Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate

Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate

Journal Pre-proofs Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate Zemin Feng, We...

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Journal Pre-proofs Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate Zemin Feng, Weifeng Shen, G.P. Rangaiah, Lichun Dong PII: DOI: Reference:

S1383-5866(19)34089-4 https://doi.org/10.1016/j.seppur.2020.116655 SEPPUR 116655

To appear in:

Separation and Purification Technology

Received Date: Revised Date: Accepted Date:

9 September 2019 15 December 2019 30 January 2020

Please cite this article as: Z. Feng, W. Shen, G.P. Rangaiah, L. Dong, Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate, Separation and Purification Technology (2020), doi: https://doi.org/10.1016/j.seppur.2020.116655

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Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate Zemin Feng,a,b Weifeng Shen,b G.P. Rangaiah,a,c* Lichun Dongb,d* a

Department of Chemical and Biomolecular Engineering, National University of Singapore,

Singapore 117576. b

School of Chemistry and Chemical Engineering, National-Municipal Joint Engineering

Laboratory for Chemical Process Intensification and Reaction, and Key Laboratory of Lowgrade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing, 400044, China. c

School of Chemical Engineering, Vellore Institute of Technology, Vellore 632014, India.

d Green

Intelligence Environmental School, Yangtze Normal University, Fuling, Chongqing,

408100, China. * Corresponding author: G.P. Rangaiah, Email: [email protected] Lichun Dong, Email: [email protected] Abstract: Ethyl acetate and n-hexane are two widely used organic solvents in chemical and pharmaceutical industries; however, separation of their mixtures is very challenging due to the formation of a minimum boiling azeotrope. This study proposes and develops an extractive distillation (ED) process for efficiently separating the mixture of ethyl acetate and n-hexane using N-methyl-2-pyrrolidone as entrainer. Subsequently, vapor recompression heat pump (VRHP) is used to further improve the thermal efficiency of the developed ED process. Simulation results revealed that the VRHP assisted ED (VRHP-ED) process is generally better than the conventional ED process in terms of lower operating and total annual costs, and also higher thermal efficiency. Compared to azeotropic distillation and heat integrated pressure-swing distillation studied in a recent paper, VRHP-ED process can save 93.78% and 82.67% of operating cost, respectively. Finally, two alternative control strategies were developed and assessed for VRHP-ED operation, demonstrating that the control scheme with feedforward structure exhibits better performance for the operation of the challenging VRHP-

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ED process than that without feedforward structure, for handling large feed flow rate and feed composition disturbances. Keywords: Extractive distillation; Azeotropic distillation; Vapor recompression heat pump; Process design; Proportional-Integral control

1. Introduction Ethyl acetate and n-hexane are two widely used organic solvents; however, separation of their mixtures is challenging as they form a homogeneous minimum-boiling azeotrope with 65.7 mol% n-hexane at 64.85°C and 1 atm [1]. For separating such mixtures with azeotropes or low relative volatility, unconventional distillation technologies, e.g. azeotropic distillation, pressure-swing distillation (PSD) and extractive distillation (ED), have been extensively applied. Rodriguez-Donis et al. [2] investigated the feasibility of separating ethyl acetate and n-hexane mixture via heterogeneous batch azeotropic distillation, demonstrating that high purity products can be obtained by using acetonitrile as heterogeneous entrainer. Lü et al. [3] compared the continuous homogeneous azeotropic distillation (HAD) and PSD for the separation of ethyl acetate and n-hexane mixture, showing that the heat integrated PSD (HIPSD) is better than HAD in terms of lower total annual cost (TAC) and energy consumption. ED has been regarded as a predominant alternative technology for separating mixtures with azeotropes and/or low relative volatility by adding an additional entrainer (also known as solvent) to break the azeotrope(s) and/or alter relative volatility of components in these mixtures. Several studies have compared economics and energy consumption of ED and PSD for various azeotropic systems. Luyben [3, 4] showed that ED is better than PSD for the separation of acetone-methanol and acetone-chloroform systems from the stand point of both capital investment and energy consumption. Iqbal et al. [6] concluded that, for separating dichloromethane and methanol mixture, ED process can save 27.62% and 30.84% of TAC and also reduce 4.09% and 51.9% of energy requirement compared to HIPSD and azeotropic distillations, respectively.

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However, some other studies found that PSD is more economical than ED for the separation of ethanol and tetrahydrofuran [7], acetonitrile and n-propanol [8], and isopropyl alcohol and diisopropyl ether [9]. The economics of PSD for separating the mixtures with azeotropes significantly depends on the pressure sensitivity of the azeotrope. If a good entrainer can be found for separating such mixtures, ED can be a better route than PSD and azeotropic distillation in terms of lower TAC and energy consumption. Therefore, the motivation of this study is to find a suitable entrainer and propose an energy-saving ED process for the separation of ethyl acetate and n-hexane mixture. The design and optimization of ED processes have been studied by a number of researchers. Shen et al. [10] proposed a systematic procedure for the design of ED processes, from solvent screening to column configuration. Hu et al. [11] reported an approach for screening organic and ionic liquid solvents for homogeneous ED. Kiss and Suszwalak [12] studied extractive dividing-wall column (EDWC) to enhance bioethanol dehydration leading to more than 10% energy savings compared to conventional ED. Loy et al. [13] compared EDWC and pressure-swing adsorption for bioethanol dehydrogenation. Wu et al. [14] assessed the energy saving potential of EDWC, pointing out that all the required thermal energy is supplied by high temperature steam in the bottom reboiler of EDWC, which may result in higher operating cost compared to conventional ED. Recently, several studies have further verified the potential of ED for the separation of multi-azeotrope mixtures [15–18]. However, ED is still an energy intensive process, which consumes large amounts of energy for regenerating the entrainer. Gu et al. [19, 20] analyzed the impact of operating pressure on the energy saving of heat integrated ED, showing that the energy consumption of ED can be significantly reduced by operating the entrainer recovery column under vacuum. You et al. [21] applied heat pump technology to reduce energy consumption and consequently carbon dioxide emission from ED process. Yang et al. [22] and Feng at al. [23] also investigated vapor recompression heat pump assisted reactive dividing-wall column for improving energy efficiency. These studies demonstrated that heat pump technology has good potential for improving thermal efficiency of distillation processes since it can upgrade the condensing latent heat released in the condenser, which is commonly removed by cooling

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water as waste heat, to supply thermal energy to reboiler. Luyben [24] and Iulian et al. [25] investigated controllability of vapor recompression assisted EDWC demonstrating that it can be controlled smoothly by using PI controllers via temperature control scheme. However, due to the complex dynamic behavior of heat pump system, studies focused on the control of heat pump assisted ED are scanty. Accordingly, the aim of the present study is to find a suitable entrainer and then design an ED process for effectively separating ethyl acetate and n-hexane mixture. Next, the vapor recompression heat pump was used to intensify the proposed ED process for improving its thermal efficiency, which was evaluated by conducting exergy analysis. Furthermore, the proposed ED processes were optimized by using a mesh adaptive search algorithm to solve the mixed integer nonlinear programming problem. Finally, the controllability of the optimal vapor recompression heat pump assisted ED was evaluated for feed flow rate and feed composition disturbances.

2. Thermodynamic data and entrainer selection Acosta et al. [1] reported experimental vapor-liquid equilibrium (VLE) data for n-hexane (HE) and ethyl acetate (EA) binary system and also demonstrated that the correlated NRTL, Wilson and UNIQUAC models are all good to fit the experimental data. Of these models, results predicted by NRTL model give relatively smaller root mean squared error (RMSE) for temperature and n-hexane mole fraction in vapor phase than that by Wilson and UNIQUAC models [1]. In addition, binary parameters of these three thermodynamic models are also available in Aspen Plus built-in databank, which predict same VLE behavior for n-HE/EA system. Therefore, NRTL model was chosen to predict the thermodynamic properties of the studied systems in this study. For accurately describing the experimental VLE data of HE/EA in [1], the parameter regression was conducted again in Aspen Plus V8.8 to obtain suitable NRTL parameters for this pair. The maximum likelihood algorithm was used to minimize the objective function for regressing the NRTL model parameters in Aspen Plus.

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Figure 1. T-xy plots for HE-EA system at 101.32 kPa predicted by NRTL model Figure 1 shows the T-xy plots for HE/EA system at 101.32 kPa predicted by NRTL model with the default Aspen Plus binary parameters and regressed parameters (labelled as Aspen and user in Figure 1, respectively) as well as the experimental data of Acosta et al. [1]. It is clear that the NRTL model with regressed parameters is good (and marginally better than that with default parameters) to fit the experimental data, giving RMSE of 0.0040 and average absolute error (AAE) of 0.0034 for n-hexane mole fraction in vapor phase. In the present study, the correlated NRTL binary parameters for HE/EA system (Table S1 in the Supporting Information) are used in the process simulation. The screening of entrainers is crucial for the design of ED processes since it can significantly affect their feasibility and energy consumption of the designed processes. The criteria and approaches for selecting a suitable entrainer has been discussed by Shen et al. [10]. Here, three potential and commonly used entrainers, namely, N-methyl-2-pyrrolidone (NMP), N,N-dimethyl formamide (DMF) and dimethyl sulfoxide (DMSO), were chosen to investigate their suitability for the separation of n-hexane to ethyl acetate by ED. The binary parameters of NRTL model for EA/NMP system are not available in Aspen Plus. Moreover, the VLE values predicted by NRTL model with default Aspen Plus binary parameters for EA/DMSO do not well agree with the experimental data. Hence, binary parameters of NRTL model for EA/DMSO and EA/NMP systems were correlated in Aspen Plus by using the VLE

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experimental data reported by Wang et al. [26] and Yang et al. [27], respectively, and their values are given in Table S1 in the Supporting Information as well as their respective root mean squared errors (RMSEs) and average absolute errors (AAEs) for ethyl acetate mole fraction in vapor phase. Figures 2a and b show the T-xy plots (Figure 2a) for EA/DMSO and EA NMP systems at 101.3 kPa predicted by NRTL model with the correlated binary parameters, indicating that the predicted values are very close to the experimental data.

Figure 2. T-xy and P-xy plots for various binary pairs; square symbols are the experimental data, and solid lines are the predictions by NRTL model Although the VLE experimental data for HE/NMP [28] and EA-DMF [29] systems are available in the literature, number of sample points is too little to correlate NRTL model. Hence, the default Aspen Plus binary parameters of NRTL model were used in the present study for these two pairs, which are given in Table S2 in the Supporting Information. Figures

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2c and 2d show the T-xy and P-xy plots for HE/NMP and EA/DMF systems, respectively, predicted by NRTL model with the default Aspen Plus binary parameters; they indicate that the predicted values agree with the experimental data reported by Harris et al. [28] for HE/NMP system and by Shealy et al. [29] for EA/DMF system. The T-xy plots in Figure 3 show that the liquid-liquid equilibrium (LLE) exists for HE/DMSO and HE/DMF systems, whose binary parameters are taken from Aspen Plus builtin databank, given in Table S2 in Supporting Information. It can be observed from the T-xxy plot for HE/DMF system (Figure 3b) that the results predicted by NRTL model with default Aspen Plus binary parameters are good to fit the available experimental data by Harris et al. [28]. Although VLE and vapor-liquid-liquid equilibrium (VLLE) experimental data for HE/DMSO system are not available in the literature, VLLE plot for n-hexane/DMSO system is similar to that for HE/DMF system (Figure 3), which indicates that the VLLE predictions for HE/DMSO are reasonable and that NRTL model can be used in the conceptual design of ED process for separating n-hexane and ethyl acetate mixtures.

Figure 3. T-xxy plots for HE/DMSO (a) and HE/DMF (b) at 101.3 kPa; square symbols are the experimental data and solid lines are predictions by NRTL model The residue curve maps of the n-hexane (A), ethyl acetate (B) and the potential entrainer (E, which can be DMSO, DMF or NMP) systems at 1 atm are displayed in Figure 4. The intersection point, xP of iso-volatility curve 𝛼AB = 1 and binary side A−E can be used to

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assess the performance of the entrainer; the closer this point is to the target component nhexane (HE in Figure 4), a less amount of entrainter is required to achieve the desired separation objective, implying lower energy consumption [17]. The ternary diagram (ABE) of HE, EA and each candidate entrainer systems (Figure 4) is divided by the iso-volatility curve into two parts: ABE and BAE. According to the volatility order of HE, EA and entrainer, if the feed composition lines in ABE zone, the possible product at the top of ED column (EDC) is nhexane (A) based on the volatility order of ABE while the possible product at the top of entrainer recovery column (ERC) is ethyl acetate (B). It can be observed from Figure 4 that NMP is the best potential entrainer for separating n-hexane and ethyl acetate since it gives largest xP, i.e., closest to pure n-hexane product, than that for DMSO and DME (xP=0.55, 0.73, 0.80 for DMSO, DMF and NMP, respectively). Detailed conceptual design of ED process for separating ethyl acetate and n-hexane using NMP as entrainer is described in Section 4.

Figure 4. Thermodynamic features of the ternary systems for three alternate entrainers at 1 atm

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3. Evaluation criteria 3.1. Economic Evaluation In this study, TAC defined in Eq. 1 is minimized to obtain the optimal design of the steady-state process. TCC TAC = + AOC payback period

(1)

Here, the total capital cost (TCC) includes the capital costs of columns, trays, condensers, reboilers, heat exchangers, compressor and vacuum system including their installation costs; the annual operating cost (AOC) is the annual cost of steam, cooling water and electricity. The payback period and annual operating time are set as 3 years and 8000 hours/year, respectively. The tray spacing in the columns is assumed as 0.6096 m, and the column diameter is calculated by tray sizing in Aspen Plus with maximum flooding of 80%. In addition, all stages of the columns are assumed to be idel trays, i.e., Murphree efficiency of each component on all stages is 100%. The condenser and reboiler are also assumed as two ideal trays in both steady-state and dynamic simulations. Pressure drop in them is taken to be 0.0068 bar per stage/tray in steady-state simulation. The capital costs of heat exchangers, compressor and column shell and trays are calculated by using the correlations provided by Douglas [30]. The steam jet ejector is used to maintain the vacuum in ERC. The amount of gas leakage, and the operating and capital costs of the vacuum system are calculated using the equations recommended by Seider et al. [31]. Heat transfer coefficient of reboilers is assumed as 0.568 kW/(K∙m2) whereas that of condensers and heat exchangers is assumed to be 0.852 kW/(K∙m2) [32]. Marshall & Swift index (M&S) is set as 1593.6 in 2017 [33]. Efficiency of the compressor and its motor is assumed to be 0.8 and 0.9, respectively [34]. Unit prices of the used utilities are given in Table 2. Detailed equations and data for calculating operating and capital costs are given in the Supporting Information.

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Table 2 Utilities and their unit prices [35] Utility Low pressure steam (160°C, 5 barg) Medium pressure steam (184°C, 10 barg) High pressure steam (254°C, 41 barg) Cooling water (at 30°C and return at 40°C) Electricity

Unit price 13.28 $/GJ 14.19 $/GJ 17.70 $/GJ 0.354 $/GJ 16.67 $/GJ

For the optimization of the ED process, decision variables for the objective function defined in Eq. 1 include both discrete variables (i.e. number of stages and feed locations in columns) and continuous variables (i.e. flow rates, S/F ratio and column pressure). Therefore, the minimization of TAC is a mixed integer nonlinear programming (MINLP) problem, which can be mathematically expressed as follows. min TAC = 𝑓(𝑥) 𝑥

(2a)

s.t. ℎ(𝑥) = 0

(2b)

𝑔(𝑥) ≤ 0

(2c)

𝑥min ≤ 𝑥 ≤ 𝑥max

(2d)

Here, x is the decision variables vector; 𝑓(𝑥) represents the objective equation defined in Eq. 1; ℎ(𝑥) represents the equality constraints, i.e., the black-box nonlinear process model built in Aspen Plus; 𝑔(𝑥) represents the inequality constraints in the optimization problem; and Eq. 2d are the bounds on the decision variables. Since derivatives of the nonlinear equations embedded in Aspen Plus model of the process cannot be calculated and/or retrieved via the interfaced software for performing optimization of the process, e.g., in Matlab. Further, discrete variables are involved in the optimization problem. Hence, it is difficult to use deterministic optimization methods for optimizing the ED process using Aspen Plus simulator. Therefore, in the present study, the MINLP problem in Eq. 2 was solved in Matlab 2019a by using the optimization package: NOMAD, which is based on the Mesh Adaptive Direct Search (MADS) algorithm for solving constrained nonlinear optimization. This algorithm belongs to the category of stochastic optimization methods, and is capable of robustly optimizing complex nonlinear processes without requiring derivatives [36]. All simulations and optimizations were carried out using 10

a computer system running Windows 10, having an Intel Core i7 processor at 2.5 GHz and 12 GB of RAM.

Figure 5. Scheme of the optimization procedure using the optimizer in Matlab interfaced with process simulation in Aspen Plus Figure 5 shows the scheme for the implementation of the MINLP optimization procedure for the ED process, in which the process simulation was implemented in Aspen Plus 8.8 while the optimization was carried out in Matlab 2019a that was directly interfaced with Aspen Plus via COM technique. In addition, the calculation of objective function and constraints in Figure 5 are performed in Matlab with the required stream flow rates, temperatures, product purities and heat exchanger duties of the process retrieved from Aspen Plus process simulator. 3.2. Exergy Analysis Exergy analysis, based on the second law of thermodynamics, is a fundamental tool for analyzing and improving the thermodynamic efficiency of a chemical process. It measures the entropy generation of the system to evaluate the quality and utility efficiency of the energy of the system that cannot be measured by energy balance, i.e., first law of thermodynamics. In this study, exergy analysis was conducted by following the method in the book of Seider et al. [31]. The thermodynamic efficiency of the system, η is defined as: 𝑊SEP 𝜂= 𝑊SEP + ∆𝐸𝑥loss

(3)

Here, 𝑊SEP is the required minimum separation work, ∆𝐸𝑥loss is the exergy loss of the system. In the present study, the surrounding temperature and pressure are assumed to be 25°C and 1 atm, respectively. The average temperature of cooling water is taken to be 35°C (based on its

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inlet and outlet temperatures of 30°C and 40°C, respectively) while the temperatures of low and medium pressure stream are 160°C and 184°C (as given in Table 2), respectively.

4. Steady-state process design 4.1. ED process The flowsheet of the ED process for the separation of ethyl acetate and n-hexane is shown in Figure 6. The fresh feed is fed to the middle section of EDC after preheated by the recovered recycling entrainer, whereas the cooled recycling entrainer (NMP) is fed to the top section of EDC after mixing with make-up NMP. High purity n-hexane is obtained at the top of EDC while the mixture of ethyl acetate and NMP with a tiny amount of n-hexane is obtained at the bottom of EDC, which is then fed to ERC. High purity ethyl acetate is obtained at the top of ERC whereas pure NMP is obtained at the bottom of ERC.

Figure 6. Conceptual flowsheet of ED process showing important design variables Here, the flow rate of the fresh feed (61 wt% n-hexane and 39 wt% ethyl acetate) is set as 100 kmol/h (= 8691.94 kg/h), and the desired product purity of ethyl acetate and n-hexane is set as 99.9 wt% as in Lü et al. [3]. The temperature of the fresh feed stream is set as 25°C whereas the feed temperature after preheated by the recycling entrainer from the bottom of ERC is fixed at 64°C, which is the saturation temperature of the fresh feed at 1 atm. The operating pressure of EDC is fixed at 1 atm in industrial practice for operation flexibility and process safety. The minimum temperature approach of all heat exchangers (including

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reboilers and condensers) is chosen as 10°C for adequate temperature driving force for heat transfer. The recycling entrainer temperature at cooler inlet (𝑇c ― in) should be more than or equal to its temperature at the cooler outlet (𝑇c ― out); if 𝑇c ― in is equal to 𝑇c ― out after process optimization, the cooler can be removed from the ED process (Figure 6). Alternately, the cooler will become a heater if 𝑇c ― in is less than 𝑇c ― out, which should be avoided in the optimization of ED process. The entrainer, NMP used in the present study has high chemical and thermal stability. In fact, boiling point of NMP is 204.27°C at 1 atm; this means ERC reboiler duty has to be supplied by high pressure steam (HPS), which increases operating cost of ERC since the price of HPS is higher (by ~20%) than that of low and medium pressure steam (LPS and MPS). Moreover, vacuum increases the relative volatility of ethyl acetate to NMP and can also improve the thermal efficiency by avoiding the use of high-grade steam to supply thermal energy to ERC reboiler. On the other hand, vacuum operation of ERC (𝑃ERC in bar) increases the capital and operating costs for the required vacuum system. Overall, vacuum operation of ERC is expected to be better. Therefore, ERC is considered to be under vacuum in this study, and 𝑃ERC value is determined by TAC minimization under the constraint that ERC condenser temperature (𝑇CERC) should be more than 40°C in order to use the inexpensive cooling water as the coolant. In summary, inequality constraints, 𝑔(𝑥) in Eq. 2c for ED process optimization are: 0.999 ― 𝑤HE 𝑔1 0.999 ― 𝑤EA 𝑔2 40 ― 𝑇CERC 𝑔(𝑥) = 𝑔3 = (4) 𝑔4 10 ― ∆𝑇pre 𝑔5 𝑇c ― out ― 𝑇c ― in

[][

]

Here, ∆𝑇pre is the minimum temperature difference/approach of the preheater shown in Figure 6, 𝑤HE and 𝑤EA are the mass fraction of n-hexane and ethyl acetate products, respectively. In order to simplify the optimization of the two columns, EDC was divided into three sections: rectifying section from the condenser to heavy entrainer feed tray with 𝑁S stages, extractive section just after the heavy entrainer feed tray to fresh feed tray with 𝑁F1 stages and stripping section just after the fresh feed tray to reboiler with 𝑁b1 stages. Hence, the total

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number of theoretical stages in the EDC is 𝑁S + 𝑁F1 + 𝑁b1, which includes both condenser and reboiler. Similarly, ERC was divided into two sections: upper section above feed stage with 𝑁t2 stages and lower section below the feed stage with 𝑁b2 stages. Thus, total number of theoretical stages in the ERC is 𝑁t2 + 𝑁b2, which includes both condenser and reboiler. The recycling entrainer temperature at the cooler outlet (𝑇c ― out) should be determined to minimize the energy consumption of the ED process since a lower 𝑇c ― out leads to a larger reboiler duty of EDC and a smaller amount of required entrainer flow rate. Selection of the required steam grade (i.e., LPS, MPS or HPS) for supplying thermal energy to reboilers is directly implemented in the optimization of the ED process via the following logic:

{

𝑇S = 𝑇MS

𝑇LS if (𝑇R ≤ 𝑇LS ― 10) if (𝑇LS ― 10 ≤ 𝑇R ≤ 𝑇MS ― 10) 𝑇HS if (𝑇MS ― 10 ≤ 𝑇R)

(5)

Here, 𝑇R is the reboiler temperature, 𝑇LS, 𝑇MS and 𝑇HS are saturation temperature of LPS, MPS and HPS, respectively, 𝑇S is the temperature of the selected steam grade, and 10 is the required minimum temperature difference between steam and reboiler temperatures. In addition, stream price of the selected steam grade is determined by the following logic:

{

𝐶S = 𝐶MS

𝐶LS if (𝑇R ≤ 𝑇LS ― 10) if (𝑇LS ― 10 ≤ 𝑇R ≤ 𝑇MS ― 10) 𝐶HS if (𝑇MS ― 10 ≤ 𝑇R)

(6)

Here, 𝐶S is the unit price of the selected steam medium, 𝐶LS, 𝐶MS and 𝐶HS are the unit price of LPS, MPS and HPS, respectively. In total, the decision variables, x in Eq. 2 for ED process optimization are as follows. 𝑥 = [𝑁S,𝑁F1,𝑁b1,𝑁t2,𝑁b2,𝐹DEDC,𝑅𝑅EDC,𝑅𝑅ERC,𝑤bNMP,𝑆 𝐹,𝑇c ― out,𝑃ERC]

𝑇

(7)

Here, 𝐹DEDC[kmol/h] is the distillate flow rate of EDC, 𝑆 𝐹 is the molar flow rate ratio of the entrainer fed to EDC and the fresh feed, and 𝑅𝑅EDC and 𝑅𝑅ERC are the reflux ratios of EDC and ERC, respectively. 𝑤bNMP is the mass fraction of NMP in the bottom stream of ERC, which is used as a specification for ERC simulation in Aspen Plus; to meet this specification, distillate flow rate of ERC is adjusted during ERC simulation throughout the optimization of ED process. The lower and upper bounds on decision variables for the optimization of ED

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process are listed in Table 3. In this study, for a single objective problem, the NOMAD solver directly gives the optimal solution based on the specified minimum mesh grid. Table 3. Bounds on and optimal values of decision variables of the optimization problems for ED and VRHP-ED processes Process

ED

VRHP-ED

Decision variable Lower bound Optimum found Upper bound NS

4

5

20

NSF1

4

14

25

Nb1

4

10

25

Nt2

3

4

15

Nb2

3

4

15

𝐹DEDC (kmol/h)

61.5

61.58

61.6

RREDC

0.3

0.79

2

RRERC

0.2

0.26

2

𝑤bNMP

0.999

0.9997

0.99999

S/F

0.7

0.86

4

Tc-out (°C)

40

100.77

150

PERC (bar)

0.2

0.28

0.4

Pdis

4

4.84

6

Δ Thex

10

14.46

40

Figure 7. Variation of TAC of ED process with increasing iteration number during the optimization Figure 7 displays variation of TAC of ED process with increasing iteration number during the optimization excluding the iterations wherein Aspen Plus model of ED model did 15

not converge. It can be seen from this figure that TAC decreases quite fast, i.e., the optimization algorithm can converge quickly. Figure 8 presents the optimal ED process along with stream data (i.e., temperature, pressure, flow rate and composition), heat duties, column sizes and operating conditions. The EDC has a total of 29 stages including condenser and reboiler whereas the ERC has 8 stages including condenser and reboiler. The entrainer and preheated fresh feed are respectively fed on 5th and 19th stages of EDC while the bottom product of EDC is fed on 4th stage of ERC. The product purity of both ethyl acetate and n-hexane is 99.9 wt% while the recovered NMP purity is practically pure at 99.97 wt%. The reboiler temperature of EDC is 114.65oC, which makes it possible to use LPS in the reboiler. However, the reboiler temperature of ERC is 161.89oC, which is more than the saturation temperature of LPS; hence, MPS is used to supply thermal energy to ERC reboiler. The recycling NMP after subcooling by cooling water in the cooler to 100.77 oC is mixed with the make-up NMP, and then fed on 5th stage of EDC.

Figure 8. Optimal ED process flowsheet with data on streams, heat duties and columns 4.2. VRHP assisted ED process As shown in Figure 8, 882.15 kW of condensing latent heat is removed by cooling water as waste heat in the EDC condenser while the EDC reboiler requires 995.43 kW, which is 16

supplied by LPS. Although the duty of EDC condenser is lower than that of EDC reboiler, it still possible to use VRHP to upgrade the condensing latent heat of the condenser to partially supply the thermal energy to EDC reboiler, which will improve the thermodynamic efficiency of the ED process. Feasibility of this is indicated by the moderate difference between condenser temperature (68.72oC) and reboiler temperature (114.65oC). On the other hand, the operating temperature of ERC condenser (42.77oC) is much lower than that of both EDC and ERC reboilers, which indicates that the utilization of condensing latent heat removed by cooling water in ERC condenser via VRHP is uneconomical, i.e. more compression work is needed due to the larger difference between reboiler and condenser temperatures. Therefore, only condensing latent heat of EDC is used to improve the thermodynamic efficiency of the ED process.

Figure 9. Variation of condensing temperature (Tcon) with discharge pressure (Pdis) (a) and of discharge temperature (Tdis) with suction temperature (Tsuc) (b), of the compressor for the vapor stream at the top of EDC The compression ratio of the compressor in heat pump (HP) system is an important operating condition that significantly affects the economics of the HP system as well as the discharge temperature (Tdis) and pressure (Pdis) of the compressor. The variation of condensing temperature of the compressed overhead vapor stream (Tcon) at various Pdis (Figure 9a) indicates that, when Pdis increases to 4.384 bar, Tcon increases to 124.65oC, which is 10oC higher than the temperature of EDC reboiler (𝑇REDC) of 114.65°C, satisfying the required temperature driving force for heat transfer in EDC reboiler. Figure 9b shows the variation of Tdis with suction temperature of the compressor (Tsuc) at a fixed Pdis of 4.384 bar. 17

It can be seen from Figure 9b that, when Tsuc increases to 91.96 °C, Tdis increases to 124.65oC that meets the required temperature difference deriving force for heat transfer in EDC reboiler when Pdis is at 4.384 bar. However, the temperature of the overhead vapor stream from the top of EDC is 68.72°C, lower than the required minimum Tsuc of 91.96°C; hence, the overhead vapor stream from the EDC top must be preheated by the condensate of the compressed overhead vapor from EDC reboiler, before feeding into the compressor. On the other hand, Tdis cannot exceed 150°C for the safe operation of the compressor [37], which should be included as the constraint for the optimization of VRHP assisted ED (VRHP-ED) process.

Figure 10. Optimal VRHP-ED process flowsheet with data on streams, heat duties, columns and compressor The flowsheet of the designed VRHP-ED process is shown in Figure 10, where the overhead vapor stream of EDC is preheated by the condensate from EDC reboiler and then compressed by the compressor. The compressed overhead vapor at higher temperature and pressure is then used to supply major part of EDC reboiler duty by releasing the latent heat. The resulted condensate is first used to preheat the overhead vapor stream (before feeding into compressor); then, its pressure is reduced to the operating pressure of EDC reflux drum, it is cooled using cooling water and finally it flows into the reflux drum.

18

In VRHP-ED process, the number of theoretical stages, feed locations and operating conditions of the columns are maintained same as those in the optimized ED process since the addition of VRHP does not affect the column operation. Therefore, as discussed above, there are two decision variables, Tsuc and Pdis to be determined by optimization while meeting the constraints on the maximum Tdis, and the minimum temperature approach of the reboiler (EDC reboiler2 in Figure 10) and the preheater of the overhead vapor stream (Hex in Figure 10). However, Tsuc is determined by the temperature difference, ΔThex between the temperatures of the condensate from reboiler (Tcon) and the preheated overhead vapor stream (Tsuc). Therefore, based on the optimal configuration of ED process, the decision variables vector for the VRHP-ED optimization is 𝑥 = [𝑃dis, ∆𝑇hex]𝑇, whose bounds and constraints vectors are given in Table 3 and Eq. 8, respectively. 𝑔1 10 ― (𝑇con ― 𝑇RERC) 𝑔(𝑥) = 𝑔 = 𝑇suc ― 150 2

[ ] [

]

(8)

The variation of TAC with number of iterations for the optimization of VRHP-ED process is shown in Figure 11. Since there are only two decision variables that are directly constrained by Tdis of the compressor and the minimum temperature approach of Hex, ΔThex, optimization of VRHP-ED process is relatively simple; it only requires 199 iterations to reach its optimal solution.

Figure 11. Variation of TAC of VRHP-ED process with with increasing iteration number during the optimization The optimal flowsheet of VRHP-ED with design and operating conditions is shown in Figure 10. Here, the overhead vapor from EDC top is preheated from 68.98°C to 114.75°C by

19

the condensate from EDC reboiler and then compressed to 4.84 bar giving Tdis of 150°C, which is the bound of the constraint on the Tdis. Furthermore, Tcon (129.20°C) is 14.55°C more than the reboiler temperature of 114.65°C, which satisfies the required temperature difference for heat transfer in the EDC reboiler. Here, the optimized discharge pressure of compressor (4.84 bar) is higher than that obtained by analysis (4.384 bar) from Figure 9. This is because the condensing temperature of the compressed overhead vapor stream increases along with the increase of the compressor discharge pressure, resulting in larger temperature difference for heat transfer in EDC reboiler, and consequently lower reboiler capital cost. Hence, the increased compression cost for higher discharge pressure can be offset by the lower reboiler cost. The pressure ratio of the compressor is 4.7502, which requires 221.22 kW of work input. In VRHP-ED process, major part (i.e., 856.75 kW) of the EDC reboiler duty is supplied by the HP system, while the remaining 138.68 kW is supplied by LPS in an auxiliary reboiler of EDC (EDC reboiler1 in Figure 10). 4.3. Comparison of Steady-State Results The economic performance and thermodynamic efficiency of the conventional ED and VRHP-ED processes are compared in detail in Table 4. Compared to the conventional ED process, steam and cooling water consumptions of VRHP-ED decrease by 51.39% and 46.06%, respectively. Even with additional annual electricity cost for the compressor, AOC of VRHP-ED process decreases by 34.99% compared to the conventional ED process. However, in comparison with the conventional ED process, TCC of VRHP-ED process increases by 65.84% due to the addition of one heat exchanger, one reboiler and one compressor. The condensing temperature of the compressed overhead vapor stream (129.20°C) is 30.8°C lower than the temperature of LPS (160°C) used in ED process, which leads to lower driving force for heat transfer in EDC reboiler, and consequently increases heat transfer area and capital cost of EDC reboiler. Moreover, VRHP-ED process requires two reboilers for EDC. All these factors contribute to substantial increase in TCC of VRHP-ED process.

20

Table 4. Comparison of costs and thermodynamic efficiency of ED and VRHP-ED Item

ED

Feed flow rate (kmol/h)

VRHP-ED

100

300

100

300

Column Shells (103 $)

406.55

740.65

406.55

740.65

Trays (103 $)

30.46

72.76

30.46

72.76

Cooler (103 $)

16.24

33.02

16.24

33.02

Condensers (103 $)

360.20

735.65

282.21

576.37

Reboilers (103 $)

460.96

941.44

712.38

11454.95

Vacuum system (103 $)

0.81

0.95

0.81

0.95

Heat exchangers (103 $)

27.21

55.59

102.46

209.28

Compressor (103 $)

0.00

0.00

608.78

1498.63

380.71

1142.14

53.04

159.12

8.44

12.34

8.44

12.34

248.42

745.26

248.42

745.26

14.56

43.65

7.85

23.54

0.00

0.00

106.19

318.56

AOC (103 $/year)

652.14 (0%)

1943.40 (0%)

423.95 (−34.99%)

1258.82 (−35.23%)

TCC (103 $)

1302.43 (0%)

2580.07 (0%)

2159.90 (+65.84%)

4586.62 (+77.77%)

1086.28 (0%)

2803.42 (0%)

1143.91 (+5.31%)

2787.69 (−0.56%)

977.74 (0%)

2588.41 (0%)

963.92 (−1.41%)

2405.48 (−7.07%)

41.65

124.96

41.65

124.96

433.68

1301.09

409.22

1227.72

LPS for EDC reboiler (103 $/year) LPS for vacuum system (103 $/year) MPS for ERC reboiler (103 $/year) Cooling water (103 $/year) Electricity (103 $/year)

TAC with payback period of 3 years (103 $/year) TAC with payback period of 4 years (103 $/year) WSEP (kW) Exloss (kW) η (%)

8.76

9.24

In summary, compared to the conventional ED process, TAC of VRHP-ED with a payback period of 3 years increases by 5.31% due to the substantial increase in capital cost; on the contrary, with a payback period of 4 years, TAC decreases by 1.41%. Moreover, owing to the effect of “economy of scale”, large plants are more economical in terms of TCC required for unit capacity although AOC increases nearly proportionately. Hence, even with a

21

payback period of 3 years, the VRHP-ED process could be more economical than ED for larger throughput. As shown in Table 4, when the fresh feed flow rate increases to 300 kmol/h (3 times of that used in Figures 8 and 10), TAC of VRHP-ED process with payback period of 3 and 4 years reduces by 0.56% and 7.07%, respectively, compared to conventional ED process. Key process conditions including reboiler and condenser duties and compressor work for feed flow rate of 300 kmol/h are given in Figures S1 and S2 in the Supporting Information. Moreover, by utilizing VRHP technology, the thermodynamic efficiency increases from 8.76% of the conventional ED process to 9.24% of VRHP-ED process (Table 4), demonstrating another benefit of integrating VRHP with ED, which is mainly due to the reduction of required steam and cooling water. For further comparing the energy requirements of the two processes, thermoelectric efficiency is considered by assuming that the required external work input (i.e., electricity) of the compressor (𝑄com) is equivalent to thermal energy by a factor 3.0 [38]. Hence, the equivalent total thermal energy consumption of two reboilers and compressor for VRHP-ED process (𝑄T) can be calculated by 𝑄T = 𝑄RS + 3𝑄com

(9)

Here, 𝑄RS is the total reboiler duty in VRHP-ED, which is supplied by steam. Thus, the total equivalent thermal energy for two reboilers and compressor of the VRHP-ED process (Figure 10) is 1410.22 kW. Therefore, even though VRHP-ED requires 221.22 kW of electricity to drive the compressor motor, its total thermal energy consumption is still lower by 12.04% compared to 1603.31 kW of thermal energy (steam) required for the two reboilers in the conventional ED process (Figure 8). In summary, for separating ethyl acetate and n-hexane, VRHP-ED is better than ED process in terms of lower energy consumption (and consequently lower CO2 emissions), improved thermodynamic efficiency and reduced TAC, particularly for a payback period (≥ 4 years) and/or larger throughput. Recently, Lü et al. [3] compared HAD and HIPSD processes for separating the n-hexane and ethyl acetate mixture with a feed flow rate of 1000 kg/h. Their results show that HAD process requires 580.13 kW of cooling water in the condenser and 2238.17 kW of LPS in the reboiler whereas HIPSD requires 726.48 kW of cooling water and 789.88 kW of LPS. Using

22

the utility cost data in Table 2 for fair comparison, AOC of HAD and HIPSD processes is 861,932 and 309,507 $/year, respectively. In the present study, the feed composition and product purity specifications are same as those by Lü et al. [3], but feed flow rate of 100 kmol/h or 8691.94 kg/h is much larger. Hence, for comparison, VRHP-ED process is simulated for a feed flow rate of 1000 kg/h, and key design conditions including reboiler and condenser duties are presented in Figure S3 in the Supporting Information. By decreasing the feed flow rate of the process in Figure 10 to 1000 kg/h as that in the study of Lü et al. [3], VRHP-ED process requires 15.95 and 15.22 kW of LPS in EDC reboiler and vacuum system, respectively, 69.94 kW of MPS in ERC reboiler, 8.93 and 79.77 kW of cooling water in cooler and condensers, and 25.45 kW of electricity for compressor. These values give AOC of 53,624 $/year for VRHP-ED process, which is much lower by 93.78% and 82.67%, respectively, compared to AOC of HAD and HIPSD processes [3]. In terms of equivalent total thermal energy consumption, VRHP-ED, HAD and HIPSD require 177.46 kW, 2238.17 kW and 789.88 kW, respectively, for a feed flow rate of 1000 kg/h. The above comparative results clearly demonstrate that VRHP-ED is a significantly economical and energy saving process for separating ethyl acetate and n-hexane. In HAD process, a large amount of thermal energy is required due to the addition of huge quantity of water (at 18,875 kg/h for a feed flow rate of 1,000 kg/h) to the decanter and subsequent separation of water by distillation. In HIPSD process, composition difference of the recycle stream and fresh feed stream is only ~5% leading to excessive recycle of azeotropic mixture (at 3006.4 kg/h for a feed flow rate of 1,000 kg/h) and consequently large duties of reboiler and condenser of both columns in the process. On the contrary, in VRHP-ED process, NMP (entrainer) recycle is relatively lower (at 979.13 kg/h for a feed flow rate of 1,000 kg/h); further, relative volatility of both ethyl acetate and n-hexane to NMP is much larger than that of the lighter component to heavier component in HAD and HIPSD processes, which makes the separation of the mixture in VRHP-ED process easier than that in HAD and HIPSD processes. In effect, this easier separation, relatively lower recycle of NMP and use of VRHP are the reasons for significantly lower energy requirement of the developed VRHP-ED process.

23

5. Plant-wide control scheme In this section, controllability of the designed VRHP-ED process shown in Figure 10 is tested using two control schemes by introducing changes in feed flow rate (F±20%) and feed composition (HE±10%). Here, the feed flow rate disturbances (F±20%) refer to 20% increase or decrease of feed flow rate used in the design, and feed composition changes (HE: ±10%) refer to 10 wt% increase (71% n-hexane and 29% ethyl acetate) or decrease (51% n-hexane and 49% ethyl acetate) of n-hexane mole fraction (with corresponding change in ethyl acetate mole fraction) in the feed stream. All the disturbances were introduced at the simulation time of 1 hour whereas the total simulation time was set as 12 hours. In this section, composition dynamic responses were plotted for the entire simulation time whereas dynamic responses of other variables were plotted until 5 hours only, to clearly compare the transient performance under various disturbances. Before converting the steady-state simulation model of the process built in Aspen Plus to the dynamic model in Aspen Dynamics 8.8 using pressure-driven simulation, the required pumps and valves were added into the simulation model of the process. Moreover, the reflux drums and sumps of columns were sized to give 15 min liquid holdup with 50% liquid level. The initial steady-state pressure drop of valves excluding pressure-reducing valve (installed on the condensate stream before feeding into EDC condenser shown in Figure 10) was set as 2 bar to satisfy the required pressure driving force for fluid flow through them. The overall control structure of a process plant commonly consists of two hierarchies, i.e. inventory control and quality control. The inventory control includes level, pressure and flow control loops, for keeping material balance in the process; and the quality control is to maintain desired product purities using temperature or composition control loops. However, composition control requires composition analyzers such as the gas chromatographs that commonly have large measurement delay, high capital cost and maintenance cost. Consequently, in the present study, temperature control (and not composition control) was used in all quality control loops. Here, two control schemes with or without feedforward control were proposed and evaluated in detail for the operation of VRHP-ED process.

24

5.1. Inventory control loops Solvent-to-feed ratio (S/F, molar flow rate ratio of entrainer to fresh feed) is a crucial process variable and has significant impact on the product purities and energy consumption of an ED process. The larger S/F can increase the relative volatility of the light key to the heavy key but also increases ERC reboiler duty for regenerating entrainer. Xia et al. [39] used the sensitive tray temperature in the middle section of EDC to manipulate S/F demonstrating that the sensitive tray temperature can be controlled at the set point by the proposed control scheme. Wang et al. [40] also compared the control performance of an ED process with fixed S/F or variable S/F for controlling the sensitive tray temperature in the middle section of EDC. Although these studies indicated that ED process can be operated by using the control scheme with variable S/F, the control scheme with fixed S/F exhibits more robust performance for the control of most ED processes; therefore, S/F is often fixed for ED process operation [38, 39]. In the present study, S/F is fixed at a constant value in the control schemes presented. The overall inventory control loops were designed as follows (Figures 13 and 14). (1) The feed flow rate (FC1) is flow-controlled. (2) The entrainer flow rate fed on 5th stage of EDC (FT2) is ratioed to fresh feed flow rate (FT1) with a fixed ratio of 0.86 (S/F), which is the optimal value for the steady state process, via manipulating the bottom entrainer flow rate of ERC (FC3). (3) The operating pressure of EDC is controlled by manipulating the compressor power (PC1) in direct action while the discharge pressure of the compressor is controlled at 4.84 bar by manipulating the opening of the pressure-reducing valve with direct action (PC4). Thus, the suction and discharge pressures of the compressor are maintained at their specified values so that the condensing temperature of the compressed overhead vapor stream remains at the desired value to satisfy the required temperature driving force for heat transfer in EDC reboiler. (4) The reflux drum pressure of EDC is controlled at 1.01 bar by manipulating the duty of EDC condenser (PC3) while that of ERC is controlled at 0.28 bar by manipulating the duty of ERC condenser (PC2). (5) The reflux drum levels are controlled by manipulating their respective distillate flow

25

rates (LC1 and LC3). (6) The sump level of EDC is controlled by manipulating its bottom product flow rate (LC2) whereas that of ERC is controlled by make-up NMP flow rate with reverse action (LC4). (7) The temperature of the recycling entraner, NMP, before mixing with make-up NMP is controlled by manipulating the cooler duty, QCE (TC5). In the control schemes, proportional-integral (PI) controllers with proportional gain of 𝐾c = 0.5 and integral time of 𝜏I = 0.3 min are used in all flow rate control loops whereas PI controllers with 𝐾c = 20 and 𝜏I = 12 min are used in all pressure control loops. However, for level control loops, P-only controllers with 𝐾c = 5 are used [23]. 5.2. Quality control loops 5.2.1. Sensitive tray temperature selection Before designing a temperature control loop for quality control, the sensitive tray temperatures of the columns must be selected. Here, the open-loop sensitivity tests were implemented to choose the sensitive tray temperatures of all temperature/quality control loops by introducing ±0.1% step change in each manipulated variable while keeping the others at their nominal steady-state values. Figures 12a and 12b display the variation of temperature difference (between the stage temperatures after introducing a step change and their corresponding normal temperatures) with stage number of EDC for ±0.1% step change in its reflux ratio (RREDC) and reboiler duty, respectively. It is clear that 14th (T14) and 24th (T24) stage temperatures are more sensitive for changes in RREDC and reboiler duty, respectively. Since T14 is closer to the top of EDC, it should be controlled by manipulating RREDC whereas T24 is nearer to the bottom of EDC which should be controlled by manipulating the auxiliary reboiler duty of EDC, QR1-EDC. It can also be observed from Figures 12c and 12d that the temperature of 3rd (T3) stage is more sensitive to changes in the reflux ratio (RRERC) while the temperature of 6th (T6) is more sensitive to changes in reboiler duty (QR-ERC) of ERC. Hence, T3 and T6 are chosen to manipulate RRERC and QR-ERC, respectively.

26

Figure 12. Open-loop sensitivity test results for ± 0.1% step change in reflux ratio (left-side plots) and reboiler duty (right-side plots) of EDC and ERC 5.2.2. Basic control scheme (CS1) The basic control scheme (CS1) for the operation of VRHP-ED process was designed without considering feedforward control structure, and it is shown in Figure 13. The temperatures, T14 and T24 of EDC are respectively controlled by manipulating RREDC (TC1) and QR1-EDC (TC2) while T3 and T6 of ERC are respectively controlled by manipulating RRERC (TC3) and QR-ERC (TC4). PI controllers are used in all temperature control loops; they were tuned by the closed-loop ATV test, wherein tuning parameters are calculated by TyreusLuyben PI tuning rules [43], all these were carried out within Aspen Dynamics. The deadtime of all temperature measurements were assumed as 1 min. The tuning parameters of all temperature controllers in CS1 are listed in Table 5.

27

Table 5. Parameters of the temperature controllers for CS1 Control loop Controlled and manipulated variables

Gain, Kc

Integral time, τI (min)

TC1

T14 and RREDC

1.09

21.12

TC2

T24 and QR1-EDC

8.24

25.08

TC3

T3 and RRERC

1.52

11.88

TC4

T6 and QR-ERC

0.38

8.71

TC5

T and QCE

5.01

0.26

Figure 13. Basic control scheme (CS1) for the operation of VRHP-ED 5.2.3. Improved control scheme with feedforward (CS2) The dynamic performance of CS1 discussed in Section 5.3 showed that there are large transient deviations in the purity responses of two products and recycling NMP under feed flow rate disturbances due to the time delay of temperature controllers for feed flow rate changes. Therefore, feedforward control structure was used to minimize the effect of these time delays in the improved control scheme (CS2) shown in Figure 14. Here, T14 of EDC is used to manipulate the mass flow rate ratio of EDC reflux rate to fresh feed, REDC/F (TC1) while T24 of EDC is used to manipulate the ratio of the auxiliary reboiler duty to the mass flow rate of fresh feed, QR1-EDC/F (TC2). Similarly, T3 of ERC is used to manipulate the ratio of its reboiler duty to column feed mass flow rate, QR-ERC/FERC (TC4). Preliminary simulation 28

results indicated that feedforward structure for TC3 has no obvious improved control performance for eliminating transient deviations in the responses of ethyl acetate purity. Hence, T3 of ERC is still controlled by manipulating RRERC (i.e., without ratioing to feed flow rate). The tuning parameters of the four temperature controllers are listed in Table 6. Table 6. Parameters of the temperature controllers for CS2 Control loop Controlled and manipulated variables

Gain, Kc

Integral time, τI (min)

TC1

T14 and REDC/F

0.61

19.80

TC2

T24 and QR1-EDC/F

7.30

27.72

TC3

T3 and RRERC

1.63

11.88

TC4

T4 and QR-ERC/FERC

0.28

9.77

TC5

T and QCE

5.01

0.26

Figure 14. Improved control scheme with feedforward action (CS2) for the operation of VRHP-ED

29

Figure 15. Dynamic responses of purities of two products and recycling NMP by CS1 (leftside plots) and CS2 (right-side plots), under the changes of ±20% feed flow rate; note that yaxis scale is different in each plot, for clarity of curves 5.3. Dynamic performance evaluation 5.3.1. Feed flow rate disturbances From the dynamic responses of purities of two products and recycling NMP under the changes of ±20% feed flow rate (Figure 15), it can be observed that, when the feed flow rate increases by 20%, n-hexane purity under CS1 decreases from 99.9 wt% to 99.6 wt% before returning to the set point whereas that under CS2 shows negligible transient deviation. However, when the feed flow rate decreases by 20%, n-hexane purity under both CS1 and CS2 decrease to 98.1 wt% and 99.3 wt%, respectively, showing large negative transient deviations, and then smoothly return to the desired product purity. Ethyl acetate purity under both CS1 and CS2 shows similar dynamic responses for feed flow rate changes; however, transient deviations under CS2 (about -0.03 wt% for F+20% and +0.02 wt% for F-20%) are

30

relatively smaller than those under CS1 (about -0.06 wt% for F+20% and +0.03 wt% for F20%). Subsequently, they all return to their respective new steady state value. Moreover, as shown in Figures 15c and f, dynamic responses of NMP purity under CS1 show relatively larger transient deviations than those under CS2. These observations indicate that CS2 provides better performance for handling large feed flow rate disturbances than CS1 in terms of smaller transient deviations. Note that the very small offset in NMP purity in Figure 15f is probably due to the dynamic simulation tolerance of 1.0×10-5 in Aspen Dynamics.

Figure 16. Dynamic responses of temperatures, pressures and reboiler duties by CS1 and CS2 under ±20% changes of feed flow rate Figures 16a-d show the dynamic responses of four sensitive tray temperatures (controlled variables of quality control loops). It can be observed from Figure 16a that, when the feed flow rate increases by 20%, T14 under CS1 (black dash-dotted line) increases quickly showing large transient deviation, and then gradually returns to its set point while that under CS2 shows only slight oscillations with relatively small amplitude (blue solid line). These variations result in large transient deviation in the dynamic response of n-hexane purity under

31

CS1 (blue solid line in Figure 15a) whereas that under CS2 shows negligible transient deviation (blue line in Figures 15d). On the contrary, T14 under both CS1 and CS2 show large transient deviations when the feed flow rate decreases by 20%, resulting in large transient deviation in the dynamic responses of n-hexane (red lines in Figures 15a and d). However, T14 under CS2 can respond to feed flow rate changes faster than that under CS1, resulting in smaller transient deviation in the dynamic responses of n-hexane purity under CS2 compared to that under CS1. This is because EDC reboiler duties (Figures 16g and h) under CS2 can respond to feed flow rate changes faster than those under CS1. Furthermore, T24 under both CS1 and CS2 shows similar dynamic responses for feed flow rate changes. However, transient deviations in T3 and T7 under CS2 are much smaller than those under CS1 for feed flow rate changes, resulting in smaller transient deviations in the responses of ethyl acetate and NMP purities under CS2 compared to those under CS1 (Figures 15b, c, e and f). In addition, Figure 16e shows that changes in compressor discharge pressure (Pdis) are very small in both CS1 and CS2 schemes, leading to good regulation of the condensing temperature of the compressed overhead vapor stream (Tcon in Figure 16f) at the desired temperature to satisfy the required temperature driving force for heat transfer in the reboiler of EDC. However, large transient variations of the overhead vapor stream composition for the decrease in feed flow rate (F-20%) lead to relatively large transient deviations of Tcon, especially for that under CS1 (approximately -2℃ ). 5.3.2. Feed composition disturbances Figure 17 displays dynamic responses of purities of two products and recycling NMP under ±10% changes in the n-hexane mass fraction in feed stream (HE±10%). The two product purities are well controlled, and they smoothly reach their respective new steady states in both CS1 and CS2. The offset in n-hexane purity is about 0.098 wt% and 0.041 wt% for the increase and decrease of n-hexane mass fraction in the feed stream, respectively, whereas that of ethyl acetate is 0.032 wt% and 0.047 wt%, respectively. T14 under CS2 responds to the decrease of n-hexane mole fraction in feed stream faster than that under CS1 (Figure 18a), resulting in smaller transient deviation in the dynamic response of n-hexane purity under CS2 (Figure 17d) than that under CS1 (Figure 17a). In addition, dynamic 32

responses of T14 and T24 (Figures 18a and b) as well as two reboiler duties of EDC (Figures 18g and h) exhibit some oscillations before reaching a new steady state, in both CS1 and CS2. However, compared to CS2, CS1 displays relatively larger transient deviations in the dynamic response of NMP purity when n-hexane mass fraction in feed stream changes. This is because of much smaller transient deviations in dynamic responses of T6 under CS2 than those under CS1 (Figure 18d). Moreover, Figures 18e and f show that Pdis and Tcon are well controlled with small transient deviations under feed composition disturbances in both CS1 and CS2.

Figure 17. Dynamic responses of purities of two products and recycling NMP by CS1 (left-side plots) and CS2 (right-side plots) under ±10% feed composition disturbances Figure 19 shows the dynamic responses of ERC sump level and make-up valve opening under various disturbances. It can be seen from this figure that the ERC sump level and makeup valve opening vary significantly for changes in feed flow rate and composition, especially for changes in feed composition. The is because changes in feed flow rate or composition can significantly affect the total material balance, leading to variations in the inventory (buffer

33

volume) in EDC sump and consequently the inventory in ERC sump. For example, when the mole fraction of n-hexane in the feed stream increases, the corresponding decrease in the ethyl acetate product flow rate would result in the decrease of the inventory in EDC sump and then in the level of ERC sump. This is because more liquid in EDC sump, i.e., n-hexane vaporizes and ascends to the top of EDC, which leads to the decrease of EDC sump level and consequently the ERC sump level.

Figure 18. Dynamic responses of temperatures, pressures and reboiler duties by CS1 and CS2 under ±10% feed composition disturbances In Figures 19a and 19b, the sump level does not return to its set point within 12 hours because the flow rate of the make-up entrainer is much smaller than that of the recycling entrainer, which leads to requiring a long time for the ERC sump level to return to its set point. This is acceptable as long as the level stays within its lower and upper limits. Further, it can be seen from Figure 19d that the opening of make-up valve could reach its upper limit as the n-hexane mole fraction in feed stream increases; this does not have much effect on the

34

dynamic response of product purities because the make-up flow rate is very small. If required, reaching the upper limit can be avoided by increasing the flow capacity of the make-up valve.

Figure 19. Dynamic responses of ERC sump level and make-up valve opening under disturbances in feed flow rate (plots a and c), feed composition (plots b and d) for both CS1 and CS2 5.3.3. Quantitative performance comparison of CS1 and CS2 The integral of absolute error (IAE) in Eq. 9 is used as the quantitative criterion to evaluate performance of the two control schemes. 𝑇

IAE =

∫ |𝑒(𝑡)|𝑑𝑡

(10)

0

Here, e(t) is the deviation of the controlled variable from its set point and T is the total simulation time. The IAE values at T = 12 h (including one hour before the disturbance was introduced) for the dynamic responses of the purities of two products and recycling NMP are summarized in Figure 20. Figure 20a clearly shows that, when the feed flow rate changes, IAE values for CS2 are much smaller than those for CS1; however, both CS1 and CS2 give similar performance for controlling n-hexane purity under feed composition disturbances. For the control of ethyl acetate purity, CS2 gives better control performance with smaller IAE values than CS1 for handling feed flow rate disturbances; however, both CS1 and CS2 give similar

35

control performance for handing feed composition disturbances. Moreover, CS2 exhibits better performance for the control of NMP purity under various disturbances studied than CS1, especially for feed flow rate changes.

Figure 20. Comparison of IAE by CS1 and CS2 for n-hexane purity (a), ethyl acetate purity (b) and NMP (c) due to tested disturbances in feed flow rate and composition Finally, to compare the overall performance of CS1 and CS2 for handling various disturbances tested, sum of IAE values (SIAE) of component purity for the four tested disturbances is given in Table 7; they are calculated by: 𝑛

SIAE𝑖 =

∑𝐼𝐴𝐸

(11)

𝑖,𝑗

𝑗=1

36

Here, i represents the purity of n-hexane, ethyl acetate and recycling NMP, j represents various tested disturbances and n represents the number of tested disturbances. Table 7 shows that SIAEs for the three components are all small, indicating that product purities are well controlled in both control schemes. Furthermore, SIAEs for all the three components in CS2 are smaller (with total SIAE lower by ~40%) than those in CS1, confirming that CS2 is better than CS1 for the operation of the proposed VRHP-ED process. Table 7. Sum of IAE values under four different disturbances tested for each component purity Component purity

SIAE for CS1

CS2

N-hexane

0.0445

0.0226

Ethyl acetate

0.0086

0.0083

NMP

0.0033

0.0026

Total

0.0564

0.0335

6. Conclusions In this study, VLEs and VLLEs of n-hexane and ethyl acetate system and of a ternary system of n-hexane and ethyl acetate with each of three candidate entrainers: NMP, DMF and DMSO, were systematically investigated including verification of NRTL model predictions against experimental data. Residue curve maps of the ternary systems with each of the three entrainers indicate that NMP has better extractive performance for separating n-hexane and ethyl acetate mixtures. Therefore, conventional ED process and VRHP assisted ED (VRHPED) process were developed for separating n-hexane and ethyl acetate using NMP as the entrainer. Both these processes were designed to minimize TAC using a mesh adaptive direct search algorithm in Matlab, which interfaces with Aspen Plus. Optimization results show that VRHP-ED process is better than conventional ED process with lower TAC (by 1% to 7%) for payback period of 4 years and higher thermal efficiency (from 8.76% of ED process to 9.24% of VRHP-ED process). Moreover, the present study shows that VRHP-ED process can reduce operating cost by 93.78% and 82.67% compared to the azeotropic distillation and heat integrated pressure-swing distillation, respectively, reported recently by Lü et al. [3]. 37

Finally, controllability of the designed VRHP-ED process with PI controllers was tested for ±20% changes in feed flow rate and ±10% changes in n-hexane mole fraction in feed stream. Transient responses demonstrate that the control scheme with feedforward structure (CS2) has much better performance than that without feedforward structure (CS1) for rejecting feed flow rate and composition disturbances in terms of smaller transient deviations and oscillations (with total SIAE lower by ~40%). In summary, present study reveals that VRHP-ED process is economical, energy-efficient and controllable for separation of n-hexane and ethyl acetate in process industries.

7. Acknowledgments This work is financially supported by the National Natural Science Foundation of China (Nos. 21776025, 21606026, 21878028), the Fundamental Research Funds for the Central Universities (2018CDKYGL0002, 2018CDKYHG0028), and the Chongqing Research Program of Basic Research and Frontier Technology (CSTC2016JCYJA0474). The authors also acknowledge the China Scholarship Council (CSC file no: 201706050043) for providing scholarship to Z. Feng to conduct research at the National University of Singapore.

CORRESPONDING AUTHORS G.P. Rangaiah, Email: [email protected] Lichun Dong, Email: [email protected] Notes: The authors declare no competing financial interest.

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43

Highlights 

Extractive distillation (ED) process for separating ethyl acetate and n-hexane



Selected N-methyl-2-pyrrolidone as entrainer, and studied ED process design and control



ED process rigorously optimized. Vapor recompression assisted ED (VRHP-ED) proposed



Compared to ED, VRHP-ED process requires lower energy and total annual cost



For VRHP-ED process, control scheme with feedforward action has better performance

44

Table graph

45

Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate Zemin Feng,a,b Weifeng Shen,b G.P. Rangaiah,a,c* Lichun Dongb,d* a Department

of Chemical and Biomolecular Engineering, National University of Singapore, Singapore

117576. b

School of Chemistry and Chemical Engineering, National-Municipal Joint Engineering

Laboratory for Chemical Process Intensification and Reaction, and Key Laboratory of Lowgrade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing, 400044, China. c

School of Chemical Engineering, Vellore Institute of Technology, Vellore 632014,

India. d

Green Intelligence Environmental School, Yangtze Normal University, Fuling,

Chongqing, 408100, China.

CRediT author statement Zemin Feng: Methodology, Software, Formal analysis, Writing – Original. Weifeng

Shen:

Writing



Review

&

Editing.

G.P.

Rangaiah:

Conceptualization, Writing – Review & Editing, Supervision. Lichun Dong: Supervision, Writing – Review & Editing, Funding acquisition

46

Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate Zemin Feng,a,b Weifeng Shen,b G.P. Rangaiah,a,c* Lichun Dongb,d* a Department

of Chemical and Biomolecular Engineering, National University of Singapore, Singapore

117576. b

School of Chemistry and Chemical Engineering, National-Municipal Joint Engineering

Laboratory for Chemical Process Intensification and Reaction, and Key Laboratory of Lowgrade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing, 400044, China. c

School of Chemical Engineering, Vellore Institute of Technology, Vellore 632014,

India. d

Green Intelligence Environmental School, Yangtze Normal University, Fuling,

Chongqing, 408100, China.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Nil

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48

49