Energy-saving thermally coupled ternary extractive distillation process using ionic liquids as entrainer for separating ethyl acetate-ethanol-water ternary mixture

Energy-saving thermally coupled ternary extractive distillation process using ionic liquids as entrainer for separating ethyl acetate-ethanol-water ternary mixture

Separation and Purification Technology 226 (2019) 337–349 Contents lists available at ScienceDirect Separation and Purification Technology journal ho...

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Separation and Purification Technology 226 (2019) 337–349

Contents lists available at ScienceDirect

Separation and Purification Technology journal homepage: www.elsevier.com/locate/seppur

Energy-saving thermally coupled ternary extractive distillation process using ionic liquids as entrainer for separating ethyl acetate-ethanol-water ternary mixture Shoutao Maa, Xianyong Shanga, Lumin Lib, Yunfei Songa, Qi Pana, Lanyi Suna, a b

T



State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering, China University of Petroleum (East China), Qingdao, Shandong 266580, China School of Resources and Chemical Engineering, Sanming University, Sanming, Fujian 365004, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Extractive distillation Thermally coupled Ionic liquids Azeotrope Process optimization

The major intrinsic obstacle of extractive distillation (ED) is high energy consumption. Thus it is significant to reduce the energy consumption of ED processes as low as possible. In this work, using green solvent ILs which have high selectivity, process intensification and optimizing operation parameters are adopted in the ED of ethyl acetate-ethanol-water mixture. For high-selective entrainers, ionic liquid (IL) 1-butyl-3-methylimidazolium acetate ([bmim][OAc]) is used as the solvent for the ED process of ethyl acetate-ethanol-water, and the physical properties of [bmim][OAc] are accurately defined in Aspen Plus by correlating the experimental data. To optimize the distillation sequence for process intensification, thermally coupled ternary extractive distillation (TCTED) process is adopted for the separation of the ternary mixture. Moreover, multi-objective generic algorithm with total annual cost (TAC), CO2 emissions (ECO2) and thermodynamic efficiency (η) as objective functions is used to optimize the operation parameters in order to evaluate the economy, the energy efficiency and environmental impact of alternative ternary extractive distillation processes. The result shows that the TCTED process can result in the reduction of both TAC and ECO2. So, this study can provide a reference for the separation of ethyl acetate-ethanol-water mixture in industry.

1. Introduction

requirements for the azeotrope system [12]. PSD can take advantage of the characteristic that the composition of azeotrope may have a significant shift with pressure changing, hence it is suitable for the systems in which the azeotropic mixture is sensitive to pressure [7,8]. ED is an important technique in which the relative volatility of the original components can be altered by adding a solvent, and it is widely used to separate binary or multiple azeotropes in chemical industry due to the flexible selection of the possible entrainers [9–12]. Arifin and Chien [13] adopted ED and heterogeneous AD for the separation of isopropyl alcohol-water azeotrope, and the results showed that ED had better economy compared with AD. Luyben [14] made a comparison of ED and PSD for separating acetone and methanol, which showed that ED process had lower costs compared with PSD process. Although ED has been widely used as a special method for the separation of binary and ternary azeotropes, its major intrinsic obstacle is still the high energy consumption [12]. Therefore, it is critical to reduce the energy consumption of ED processes as much as possible. There are two ways which can be used for reducing the energy consumption in ED process. The first one is that some energy-saving technologies have

Ethyl acetate is an important chemical solvent commonly used in coatings, synthetic fibers and other production processes because of its excellent solubility [1,2]. Ethanol, an important biomass energy source, can be obtained by the conversion of various biomasses through microbial fermentation [3]. At present, ethyl acetate is mainly obtained by esterification reaction with acetic acid and ethanol as raw materials [4]. However, it is difficult to obtain high purity compounds because of the azeotrope of ethyl acetate-ethanol-water. So it is meaningful to separate the ternary mixture [5]. Common distillation is widely used for the separation of mixtures in the chemical and pharmaceutical industries, while it is difficult to separate the azeotrope and closing boiling mixtures. Some special distillation processes, such as azeotropic distillation (AD) [6], pressureswing distillation (PSD) [7–8], and extractive distillation (ED) [9–11] have been used to solve this problem. AD can be adopted to separate the azeotropic mixture with the entrainer, however, there are also challenges such as the existence of multi-stable and the high energy



Corresponding author. E-mail address: [email protected] (L. Sun).

https://doi.org/10.1016/j.seppur.2019.05.103 Received 12 January 2019; Received in revised form 10 April 2019; Accepted 30 May 2019 Available online 03 June 2019 1383-5866/ © 2019 Elsevier B.V. All rights reserved.

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500 Exp Calculated data

3000

Exp Calculated data

2500 2000

ητcPτ

Cp(J•mol-1K-1)

450

400

1500 1000 500 0

350

320

340

360

380

400

280

300

320

340

360

T(K)

T(K)

a

b 198

38.0

Exp Calculated data

Exp Calculated data

196

Vm(cm /mol)

37.0

194

3

δ (mN•m-1)

37.5

36.5

192 190

36.0

188 290

300

310

320

330

300

320

T(K)

340

360

380

T(K)

c

d 1.4x10-12

Calculate by COSMO-SAC

Vapor Pressure(Pa)

-12

1.2x10

-12

1.0x10

8.0x10-13 6.0x10-13 -13

4.0x10

2.0x10-13 0.0 275

300

325

350

375

400

425

T(K)

e Fig. 1. Heat capacity (a), viscosity (b), surface tension (c), liquid molar volume (d) and vapor pressure of [bmim][OAc] (e). Symbols: experimental data from literature or theoretical estimation of this work; Curves: fitting results with the equations embedded in Aspen Plus.

and design. Timoshenko et al. [21] proposed many ED flowsheets with partially thermally coupled columns for the separation of ternary azeotropic mixtures with different type of vapor-liquid equilibrium diagrams, and the results demonstrated the advantages of process intensification. The other one except process intensification is that it is implemented by using green solvent which have high-selectivity. At present, the existing traditional solvents have the disadvantages of being volatile, polluting the environment and high energy consumption for recycling. Therefore, it has become a consensus to use a green solvent. Ionic liquids (ILs) are a purely ionic material with a melting

been proposed based on process intensification, such as divided wall column [15], thermal coupling of columns [16], heat-integrated distillation [17], and heat pump-assisted distillation [18]. Zhao et al. [12] adopted thermally coupled distillation for the separation of tetrahydrofuran-ethanol-water azeotrope, and it was found that a thermally coupled extractive distillation sequence with a side rectifier presented the best results. Sun et al. [19] separated benzene-cyclohexane mixtures by extractive distillation dividing wall column. The results showed that it could achieve 22% energy saving. To obtain an optimized extractive dividing-wall column configuration, Gordenia et al. [20] proposed a systematic procedure based on stage equilibrium in terms of operability

338

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1.0

0.8

Vapor fraction of ethyl acetate

Vapor fraction of ethyl acetate

1.0

0.6 xIL=0

0.4

xIL=0.1 xIL=0.3 xIL=0.5

0.2 0.0 0.0

y=x

0.2

0.4

0.6

0.8

0.8 0.6

xIL=0.1 xIL=0.3 x IL=0.5

0.2 0.0 0.0

1.0

xIL=0

0.4

y=x

0.2

0.4

0.6

0.8

Liquid fraction of ethyl acetate

Liquid fraction of ethyl acetate

(a) ethyl acetate-ethanol

(b) ethyl acetate-water

Vapor fraction of ethanol

1.0 0.8 0.6 xIL=0 xIL=0.1

0.4

xIL=0.3 xIL=0.5 y=x

0.2 0.0 0.0

0.2

0.4

0.6

0.8

1.0

Liquid fraction of ethanol

(c) ethanol-water Fig. 2. y-x plots of different component pairs before and after adding IL. QC= -880.34 kW T=76.8 RR1=0.838 53.60 kmol/h Ethyl acetate 0.9995 Ethanol 451PPM Water 4 PPM

F=100 kmol/h T=35 Ethyl acetate 53.58 mol% 32 Ethanol 18.52 mol% Water 27.9 mol%

34

Flash

20

T=140 27.92 kmol/h Ethyl acetate trace Ethanol 0.001 Water 0.9987 IL trace

4 F2S=5.01 kmol/h T=65

ED column 2

5

ED column 1

F1S=12.73 kmol/h T=65

QC= -413.02 kW T=77.97 RR2=1.052 18.48 kmol/h Ethyl acetate 157 PPM Ethanol 0.9991 Water 791 PPM

Qflash=349.44 kW P=1 kPa T=140

45

QR=564.82 kW 145.58 45.67 kmol/h Ethyl acetate trace Ethanol 791 PPM Water 0.611 IL 0.388

QR=1009.60 kW T=97.1 59.13 kmol/h Ethyl acetate 49 PPM Ethanol 0.313 Water 0.472 IL 0.215

Fig. 3. The flowsheet of CTED 1 process.

339

T=140 17.74 kmol/h Ethyl acetate trace Ethanol 36 PPB Water 254 PPM IL 0.9997

1.0

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temperature below 100 °C, which are composed with organic cations and inorganic anions. They have advantages of low melting point, low volatilization, easy recovery, good selectivity and functional design [22–24]. ILs have been found for > 100 years [25], but over the past two decades, they started to get attention for using in many applications such as extractive separation. In 2006, Aniya et al. [25] studied the t-butanol dehydration process using triethylene glycol and IL 1ethyl-3-methy-limidazolium chloride ([EMIM][Cl]) as entrainer. The results showed that using [EMIM][Cl] as entrainer could reduce 13.9% of the total annual cost (TAC) compared with using triethylene glycol as entrainer. Zhu et al. [26] adopted IL 1-ethyl-3-methy-limidazolium tetrafluoroborate ([EMIM][BF4]) as solvent to separate ethanol-water mixture, and the results showed that energy consumption could decrease 14.6% compared with the traditional solvent ethylene glycol. Chen et al. [27] adopted IL 1,3-dimethylimidazolium dimethylphosphate ([MMIM][DMP]) to separate the mixture of isopropanol-water. The results indicated that the process using [MMIM][DMP] as entrainer reduced 7.92% of TAC compared with that using traditional solvent. The excellent properties of ILs lay the foundation for its application as excellent solvents. In addition, appropriate optimization algorithm can optimize the operating parameters and thus reduce energy consumption. There are several optimization methods used in the published literatures to find the optimal design variables [28–30], such as sensitivity analysis, sequential iteration optimization method, simulated annealing algorithm [31–33], and genetic algorithm [34–35]. As we known, single target optimization can only optimize one objective function such as TAC, while the optimization of complex distillation process may have more than one objectives related to environment, cost, energy, safety, and controllability, etc., and a trade-off among them needs to be considered. Moreover, conventional methods are easy to get into the local optimal solution of extractive distillation process, and it cannot get the global optimal solution. Compared with the sequential iterative optimization procedure dominantly used in the extractive distillation literature, genetic algorithms (GA) are attractive because they are able to perform a multi-objective optimization where the influence of each parameter on the solution is evaluated simultaneously rather than sequentially. GA [36] is a random search method based on the evolutionary law of the genetic mechanism of survival of the fittest in the biological world. In GA, the individual is composed of a set of optimization variables. GA begin with the generation of a random population (consisting of a certain individuals) and make use of three basic genetic operators, namely selection, crossover and mutation, to carry out continuous and repetitive evolutionary process [37–38]. Therefore, multi-objective optimization can give many optimal solutions if the objectives are

Fig. 4. Pareto front of the separation, ECO2-η-TAC in the CTED 1.

6.80

6.80

6.75

6.75

6.70

6.70

TAC/105$

TAC/105$

Fig. 5. Effects of key variables RR1 and solvent flow 1 on TAC of the separation system in the CTED 1.

6.65 6.60

6.65 6.60

6.55

6.55

6.50

6.50

30

35

45

40

N1stage

50

55

N2 stage

a

b

Fig. 6. The relationship between Nstage and TAC in the CTED 1. 340

60

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48

24

46 22

44

F2solvent/F2feed

F1solvent/F1feed

42 20 18 6

40 38 36 34 6 4

4 30

35

2

40

45

50

55

60

N2stage

N1stage

Fig. 7. The relationship among Nstage, feed stage and solvent feed stage in the CTED 1.

due to their relative novelty and the shortage of experimental data or predictive methods. Therefore, some thermodynamic properties of ILs, such as critical properties and boiling point, are required to be predicted and implanted in Aspen Plus. The critical properties (Tc, Pc and Vc), normal boiling temperature (Tb), and acentric factor (ω) of [bmim] [OAc] are calculated [39–43] and shown in Table S1. The temperaturedependent properties, such as density, viscosity, surface tension, heat capacity, liquid molar volume and extended antoine equation [44] have been measured experimentally, and all the available parameters of [bmim][OAc] for the calculation at different temperatures were collected and listed in Table S2. Fig. 1 shows the relationship between the experimental data and calculated data, it can be seen that the calculated data are consistent with the experimental data, which shows the accuracy of the associated equation parameters. After evaluation, the consistent experimental data of density, viscosity, surface tension and heat capacity were used to regress the equations embedded in Aspen Plus, and all the fittings were conducted within Aspen Plus [45–48].

Table 1 Design parameters of the CTED 1 process.

N1stage N2stage F1solvent F2solvent F1feed F2feed Reboiler duty of ED column 1 (kW) Reboiler duty of ED column 2 (kW) Duty of flash (kW) The purity of ethyl acetate The purity of ethanol The purity of water Solvent flow in ED column 1 (kmol) Solvent flow in ED column 2 (kmol) RR1 RR2 Capital cost ($) Operating cost ($) TAC ($) ECO2 (kg/h) η

case1

case2

case3

33 45 5 4 20 34 1009.60 564.82 349.44 0.9995 0.9991 0.9987 12.73 5.01 0.838 1.052 611,590 445,910 649,770 691.041 0.241

36 48 5 4 21 37 1005.25 564.95 349.27 0.9994 0.9993 0.9996 12.76 4.91 0.828 1.056 631,230 444,880 655,289 689.459 0.242

38 53 5 4 22 39 1062.90 554.38 355.66 0.9994 0.9991 0.9998 11.66 4.95 0.961 1.054 649,370 457,410 673,865 708.669 0.246

2.2. The feasibility of ED process In the ED process, thermodynamic model NRTL is used to describe the nonideality of the liquid phase and the vapor is assumed to be ideal. The NRTL binary parameters for ethyl acetate-[bmim][OAc], ethanol[bmim][OAc] and water-[bmim][OAc], which are taken from Zhang’s work [49], are used for ED process simulation by Aspen Plus (see Table S3 in the supporting information). The binary interaction parameters of ethanol-water and ethanol-ethyl acetate are obtained as fix values from APV VLE-IG in Aspen Plus database. In order to verify whether [bmim] [OAc] can be used as an excellent solvent to break azeotropic point of ethyl acetate-ethanol-water system and get the high purity product, the VLE curves of ethyl acetate-ethanol, ethyl acetate-water and ethanolwater are studied when the molar ratio of solvent-to-feed is 0.1 ~ 0.5. It can be seen from Fig. 2 that [bmim][OAc] breaks the azeotrope and the relative volatility of azeotropic system increases with the amount of IL increasing.

conflicting [36–38]. In this paper, energy-saving technologies, namely using high-selective entrainers, process intensification and optimizing operational parameters, are adopted for the separation of ethyl acetate-ethanolwater mixture. The IL [bmim][OAc] is selected as entrainer for the three ED processes which are called conventional ternary extractive distillation process 1 (CTED1), conventional ternary extractive distillation process 2 (CTED2) and thermally coupled ternary extractive distillation (TCTED). Multi-objective genetic algorithm (MOGA) is adopted for the processes optimization to determine design operation parameters which can meet the different constraints and objectives. This work can provide a reference in some chemical and pharmaceutical processes.

3. Multi-objective genetic algorithm for process optimization 2. Thermo-physical properties and phase equilibria In order to prevent sensitivity analysis falling into the local optimal solution, MOGA is used for the process optimization [50]. In this section, total annual cost (TAC) minimum, CO2 emissions (ECO2) minimum and thermodynamic efficiency (η) maximum are set as the objective functions [51]. The reason is as follows: Although three objective functions are adopted for the ED process optimization, the desired design is the one with the minimum TAC from the point of economy. ECO2 accounts for the ED columns and also reflects energy consumption, which can reflect the effect of distillation energy saving to a certain

2.1. Properties of pure components In the ED processes which use [bmim][OAc] as entrainer, the components involved are ethyl acetate, ethanol, water and IL. The thermophysical properties of the components other than [bmim][OAc] (i.e. ethyl acetate, ethanol and water) are calculated using the parameters from the property databank in Aspen Plus. ILs are not included in the component databases of process simulators such as Aspen Plus 341

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QC=-831.04 kW T=76.8 RR1=0.735 53.59 kmol/h Ethyl acetate 0.9998 Ethanol 0.0002 Water trace

QR=966.86 kW T=98.86 60.25 kmol/h Ethyl acetate 0.00006 Ethanol 0.3072 Water 0.4631 IL 0.2296

28

13.84 kmol/h T=140 Ethyl acetate trace Ethanol 0.00001 Water 0.0002 IL 0.9998

27.94 kmol/h T=140 Ethyl acetate trace Ethanol 0.0016 Water 0.9984 IL trace

34

QR=447.34 kW T=119.05 35.76 kmol/h Ethyl acetate trace Ethanol 0.0013 Water 0.7800 IL 0.2186

Flash2

F=100 kmol/h T=35 Ethyl acetate 53.58 mol% Ethanol 18.52 mol% 30 Water 27.9 mol%

46.41 kmol/h T=140 Ethyl acetate 0.00007 Ethanol 0.3988 Water 0.6011 IL trace Qflash=685.604 kW P=1 kPa T=140

5

Flash1

19

F2S=7.82 kmol/h T=65

ED column 2

6 ED column 1

F1S=13.83 kmol/h T=65

QC=-419.49 kW T=77.97 RR2=1.075 18.47 kmol/h Ethyl acetate 0.0002 Ethanol 0.9995 Water 0.0003

Qflash=380.40 kW P=1 kPa T=140

7.82 kmol/h T=140 Ethyl acetate trace Ethanol trace Water 0.0003 IL 0.9997

Fig. 8. Flowsheet of conventional ternary extractive distillation process 2.

Fig. 10. Effects of key variables RR1 and solvent flow 1 on TAC in the CTED 2.

Fig. 9. Pareto front of the separation, TAC-ECO2-η in the CTED 2.

extent, and also understand the impact of the energy-saving system on environmental benefits [52]. Seader [53] proposed the thermodynamic efficiency that could be used to evaluate the economics of distillation systems.. Moreover, the purity of each compound and the recovery ratio of ethyl acetate and ethanol are set as constraints.

TAC ($/ year ) =

CapitalCost + OperatingCost PaybackPeriod

(1)

where the operating cost includes the cost of vapor steam and cooling water, and the capital cost covers the cost of the shell, stages, condensers, reboilers and other costs. In this work, the payback period is set three years with the assumed operation time of 8000 h/year. The main cost estimation formula and design parameters are shown in Table S4 of the supporting information.

3.1. Process evaluation 3.1.1. Economical evaluation TAC is a key indicator for evaluating the economic performance of chemical processes, and it is along with higher thermodynamic efficiency (η) and less greenhouse gas emissions. TAC is based on the formulation and constants presented by Seader [53], and the calculation formula is shown as follow:

3.1.2. Thermodynamic efficiency Thermodynamic efficiency (η) is an indicator which can be used to evaluate the effective energy [54–55]. It is computed based on the laws of thermodynamics. As Seader defined [53,56], the equations are shown as follow: 342

Separation and Purification Technology 226 (2019) 337–349

8.00

8.00

7.95

7.95

7.90

7.90

7.85

7.85

TAC,105$/y

TAC,105$/y

S. Ma, et al.

7.80 7.75 7.70

7.80 7.75 7.70

7.65

7.65

7.60

7.60 28

30

32

34

36

38

40

42

44

34

36

38

40

42

44

46

48

50

N2stage

N1stage Fig. 11. The relationship between Nstage and TAC in the CTED 2.

22

32 30

18

F2feed

F1feed

20

16

28

F2solvent

F1solvent

26 6 4 2

28

30

32

34

36

38

40

42

44

6 4 34

36

38

40

N1stage

42

44

46

48

N2stage

Fig. 12. The relationship among Fsolvent, Ffeed and Nstage in the CTED 2.

calculated as follow:

Table 2 Design parameters of the CTED 2 process.

N1stage N2stage F1solvent F2solvent F1feed F2feed Reboiler duty of ED column 1 (kW) Reboiler duty of ED column 2 (kW) Duty of flash 1 (kW) Duty of flash 2 (kW) The purity of ethyl acetate The purity of ethanol The purity of water Solvent flow in ED column 1 (kmol) Solvent flow in ED column 2 (kmol) RR1 RR2 Capital cost ($) Operating cost ($) TAC ($) ECO2 (kg/h) η

η=

Wmin LW + Wmin

Wmin =

case 1

case 2

case 3

31 35 6 5 19 28 966.86 447.34 685.60 380.40 0.9998 0.9995 0.9984 13.83 7.82 0.735 1.075 558,490 576,230 762,394 890.871 0.207

33 40 4 5 19 27 943.78 447.77 687.84 378.37 0.9997 0.9993 0.9996 14.77 9.05 0.674 1.101 575,600 570,978 762,845 882.810 0.208

42 40 4 4 18 28 1006.86 451.14 682.00 378.20 0.9998 0.9998 0.9999 12.74 7.00 0.832 1.119 627,640 585,137 794,350 904.518 0.211



(Ex ) −

products



(Ex ) =

∑ ΔH − T0ΔS (3)

feeds

where ΔH (kJ/kmol) and ΔS (kJ/kmol⋅K) represent the enthalpy change and the entropy change of the system, respectively. T0(K) is ambient temperature.

EXQ = Wmin + LW =

∑ into system

QR (1−

T0 )− TR

Ex = H − T0 S

∑ out of system

QC (1−

T0 ) TC (4) (5)

EXQ and Ex are the effective energy input and the effective energy of flow, respectively. Thermodynamics properties like enthalpies and entropies of the streams of the distillation sequences are evaluated through the process simulator in Aspen Plus [57–58]. 3.1.3. CO2 emissions ECO2 can be used to evaluate the environmental effect of ED process. The explicit estimation of CO2 emissions, which is associated with various energy-intensive distillation-based operations in process industries, is carried out according to the method proposed by Gadalla [58]. The fuel combusts when mixed with air, and the CO2 producing is obtained according to the following stoichiometric equation:

(2)

Cx Hy + (x +

where LW (kJ/h) represents the lost work of the system. Wmin (kJ/h) is the minimum separation work which equals to the difference between product effective energy and feed effective energy, and it can be

y y )O2 → x CO2 + H2 O 4 2

(6)

Where x, y are on representative of the number of C and H atoms in the fuel. In the combustion of fuels, air is assumed to be in excess and to ensure the complete combustion, so that no carbon monoxide (CO) is 343

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QC=-848.15 kW T=76.8 RR1=0.771 53.592 kmol/h Ethyl acetate 0.9997 Ethanol 257 PPM Water 3 PPM IL trace

F1S=13.32 kmol/h T=65

6 MC

22 F=100 kmol/h T=35 Ethyl acetate 53.58 mol% Ethanol 18.52 mol% Water 27.9 mol%

38

QC=-363.74 kW T=77.97 RR2=0.809 18.46 kmo/h Ethyl acetate 106 PPM Ethanol 0.9991 Water 844 PPM

6 F2S=4.45 kmol/h T=65

18.89 kmol/h T=96.75 Ethyl acetate trace Ethanol 0.4537 Water 0.3290 IL 0.2353

Flash

46

QR=1492.92 kW T=145.5 45.717 kmol/h Ethyl acetate trace Ethanol 0.001 Water 0.610 IL 0.389

SC

45

32.91 kmol/h T=83.91 Ethyl acetate trace Ethanol 0.8106 Water 0.1893 IL trace

27.948 kmol/h T=140 Ethyl acetate trace Ethanol 0.002 17.769 kmol/h Water 0.9977 T=140 Qflash=350.05 kW ILs 0.9997 P=1 kPa Ethyl acetate trace T=140 Ethanol 63PPB Water 253PPM

Fig. 13. The flowsheet of thermally coupled ternary extractive distillation process. Table 3 Design parameters of the TCTED process.

Nstage of MC Nstage of SC Fsolvent of MC Fsolvent of SC Ffeed of MC Location of production The amount of production (kmol/h) Solvent flow in MC (kmol/h) Solvent flow in SC (kmol/h) Reboiler duty of MC (kW) Duty of flash (kW) The purity of ethyl acetate The purity of ethanol The purity of water RR of MC Capital cost ($) Operating cost ($) TAC ($) ECO2 (kg/h) η

case1

case2

case3

47 45 6 6 22 38 32.91 13.32 4.45 1492.92 350.05 0.9997 0.9991 0.9977 0.771 610,700 426,950 630,519 661.981 0.192

61 49 6 7 25 46 31.55 13.36 5.90 1484.45 338.30 0.9996 0.9992 0.9996 0.762 717,760 422,220 661,470 654.718 0.190

70 60 7 6 27 51 33 13.06 4.42 1501.43 350.50 0.9999 0.9997 0.9997 0.794 715,970 429,060 667,710 665.201 0.193

Fig. 14. Pareto front of the separation, TAC- ECO2-η in the TCTED.

QFuel =

formed, and the CO2 emissions can be calculated as follow:

ECO2

Q C% )α = ( Fuel )( NHV 100

QProc T − T0 (hProc − 419) FTB λProc TFTB − TStack

(8)

where λProc (kJ/kg) and hProc (kJ/kg) are the latent heat and enthalpy of steam delivered to the process, respectively. TFTB (oC), TStack (oC) and T0 (oC) are the flame temperature of the boiler flue gases, stack temperature and the environment temperature, respectively. In the calculation, the theoretical flame temperature is 1800 °C and the stack temperature is 180 °C. 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. Based on the formula shown in Eq. (9), the heating efficiency of the boiler is assumed to be 0.8 in

(7)

where α (=3.67) represents the ratio of molar mass of CO2 and C, NHV (kJ/kg) represents the net heating value of the fuel with C% carbon content. QFule (kW) is the heat released by fuel combustion. The values of NHV and C% are different due to the difference of fuels. In this work, the value of NHV and C% is 51600 kJ/kg and 75.4, respectively [59–60]. QFuel is the amount of fuel burnt (kW) and it depends on the heat duty (QProc) according to Eq. (8). 344

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6.8

0.1 mol% water to reduce the viscosity. In the ED process, the design variables includes the number of stage (N1stage), fresh feed stage (F1feed), solvent feed stage (F1solvent) and reflux ratio (RR1) of ED column 1, the number of stage (N2stage), fresh feed stage (F2feed), solvent feed stage (F2solvent) and reflux ratio (RR2) of ED column 2. The value ranges of operating variables can be obtained by sensitivity analysis. In this section, the population, crossover and mutation fractions parameters of the genetic algorithm are tuned after several preliminary tests. After tuning, 200 individuals, 200 generations, 0.8 for crossover fraction and 0.1 for mutation fraction are employed for the process 1 and process 3, while 100 individuals are adopted for the process 2. At last, the solutions from Pareto front are chosen based on the objective functions and constraints.

6.7

TAC/105$

6.6 6.5 6.4 6.3 0.76

0.77

0.78

0.79

0.80

0.81

RR of MC

4.1. Conventional ternary extractive distillation process 1

Fig. 15. The effects of RR on TAC in the TCTED.

Conventional ternary extractive distillation 1 (CTED1) process contains ED column 1, ED column 2 and flash. The flowsheet is shown in Fig. 3. The ternary mixture is fed at the lower section of ED column 1, the solvent is fed at the upper section of ED column 1, and pure ethyl acetate is obtained at the top of ED column 1. The mixture of ethanolwater-solvent is fed into ED column 2 and pure ethanol is obtained at the top of ED column 2. Flash is used for solvent recycling since IL has low saturated vapor pressure. In the CTED1 process, the optimization range of the design variables is shown in Table S5. Results belonging to the Pareto front of the stochastic optimization are displayed in Table S6 and Figs. 4–7. It can be seen from Table S6 that when the program runs to 170 generations, the number of solutions reaches 200, and the objective functions do not change, which indicates that MOGA achieves the optimal value. Fig. 4 shows the Pareto front of the CTED1 system, TAC versus ECO2 and η. Fig. 5 shows the effects of key variables RR1 and solvent flow 1 on the TAC of CTED 1. The details of the lowest TAC design, the lowest ECO2 and the highest η, namely case 1, case 2 and case 3, are shown in Table 1. From Table 1, we can obtain the following conclusions: The design case 1 is the one with the lowest TAC (6.4977 × 105 $), the design case 2 is the one with the lowest ECO2 (689.459 kg/h) and the design case 3 is the one with the highest η (0.246). It can be seen from Fig. 4 that TAC increases with the increase of ECO2 since ECO2 is related to the reboiler duty which can directly affect the operating cost. It is worth noting that TAC and ECO2 are consistent but not equivalent. The highest η corresponds to the larger TAC and ECO2. The reason is that η only reflects the loss of energy, which is related to the enthalpy of import and export. In addition, it is also worth noting that η competes with TAC and ECO2, which illustrates the accuracy of the selected objective functions. Fig. 5 shows the effects of RR1 and solvent flow 1 on TAC, it can be seen that TAC increases with the increase of RR1 and decreases with the

this section [61].

QFuel =

QProc 0.8~0.9

(9)

3.2. Objective functions and constraints MOGA is adopted for process optimization of the ED processes of ethyl acetate-ethanol-water ternary mixture. In the multi-objective program, TAC and (ECO2) are minimized and η is maximized. The objective functions and constraints are shown as follows:

Objective function: Min TAC Min ECO2 Max η Subject to: xethyl acetate > 0.999 (mole fraction) and recovery ratio > 0.995 xethanol > 0.999 (mole fraction) and recovery ratio > 0.995 xwater > 0.995 (mole fraction)

4. Process simulation In this paper, the commercial software Aspen Plus V8.4 is used to simulate the ED processes for separating ethyl acetate-ethanol–water ternary mixture based on NRTL model. The feed flow is 100 kmol/h and the feed composition is ethyl acetate 53.58 mol%, ethanol 18.52 mol% and water 27.90 mol%. The temperature of feed is 35 °C. The solvent flow is set to be 13 kmol/h at first, and the purity is 99.9 mol% with 6.8

6.7 6.7

6.6

TAC/105$

TAC/105$

6.6 6.5

6.5

6.4

6.4

6.3

6.3

45

50

55

60

65

70

75

45

50

Nstage of SC

Nstage of MC Fig. 16. The relationship between Nstage and TAC in the TCTED. 345

55

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10

28 26

22

F2solvent

F1solvent/F1feed

8 24

20

6

8

4 6 4 45

50

55

60

65

70

2

75

Nstage of MC

45

50

55

60

Nstage of SC

Fig. 17. The relationship between Nstage and Ffeed in the MC and SC of TCTED.

solvent flow 1 is related to the energy consumption of flash in the phase of solvent recovery. Fig. 6 shows the effects of the number stages of ED column on TAC. The Pareto front in Fig. 6-a shows that no solutions are found when N1stage is lower than 31 (the lower bound is set at 15) or > 42 (the upper bound is set at 45) in ED column 1. Fig. 6-b shows that no solutions are found when N2stage is lower than 45 (the lower bound is set at 20) or > 60 (the upper bound is set 65) in ED column 2. The main reason is that to achieve a certain separation requirement, the number of theoretical plates must reach a certain value. Fig. 6 also shows that TAC increases with the raise of the number of stages (Nstage) because high number of stages leads to high capital cost. Fig. 7 shows the relationship among Nstage, feed stage and solvent feed stage. For ED column 1, F1solvent is mainly concentrated between 4 and 6, while F1feed is mainly concentrated between 18 and 24. For ED column 2, F2solvent is mainly concentrated between 3 and 6, while F2feed is mainly concentrated between 45 and 60. The Pareto front demonstrate the advantages of MOGA optimization, which has different solutions at different design range.

Location of coupling stream

50

45

40

45

50

55

60

65

70

75

Nstage of MC Fig. 18. The relationship between the location of coupling stream and Nstage of MC in the TCTED.

Solvent flow in SC/kmol·h-1

6.0 5.8

4.2. Conventional ternary extractive distillation process 2

5.6 5.4

In the CTED 1 process, since the amount of entrainer is large and the ED column 1 temperature is lower than the temperature of ED column 2, which may lead to the increase of energy consumption of ED column 2. For CTED1 process, the entrainer is not separated after the mixture is obtained from the bottom of ED column 1, therefore, conventional ternary extractive distillation process 2 (CTED 2) process is adopted and the flowsheet is shown in Fig. 8. In CTED 2 process, the entrainer is separated at the bottom of ED column 1 using a flash and the mixture of ethanol-water at the top of flash is fed into ED column 2. The optimization range of design variables in CTED 2 process is the same with that of CTED 1 process. Results belonging to the Pareto front of the stochastic optimization are displayed in Table S7 and Figs. 9–12. It can be seen from Table S7 that when the program runs to 70 generations, the number of solutions reaches to 100. And the value of objective functions does not change, which also indicates that the optimal value is obtained by MOGA. Fig. 9 shows the Pareto front of the CTED 2 system, TAC versus ECO2 and η. Fig. 10 shows the effects of key variables RR1 and solvent flow 1 in ED column 1 on TAC. The lowest TAC, the lowest ECO2 and the highest η, namely case 1, case 2 and case 3, are shown in Fig. 9. The relationship among TAC, ECO2 and η is the same with CTED 1. More details of cases 1–3 are presented in Table 2. As Table 2 shown, the design case 1 is the one with the lowest TAC (7.6239 × 105 $), the design case 2 is the one with the lowest ECO2 (882.809 kg/h) and the design case 3 is the one

5.2 5.0 4.8 4.6 4.4 4.2 31.4 31.6 31.8 32.0 32.2 32.4 32.6 32.8 33.0 33.2

Vapor flow of side-draw, kmol/h Fig. 19. The relationship between solvent flow and amount of production in the TCTED.

increase of solvent flow 1. The main reason is that the reduction of RR1 leads to the decrease of reboiler duty which can directly affect the operating cost. High solvent flow 1 infers a high energy cost in the solvent recovery phase, which leads to a growth trend of TAC consequently. More importantly, the increase of solvent flow 1 will increase the relative volatility of components, which can also reduce RR1. Therefore, the amount of solvent affects TAC more obvious than the energy consumption of recovering solvent. It also reflects that the effect of RR1 on TAC is greater than that of solvent flow 1 on TAC since 346

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1.0

1.0

Liquid molar fraction

0.8 0.6 0.4 0.2 0.0

0.6 0.4 0.2 0.0

0

10

20

30

N1stage

ETHYL-01 ETHANOL WATER IL

0.8

Liquid molar fraction

ETHYL-01 ETHANOL WATER IL

0

10

20

30

40

N2stage

Fig. 20. Liquid composition profile in the CTED 1.

1.0

1.0 ETHYL-01 ETHANOL WATER IL

0.6 0.4 0.2 0.0

0.6 0.4 0.2 0.0

0

10

20

ETHYL-01 ETHANOL WATER IL

0.8

Liquid molar fraction

Liquid molar fraction

0.8

30

0

10

20

30

N2stage

N1stage Fig. 21. Liquid composition profile in the CTED 2.

1.0

1.0 ETHYL-01 ETHANOL WATER IL

0.8

Liquid molar fraction

Liquid molar fraction

0.8 0.6 0.4 0.2 0.0

0.6 0.4 0.2 0.0

0

10

20

30

ETHYL-01 ETHANOL WATER IL

40

0

10

20

30

40

Nstage of SC

Nstage of MC Fig. 22. Liquid composition profile in the TCTED.

with the highest η (0.211). In addition, Fig. 10 shows the relationship among TAC, RR1 and solvent flow 1, Fig. 11 shows the relationship between TAC and Nstage, and Fig. 12 shows the relationship among F1solvent, F1feed and N1stage. The tendency is also the same with that of CTED 1.

investigate the energy-saving potentials. As shown in Fig. 13, similarly to CTED 1 and CTED 2 processes, the entrainer is fed at the upper section of ED column and ternary mixture is fed at the lower section of the column. ED column contains a main column (MC) and a side column (SC). Partial vapor in MC is drawn out and fed into the bottom of SC, and liquid from the bottom of SC returns to MC. The pure ethyl acetate and ethanol are obtained at the top of MC and SC, respectively. The mixture from the bottom of MC, which contains mostly water and solvent, is fed into flash for solvent recovery. Water is obtained at the top of flash and high-purity solvent from the bottom of flash is split and

4.3. Thermally coupled ternary extractive distillation process Thermally coupled ternary extractive distillation (TCTED) is used for the separation of ethyl acetate-ethanol–water ternary mixture to 347

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entrainer with process intensification for the separation of ethyl etherethanol-water were explored and MOGA was adopted to optimize operating parameters to reduce the energy consumption as much as possible. In order to accurately define ILs in Aspen Plus, temperature-dependent properties were correlated with the experimental data. The TAC, CO2 emissions and thermodynamic efficiency index were used to evaluate the performance of the ED processes from the perspective of economic, energy efficiency and environmental impacts. Comparing the three processes with the same entrainer, the TCTED can achieve 3.05% TAC savings and 6.68% CO2 emissions decrease compared with the CTED1, and achieve 20.92% TAC savings and 34.84% CO2 emissions reducing compared with the CTED 2. It is worth noting that the thermodynamic efficiency of TCTED is 0.193 which is lower than that of CTED 1 and CTED 2 processes. The main reason is that the temperature at the bottom of ED column is higher than that of CTED 1 and CTED 2 processes, and effective energy will increase, which leads to the lower thermodynamic efficiency. Remixing effect may be an important factor influencing the energy consumption of the ED process as seen from the results of liquid composition profiles, and the CTED can avoid the remixing effect significantly. So the TCTED process is the most energysaving configuration and also shows apparent benefits on economic and environment aspects. Consequently, the TCTED process with IL as entrainer can make the energy consumption as low as possible for the separation of ethyl acetate-ethanol-water ternary azeotrope, and thus the work can provide a reference for the industry applications of ILs as entrainers.

recycled back to MC and SC after being cooled. In this section, MOGA is also adopted for the optimization of operating parameters in TCTED process. The optimization range of the design variables is shown in Table S8. Table S9 shows the solving progress along the generations. It can be seen that the number of solutions still cannot meet the requirement when the program runs 200 generations, so the program also runs basing on the result. When the program runs to 250 generations, the values of objective functions remain constant and the solutions meet the requirement, which indicates the optimization process has been fished. More design parameters of cases 1–3 are presented in Table 3. As Table 3 shown, it can be seen that the design case 1 is the one with the lowest TAC (6.3051 × 105 $), the design case 2 is the one with the lowest ECO2 (654.718 kg/h) and the design case 3 is the one with the highest η (0.193). Fig. 14 shows the relationship among TAC, ECO2 and η. It can be seen that TAC decreases with the increase of ECO2. This tendency is also the same with that in CTED 1 and CTED 2 processes. Fig. 15 shows the relationship between TAC and RR of MC, and it can be seen that TAC decreases with the increase of RR at first, but increases when RR is higher than 0.77. This demonstrates that RR is suitable around 0.77. Fig. 16 shows that TAC increases with the increase of Nstage of MC and SC because the increase of Nstage will lead to the increase of capital cost. As seen in Fig. 17, Nstage of MC is located between 45 and 75, and F1feed is located between 20 and 26 when meets constraints. This demonstrates that there is a certain ratio between N1stage and F1feed. F1solvent is located between stage 5 and stage 7, so the Nstage in extraction section must be more than a certain value. Fig. 17 also shows the relationship between F2solvent and Nstage in SC, and it can be seen as well that F2solvent is located at the higher location of SC. The location of coupling stream and the Nstage of MC are the important parameters in TCTED process. Fig. 18 shows the relationship between Nstgae of MC and the location of coupling stream, it can be seen that the location of production moves to the bottom of column with the increase of Nstage of MC, which demonstrates that there may be a ratio range between the location of production and Nstage. As seen in Fig. 19, the solvent flow which is fed into SC increases with the decrease of the vapor flow of side-draw. The main reason is that, when the amount of product at the top of SC is fixed, the larger the flow of side-draw vapor is, the more the amount of liquid from SC returns to MC is, which is equivalent to increase the RR of SC.

Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant: 21676299 and Grant: 21476261). Finally the authors are grateful to the editor and the anonymous reviewers. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.seppur.2019.05.103. References [1] Z. Zhu, Y. Ri, H. Jia, et al., Process evaluation on the separation of ethyl acetate and ethanol using extractive distillation with ionic liquid, Sep. Purif. Technol. 181 (2017) 44–52. [2] S. Brandt, S. Horstmann, S. Steinigeweg, et al., Phase equilibria and excess properties for binary systems in reactive distillation processes. Part II. ethyl acetate synthesis, Fluid Phase Equilib. 376 (2014) 48–54. [3] S. Guo, B. He, J. Li, et al., Esterification of acetic acid and ethanol in a flow-through membrane reactor coupled with pervaporation, Chem. Eng. Technol. 37 (3) (2014) 478–482. [4] L.H. Horsley, Azeotropic Data III, advances in chemistry series 116, Am. Chem. Soc., Washington DC (1973). [5] X. Huang, W. Zhong, W. Du, et al., Thermodynamic analysis and process simulation of an industrial acetic acid dehydration system via heterogeneous azeotropic distillation, Ind. Eng. Chem. Res. 52 (8) (2013) 2944–2957. [6] W. Li, L. Zhong, Y. He, et al., Multiple steady-states analysis and unstable operating point stabilization in homogeneous azeotropic distillation with intermediate entrainer, Ind. Eng. Chem. Res. 54 (31) (2015) 7668–7686. [7] A.M. Fulgueras, J. Poudel, D.S. Kim, et al., Optimization study of pressure-swing distillation for the separation process of a maximum-boiling azeotropic system of water-ethylenediamine, Korean J. Chem. Eng. 33 (1) (2016) 46–56. [8] R. Li, Q. Ye, X. Suo, et al., Heat-integrated pressure-swing distillation process for separation of a maximum-boiling azeotrope ethylenediamine/water, Chem. Eng. Res. Des. 105 (2016) 1–15. [9] Z. Lei, C. Dai, J. Zhu, et al., Extractive distillation with ionic liquids: a review, AIChE J. 60 (9) (2014) 3312–3329. [10] S. Ma, Y. Hou, Y. Sun, et al., Simulation and experiment for ethanol dehydration using low transition temperature mixtures (LTTMs) as entrainers, Chem. Eng. Process. Process Intensif. 121 (2017) 71–80. [11] L. Berg, Separation of benzene and toluene from close boiling nonaromatics by extractive distillation, AIChE J. 29 (6) (1983) 961–966. [12] Y. Zhao, K. Ma, W. Bai, et al., Energy-saving thermally coupled ternary extractive distillation process by combining with mixed entrainer for separating ternary mixture containing bioethanol, Energy 148 (2018) 296–308.

5. Discussion Figs. 20-22 show the liquid composition profiles of the proposed three processes. For the CTED 1 and CTED 2, it can be seen that the composition of ethyl acetate and ethanol goes through a maximum in the middle of stripping and then decreases toward the column bottom in ED column 1, which indicates that remixing phenomenon in ED column 1 exhibits. For the TCTED, It can be seen that the mixing effect of ethyl acetate also exists in MC. However, the thermal coupling is used on the stage with the highest purity of ethanol in MC to avoid the mixing effect of ethanol. It is noticed that the energy consumption of CTED 2 is larger than that of CTED 1. The reason is that although the solvent is separated using flash at first and the reboiler duty of ED column 2 decreases compared with that of CTED 1, when recycling solvent, the energy used to vapor ethanol and water will increase the total energy consumption. In the TCTED process, the energy consumption is the lowest in the three processes. The energy consumption of TCED process reduces by 6.40% and 25.70% compared with CTED 1 and CTED 2, respectively. That is because ethanol is drawn out on the stage of its highest concentration and the remixing effect is disappeared. 6. Conclusions In this work, the ternary extractive distillation process using IL as 348

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