectively.

The mole fractions of acetonitrile in the vapor phase at dif-ferent IL mass contents were estimated according to the liquidphase composition, and the results were compared with theexperimental data (Figures 7–9). Table 6 lists the deviationanalysis of the vapor phase composition predictions using theactivity coefficient method of the ternary system, and the thirdcomponent is set as [EMIM]OAc, [4MePy]OAc, or [HDMI-

M]OAc (relative deviation 5 |experimental data-calculated

data|/experimental data 3 100%).As shown in Figures 7–9 and Table 6, the activity coeffi-

cient method is able to predict the vapor phase composition of

acetonitrile–water–ILs systems effectively. The overall aver-

age relative deviation of the three systems was 4.92%.Figure 10 compares the experimental and calculated relative

volatilities of acetonitrile to water with three types of ILs. It

can be concluded that if the mole fraction of acetonitrile is

less than 0.75, the ranking order of the ILs ability to eliminate

azeotropy in the acetonitrile–water system is [HDMI-

M]OAc> [4MePy]OAc> [EMIM]OAc; if the mole fraction

of acetonitrile is greater than 0.75, the ranking order becomes

[HDMIM]OAc> [EMIM]OAc> [4MePy]OAc. The results

demonstrate that the predicted data are consistent with the

Table 8. Screening Indexes of the Solvents Separating

Ethanol–Cyclohexane System

Screening indexes Index

Temperature (K) 338.05Selectivity S1A;B� 20Solubility Sp� 5

Table 9. Part Results of Molecular Design

No.Anion

No.Cation

No. r1A;S r1B;S S1A;B Sp

54 3 54 6.92697 0.06114 113.305 16.35758 3 69 6.62760 0.05602 118.317 17.85277 3 20 6.06295 0.03627 167.181 27.574

Note: For the anion number, see Appendix A; for the cation number, seeAppendix B.

Figure 11. StructureCharts of ILs(a) [OMIM]Cl(b) [BDMIM]Cl(c) [BMIM]Cl.

[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Figure 12. VLE diagram for ethanol(1)-cyclohexane(2)-[OMIM]Cl(3) system.

Figure 13. VLE diagram for ethanol(1)-cyclohexane(2)-[BDMIM]Cl(3) system.



calculated data. Thus, the molecular design method based onCOSMO-SAC was effective for the selection of ILs as a sol-vent for extractive distillation.

The molecular design of ethanol–cyclohexane system

Table 7 shows the physical properties of ethanol and cyclo-hexane. The screening indexes of the solvents separating theacetonitrile–water system are presented in Table 8.

The molecular design results of the solvents used to separatethe ethanol–cyclohexane mixture are shown in Appendix Dwith selectivity S1A;B and solubility Sp as the screening indexes.Table 9 presents the partial results of the molecular design.

According to the design results in Appendix D, No. 54,1-octyl-3-methyl imidazolium chloride ([OMIM]Cl), No. 58,1-butyl-2,3-dimethyl imidazolium chloride ([BDMIM]Cl), andNo. 77, 1-butyl-3-methyl imidazolium chloride ([BMIM]Cl) hadhigh selectivities and solubilities and were more suitable for useas solvents for extractive distillation compared with the others.In this system, ethanol is polar solvent while cyclohexane is not.It can be found from Table 9 that the activity coefficients forethanol dissolved in the ILs solutions are much less than that ofcyclohexane. This means that the attractive interaction betweenethanol and ILs is also higher than that between cyclohexane andILs, which indicates that ILs and ethanol can form hydrogenbond easily. Thus, it was confirmed that the ILs can be goodextractive distillation solvents compared to conventional solventsfor ethanol–cyclohexane system. Therefore, the VLE verificationfor the ethanol (1)-cyclohexane (2) system selected [OMIM]Cl,[BDMIM]Cl, and [BMIM]Cl as the ILs solvents. In addition,Figure 11 shows the structures of these three ILs.

The VLE data of the ethanol (1)-cyclohexane (2) systemwith different ILs at various contents (15 and 30 wt %) weremeasured at 101.3 kPa. The vapor–liquid equilibrium dia-grams are shown in Figures 12–14.

As observed in Figures 12–14, [OMIM]Cl, [BDMIM]Cl, and[BMIM]Cl enhanced the relative volatility of cyclohexane,whereas the concentration of cyclohexane increased as the IL massfraction increased. The azeotropic point vanished when the ILmass fraction reached 30 wt %. Generally, in the ethanol–cyclo-hexane system, ILs are highly miscible with ethanol and thus, caninteract with ethanol through chemical affinity and hydrogenbonds. However, because cyclohexane molecules are nonpolar andethanol molecules are polar, the ILs experience much strongerinteractions with ethanol molecules than with cyclohexane mole-cules, and therefore, the volatility of cyclohexane is enhanced.

It is clear that with the same anions, the effect order of cationson the relative volatility is [BMIM]1> [OMIM]1 >[BDMIM]1, which is similar to the effect of the alkyl chainlength of cations on the volatilization of cyclohexane. The reasonfor this observation may be that the methyl functional groups inthe imidazolium cation weaken the interaction between the ILsand ethanol and thus limits the ILs ability to eliminate azeotropy.

The Wilson model parameters (Kij) and Antoine equationparameters are shown in Tables 10 and 11, respectively.

According to the liquid phase composition, the mole fractionsof cyclohexane in the vapor phase at different IL mass fractionswere predicted, and the experimental data were compared withthe calculated data (Figures 15–17). Table 12 lists the deviationanalysis results of the vapor phase composition prediction usingthe activity coefficient method of the three ternary systems:

Figure 14. VLE diagram for ethanol(1)-cyclohexane(2)-[BMIM]Cl(3) system.

Table 10. Calculated Results of Wilson Model Parameters (Kij)

i

j Cyclohexane Ethanol [OMIM]Cl [BDMIM]Cl [HDMIM]OAc

Cyclohexane 0 0.05139 8.4113 3 10213 1.229 3 1029 2.3913 3 10211

Ethanol 0.6150 0 0.005461 1.412 3 1027 7.2887 3 1029

[OMIM]Cl 0.5517 22.3178 0 – –[BDMIM]Cl 1.0838 37.0211 – 0 –[HDMIM]OAc 1.1581 38.3314 – – 0

Table 11. Antoine Equation Parameters

A B C

Cyclohexane 6.84130 1201.531 222.647Ethanol 8.21330 1652.050 231.480

Figure 15. Comparison of VLE diagrams of experimentand calculated data ([OMIM]Cl).





ethanol–cyclohexane–[OMIM]Cl, ethanol–cyclohexane–[BDMIM]Cl, and ethanol–cyclohexane–[BMIM]Cl.

As shown in Figures 15–17 and Table 12, the activity coeffi-cient method can efficiently predict the vapor phase composi-tion of acetonitrile–water–IL systems. In addition, the overallaverage relative deviation of the three systems is 5.45%.

For a clearer perspective, Figure 18 compares the experimen-tal and relative volatilities of cyclohexane to ethanol in threetypes of ILs. By analyzing the experimental data, it can be con-

cluded that the ranking of the ability to eliminate azeotropy ofethanol–cyclohexane system is [BMIM]Cl> [OMIM]Cl> [BD-MIM]Cl. The results demonstrate that the predicted data agreewith the calculated data. The molecular design method based onCOSMO-SAC was effective in selecting ILs as a solvent forextractive distillation.

According to the presented discussion, it can be stated thatthe selection of a solvent for an extractive distillation process iseffective when based on a molecular design method. Consider-ing an acetonitrile–water system with two polar componentsand a cyclohexane–ethanol system containing a polar compo-nent and a nonpolar component, the results illustrate that themolecular design method based on COSMO-SAC is widelyapplicable. Investigations of applying the method to other sys-tems, e.g., a nonpolar system or complicated mixtures withmore than two components, will be examined in future work.

Conclusions

Based on COSMO-SAC theory, this article established ther-profiles of 24 types of anions and 176 types of cations andcalculated the infinity dilution activity coefficients of differentcomponents in the ILs. The data were subsequently used toscreen ILs using the molecular design method for the extrac-tive distillation of special systems.

For the acetonitrile–water systems, three ILs ([EMIM]OAc,[4MePy]OAc, and [HDMIM]OAc) with high selectivity andhigh solubility were chosen according to COSMO-SACmethod. The VLE data were measured for the three ternarysystems containing acetonitrile, water, and ILs at 101.3 kPa.The results showed that the three ILs were effective in enhanc-ing the relative volatility of acetonitrile to water. The azeo-tropic point was eliminated when the mass fraction of ILsreached 30%. The separation abilities of ILs were [HDMI-M]OAc> [4MePy]OAc> [EMIM]OAc when the mole frac-tion of acetonitrile in the raw material was less than 0.75. Thisorder became [HDMIM]OAc> [EMIM]OAc> [4MePy]OAcwhen the mole fraction of acetonitrile was greater than 0.75.The Wilson model parameters were calculated and used topredict the VLE data of acetonitrile–water–IL systems usingthe activity coefficient method. The overall average relativedeviation was 4.92%.

Figure 16. Comparison of VLE diagrams of experimentand calculated data ([BDMIM]Cl).



Figure 17. Comparison of VLE diagrams of experimentand calculated data ([BMIM]Cl).



Table 12. Analysis of the Calculated Results of Vapor Phase

Composition

System

Maximumrelative

deviation, %

Minimumrelative

deviation, %

Averagerelative

deviation, %

Ethanol–cyclohexane–[OMIM]Cl

24.10 0.86 4.80

Ethanol–cyclohexane–[BDMIM]Cl

16.47 1.31 6.19

Ethanol–cyclohexane–[BMIM]Cl

23.08 1.83 5.92

Figure 18. Relative volatility for compared of experi-ment data and calculated results.







For the cyclohexane–ethanol system, three ILs ([OMIM]Cl,

[BDMIM]Cl, and [BMIM]Cl) with high selectivity and high

solubility were similarly selected. The VLE data were also

measured for the three ternary systems containing cyclohex-

ane, ethanol, and ILs at 101.3 kPa. The results showed that the

three ILs were effective at enhancing the relative volatility of

the cyclohexane–ethanol system. The azeotropic point was

also eliminated when the mass fraction of ILs reached 30%.

The separation ability of the ILs were ranked

[BMIM]Cl> [OMIM]Cl> [BDMIM]Cl, which agreed with

the molecular design results. The Wilson model parameters

were used to predict the VLE data of cyclohexane–ethanol–IL

systems with an overall average relative deviation of 5.45%.In conclusion, the molecular design method based on

COSMO-SAC proposed in this article is a practical, effective,

and accurate method for solvent selection in the extractive dis-

tillation processes. This work also provided basic data and the-

oretical instructions for further industrial applications of ILs

as a mass separation agent.

Acknowledgments

This article is supported by the National Natural Science

Foundation of China (21306036), Excellent Youth Scholars

of Educational Commission of Hebei Province of China

(Y2012040), and The Research Fund for the Doctoral Pro-

gram of Higher Education of China (20131317120014).

Notation

T = temperature, KN = experiment point numberS = selectivity

Sp = solubilityV = volume, mLK = vapor–liquid equilibrium ratio

GE = mole excess Gibbs free energyp = pressure, kPa

ps = pressure, kPax = mole fraction of components in liquid phasey = mole fraction of components in vapor phase

w = mass fractionf = correction factorf = fugacity

Greek Letters

a = relative volatilityc = activity coefficientl = chemical potential, J�mol21

u = fugacity coefficientK = Wilson model parameters = NRTL model parameter

Superscript Subscripts

L = liquid phaseV = vapor phase1 = the state at infinite dilution

exp = experiment datacal = calculate data

i, j, k = component number

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Appendix A: Lists of Anions


Appendix B: Lists of Cations


Appendix B: Continued


Appendix C: Molecular Design Results ofAcetonitrile–Water System


1 24 3 0.94486 0.01736 54.418 57.5932 24 84 0.93617 0.01702 55.018 58.7693 24 154 0.77614 0.01646 47.153 60.7534 24 76 0.83320 0.01624 51.311 61.5835 24 110 0.88359 0.01524 57.982 65.6226 24 25 0.79704 0.01376 57.943 72.6987 24 98 0.76260 0.01346 56.677 74.3208 24 63 0.83062 0.01323 62.786 75.5899 24 117 0.79864 0.01303 61.306 76.76310 24 29 0.76742 0.01271 60.366 78.66111 24 11 0.80382 0.01245 64.579 80.34112 24 64 0.79822 0.01235 64.658 81.00213 24 135 0.76388 0.01163 65.703 86.01214 9 32 1.46334 0.01074 136.189 93.06715 3 162 1.97477 0.00928 212.729 107.72316 24 34 0.66383 0.00850 78.121 117.68217 24 120 0.62682 0.00849 73.827 117.78118 24 90 0.62234 0.00805 77.325 124.24819 24 31 0.63169 0.00785 80.435 127.33420 24 65 0.60488 0.00782 77.328 127.84121 24 128 0.61463 0.00735 83.669 136.13022 24 42 0.57373 0.00678 84.566 147.39723 24 43 0.55124 0.00675 81.722 148.25224 24 113 0.56826 0.00571 99.544 175.17225 24 88 0.58903 0.00550 107.137 181.88626 24 30 0.49870 0.00470 106.124 212.80027 24 19 0.45001 0.00414 108.808 241.79128 24 75 0.45050 0.00375 120.280 266.99429 9 8 0.98238 0.00293 335.550 341.56830 3 54 1.20006 0.00274 437.377 364.46431 3 123 1.44081 0.00272 530.540 368.22432 9 63 0.69620 0.00269 258.819 371.75833 3 38 1.24944 0.00263 475.323 380.42934 3 111 1.41890 0.00251 565.388 398.47035 3 29 1.24574 0.00249 500.416 401.70336 3 136 1.84458 0.00244 756.169 409.94137 3 3 1.13565 0.00242 470.023 413.87838 3 174 1.23049 0.00233 528.188 429.24939 3 150 1.40296 0.00226 619.856 441.82140 3 65 1.20817 0.00209 577.536 478.02541 9 6 1.71883 0.00207 830.444 483.14642 3 177 1.19287 0.00201 594.638 498.49543 9 73 1.43742 0.00200 717.372 499.06944 3 1 0.83614 0.00200 417.420 499.22245 9 84 1.52080 0.00198 768.011 505.00546 3 135 1.15922 0.00196 591.578 510.32347 9 30 0.60496 0.00192 315.636 521.74548 3 71 1.13257 0.00188 602.274 531.77549 9 154 1.16434 0.00184 632.115 542.89551 3 34 0.93966 0.00164 574.101 610.96652 3 145 0.81427 0.00156 522.391 641.54153 3 131 0.86355 0.00156 554.432 642.03954 9 69 0.49635 0.00151 328.897 662.63155 3 43 0.71965 0.00144 501.132 696.35156 3 4 0.78951 0.00142 556.326 704.64557 3 20 0.78265 0.00141 555.950 710.34358 9 15 0.62512 0.00140 445.546 712.74159 9 85 1.22560 0.00139 880.988 718.82460 3 60 0.77389 0.00133 583.026 753.37561 3 42 0.73437 0.00133 554.064 754.47262 9 138 1.17388 0.00132 890.531 758.62063 9 23 1.34318 0.00132 1021.101 760.21464 9 77 1.22465 0.00127 960.889 784.62165 9 129 1.18815 0.00120 989.006 832.39466 9 11 1.22257 0.00117 1047.781 857.03467 9 14 1.24370 0.00115 1085.159 872.52668 3 58 0.97692 0.00112 873.008 893.636

(Continued)

Appendix (Continued)


69 3 113 0.54515 0.00108 502.520 921.80970 9 35 1.17043 0.00104 1123.784 960.14771 3 67 0.53476 0.00093 574.817 1074.91572 3 79 0.42098 0.00078 538.454 1279.04773 3 15 0.61142 0.00078 788.911 1290.29274 9 4 0.84508 0.00064 1317.176 1558.64075 9 111 0.96382 0.00059 1639.969 1701.52576 9 53 0.83310 0.00055 1525.805 1831.47377 9 131 0.89163 0.00051 1748.215 1960.69578 9 64 0.83136 0.00050 1662.565 1999.80979 9 88 0.84395 0.00045 1885.018 2233.55580 9 42 0.76459 0.00039 1953.567 2555.05881 9 65 0.73197 0.00038 1935.920 2644.826

Appendix D: Molecular Design Results ofEthanol–Cyclohexane System


1 3 32 10.07211 0.24276 41.490 4.1192 9 137 7.58098 0.20936 36.210 4.7763 3 61 6.38958 0.16724 38.207 5.9804 9 31 6.29137 0.13685 45.974 7.3075 3 16 8.33154 0.13640 61.084 7.3326 9 54 6.31426 0.13238 47.697 7.5547 3 44 5.40730 0.13179 41.029 7.5888 3 56 5.20830 0.12932 40.275 7.7339 9 112 6.51997 0.12903 50.530 7.75010 9 6 12.84015 0.12707 101.047 7.87011 3 138 4.96103 0.12155 40.815 8.22712 3 12 3.61406 0.11878 30.427 8.41913 3 90 3.82162 0.11567 33.039 8.64514 9 10 5.70917 0.11510 49.601 8.68815 3 129 4.31249 0.11442 37.689 8.74016 9 83 5.74131 0.11318 50.729 8.83617 3 25 4.27731 0.11207 38.167 8.92318 3 70 3.30042 0.10921 30.221 9.15719 9 44 4.88108 0.10853 44.974 9.21420 3 93 3.46499 0.10269 33.741 9.73821 3 135 3.63755 0.10234 35.544 9.77122 3 28 6.08209 0.10185 59.718 9.81923 3 4 10.33512 0.10112 102.202 9.88924 3 17 3.28034 0.10031 32.703 9.96925 9 149 5.96731 0.10011 59.607 9.98926 9 25 4.97263 0.09872 50.370 10.12927 9 157 5.20382 0.09766 53.288 10.24028 3 14 2.83350 0.09658 29.339 10.35429 9 54 4.17695 0.09595 43.532 10.42230 9 68 11.57489 0.09438 122.641 10.59531 9 14 5.76319 0.09436 61.075 10.59732 9 38 5.42324 0.09380 57.817 10.66133 9 116 4.87487 0.09344 52.171 10.70234 9 103 5.61124 0.09290 60.404 10.76535 9 33 5.22977 0.08975 58.272 11.14236 3 77 2.38567 0.08699 27.426 11.49637 9 125 4.67605 0.08630 54.182 11.58738 3 50 2.96784 0.08364 35.485 11.95639 3 45 3.13476 0.08071 38.841 12.39040 3 165 5.59056 0.07939 70.422 12.59741 3 31 2.70623 0.07814 34.633 12.79842 3 108 2.75471 0.07568 36.398 13.21343 3 73 2.79628 0.07556 37.007 13.23444 3 66 3.07211 0.07414 41.434 13.48745 3 127 2.50816 0.07343 34.159 13.61946 3 42 2.79632 0.07287 38.374 13.72347 3 20 8.34527 0.07092 117.676 14.101

(Continued)




48 3 128 2.20368 0.06933 31.783 14.42349 9 115 7.71845 0.06528 118.242 15.31950 3 30 4.16608 0.06476 64.334 15.44251 3 86 1.50780 0.06386 23.612 15.66052 9 47 3.72274 0.06301 59.083 15.87153 9 134 4.00443 0.06287 63.692 15.90554 3 54 6.92697 0.06114 113.305 16.35755 9 71 3.98033 0.05869 67.824 17.04056 9 140 3.38759 0.05781 58.601 17.29957 3 118 1.65939 0.05654 29.347 17.68658 3 69 6.62760 0.05602 118.317 17.85259 9 45 3.76959 0.05590 67.439 17.89060 9 176 3.81972 0.05489 69.590 18.21961 9 130 3.75170 0.05465 68.652 18.29962 3 51 1.36901 0.05446 25.138 18.36263 3 69 1.48161 0.05030 29.454 19.88064 9 152 3.31627 0.04887 67.857 20.462

(Continued)



65 9 121 3.20012 0.04746 67.432 21.07266 9 43 3.49343 0.04696 74.391 21.29567 3 70 1.24983 0.04648 26.888 21.51468 9 118 3.28385 0.04495 73.049 22.24569 3 142 1.03941 0.04419 23.519 22.62870 9 90 2.46802 0.04349 56.744 22.99271 3 139 1.19748 0.04101 29.200 24.38472 9 59 2.11785 0.03758 56.359 26.61273 9 13 2.37645 0.03750 63.376 26.66974 9 159 2.11007 0.03646 57.867 27.42475 3 119 0.91252 0.03644 25.045 27.44676 9 42 2.53030 0.03632 69.664 27.53277 3 20 6.06295 0.03627 167.181 27.57478 3 13 0.77096 0.03530 21.842 28.332

Manuscript received July 15, 2015, and revision received Feb. 25, 2016.


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A Molecular Design Method Based on the COSMO‐SAC Model...

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