Process analysis of extractive distillation for the separation of ethanol–water using deep eutectic solvent as entrainer

Process analysis of extractive distillation for the separation of ethanol–water using deep eutectic solvent as entrainer

Chemical Engineering Research and Design 1 4 8 ( 2 0 1 9 ) 298–311 Contents lists available at ScienceDirect Chemical Engineering Research and Desig...

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Chemical Engineering Research and Design 1 4 8 ( 2 0 1 9 ) 298–311

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Process analysis of extractive distillation for the separation of ethanol–water using deep eutectic solvent as entrainer Xianyong Shang, Shoutao Ma, Qi Pan, Jinfang Li, Yinhai Sun, Kai Ji, Lanyi Sun ∗ State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China

a r t i c l e

i n f o

a b s t r a c t

Article history:

Deep eutectic solvents (DESs) as novel entrainers can be used for the separation of azeotropic

Received 3 September 2018

mixture, and have received much attention in recent years. However, the researches about

Received in revised form 2 April

DESs mainly focus on vapor–liquid equilibrium (VLE) and extractive distillation experi-


ment. In this work, an overall extractive distillation process for ethanol dehydration using

Accepted 11 June 2019

ChCl/Urea (1:2) as entrainer is investigated. The physical properties of the DES are defined

Available online 18 June 2019

by correlating the experimental data. Three thermodynamic methods are employed to describe the phase behavior of ethanol–water–ChCl/Urea (1:2) system. COSMO-based theory


is adopted to explain the differences in separation performance among ChCl/Urea (1:2), glyc-


erol and [EMIM][BF4 ]. The design parameters of extractive distillation process are optimized

Ethanol dehydration

by multi-objective generic algorithm (MOGA) with minimum total annual cost (TAC), min-

COSMO-based theory

imum CO2 emissions (ECO2 ) and maximum efficiency indicator of extractive section (EExt )

MOGA optimization

as objective functions. In addition, the control structure of extractive process is studied

Dynamic performance

by Aspen Dynamics, and the proposed control structure could resist fresh feed flow rate and composition disturbances. The results show that ChCl/Urea (1:2) exhibits better separation performance in ethanol dehydration compared with glycerol and [EMIM][BF4 ]. Both COSMOSAC model relying on molecular information and NRTL model based on experimental data can well describe the vapor–liquid behavior containing ChCl/Urea (1:2). Entrainer purity plays an important role in extractive distillation process, and a proper concentration rather than nearly pure entrainer should arouse our attention in extractive distillation process. DESs as promising novel entrainers can be used in extractive distillation industrial for separating azeotrope. © 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.



The separation of azeotropic mixture is one of the main challenges in the field of separation technology. Ethanol from the separation of ethanol–water mixture commercially produced by either catalytic hydration of ethylene or fermentation of sugars, is used as fuel, chemical reagent, organic solvent and raw material in many chemical industries (Kumar et al., 2010). Considering the azeotrope formed by

ethanol and water, the mixture cannot be separated by conventional distillation, some special separation methods have been adopted, such as azeotropic distillation (Luyben, 2012a; Kiss and Suszwalak, 2012), pressure-swing distillation (Arifeen et al., 2007), and pervaporation membranes (Leland, 2005). Extractive distillation is a special separation method in which the relative volatility of original components is altered by adding an entrainer, and is widely used to separate azeotropic mixtures because of low energy consumption and flexible selection of possible entrainers (Lei et al., 2003, 2014). Meirelles et al. (1992) and Lynn and Hanson (1986) studied the ethanol dehydration process, which used ethylene glycol as entrainer. Uyazán et al. (2006) and García-Herreros et al. (2011) used glycerol as entrainer for ethanol

Corresponding author. E-mail address: [email protected] (L. Sun). 0263-8762/© 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Chemical Engineering Research and Design 1 4 8 ( 2 0 1 9 ) 298–311

dehydration and achieved favorable energy consumption in comparison to the ethylene glycol. Zhu et al. (2016) adopted ionic liquids (ILs) as entrainers and found that [EMIM][BF4 ] is superior to conventional entrainers in the separation of ethanol and water by extractive distillation. The researches above indicate that the suitable entrainer is one of


2. Thermophysical properties and comparison of VLE calculation methods 2.1.

Properties of ChCl/Urea (1:2)

the key factors to ensure the separation performance (Ma et al., 2017a). ILs as a new type of solvents have attracted more attention because the advantages of low melting point, low volatility, easy recovery, good ´ selectivity and functional design (Jelinski and Cysewski, 2017). However, some studies (Couling et al., 2006; Dong et al., 2017) also pointed out that the use of ILs was limited in extractive distillation process due to their high cost, complicated synthesis process, poor biodegradability and potential toxicity. At present, there is a general consensus on developing green solvents to overcome the disadvantages of solvents (Mondal and Saha, 2018; Kerton and Marriott, 2013). DESs, which are consisted of hydrogen bond donors and hydrogen bond acceptors, are a new class of solvents with similar properties to ILs. Besides, DESs have advantages of low price, good biodegradability and simple preparation compared to ILs (Abbott et al., 2003; Francisco et al., 2013). In 2015, Kroon et al. first evaluated DESs as novel entrainers for extractive distillation (Rodríguez et al., 2015). The feasibility of DESs used as entrainers for the separation of azeotropic mixtures have been reported in some literatures (Francisco et al., 2013; Rodríguez et al., 2015; Peng et al., 2017). Those studies mainly considered the liquid–liquid equilibrium (LLE) and VLE experiments. However, observations on the modeling of extractive distillation process using DESs as entrainers are infrequent, due to the absence of physical properties and difficulties in modeling (Ma et al., 2017a). ChCl/Urea (1:2) is a typical DES and many properties about it have been reported. ChCl/Urea (1:2) is liquid state at a room temperature and is thermally stable at a higher temperature, with the melting point and thermal decomposition temperature of ChCl/Urea (1:2) are 12.7 ◦ C and 211.02 ◦ C, respectively (Chemat et al., 2016a). Moreover, the literature (Peng et al., 2017) has proved that it can break the ethanol–water azeotrope composition and achieve separation. Therefore, ChCl/Urea (1:2) is selected as the entrainer for ethanol dehydration process to execute technical and economic evaluation via process simulation for the design of industrial-scale applications. COSMO-based theory can be used to analysis the interaction among components (Zhou et al., 2012; Fang et al., 2016). In 2002, Lin and Sandler (2002) proposed a new activity coefficient model known as the COSMO-SAC model based on the COSMO-RS framework (Klamt, 1995; Eckert and Klamt, 2002). Those models could analysis the interaction among components and obtain the activity coefficient based on only molecular structure and several adjustable parameters. Besides, the COSMO-based/Aspen Plus integrated computational tool could predicted VLE data of the mixtures, which is significantly advanced for VLE calculations compared with the group contribution method as most group interaction parameters in DESs are absent (Fang et al., 2016). In this work, the physical properties of ChCl/Urea (1:2) are defined by correlating the experimental data of key physical properties. A suitable method to describe the phase behavior of this system is selected, by comparing the predicted VLE data of three different thermodynamic methods with the experimental VLE data. COSMO-based theory is used to explain the discrepant capacity of different entrainers used in extractive distillation for the separation of ethanol and water. Process parameters are determined and analyzed by MOGA optimization, including the purity of the circulated entrainer, parameters of distillation column and so on, which provides the basis for the design of the distillation process of ethanol water extraction. And a heat integrated flowsheet is then proposed to make the process more energy efficient. In addition, the dynamic performance of the process is also studied by Aspen Dynamics V8.4. All of these are carried out to evaluate the extractive distillation process with ChCl/Urea (1:2) as entrainer.

DESs as novel solvents are not available in the database of the Aspen Plus process simulator. The appropriate component definition and property model specification for DESs can directly affect the accuracy and reliability of extractive distillation process. Theoretical estimation and semi-empirical equations correlation are conducted to meet the simulation requirements of Aspen Plus.


Scalar properties of ChCl/Urea (1:2)

ChCl/Urea (1:2) is created as user-defined components in Aspen Plus referring to ILs (Lei et al., 2014; Zhu et al., 2016; Ma et al., 2017a). The scalar properties of pure components are required as one of inputs in the process simulation with Aspen Plus. Mirza et al. (2015) had estimated the critical properties, normal boiling temperatures and acentric factors of DESs including ChCl/Urea (1:2) by a combination of the modified Lydersen–Joback–Reid method with the Lee–Kesler mixing rules, and the accuracy was verified by comparison of theoretical densities determined from the estimated critical properties with experimental values. The scalar properties of ChCl/Urea (1:2) used in this work are listed in Table S1 in Supporting information.

2.1.2. (1:2)

Temperature-dependent properties of ChCl/Urea

The properties depended on temperature, such as density, viscosity, surface tension, vapor pressure and heat capacity, which affecting the mass transfer and the energy consumption of the process (Ma et al., 2017a; Ali et al., 2018), are also fitted in Aspen Plus. The equations and parameters fitted in this work are listed in Table S2 in Supporting information (Chemat et al., 2016b; Shahbaz et al., 2016; Ma et al., 2017b). Fig. 1 shows the calculated and experimental results. It can be seen that the model calculated results agree well with the experimental data, which demonstrates the fitted parameters can be used to conduct further simulations. It is worth noting that the liquid molar volume is first converted from the density, and then fitted the liquid molar volumes of ChCl/Urea (1:2). The remaining non-critical properties of ChCl/Urea (1:2) are estimated by the methods and models used implicitly in Aspen Plus (Aspen Technology, Inc., 2019).


Comparison of VLE calculation methods

A correct description for the vapor–liquid behavior, is another crucial factor to ensure the validity of modelling for the separation process. NRTL model can be used to calculate VLE in DESs systems based on binary interaction parameter. The regressed binary interaction parameters based on experimental data are listed in Table S3 in Supporting information. The implemented COSMOSAC property model in Aspen Plus can also be used to predict VLE by importing the molecular volumes and the sigma-profiles of DESs. COSMO data of ChCl/Urea (1:2) are obtained by quantum mechanical calculation. The detailed calculation steps can be found in our previous literatures (Ma et al., 2017c, 2018). COSMO data used


Chemical Engineering Research and Design 1 4 8 ( 2 0 1 9 ) 298–311

Fig. 1 – Temperature-dependent properties of ChCl/Urea (1:2). (a) Heat capacity; (b) vapor pressure; (c) density; (d) viscosity; (e) surface tension. Symbol: experimental data; curve: fitting result of Aspen Plus. are listed in Table S4 in Supporting information (Moreno et al., 2018). COSMO-SAC developed on the COSMO-RS framework, can be used to calculate the infinite dilution activity coefficient based on surface charge interactions. To achieve VLE calculation, the NRTL binary interaction parameters of the system are also regressed based on the calculated infinite dilution activity coefficient (Aspen Technology, Inc., 2019). The calculated infinite dilution activity coefficient and regressed NRTL binary interaction parameters are listed in Tables S5 and S6 in Supporting information. The comparisons of experimental data and calculated data by the above three methods are shown in Fig. 2. The rootmean-square deviations (RMSD) of vapor phase composition for the system was applied to evaluate the quality of correlation for the VLE calculation methods. Both the COSMOSAC model and the NRTL model based on experimental data are well consistent with the experimental data. Compared with the other two methods, the RSMD of the NRTL model based

on experimental data has the minimum values with different E/F, which indicated the best agreement with the experimental data. Thus, the NRTL model based on experimental data is selected in this paper to describe the vapor–liquid behavior for extractive distillation process simulation.


Process simulation and optimization


The feasibility of the extractive distillation process


Separation performance of ChCl/Urea (1:2)

Glycerol and [EMIM][BF4] display a better performance as entrainer for the separation of ethanol and water. In order to verify whether ChCl/Urea (1:2) can be used as an excellent entrainer, the VLE curves of ethanol–water are obtained with an entrainer to feed mole ratio 1. As shown in Fig. 3 (Rodríguez et al., 2015; Peng et al., 2017; Ge et al., 2008), it can be seen that three kind solvents all can break azeotrope and can be used

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Fig. 2 – Experimental data and calculated data of (1) ethanol, (2) water, (3) ChCl/Urea (1:2) mixture. (a) E/F = 0.1 (mass/mass); (b) E/F = 0.2 (mass/mass); (c) E/F = 0.3 (mass/mass).

Fig. 3 – The VLE of (1) ethanol, (2) water, (3) entrainer mixture. as entrainer. Moreover, the effect of ChCl/Urea (1:2) on the relative volatility is bigger than the other two entrainer. Thus, ChCl/Urea (1:2) has the application potential as entrainer for ethanol dehydration process. In addition, the separation performance of ChCl/Urea (1:2) can be explained by the interaction between molecules with respect to the ␴-profile (Zhou et al., 2012; Fang et al., 2016). ␴-profile obtained from quantum chemical calculations is one of the most important molecule-specific properties. It is the distribution of the ␴ of molecule, which is divided into three regions, namely the nonpolar region (−0.0084 e/Å2 < ␴ < 0.0084 e/Å2 ), the hydrogen bond donor (HBD) region (␴ < −0.0084 e/Å2 ) and the hydrogen bond acceptor (HBA) region (␴ > 0.0084 e/Å2 ). According to COSMO-

RS, a molecule ␴-profile in the region of ␴ < −0.0084 e/Å2 and ␴ > 0.0084 e/Å2 indicates the HBD ability and the HBA ability, respectively. Generally, a broader distribution and high peaks of ␴-profile outside the nonpolar region indicate a higher polarity of the molecule. Therefore, as seen in Fig. 4(a), the range of ethanol screening density is narrower than that of water, which demonstrates the polarity of ethanol is smaller than water. In addition, water also has peaks in the polar region, which is the reason that water can be deemed as hydrogen bond accepted and hydrogen bond donor simultaneously. Fig. 4(b) shows the ␴-profile of ChCl/Urea (1:2), glycerol and [EMIM][BF4 ]. It can be seen that ChCl/Urea (1:2) has a peak at 0.017 e/Å2 , while the peaks of glycerol and [EMIM][BF4 ] are located at 0.012 e/Å2 nearby. Therefore, the polarity of ChCl/Urea (1:2) is higher than others, so the binary interaction of ChCl/Urea (1:2) with water is stronger than that of glycerol and ILs. Moreover, the peak at −0.017 e/Å2 for ChCl/Urea (1:2) can also result in a favorable interaction with the right peak of water (0.015 e/Å2 ). Those ␴-profile analyses also demonstrated that ChCl/Urea (1:2) shows a higher separation capability than glycerol and [EMIM][BF4 ].


Residue curve map analysis

The residue curve map (RCM) of ChCl/Urea (1:2)-ethanol–water is used as a tool to further verify the feasibility of ChCl/Urea (1:2) used as entrainer in the extractive distillation process, and the result is shown in Fig. 5. Different points on the residual curve correspond to different distillation time, and the direction indicated by arrow is the both time extension and temperature rising. It can be seen from the RCM that the azeotropic point of ethanol–water is unstable point; ethanol and water are saddle points; ChCl/Urea (1:2) is stable


Chemical Engineering Research and Design 1 4 8 ( 2 0 1 9 ) 298–311

Fig. 4 – ␴-profiles for pure components. (a) ␴-profiles for ethanol and water; (b) ␴-profiles for ChCl/Urea (1:2), glycerol and [EMIM][BF4 ].

Fig. 5 – The residue curve map of ethanol–water–ChCl/Urea (1:2). point. There is no distillation boundary among all the residual curves, indicating that extractive distillation process may be an efficient method for the separation of ethanol–water mixture.


Process design

In this paper, the extractive distillation processes are conducted using the Aspen Plus (V 8.4) based on the thermodynamic/physical properties and methods discussed in Section 2. The fresh feed flow rate and temperature are 100 kmol/h and 47 ◦ C, respectively, with the composition of 80 mol% ethanol and 20 mol% water. The specification for ethanol are no less than 99.5 mol% purity and 0.995 (mol/mol) recovery yield (Luyben, 2012a). As shown in Fig. 6, ChCl/Urea (1:2) as entrainer is fed to the upper section of extractive distillation column (EDC), which can enhance the volatility between ethanol and water much more than glycerol and [EMIM][BF4 ], and

ethanol and water are separated under atmospheric pressure. Entrainer temperature entering the EDC is set 68 ◦ C according to Donherty and Malone’s guideline (Doherty and Malone, 2001). High purity ethanol is obtained at the top of column; the bottom stream of the EDC is fed to next operating unit to complete entrainer recovery. Considering the thermal stability of ChCl/Urea (1:2), the lower pressure flash tank is selected instead of recovery column. The ChCl/Urea (1:2) is recovered and recycled to the EDC.


MOGA optimization and process evaluation


MOGA approach for extractive distillation processes

The effects of design variables in extractive distillation processes on the solution set are evaluated simultaneously rather than sequentially, thus, MOGA based on non-dominated sorting genetic algorithm (NSGA) II is adopted for process

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excess and to ensure the complete combustion, so that no CO is formed, and the CO2 emission can be calculated as follows: ECO2 = (

QFuel C% )( )˛ NHV 100


where, ˛ is the ratio of molar masses of CO2 and C and its value is 3.67. The NHV (kJ/kg) represents the net heating value of the fuel with carbon content C% (dimensionless). QFuel (kW) is the heat released by the fuel combustion. The value of NHV and C%, depending on fuels, is 39,771 kJ/kg and 86.5 respectively for heavy fuel foil (Gadalla et al., 2006b). In this distillation systems, steam is used for heating purposes, indirectly for reboilers, and is also used as motive stream to evacuate vapor in vacuum system. The amount of fuel burned can be calculated as follows: Fig. 6 – Extractive distillation process flowsheet for ethanol dehydration. optimization (Deb et al., 2002; Gómez-Castro et al., 2008; Gutiérrez-Antonio and Briones-Ramírez, 2009). Zaman and Rangaiah (2009) reported the limitations and advantages of MOGA for the design and operation of energy efficient chemical processes. Comparing with single objective optimization method, it can perform multi-objective optimization simultaneously, which can obtain a set of non-dominated solutions which satisfying the constraints with different operating parameters. Meanwhile, the MOGA can break the restriction which sequential iterative optimization procedure is easy to fall into a local optimal solution (You et al., 2018, 2015; Sun et al., 2014).

3.2.2. Process evaluation Economical evaluation. TAC is a key indicator for evaluating the economic performance of chemical processes. The TAC, including operating cost and capital cost, is calculated based on Luyben’s book (Luyben, 2012b). The calculation formula is shown as follows: TAC($ · year−1 ) =

Capital Cost + Operating Cost Payback Period


In the entrainer recovery section, flash tank should be kept under lower pressure, which can increase the operating cost and capital cost. The cost of vacuum system is calculated by reference to Seider et al.’s book (Seider et al., 2009). The payback period is set 3 years with the operation time of 8000 h per year. Detailed information is listed in Table S7 in Supporting information. CO2 emission evaluation. CO2 emission is another indicator for analyzing the environmental performance of extractive distillation. In addition, CO2 emissions can reflect the effect of energy-saving distillation and help to understand the impact of energy-saving system on environment benefits. The explicit estimation of CO2 emission is carried out according to the method of Gadalla et al. (2006a). The fuel combusts when mixed with air, and CO2 producing is obtained according to the following stoichiometric equation: Cx Hy + (x +

y y )O2 → xCO2 + H2 O 4 2


where, x, y are on representative of the number of C and H in the fuel. In the combustion of fuels, air is assumed to be in

QFuel =

QProc TFTB − T0 (hProc − 419) Proc TFTB − TStack


where, Proc (kJ/kg) and QProc (kJ/kg) are the latent heat and enthalpy of steam delivered to the process, respectively. TFTB (o C) and TStack (o C) represent the flame temperature of the boiler flue gases and stack temperature. T0 (o C) is the environment temperature. In addition, the concept of heating efficiency of the boiler is usually used to simplify the calculation difficulty, which is about 0.8–0.9 in the calculation process. The formula is shown in Eq. (5). In this work, the heating efficiency is assumed to be 0.8 (Gadalla et al., 2006a). QFuel =

QProc 0.8∼0.9

(5) Separation efficiency evaluation. It is very important to monitor the efficiency of extractive section for the separation of azeotropic mixture of extractive distillation process, which can affect the process energy consumption (You et al., 2015). Knapp and Doherty (1994) reported the minimum amount of entrainer used for separating azetropic mixture. You et al. (2018) and (2015) used the efficiency indicator of extractive and efficiency indicator of per tray in extractive section as objectives to study the extractive distillation process. To eliminate the impact of entrainer on the separation efficiency, a new efficiency indicator is propose based on You’s paper. That represents the ability of the extractive section to discriminate the desired product in EDC. The calculation procedures are shown as follows: xP + xUn-P + xE = 1


∗ xP,H =

xP,H xP,H + xUn-P,H


∗ xP,L =

xP,L xP,L + xUn-P,L


EExt =

∗ − x∗ xP,H P,L

NExt − 1


where, EExt is the efficiency indicator of the extractive section. xP , xUn-P , and xE are the desired product, undesired products and entrainer mole fraction at the extractive section. The subscript H and L identify the entrainer feed and the fresh feed tray location, respectively. NExt is the number of trays in the extractive section.


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xsolvent=0.995 xsolvent=0.99





Bottom temperature of EDC( o C)

E/F=0.25 E/F=0.2

mole fraction of ethanol

Molar fraction of ethanol


0.985 0.980



xE=0.99 140 xE=0.975











0.975 0.15














Reflux ratio



Fig. 7 – Eff ;ect of E/F, reflux ratio and entrainer purity on purity of ethanol. (a) Eff ;ect of E/F and reflux ratio on purity of ethanol; (b) eff ;ect of entrainer purity on purity of ethanol.


Process optimization

The global optimal solutions are mainly based on random search within the specified range. If an appropriate ranges of optimization variables is selected, the Pareto front can be computed more efficiently. A sensitivity analysis of the extractive distillation process with ChCl/Urea (1:2) as entrainer is carried out, and the optimization value ranges of design variables are determined to provide the basic reference for the multiobjective optimization of extractive distillation process (Sun et al., 2014; Cui et al., 2018).


Preliminary analysis

The entrainer-to-fresh feed molar ratio (E/F) as a continuous variable is an important parameter in extractive distillation process, which directly determine the purity of the product. Fig.7(a) shows the sensitivity analysis result. It can be seen that there is an optimum RR that gives a maximum at the given E/F. Meanwhile, the purity of ethanol cannot meet the qualification at a proper reflux ratio (RR), if E/F is less than 0.15. Owing to the high boiling point and low vapor pressure of ChCl/Urea (1:2), it is unnecessary to feed in nearly pure entrainer. The purity of entrainer affects the distillate ethanol purity. Furthermore, a higher purity target could consume more energy in the recovery section. It can be seen from Fig. 7(b) that the entrainer purity should not less than 0.975. In addition, the bottom temperature of EDC is higher with the increase of entrainer purity and flow. In order to harness low pressure steam (5 bar, 160 ◦ C) as a source of heat and keep heat transfer temperature difference not less than 10 ◦ C, the upper limit of E/F is 0.35. The temperature and pressure of the flash influence the purity and recovery of the entrainer. The lower pressure of the flash will result in an increase in vacuum operating cost. Steam-jet ejectors is adopted to achieve a certain degree of vacuum for the flash, and the lower limit of suction is 0.266 kPa (Seider et al., 2009). Moreover, to ensure the molar purity of recycled ChCl/Urea (1:2) not less than 0.975, the operating pressure should not be higher than 3.8 kPa based on sensitivity analysis. Correspondingly, the temperature of the flash is set to 95–150 ◦ C.


Multi-objective optimization

In the multi-objective problem, TAC and ECO2 are minimized while EExt is maximized. The objective functions, constraints and variable bounds are shown as follows:

Objective functions: minTAC


min ECO2




Constraints: xethanol ≥ 0.995


xE ≥ 0.975


Rethanol ≥ 0.995


RE ≥ 0.99


where, xethanol represents the purity of ethanol at the distillate and xE is the entrainer purity. Rethanol and RE represent the percent recovery of ethanol and entrainer, respectively. Variable bounds: Nine variables used in this work and bounds based on preliminary analysis are listed in Table 1. The ranges of each design variables are set to generate the first population, and the simulation of distillation scheme, which is based on all the members of the first population, is carried out. The population, crossover and mutation fractions parameters of the genetic algorithm are tuned after several preliminary tests. After tuning, 100 individuals, 0.8 for crossover fraction and 0.15 for mutation fraction are employed. When the values of objective functions could not be remarkably improved, the optimization completed.


Results and discussion

As the result of MOGA optimization of extractive distillation process, the Pareto front is obtained. Any optimal solution belonging to the Pareto front satisfy the specified constraints, and it could not be improved through one objective function without worsening the other objectives. Fig. 8 shows the Pareto front of three objectives. The blue dot representing the optimal value of each objective, does not coincide, indicating a nonlinear relationship among three objectives. Although there is a trade-off among the TAC, CO2 emission and separation efficiency, the desired design is the one with the minimum TAC from the economical view.

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Fig. 9 – Design result of total stages of EDC, fresh feed stage and entrainer feed stage. Fig. 8 – Pareto front of three objectives, TAC versus ECO2 and EExt . Effects of some operation parameters on TAC are discussed as a guidance for extractive distillation with ChCl/Urea (1:2) as entrainer. For the total stage of EDC, fresh feed stage and entrainer feed stage from Fig. 9, it is observed that three parameters vary in a very narrow range despite that we set a wider range. When the total stage of EDC is only 17, the separation requirements can be achieved. It can be seen from Fig. 10 that E/F, entrainer purity and reflux ratio exhibit a non-linear effect on TAC, because of the complexity of the design problem. A little amount of entrainer will lead to an increase in reflux ratio to meet the requirement of ethanol purity and recovery. Meanwhile, a more reboiler duty will increase to keep a certain reflux rate.

At a relatively high entrainer flow rate, the separating cost in the entrainer recovery section increases. A suitable E/F range (0.3–0.35), namely, (30–35) for entrainer flow rate, is obtained. It is noticed that when entrainer purity is approximately 0.987, the minimum TAC could be found. That is consistent with the analysis in Section 3.4.2. It demonstrates entrainer purity as a process variable should be taken seriously rather than pursuing a higher purity. Reflux ratio varies in a very narrow range between 0.140 and 0.175 with a minimum TAC at 0.170. The value of reflux ratio is smaller, which can also indicate the favorable property of ChCl/Urea (1:2) in the separation of ethanol–water water mixture. The parameters of the designs, named Cases 1–3 with the lowest TAC, the lowest ECO2 and the highest EExt , are shown in Table 2. The optimal flowsheet with minimum TAC is shown in Fig. 6. With respect to economic performance, TAC of Case 1 is

Fig. 10 – Effect of partial operation parameters on TAC in extractive distillation process. (a) Effect of E/F on TAC; (b) effect of entrainer purity on TAC; (c) effect of reflux ratio on TAC.


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Fig. 12 shows the effect of E/F, entrainer purity and feed stage on EExt . Three variable parameters influence directly on the extraction section in EDC. From Fig. 12, we know the following: (1) The optimal EExt is not the one with maximum entrainer flow. Along with the increase of entrainer flow (E/F), EExt increases nonlinearly, then decreases slowly after reaching an optimum value. (2) EExt increases with the increase of entrainer purity. The main reason is that the higher purity the entrainer is, the better separation performance they possess. (3) A suitable extractive section position is important to acquire a preferable EExt . A certain number of trays in distillation section, displaying a lower entrainer feed tray location in Fig. 12(c), may be beneficial to enhance the capability of extractive section. Fig. 11 – Relation map of EExt and TAC from Pareto front. 1.75% and 2.37% lower than that of Cases 2 and 3, respectively. The detailed information of Cases 1–3 are shown in Table S8 in Supporting information. Extractive section in DEC drives the feasibility of the whole process. In order to further discuss the eff ;ect of the extractive section on this extractive distillation process, the relation map of EExt and TAC, as shown in Fig. 11, is extract from Fig. 8. As mentioned before, EExt and TAC exhibit a non-linear relation. It can be seen that there are different EExt distributions around a lower TAC and vice versa. A favorable design of extractive distillation process should have a low TAC and a high EExt , even using TAC as a decision factor. Thus, we should have a consideration of EExt when evaluating design parameters with seemingly TAC.

3.5. Optimized extractive distillation with heat integration A higher fresh feed temperature can decrease the reboiler duty. Thus, the hot recycled entrainer stream with an amount of sensible heat is used to preheat fresh feed, as shown in Fig. 13(a). Through heat exchange, the feed temperature of ethanol–water mixtures increases to 63 ◦ C and the temperature of the recycled entrainer reduces to 68 ◦ C, without temperature crossover. Correspondingly, the preheated fresh feed results in a 55 kW reduction in reboiler duty of EDC. Besides, there is no obvious change in stream composition after heat integration. The temperature and liquid composition profiles of the EDC are shown in Fig. 13(b).

Fig. 12 – Effect of design variables on EExt in extractive distillation process. (a) Effect of E/F on EExt ; (b) effect of entrainer purity on EExt ; (c) effect of feed stage on EExt .

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Fig. 13 – The heat integrated flowsheet and profiles of EDC for separating ethanol–water with ChCl/Urea (1:2). (a) The heat integrated flowsheet; (b) Temperature and liquid composition profiles of EDC.

Fig. 14 – Control structure of extractive distillation process.


Control system design

Process dynamics are not typically considered at the design stage, which may return economically attractive processes that are dynamically inoperable (Narraway et al., 1991; Luyben,

2013). Although the design and operability are highly connected goals, they should be considered together to provide the best possible operational process design. It is challenging to consider both economic and operational design, so it is important to run a posteriori operability analysis such that the


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Fig. 15 – Dynamic performance for flow rate and composition disturbances. (a) Dynamic performance for ±10% fresh feed flow rate disturbances; (b) dynamic performance for ±10% fresh feed composition disturbances.

designer gains insight on potential controllability problems. The purpose of this part is to study on the dynamic performance of the extractive distillation using ChCl/Urea (1:2) as entrainer. Before starting a dynamic simulation, the sizes of column sumps and reflux drums are specified to provide a 5 min holdup if liquid level reaches a half-full (Chen et al., 2017). The steady-state simulation is exported into Aspen Dynamics as a pressure driven simulation after appropriate valves and

pumps are set. According to the “slope criterion” suggested by Luyben (2013), stage 17 is selected as control stage to keep the temperature constant.


Control structure of extractive distillation process

The base level and pressure of ED process must be controlled, and several variables, such as the flow rate of fresh feed, the makeup stream, are also controlled. The details of con-

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trol structure are shown in Fig. 14. The control structure is described as follows: 1 The EDC: the pressure is controlled by the condenser duty; the reflux ratios is kept constant; the base level of reflux drum and the base level in the bottom are controlled by manipulating the distillate flow rates and bottom flow rate, respectively; the temperature of stage 17 (TEDC.17 ) is controlled by the reboiler duty of EDC. 2 The flash tank: the pressure is controlled by the output of product at the top of flash tank; the temperature is controlled by the reboiler duty of flash tank; the level of flash is controlled by makeup entrainer flow rate. 3 Feed flow rate is controlled by flow controller; ratio controller is added for the fresh feed and the recycled entrainer flow ratio. PI controllers are used for all the flow control loops with the settings: KC = 0.5 and  I = 0.3 min. All the level controllers are P-only with KC = 2, and all the pressure controllers are proportional-integral with the default values. Dead time elements are inserted in the temperature control loops with the dead time of 1 min (Chen et al., 2017). Relay-feedback tests are used to determine ultimate gains and periods of the two temperature controllers.


The results of control structure


The effect of flow rate disturbances

After the closed-loop dynamic running 1 h, the ±10% fresh feed flow rate disturbances are introduced, and the results are shown in Fig. 15(a). It could be seen that a close desired product purities at the new steady state can be achieved after 3 h. At the new steady state, the purities of ethanol and water are 99.30 mol% and 99.79 mol% corresponding to the 10% flow rate increase in fresh feed, whereas they are 99.59 mol% and 99.72 mol% corresponding to the 10% flow rate decrease in fresh feed. These results demonstrate the proposed control structure can achieve the controllable operating of the EDC configuration.


The effect of fresh feed composition disturbance

The effect of fresh feed composition disturbance is observed and the results are shown in Fig. 15(b). It can be seen that temperature control point return quite eff ;ectively and all the product compositions are maintained close to the specified values under fresh feed composition disturbances. The control structure can also resist fresh feed composition disturbances. At last, although the proposed control structure for the extractive distillation configuration is eff ;ective in managing operations to achieve the separation goal, there are minor deviation from expected values and can be further improved.



In this work, extractive distillation process using ChCl/Urea (1:2) as entrainer for separating ethanol–water mixture is discussed. The TAC, CO2 emissions and efficiency indicator of extractive section are introduced to evaluate the performance of the extractive distillation process from different perspective. In order to accurately define ChCl/Urea (1:2) in Aspen Plus, temperature-dependent properties are correlated based on experimental data. Three methods are adopted to describe the


vapor–liquid behavior, and both COSMO-SAC model relying on molecular information and NRTL model based on experimental data have a good consistency with the experimental data. COSMO-based theory could well explain the better separation performance of ChCl/Urea (1:2) compared with glycerol and [EMIM][BF4 ]. MOGA is used to solve nonlinear optimization problems and explore the relationships between design variables and objective functions. In addition, entrainer purity plays an important role in TAC and separation efficiency, a proper concentration rather than nearly pure entrainer should arouse our attention in extractive distillation process. The process with heat integration can achieve 3.89% savings of the total reboiler duty compared with the process without thermal integration. The proposed control structure can resist both ±10% fresh feed flow rate and composition disturbances. All studies show that ChCl/Urea (1:2) as a novel entrainer is promising to use in extractive distillation industrial for separating azeotrope.

Acknowledgments This work was supported by the National Natural Science Foundation of China (Grants 21676299 and 21476261) and supported by the Fundamental Research Funds for the Central Universities (Grant 17CX06025). Finally the authors are grateful to the editor and the anonymous reviewers.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi: 2019.06.014.

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