Energy-Saving Optimal Design and Effective Control of Heat

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Separations

Energy-Saving Optimal Design and Effective Control of Heat Integration-Extractive Dividing Wall Column for Separating Heterogeneous Mixture Methanol/Toluene/Water with Multiazeotropes Ao Yang, Renxing Wei, Shirui Sun, Shun'an Wei, Weifeng Shen, and I-Lung Chien Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00668 • Publication Date (Web): 02 May 2018 Downloaded from http://pubs.acs.org on May 4, 2018

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Industrial & Engineering Chemistry Research

Energy-Saving Optimal Design and Effective Control of Heat Integration-Extractive Dividing Wall Column for Separating Heterogeneous Mixture Methanol/Toluene/Water with Multiazeotropes Ao Yang,† Renxing Wei,† Shirui Sun,† Shun’an Wei,† Weifeng Shen,*,† I-Lung Chien§ †

School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, P. R.

China §

Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan

Corresponding Author: *(W.S) E-mail: [email protected] Abstract To the best of our knowledge, very few efforts have been investigated for separating heterogeneous mixtures methanol/toluene/water with multiazeotropes using extractive dividing-wall column (EDWC). In this work, we propose a systematic approach for the energy-efficient EDWC to achieve less capital cost and operating cost in separating heterogeneous multiazeotropes mixtures, which involves thermodynamic feasible insights via residue curve maps to find separation constraints, global optimization based on a proposed CPOM model, and a dynamic control through Aspen Dynamics simulator to better maintain product purities. Then, an energy-saving EDWC with heat integration (HI-EDWC) flowsheet is proposed to achieve the minimum total annualized cost (TAC). The computational results show that the TAC of the proposed HI-EDWC is significantly reduced by 15.14% compared with the optimal double-column extractive distillation with an additional decanter. Furthermore, an effective control strategy CS3 with a fixed reboiler duty-to-feed ratio and temperature/(S/F) cascade is proposed to better handle the methanol, toluene, and water product purities than basic control structures CS1 and CS2 while feed flowrate and composition disturbances are introduced in the proposed HI-EDWC process. 1 / 50

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Keywords: extractive dividing-wall columns; dynamic control; global optimization; residue curve maps; heterogeneous multiazeotropes; heat integration 1. Introduction As is well-known, both toluene and methanol products are frequently applied as important organic solvents and synthetic auxiliaries in fine chemical processes and pharmaceutical industries.1 Industrial wastes produced in pharmaceutical processes usually contain considerable amounts of methanol, toluene, and water mixtures, such as the wastewater generated in the production process of methyl anthranilate.1 The recovery of methanol and toluene from the wastewater can not only improve economic efficiency but also protect the environment against pollution. However, methanol/toluene/water can form a single homogeneous azeotrope (i.e., methanol/toluene azeotrope) and a single heterogeneous azeotrope (i.e., toluene/water azeotrope) at atmospheric pressure,2 which makes the separation of such nonideal systems more complex than other azeotrope mixtures via conventional distillations. Conventional extractive distillations (ED) are widely used and investigated for separating azeotropic or close-boiling mixtures by introducing a third component also called as entrainer (see Figure 1).3–5,7 However, conventional ED has a relatively low thermodynamic efficiency, and the high quality energy input is required in the reboiler to perform the separation task.35–37,41 Furthermore, the increasing awareness of the energy crisis has made process intensification a promising trend for energy savings strategies in chemical engineering. Therefore, a separation technology, called "dividing-wall columns or DWCs" separation configuration was proposed and could be applied in the separation of multicomponent or nonideal azeotrope mixtures and have significant advantages than conventional distillation.10–13, 29–30

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Figure 1. Flowsheet of the conventional two-column extractive distillation process

Figure 2. Flowsheet of (a) the EDWC and (b) the thermodynamically equivalent process of EDWC 3 / 50



The divided-wall at the top of the DWC configuration divides the column into two rectifying sections and a shared stripping section, and the dividing-wall is often implemented in the extractive separation denoted as extractive dividing-wall column (EDWC) for separating an azeotropic mixture (see Figure 2a).14–20 Thermodynamically equivalent process of the EDWC in Aspen Plus is demonstrated in Figure 2b. Note that MC and RC in Figure 2 represent main column and rectifying column of the EDWC, respectively. Correspondingly, the comparison between EDWC and ED processes for separating azeotrope mixtures are frequently investigated. For instance, Xia et al.18 investigated the EDWC process for the separation of methylal/methanol with dimethyl formamide as an entrainer, and they found that the EWDC had an 11.60% of total annualized cost (TAC) savings compared with that of conventional ED. Sun et al.17 studied the design and control of the EWDC for separating benzene/cyclohexane mixtures, and it is found that the optimal flowsheet of the EWDC can achieve up to 22.00% savings of the total reboiler duty, meanwhile, the steam cost and the TAC are reduced by 1.80% and 4.80%, respectively. Tututi-Avila et al.21 studied the design and control of EDWC for ethanol dehydration process, and they reported that the EWDC showed a 12.42% of reduction in TAC compared with that of conventional ED. Wu et al.19 studied the EWDC for separation of acetone/methanol and the 1.79% of reduction in TAC by the EWDC process is observed compared with ED process. Besides, the space of installation or capital cost will be significantly decreased by the EWDC because of the ED and solvent recovery columns are integrated into one single shell. Furthermore, in order to prove the feasibility and controllability of the EWDC, different control strategies were proposed in the EWDC by researchers. Zhang et al.16 investigated the design and control of EWDC for separating ethyl acetate/isopropyl alcohol mixtures, and they found two improved control strategies can achieve satisfactory performance with much smaller deviation and 4 / 50


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shorter settling time by comparing with the basic control structure. Dynamic results also revealed that it is useful to adjust entrainer flowrate (FE) or αV to hold the purity speciﬁcations when the disturbances are introduced. Sun et al.17 studied three control strategies of the EWDC for separating benzene/cyclohexane mixtures, and αV is more practical in industry than two basic control strategies CS2 and CS1. Xia et al.18 proposed basic control structure with fixed VR/QR ratio (also denoted as αV) and improved control structure with QR/F ratio for the EWDC process, and revealed that the αV at the bottom edge of dividing wall must be adjusted at least for composition disturbances of key feed components. Li et al.8 discussed that an improved control structure CS3 with reboiler duty-to-feed (QR/F) ratio and reflux-to-feed ratio (R/F) can maintain the products at high purities with small deviations and short settle time by introducing the feed disturbances. Besides, Xia et al.22 investigated temperature control for the EWDC with adjustable vapor split ratio αV. The new contribution of this work is focused on proposing a novel process using EDWC with an additional decanter for separating heterogeneous mixtures methanol/toluene/water with multiazeotropes as compared to the widely used EDWC for separating binary mixtures with single azeotrope. We built a systematic procedure for optimal design of the proposed energy-efficient EDWC to achieve less capital cost and operating cost, which involves thermodynamic feasible insights via residue curve maps to find separation constraints, global optimization based on a proposed CPOM model. Furthermore, an effective control strategy CS3 with a fixed reboiler duty-to-feed ratio and temperature/(S/F) cascade is proposed to better handle the methanol, toluene, and water product purities than basic control structures CS1 and CS2 while feed flowrate and composition disturbances are introduced in the proposed HI-EDWC process. In this work, we propose a systematic approach to optimize, design, and control the energy-efficient EWDC with an additional decanter for separating heterogeneous mixtures 5 / 50



methanol/toluene/water azeotropes. The proposed systematic strategy for the optimal design and effective control of a EWDC process is preceded by three steps. Step 1 mainly involves the investigation of the thermodynamic feasibility insight in ED processes (e.g., univolatility curve and residue curve maps) for separating methanol/toluene/water system using n-methyl-2-pyrrolidone (denoted as NMP) as an entrainer. Step 2 is focused on the global optimization of the proposed EWDC process. In this step, an objective function, OF, is introduced to evaluate the energy total energy consumption per unit flowrate of products of the EWDC due to it can avoid highly nonlinear complexity.9,27–28,38–40 Then, a novel EWDC with heat integration (HI-EWDC) process is proposed in Step 3. Eventually, an improved control strategy is proposed based on the two basic control structures of the HI-EWDC and the dynamic simulations are performed using Aspen Dynamics simulator. Meanwhile, two kinds of disturbances involving feed flowrate and composition are introduced to evaluate the controllability of the three control structures for the proposed HI-EWDC process. 2. Existing Processes for Separating Methanol/Toluene/Water System 2.1 Triple-Column Extractive Distillation

6 / 50


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Figure 3. Triple-column extractive distillation process to separate methanol/toluene/water using DEG as an entrainer To achieve the separation of heterogeneous mixtures methanol/toluene/water with multiazeotropos, the triple-column extractive distillation (TCED) process using entrainer diethylene glycol (abbreviated as DEG) has been proposed by Wang at al.23. As can be seen from Figure 3, both of columns C2 and C3 are the extractive distillation column, and the column C3 is the solvent regeneration column. A make-up stream of the entrainer is mixed with the recycled stream from the bottom stream of the column C3 to form a new feed stream and then is sent to the top of the column C1, which is operated at the pressure of 0.1 atm. The fresh feed is sent to the bottom section of the column C1 operating at the pressure of 0.1 atm. Therefore, the solvent is selected to separate toluene out of the mixture and take it out as the distillate product at the top of the column C1. Then, the methanol and water products are distillated at the top of the columns C2 and C3, which are operated under the pressure of 1.0 and 0.5 atm, respectively. 7 / 50



2.2 Double-Column Extractive Distillation with a Decanter

Figure 4. The process of double-column extractive distillation with a decanter to separate methanol/toluene/water using NMP as an entrainer To obtain the economic optimal design, the flowsheet of double-column extractive distillation process with a decanter (DCED-D) process has been explored.23 Some detailed information such as heat duties, tray sizes, and operating pressures are represented in Figure 4. The heterogeneous toluene/water azeotrope will become miscible at higher temperature, and thus, a cooler is installed into the flowsheet to cool the distillate product down to 283.15 K in the column C2.23 The fresh feed flowrate, temperature, and composition are kept as the same as the TCED process. An NMP entrainer stream and the fresh feed stream are respectively fed to the top and bottom section of the column C1 under the pressure of 1.0 atm. Therefore, the solvent is selected to obtain the distillate product methanol of column C1. Then, the NMP component is recovered at the bottom of the column C2. The distillate stream (i.e., mixture of toluene and water) is cooled down to 283.15 K 8 / 50


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and then they are divided into organic (OR) and aqueous phases (AQ) via a decanter. 3. Methodology As is well-known, the screening of suitable entrainer is an essential and important factor to achieve successful design of ED and EDWC. The entrainer selection in this work is based on the screening results have been detailed investigated by Wang et al.23. Therefore, the main object of this work

is

extended

to

the

investigation

of

a

proposed

optimal

energy-saving

heat

integration-extractive dividing wall column (HI-EDWC).

Figure 5. Proposed procedure for optimal design of methanol/toluene/water separation process using EDWC We propose a systematic procedure for optimal deign of the methanol/toluene/water system separation through integrating extractive distillation and solvent recovery columns, as shown in Figure 5. First, thermodynamic insight and residue curve maps (RCMs) analysis in the conceptual design are conducted using Aspen Plus to provide valuable insights on process feasibility for the proposed extractive dividing-wall column (EDWC). Then, energy-saving designs of EDWC and 9 / 50



HI-EDWC are proposed and simulated. Next, detailed process simulation for the proposed methanol/toluene/water system separation based on the data from the literature is conducted in Aspen Plus to identify lower and upper bounds of key design parameters and provide initial operational conditions for optimization. Following that, process optimization is performed using complete the optimization model (CPOM) built in Aspen Plus to obtain the optimal design including optimal operating conditions. Finally, we proposed an effective dynamic control strategy to ensure the system is well controlled at (or close to) the set points in a desired time under the condition of disturbances. 3.1 Thermodynamic Insights for Residue Curve Maps RCMs and distillation regions are useful graphical techniques for the comprehension of continuous distillation operations and per batches, specifically if these are combined with other information such as bimodal curves of LLE. As an important useful tool, RCMs is often applied to assist conceptual design and feasibility analysis. As well as other separation operations where triangular composition diagrams are used, as distillation curve maps, mass balances are graphically represented by straight lines connecting the corresponding compositions. Global flowrates are found through the application of the lever rule. Mass balance lines of the distillation have two restrictions: (1) Bottom compositions, distillate, and feed shall are always on the same straight line; (2) With a very close approximation, bottom compositions and distillate are on the same residue curve. As, by definition, residue curves do not cross a distillation limit, the bottom compositions and distillate are on the same distillation region, and the intrinsic mass balance and line to the residue line are in both sites.33–35 3.2 Process Simulation In this work, the feed composition and flowrate are selected to be kept same as those used by 10 / 50


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Wang et al.23, the flowrate of the feed mixture is 100 kmol/h with a composition of 37 mol % toluene, 49 mol % methanol, 14 mol % water. Besides, the temperature of the recovered entrainer is another key design variable except for total number of stages, reflux ratio, and feed locations. Therefore, the recycle solvent stream is thus subcooled to a temperature of 293.15 K during the simulation. The UNIQUAC model is selected to describe the VLE and LLE of methanol/toluene/water ternary mixtures, and the vapor phase is assumed as an ideal gas. The missing binary interaction parameters of methanol/NMP are estimated by UNIFAC in Aspen Plus. Besides, the isobaric experimental VLE data is provided to validate the accuracy of VLE predictions by the estimation of UNIFAC (see Figure S7 in Supporting Information). Table 1. Interaction Parameters of the UNIQUAC Model for Methanol/Toluene/Water System component i

methanol

methanol

toluene

toluene

water

methanol

component j

toluene

water

water

NMP

NMP

NMP

property units

℃

℃

℃

℃

℃

℃

aij

0.00

-1.07

0.00

0.14

-3.87

0.00

aji

0.00

0.64

0.00

-0.50

1.93

0.00

bij

27.83

432.88

-950.60

-44.83

1254.31

54.44

bji

-563.01

-322.13

-350.21

171.52

-460.98

-60.12

The Aspen Plus built-in and estimated binary parameters are demonstrated in Table 1. Meanwhile, the equilibrium stage calculation of RadFrac module is selected in Aspen Plus. Following previous stuides16–18, the Murphree tray efficiency is assumed to 100% when calculating the total capital cost and determining tray temperature control points. It is noteworthy that the capital cost of the process will become larger when the actual tray efficiency is considered. Meanwhile, the tray temperature control points might be reconsidered through the open-loop sensitivity analysis. However, the determination of three control structures CS1-CS3 that are applied to control the proposed HI-EDWC process would remain unchanged. 3.3 Process Optimization 11 / 50



3.3.1 Optimization via the Sensitively Analysis. As is well-known, the reflux ratio, feed locations, and withdraw locations of sidestream of MC, as well as the flowrate of vapor sidestream, and total number of stages of both MC and RC, have significant effects on the product purities for the proposed EWDC process. Therefore, the sensitivity analysis is carried out to seek the appropriate range of optimal values of R1, NF1, NF2, NT1, NT2, NVR, VR, and FE. Then, the global optimization can be further obtained via appropriate ranges of key operation parameters.

3.3.2 Global Optimization by the CPOM. The sensitivity analysis approach may not consider effects of more complex issues in the EWDC process arising from correlated variables. Therefore, the optimization of proposed the EWDC process is carried out using complete the optimization method (CPOM) implemented in Aspen Plus simulator. Firstly, the convergence is easy to achieve in the open loop flowsheet with no recycle and make-up of the entrainer, the proposed optimization procedure CPOM is used for the process optimization with an objective function of the energy consumption by manipulating the key design parameters under specified purities. Secondly, the final optimization is obtained and it is validated by running the simulation in the close loop flowsheet where the entrainer is recycled from the rectifying column to the main column and an entrainer make-up feed is added. The close loop simulation requires adjusting the reflux ratio if necessary in order to overcome the effect of impurity in recycling entrainer on the distillate purity.38,40

Objective Function. The design of extractive distillations, pressure-swing distillations, and extractive dividing-wall columns are frequently evaluated through introducing objective function such as TAC to find the optimal processes for achieving minimum TAC.6,31–32,36 Nonetheless, objective function, TAC, frequently depends on several variant factors such as shell cost, energy price, and a set of correlated variables (e.g., total number of stages and reflux ratios). For instance, 12 / 50


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the minimum reflux ratio requires a large total number of stages and vice versa. Furthermore, the modelling of TAC will be introduced a lot of nonlinear terms such as power function, which makes the optimization more complex and difficult in the Aspen Plus.9,27–28,38–40 Therefore, to avoid these problems in process design, an alternative objective function (OF) is proposed by You et al.40 and Shen et al.9 to evaluate the total energy consumption per unit flowrate of products and it is sensitive to the key operational variables (e.g., reflux ratio, feed location, and total number of stage), which is same as that of You et al.40, Shen et al.9, and Yang et al.28. Therefore, the objective function of EDWC is defined as follow,

min OF = f ( NT1 ,NT2 ,NF1 ,NF2 ,R1 ,NVR ,VR ,FE ) =

M ⋅ QR1 + m ⋅ QC1 + m ⋅ QC2 D1 + k ⋅ OR

(1)

where m = 0.036 illustrates the energy price difference factor between the condenser and reboiler9,40;

k = 2.04 shows the price differences in two products (i.e., methanol and toluene); M may equal to 1, 1.065 or 1.280 depending low, middle, or high pressure steams are used, respectively9,40. Besides, QR1, QC1, and QC2 are heat duties in reboilers and condensers, respectively. D1 and OR represent the flowrates of methanol and toluene products, respectively, which are fixed during the optimization to avoid nonlinearity in the objective function for simplification.28

Constraints. There are also some rigorous constraints involving mass conservation, energy conservation, and thermodynamics relations, which are implicitly implemented in Aspen Plus simulator. Meanwhile, these rigorous correlations can be generally represented as follow, h(x) = 0

(2)

g(x) ≤ 0

(3)

Variable Bounds. The design specifications are defined as follow, (4)

xmethanol ≥ 99.90 mol% 13 / 50



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xtoluene ≥ 99.80 mol%

(5)

xwater ≥ 99.99 mol%

(6)

xNMP ≥ 99.99 mol%

(7)

where xmethanol represents the purity of methanol at the distillate while the toluene and water at the decanter are denoted respectively as xtoluene and xwater. The expected methanol purity is 99.90 mol % while 99.80 mol % and 99.99 mol % are the desired toluene and water purities in first and second liquid phases of the decanter, and 99.99 mol % is the desired NMP purity of the bottom stream of the EWDC. Lower and upper bounds of feed locations (i.e., NF1 and NF2), total number of stages of MC and RC (i.e., NT1 and NT2) in the EWDC, reflux ratio R1, the vapour flowrate of sidestream VR, and withdraw stages of sidestream NVR are demonstrated in eqs 8–15. m in N F1 ≤ N F1 ≤ N Fm1ax

(8a,b)

m in m ax N F2 ≤ N F2 ≤ N F2

(9a,b)

N Tm1in ≤ N T 1 ≤ N Tm1ax

(10a,b)

N Tm2in ≤ N T 2 ≤ N Tm2ax

(11a,b)

m in N VR ≤ N V R ≤ N VmRax

(12a,b)

R1m in ≤ R1 ≤ R1m ax

(13a,b)

V Rmin ≤ V R ≤ V Rm ax

(14a,b)

F Em in ≤ F E ≤ F Em a x

(15a,b)

The lower and upper bounds in eqs 8–15 are determined through the Sensitivity Analysis Tool in Aspen Plus simulator. We complete the optimization model defined as CPOM consisting of eqs 1–15. 3.4 Economic Evaluation 14 / 50


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The optimization is carried out following the minimizing objective function OF. According to the result of minimum OF, TAC is then calculated with the purpose of comparison among five separation sequences of double-column extractive distillation, triple-column extractive distillation, optimal double-column extractive distillation, extractive dividing-wall columns, and extractive dividing-wall columns with heat integration.5,18,23 Following the suggestions of Luyben26 and Douglas24, the optimal design process can be evaluated by TAC, which is computed via the eq 16. TAC =

capital cost +operating cost payback period

(16)

For computing the capital cost, the Douglas’ cost formulas24 (see eqs S1–S8 in Supporting Information) are adopted in this work. Note that the capital cost involves cost of column shells, trays, and heat exchangers (i.e., reboiler, condensers, and coolers); the operating cost mainly includes utility cost (e.g., cost of steam, cooling water, and electricity used in reboilers and condensers), which is shown in Table S1 (see in Supporting Information). Besides, the capital cost of pipes, pumps, and valves is frequently neglected due to it is much smaller compared with the capital cost of columns and heat exchangers. Meanwhile, the operating cost of pump is also ignored because it is much smaller than that in the reboilers and condensers. Assuming the payback period is 3 years.24–25

3.5 Dynamic Control Disturbances in feed flowrates and compositions frequently occur during the real plant operation, resulting in the deviation of the operating conditions from their optimal conditions (e.g., temperature and heat duty). This may cause off-specification products and safety issues. To guarantee on-specification products and maintain safe operations, dynamic control strategies are required to be implemented for the proposed optimal HI-EDWC. Several parameters such as feed 15 / 50



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flowrates, liquid levels, temperature, pressure, and vapor split ratio have to be controlled at or close to their set points. However, it is not easy to control feed compositions remaining at or close to their set points because of their low response and expensive online compositions measurement devices. On the other hand, we notice that the variation of feed compositions leads to the change of liquid compositions on each tray which depend on tray temperature and it is also known that reboiler duty has a great effect on the tray temperatures. Therefore, in this work, the variation of tray temperatures in a column along with the changes in reboiler duty is thoroughly investigated through sensitivity analysis to determine which tray temperatures should be controlled.

4. Computational Results and Discussion 4.1 Thermodynamic Insights and Residue Curve Maps Table 2. Topologic and Thermodynamic Features of Methanol/Toluene/Water System pressure (atm)

components

singular points

temperature (℃)

composition

1 atm

methanol

saddle

64.53

-

toluene

saddle

110.68

-

water

saddle

100.02

-

NMP

stable node

203.99

-

azeotrope at

methanol–toluene

unstable node

63.76

xmethanol = 0.8843

1 atm

toluene–water

unstable node

67.15

xwater = 0.5771

Topologic features of methanol/toluene/water system are demonstrated in Table 2. The normal boiling points of four components methanol, toluene, water, and NMP are 64.53, 110.68, 100.02, and 203.99℃, respectively. Besides, there is a methanol/toluene azeotrope (xazeo,methanol = 88.43 mol %) and a toluene/water azeotrope (xazeo,water = 57.71 mol %). It can be seen from Table 2 that two azeotrope points are residue curve map unstable nodes (i.e., USrcmazeoAB and USrcmazeoBC) because it is a minimum-boiling point in the mixtures; likewise, pure methanol, toluene, and water (i.e., A, B, and C) behaves as residue curve map saddle nodes (i.e., SNrcm,A, SNrcm,B, and SNrcm,C); meanwhile, the entrainer NMP is a stable node (i.e., Srcm,E) of the residue curve map. 16 / 50


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Distillation Synthesis, as an implemented tool of Aspen Plus simulator, is used to study thermodynamic characters involving RCMs and LLE of ternary heterogeneous systems in this work. Figure 6a–b illustrates the RCMs for methanol/toluene/NMP and toluene/water/NMP ternary systems, respectively. The presence of the azeotrope generates a particular behavior in residue curves trajectory, and divides the composition triangle into two regions defined by a distillation seperatrix connected between the azeotrope and the stable mode.

Figure 6. (a) Residue curve maps of methanol/toluene/NMP system and (b) liquid–liquid equilibrium of the NMP /toluene/water system at 1.0 atm It can be clearly observed from Figure 6a–b that the RCMs are changed from USrcmazeoAB and USrcmazeoBC points to the Srcm,E point and there is no presence of distillation boundary in the RCMs. The univolatility curve αAB = 1 switches the volatility order because the entrainer interacts differently with original components and the αAB =1 curve intersects the binary side AE while the intersection point is called xP. Therefore, region ABE (see Figure 6a) is the feasible region because it satisfies the two conditions of the general feasibility criterion under infinite reflux: A is connected to E by a residue curve following decreasing temperature direction from E to A (condition 1) and A 17 / 50



is the most volatile one in this region (condition 2). Consequently, A (i.e., methanol) will be the distillate product and E (i.e., NMP) will be the bottom product and there is a minimum entrainer-to-feed ratio following the location of xP.

4.2 Simulation of the Proposed Extractive Dividing-Wall Column The design of double-column extractive distillation process for methanol/toluene/water mixtures using an entrainer NMP proposed by Wang et al.23 is taken as a base case for the comparison and as initial values for simulating the EWDC process. Consequently, NT1 = 52, NF1 = 26, NF2 = 4 and NT2 = 40 are selected for main and rectifying columns of the EWDC, respectively. Also, the sidestream is withdraw at stage 40 to be as close to the bottom of the main column (MC) in EWDC as possible due to there is no methanol component existing at stage 40 based on vapor composition profiles. Then, the flowrate of vapor sidestream (VR) is initially set as 90 kmol/h. Besides, both condenser pressures of MC and RC are set at 1.0 atm leading to corresponding refluxed drum temperatures reach 337.65 and 332.85 K, respectively. Therefore, the two temperatures are high enough to allow the use of cooling water in the overhead condensers. Finally, the initial value of the reflux ratio for the MC (R1) is set as 1.51 to keep same with traditional process of double-column extractive distillation with a decanter. Under the above key operational parameters specifications of the EWDC process, the simulated purities of methanol, toluene, and water are 99.87 mol %, 99.76 mol %, and 99.83 mol %, respectively. Simultaneously, 99.99 mol % NMP can be obtained at the bottom stream of the EDWC. However, the preliminary simulation are not capable to achieve our desired set points, 99.9 mol % for methanol, 99.8 mol % for toluene, and 99.99 mol % for water, respectively. Therefore, the optimal key operation parameters (e.g., feed locations, total number of stages, and reflux ratio) will be optimized in the next step to obtain the desired product purities. 18 / 50


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As shown in the left side of Figure S1 in the Supporting Information, while the total number of stages NT1 of MC in the EWDC is less than 45, the purities of methanol, toluene, and water are all not able to reach desired values. Too many numbers of NT1 will result in the increase in the capital cost of MC in the EWDC. Therefore, the NT1 is assumed lower than 55 stages. From the right subfigure of Figure S1, it can be seen that the effects of NT2 on changing purity of product are not sufficiently distinctive. Consequently, the lower and upper bonds of NT2 fluctuate up and down at the half of initial value 20 (see Table 3). Meanwhile, it can be clearly observed from the left side of Figure S2 that the purity of methanol, toluene, and water sharply increases while R1 is increased from 0.8 to 1.5, corresponding to the reboiler duty will be significant increased. Therefore, the preliminary reflux ratio of MC between 1.5 and 2.0 is determined in this step and the exact value can be further determined in the next step of optimization. Similar observations can be made for the total number stages of RC, feed locations, flowrates of solvent, withdraw location of sidestream, and the flowrates of vapor sidestream from Figures S3–S6 in the Supporting Information. Therefore, the appropriate lower and upper bonds are summarized in Table 3.

Table 3. Lower and Upper Bounds of Key Operating Variables Obtained From the Sensitively Analysis operating variables

lower bounds

upper bounds

unit

NT1

45

55

-

NT2

9

20

-

NF1

21

28

-

NF2

3

7

-

R1

1.5

2.0

-

NVR

40

50

-

VR

65

85

kmol/h

FE

70

80

kmol/h

Lower and upper bounds of the estimation (see Table 3) have been generated via the Sensitivity

Analysis Tool, while CPOM is carried out for each set values of discrete and continuous variables 19 / 50



Page 20 of 51

within bounds. Furthermore, more detailed optimization procedure is illustrated as follows, firstly, the three constraints of products purity can be achieved in Constraints function, then, the objective function OF is defined via Fortran function, and eventually the total number of stages, feed locations, reflux ratio, withdraw locations of sidestream, and the flowrate of vapor sidestream are all optimized through Optimization function implemented in Aspen Plus.

Table 4. Optimal Key Operational Parameters for the Proposed EDWC Process optimal design parameters

value

unit

number stages of MC in the EDWC, NT1

50

-

number stages of RC in the EDWC, NT2

10

-

feed locations of fresh feed, NF1

26

-

feed location of entrainer, NF2

4

-

flowrate of the vapor sidestream, VR

70

kmol/h

withdraw location of sidestream, NVR

45

-

flowrate of entrainer, FE

79.643

kmol/h

reflux ratio of EDWC, R1

1.625

-

heat duty of reboiler, QR1

3.09

MW

heat duty of condenser 1, QC1

1.25

MW

heat duty of condenser 2, QC2

0.91

MW

objective function, OF

72079.35

kJ/kmol

Eventually, the optimal design parameters can be obtained through the Aspen Plus simulator. In the optimization process, the CPOM is adopted and the calculation is performed on the desktop with Intel Core i7-6700HQ [email protected], 8 GB memory. Additionally, the calculation time of the whole optimization process is about 6 h. Table 4 shows the optimal related design parameters (e.g., OF and total number of stages) of the EWDC process.

4.3 Results of Optimization

4.3.1 The Optimized Double-Column Extractive Distillation with a Decanter. To make it clearer that energy-saving is caused by the improvement of the configuration or the optimization of the parameters in this work, parameters of the DCED-D process are optimized. The optimal results are 20 / 50


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shown as follows.

Figure 7. The optimal flowsheet based on DCED-D for separating heterogeneous multiazeotropes mixtures methanol/toluene/water Figure 7 illustrates the optimal double-column extractive distillation with a decanter (i.e., ODCED-D) for separating heterogeneous multiazeotropes mixtures methanol/toluene/water. It can be seen from Figure 7 that the optimal total number of stages of extractive distillation column C1 is 52 while the solvent and fresh feed are fed at 4th and 26th stage, respectively. Besides, the total number stages of solvent recovery column C2 is largely decrease to 15 from 40 in referenced case based on the proposed optimization approach in this work.

4.3.2 The Optimized DCED-D with Heat Integration

21 / 50



Figure 8. The optimized ODCED-D with heat integration for separating heterogeneous multiazeotropes mixtures methanol/toluene/water Figure 8 illustrates the optimized DCED-D process with heat integration (HI-ODCED-D) for the separation of methanol/toluene/water system. A feed-effluent-heat-exchanger (FEHE) is added to utilize the energy from B2 (477.14 K) to preheat fresh feed (293.15 K). This arrangement with a counter-current heat exchanger can reduce the outlet temperature of hot stream down to 327.7 K, on other hand, the feed with higher temperature (344.79 K) results in a 1.12 MW reduction in QR1. Therefore, the cooler1 in Figure 7 is replaced with a FEHE to achieve the further heat integration.

4.3.3 The Proposed Approach Based on Extractive Dividing-Wall Column Distillation

22 / 50


Page 22 of 51

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Figure 9. The proposed flowsheet based on EDWC for separating heterogeneous multiazeotropes mixtures methanol/toluene/water As shown in Figure 9, the proposed EWDC separation process is investigated to separate heterogeneous mixtures methanol/toluene/water mixtures with multiazeotropes. It can be clearly seen that the total number of stages for EWDC is 50 with the reflux drum as the first stage and the rectifying column, RC, has total 10 stages. Note that the stage 1 in the RC of the EWDC is equivalent to the stage 36 of the EWDC since the MC and the RC in the EWDC share 5 stripping stages. The fresh stream is added at stage 26 in the middle section of the EWDC. The solvent stream is fed at the stage 4, which is located at the overhead section of the EWDC. To obtain high purities 99.9 mol % for methanol, 99.8 mol % for toluene, and 99.99 mol % for water, the optimized reflux ratio for the EWDC is 1.625. Also, the obtained optimal vapor split ratio αV is 0.2728 indicating that divided wall is not located in the middle of the cross-section area of the 23 / 50



column. Simultaneously, the optimal flowrate of vapor sidestream, VR, is 70 kmol/h, the sidestream is withdraw at stage 45 (i.e., NVR = 45), and flowrate of entrainer is 79.638 kmol/h. Furthermore, a makeup stream of the entrainer is added to compensate the entrainer losses along with products of two distillates.

Figure 10. Residue curve maps of ternary mixtures (a) methanol/toluene/NMP and (b) toluene/water/NMP It can be obviously observed from Figure 10 that the fresh feed stream (F) is mixed with the recycled entrainer (FE) from the bottom stream of the EWDC to form a new feed stream (Mix) that is fed to the MC of EWDC. Therefore, a mixture stream, Mix, is separated into a sidestream stream (VR) and an overhead product stream (D1) based on the material balance lines of the MC in EWDC (see Figure 10a). Stream VR (also denoted as F2) is fed to RC for removing the entrainer NMP to obtain toluene and water mixtures in the distillate of the RC. As illustrated in Figure 10b, the F2 stream is separated to stream B2 (pure NMP) and stream D2 in the SC of EWDC. Eventually, D2 is split into OR stream (i.e., toluene) and AQ stream (i.e., water) by the decanter.

24 / 50


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Figure 11. Liquid composition and temperature profiles in (a, c) MC and (b, d) RC The liquid composition and temperature profiles of the proposed EWDC are illustrated in Figure 11a–d, respectively. It can be clearly observed from Figure 11a that the methanol composition (marked as black squares) increases drastically between stages 26 and 4 because the extraction process is taken place in this extractive distillation section to obtain pure methanol as a distillate. At stage 1, the methanol composition achieves its highest purity of 99.9 mol %. Meanwhile, the toluene and water compositions increase from stages 26 to 42, and then significantly decrease from stages 42 to 45 due to the flowrate of vapor sidestream is removed at stages 45 in the RC of the EWDC. A steep slope can be observed in the two liquid composition profiles (see Figure 11a–b), that's why there is no need so much total number of stages of MC and RC to achieve the desired product purities. 25 / 50



4.3.4 The Proposed Approach Based on EWDC with Heat Integration

Figure 12. The proposed EWDC with heat integration for separating heterogeneous multiazeotropes mixtures methanol/toluene/water Figure 12 illustrates the proposed EWDC with heat integration (HI-EWDC) process for separating heterogeneous mixtures methanol/toluene/water mixtures with multiazeotropes. A FEHE is added to utilize the energy from B1 (477.14 K) to preheat fresh feed (293.15 K). This arrangement with a counter-current heat exchanger can reduce the outlet temperature of hot stream down to 327.7 K, on other hand, the feed with higher temperature (368.65 K) results in a 2.417 MW reduction in QR1. Therefore, the cooler1 in Figure 9 is replaced with a FEHE to achieve the further heat integration. Besides, to obtain same purities 99.9 mol % for methanol, 99.8 mol % for toluene, and 99.99 mol % for water, the optimized reflux ratio for the HI-EWDC is 1.801.

4.4 Comparative Economic Evaluation Table 5. Comparison of the Optimal Design among TCED, DCED-D, ODCED-D, HI-ODCED-D, EWDC, and HI-EWDC Processes 26 / 50


Page 26 of 51

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C1/MC

sizes

parameter TCED

DCED-

ODCED-

HI-ODCED-

s

D

D

D

1.522

0.850

0.920

0.850

1.525

1.525

stages

46

52

52

52

50

50

QC (MW)

0.422

1.060

1.21

1.14

1.250

1.250

35.633

89.506

102.172

96.26

105.54

105.549

diameter

EWDC HI-EWD C

(m)

2

AC (m )

9 QR (MW)

2.273

1.770

1.87

1.12

3.090

2.417

AR(m2)

114.84

89.546

94.605

56.66

156.32

156.326

1 shell cost

6

0.484

0.322

0.351

0.289

0.521

0.521

0.3001

0.348

0.370

0.312

0.443

0.443

0.784

0.670

0.721

0.601

0.964

0.964

0.247

0.195

0.207

0.131

0.358

0.281

0.881

0.980

1.03

1.03

0.777

0.777

stages

46

40

15

15

10

10

QC (MW)

1.121

2.072

1.16

1.16

0.911

0.911

AC (m )

94.657

120.406

97.950

97.950

76.840

76.840

QR (MW)

3.421

2.380

1.43

1.43

-

-

173.02

174.790

72.547

72.547

-

-

0.270

0.280

0.126

0.126

0.061

0.061

0.447

0.480

0.336

0.336

0.157

0.157

0.717

0.760

0.462

0.462

0.219

0.219

0.374

0.265

0.159

0.159

0.004

0.004

0.805

-

-

-

-

-

6

(10 $) HX (106 $) total cCC 6

(10 $)

C2/RC

energ

b

EC (106

y

$/y)

sizes

diameter (m)

2

2

AR(m )

1 shell cost 6

(10 $) a

HX (106

$) total cCC 6

(10 $)

C3

energ

b

EC (106

y

$/y)

sizes

diameter (m)

27 / 50



Page 28 of 51

stages

30

-

-

-

-

-

QC (MW)

0.426

-

-

-

-

-

AC (m2)

35.971

-

-

-

-

-

QR (MW)

0.858

-

-

-

-

-

AR(m )

43.407

-

-

-

-

-

shell cost

0.169

-

-

-

-

-

0.205

-

-

-

-

-

0.374

-

-

-

-

-

0.094

-

-

-

-

-

-

0.001

0.001

0.001

0.001

0.001

-

0.695

0.865

0.865

0.865

0.71

-

58.685

66.876

66.876

66.876

59.952

-

0.132

0.152

0.152

0.152

0.134

-

0.037

0.047

0.047

0.047

-

4.520

0.103

0.101

0.101

0.101

0.101

381.66

8.697

8.528

8.528

8.528

8.528

0.446

0.038

0.038

0.038

0.038

0.038

0.244

0.014

0.014

0.014

0.014

0.014

2

6

(10 $) a

HX (106

$) total cCC 6

(10 $)

decante

energ

b

y

$/y)

c

shell cost

CC

EC (106

(106 $)

r coole2

sizes

Q1 (MW) 2

/FEHE

A1 (m ) c

total CC 6

(10 $)

cooler1

energ

b

EC (106

y

$/y)

sizes

Q2 (MW) 2

A2 (m )

7 total cCC 6

(10 $) energ

b

EC (106

y

$/y)

c

6

2.321

1.901

1.374

1.254

1.365

1.355

b

6

0.959

0.514

0.427

0.351

0.417

0.299

OF (10 kJ/kmol)

1.913

1.232

0.978

0.761

0.915

0.721

6

1.733

1.044

0.885

0.769

0.872

0.751

total CC (10 $) total EC (10 $) 5

TAC (10 $) a

b

c

HX represents total capital cost of heat transfer; EC stands energy cost; CC shows capital cost. Table 5 provides a head-to-head comparison of economic evaluations among the proposed

EWDC and HI-EWDC processes, the optimized ODCED-D process, and the existing TCED and DCED-D processes from Table 5, it can be observed that the TAC for the proposed HI-EWDC 28 / 50


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process is the lowest with 15.14% savings compared to the ODCED-D process. This significant reduction mainly comes from heat and mass transfer of the vapor and liquid streams occurring in a single shell, heat integration of recycled entrainer and fresh feed streams, and capital cost.

5. Overall Control Strategy Development Once the steady-state design and optimization of the heat integration-extractive dividing-wall column (HI-EWDC) process has been achieved, it is also essential to study the dynamic control to maintain the products purities when the feed disturbances are introduced in HI-EWDC. Before exporting the stable simulation to a dynamic simulation using Aspen Dynamics, liquid holdups in the column sumps and reflux drums of main column (MC) and rectifying column (RC) in the extractive dividing-wall column are sized by 5 min of liquid holdup when they reach a half-full, and the decanter is calculated to provide a 10 min holdup if reaches a half-full.23 Then, pressure drops of pumps and valves are set as 2 bar to achieve the pressure driven mode in dynamic control.6–7,25–26 The purities of methanol, toluene, and water are 99.867 mol %, 99.781 mol %, and 99.956 mol % in the Aspen Dynamics, respectively. The results in the dynamic state are in substantial agreement with that given in the steady state (see Figure 12) due to the pumps, compressor, valves, and tray pressure drops (0.7 kPa)7 are installed in the dynamic simulation. There are some basic control structures in different dynamic control schemes.16–18 These regulatory control structures are added firstly and the detailed descriptions are represented as below: (1) Feed flowrate is flow controlled (i.e., FC controller, reverse acting). (2) The total entrainer flowrate is in proportion to the fresh feed flowrate (i.e., S/F multiplier). (3) The pressure in each column is controlled by manipulating the heat removal rate in two condensers (i.e., PC1 and PC2 controllers) which are reverse acting. (4) The distillate of two columns (direct acting) is manipulated to control the reflux drum 29 / 50



Page 30 of 51

levels (i.e., LC1 and LC3 controllers). (5) The flowrate of organic phase and aqueous phase (direct acting) is manipulated to control the first and second liquid levels in the decanter (i.e., LC5 and LC6 controllers). (6) The cooler heat duty (reverse acting) is manipulated to hold the stream temperature (i.e., TC4 controller). (7) The base level in the MC is held by manipulating the makeup flowrate (i.e., LC2 controller, reverse acting). (8) The base level in the RC is held by manipulating the bottom flowrate (i.e., LC4 controller). (9) The flowrate of vapor sidestream is controlled using the FCV controller by manipulating valve opening (direct acting).17–18 (10) The total vapor flow rate is in proportion to the flowrate of vapor sidestream (i.e., VR/QR multiplier).17–18 In general, the feed flowrate could be controlled within a few seconds and 0.3 min is thus chosen as the integral time (τ1) of flow controllers.28 In the meantime, the gain (KC) of flow controller should be set as 0.5 to reduce the relative error of products.25 Following the suggestion of Luyben26, the parameters of two pressure controllers are set as KC = 12 and τ1 = 20 min, and the parameters of all liquid level controllers are set as ΚC = 2 and τ1 = 9999 min. Furthermore, dead time of 2 min is input in temperature controllers.25 Relay-feedback tests are run on the temperature controllers to obtain several ultimate gains (KU) and periods (PU), and Tyreus–Luyben tuning method (see eqs 17–18) in Aspen Dynamics is used in several temperature controllers to obtain the KC and τ1.25–26,28 K C = KU / 3.2

(17)

τ 1 = 2.2 PU

(18) 30 / 50


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5.1 The Determination of Tray Temperature Control Points

Figure 13. Open-loop sensitivity plots for ± 0.1% changes in (a) reboiler duty in the MC of EWDC and (b) reflux in the RC of EWDC Figure 13a–b illustrates stage temperature variations of the MC and RC when the reboiler heat duties (i.e., QR) and reflux flowrate (i.e., L2) deviate from their desired values by 1%. It can be clearly seen from Figure 13a that trays 22 and 45 of MC are selected for the temperature control because maximum temperature variations occur in these two stages. The closer distance from temperature control stage to the bottom stage the smaller delay will be achieved indicating the stage 45 is the best temperature manipulating point to control the heat duty of reboiler. Then, the temperature at stage 22 is controlled by manipulating the vapor split ratio αV. Similarly, it can be obviously observed from Figure 13b that the temperature of stage 5 is controlled by manipulating the reflux in the RC of EWDC.

Table 6. Gain and Integral Time for Tray Temperature Controllers Using Tyreus–Luyben Tuning Methods in Three Control Structures CS1, CS2, and CS3 controllers TC1 TC2

tuning parameters

CS1

CS2

CS3

KC

0.349

0.357

0.397

τ1 [min]

17.160

15.83

14.520

KC

0.040

0.040

0.040

τ1 [min]

9.240

9.240

9.240

31 / 50



TC3 TC4 TC5

Page 32 of 51

KC

0.469

0.506

0.252

τ1 [min]

22.440

21.720

18.480

KC

0.075

0.075

0.075

τ1 [min]

9.240

9.240

9.240

KC

-

-

0.321

τ1 [min]

-

-

65.480

Table 6 shows the tuning parameters of the temperature controllers while these parameters are tuned through Tyreus–Luyben method implemented in Aspen Dynamics simulator.25–26 In this work, two kinds of feed disturbances are introduced to evaluate the control performance of HI-EWDC separation process. Two disturbances are added at 2 h and the corresponding dynamic performances will be illustrated below. It is worth mention that heat duty is in GJ/h and temperature is in centigrade following the Aspen Dynamics notation.

5.2 Basic Control Structure with Fixed Reflux Ratio (CS1) Figure 14 shows the basic control structure CS1 with fixed reflux ratio (R1) in the HI-EWDC. Based on the result of sensitivity analysis (see Figure 13), the temperature on stage 45 in the MC of EWDC is selected to indirectly maintain methanol purity by manipulating reboiler heat input (i.e., TC1 controller). At the same time, the temperature on stage 22 in the MC of EDWC is controlled by manipulating vapor split ratio αV (i.e., TC2 controller). The temperature at the 5th stage of the RC is controlled by manipulating the reflux (i.e., TC3 controller). For the control structure CS1, the value of reflux ratio (R1) is calculated by eq 19. For the fixed solvent-to-feed ratio (S/F) controller, the first input signal is the mass flowrate of the fresh feed (FW), the output signal is the mass flowrate of the solvent (SW), and the set point of solvent-to-feed ratio can be calculated by eq 20, R1 =

RW 2519.39 kg / h = = 1.600 DW 1574.62 kg / h

(19)

32 / 50


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S/F =

SW 7858.94 kg / h = = 1.502 FW 5231.48 kg / h

(20)

RC

Figure 14. Basic control structure CS1 with fixed reflux ratio for the proposed HI-EWDC The dynamic performances of the basic structure CS1 is evaluated by introducing disturbances in feed flowrate and feed composition. Figures 15 and 16 demonstrate the dynamic responses of two disturbances being introduced at 2 h.

33 / 50



Figure 15. Dynamic performances of CS1 under feed flowrate disturbances From Figure 15, it can be observed that the red solid and blue dash lines represent the dynamic responses in the feed flowrate by –10% and +10%, respectively. It can be clearly seen that another new steady-state can be achieved after 4 h. At the new steady-state, the purities of methanol, toluene, and water are 99.8516 mol %, 99.7816 mol %, and 99.9554 mol %, corresponding to –10% feed flowrates in fresh feed while the purities are 99.8687 mol %, 99.7786 mol %, and 99.9472 mol %, corresponding to +10% feed flowrate in fresh feed. Therefore, under the ±10% feed flowrate disturbances in fresh feed, the purities of methanol, toluene, and water are controlled to approach their desired values. Likewise, with two disturbances in feed flowrate, three temperatures 34 / 50


Page 34 of 51

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are controlled to return their specified values (see Figure 15b, d, and f).

Figure 16. Dynamic performances of CS1 under feed composition disturbances Figure 16a–f presents the closed-loop dynamic simulation results when two disturbances are introduced in the feed composition at t = 2 h. The red solid lines and blue dash lines represent the performances response to the feed composition disturbance by –10% and +10% (i.e., 44.1 mol % methanol and 53.9 mol % methanol), respectively. It can be clearly seen that the purities of methanol, toluene, and water could be reach to their desired values in about 3 h. From Figure 16a, c, and e, it can be observed that the purities of methanol, toluene, and water are controlled close to their set points. Likewise, three temperature control points are maintained to return their specified 35 / 50



values by introducing the disturbances in fresh feed (see Figure 16b, d, and f). From Figures 15 and 16, it can be clearly observed that the stable regulatory control is achieved and the compositions of the three product streams are held very close to the desired values. However, the maximum deviation and offsets are extremely serious, and it takes a long time (around 4 h) to get back to a new stable state.

5.3 Improved Control Structure with Fixed Reflux-to-Feed Ratio (CS2) In order to reduce the issues of deviation and vibrating in the CS1, the control structure CS2 with fixed reflux-to-feed ratio (R/F) is investigated based on the control structure CS1. Compared with control strategy CS1, the fixed R1 is replaced by the fixed R/F.

RC

Figure 17. Control structure CS2 with fixed the reflux-to-feed ratio (R/F) for the proposed HI-EWDC Figure 17 illustrates the improved control structure CS2 with fixed R/F for the proposed 36 / 50


Page 36 of 51

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HI-EWDC process. For the control structure CS2, a multiplier is used to fix the ratio of fresh feed and reflux and it is calculated by eq 21, R/F =

RW 2519.39 kg / h = = 0.4816 FW 5231.48 kg / h

(21)

where RW represents the mass flowrates of the reflux and FW refers to the mass flowrate of the fresh feed.

Figure 18. Dynamic performances of CS2 under ± 10% feed flowrate disturbances Figure 18 gives the dynamic performances with ±10% disturbances in fresh feed flowrate. The purities of products methanol, toluene, and water are returned to their desired values at about 3.5 h

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when the feed flowrate disturbance is increased up to 110 kmol/h (i.e., blue dash lines) and is reduced down to 90 kmol/h (i.e., red solid lines). From Figure 18, it can be observed that the purities of methanol, toluene, and water are returned close to their set points by introducing the disturbances in fresh feed at t = 2 h.

Figure 19. Dynamic performances of CS2 under feed composition disturbances Dynamic performances of the control structure CS2 with fixed R/F by adding ±10% feed composition disturbances. From Figure 19, it could be clearly observed that the purities of methanol, toluene, and water could closely reach their desired values in about 3 h. At the new steady state, the purities of methanol, toluene, and water are 99.9089 mol %, 99.7798 mol %, and 99.9472 mol % 38 / 50


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corresponding to the 10% composition decrease in fresh feed, while they are 99.7916 mol %, 99.7797 mol %, and 99.9538 mol % corresponding to the 10% composition increase in fresh feed. Although the peak transient deviations and offsets in the product purities has been improved of CS2. It can be obviously seen from Figures 18 and 19 that the purities of products correspond closely to the specified values in around 3 h. Therefore, a more robust control strategy CS3 with fixed heat duty of reboiler-to-feed ratio (QR/F) and temperature/(S/F) cascade is investigated in the following section.

5.4 Improved Control Structure with QR/F and Temperature/(S/F) Cascade (CS3)

MC

RC

Figure 20. Improved control structure CS3 with fixed QR/F and temperature/(S/F) cascade for the proposed HI-EWDC Following the suggestions of Xia et al.18, Sun et al.17, and Luyben25, the fixed QR/F and temperature/(S/F) cascade strategy could be applied to increase the controllability and stability of 39 / 50



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the HI-EWDC process. Therefore, in this work, an improved CS3 with fixed QR/F and temperature/(S/F) cascade structure (see Figure 20) is presented based on the control structure CS2. The temperature on stage 4 is controlled by manipulating the S/F ratio with reverse action to overcome the unavoidable flow measurement errors and feed composition changes in the control structure CS2. Meanwhile, the value of QR/F multiplier is computed by eq 22. QR / F =

QR 11.353 GJ / h = = 0.00217 FW 5231.48 kg / h

(22)

Figure 21. Dynamic performances of CS3 under feed flowrate disturbances Figure 21 shows the dynamic responses of the products purities and temperature control points.

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While the blue dash lines show the responses to the increase of feed flowrate from 100 kmol/h up to 110 kmol/h, the red solid lines represent the both purities and temperature responses to decrease of the flowrate from 100 kmol/h to 90 kmol/h. From Figure 21a–b it can be clearly observed that the methanol purity and tray temperature in the MC of EWDC can return to a new steady state within 2 h by introducing the disturbances in fresh feed. The methanol purities of distillate in the EWDC are about 99.8527 mol % and 99.8680 mol % approaching to their set point of 99.8600 mol %. The temperatures of stages 22 are stayed at 88.4079 and 88.4066℃ approaching the set points of 88.4092℃ under positive and negative deviations. Similar observations can be made for water and toluene from parts c–d and e–f of Figure 21.

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Figure 22. Dynamic performances of CS3 under feed composition disturbances Figure 22a–f gives dynamic performances of the ±10% feed composition disturbances when the disturbances are introduced at 2 h. While the blue dash lines show the responses to decrease in methanol composition from 49.0 mol % to 44.1 mol %, the red solid lines represent the responses to the increase of the methanol composition from 49.0 mol % to 53.9 mol %. It can be obviously seen from Figure 22a–b that the purities of methanol and tray temperature in the MC of EWDC can get back to a new steady state about 2 h while the 53.9 mol % and 44.1 mol % methanol feed composition disturbances are introduced at t = 2 h. At the new steady state, the purities of methanol are 99.9088 mol % and 99.7921 mol %, which are close to its desired specified value of 99.8600 mol %. Meanwhile, the new temperatures of stages 45 are 88.4128 and 88.4123℃, which are almost the same as the set points of 88.4092℃. Similar observations can be made for water and toluene from parts c–d and e–f of Figure 22.

Table 7. The Peak Transient Deviations and Offsets for ±10% Disturbances both in Feed Flowrate and Composition Changes in Fresh Feed products

offsets (%)

peak transient deviations (%)

CS1

CS2

CS3

CS1

CS2

CS3

+10% feed flowrate

methanol

0.0088

0.0098

0.0081

-0.0682

-0.0213

-0.0221

disturbance

toluene

-0.0013

-0.0017

-0.0020

0.0549

0.0305

0.0091

water

-0.0042

-0.0044

-0.0043

0.2738

0.0431

0.0149

methanol

-0.0083

-0.0083

-0.0072

0.0534

0.0254

0.0255

–10% feed flowrate

toluene

0.0017

0.0011

0.0017

-0.0154

-0.0118

-0.0072

disturbance

water

0.0040

0.0023

0.0040

-0.086

-0.0079

-0.0040

methanol

-0.3803

-0.0683

-0.0678

0.3814

0.0687

0.0687

+10% composition

toluene

0.0001

-0.0002

-0.0004

-0.0117

-0.0045

-0.0037

disturbance

water

0.0105

0.0024

0.0034

-0.0105

-0.0031

-0.0034

methanol

0.0785

0.0490

0.0489

-0.0798

-0.0490

-0.0489

–10% composition

toluene

-0.0043

-0.0001

-0.0010

0.0085

0.0047

0.0037

disturbance

water

-0.0380

-0.0042

-0.0039

0.0461

0.0042

0.0039

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The peak transient deviations and offsets of 10% disturbances are illustrated in Table 7 to compare the greatest peak deviations and offsets of three control structures. It can be clearly observed from Table 7 that the improved control structure CS3 with fixed QR/F and temperature/(S/F) cascade can provide a much better dynamic control for the proposed HI-EWDC process while the disturbances of both feed flowrate and composition are introduced in the fresh feed. Furthermore, additional ±20% feed flowrate and composition disturbances are introduced in CS3 to evaluate the stability and controllability of the proposed HI-EDWC sequence (see Figures S8 and S9 in Supporting Information). The computational results illustrate that this sequence can handle as well as that of ±10% feed flowrate and composition disturbance in the real situation when the feed composition of waste water varies too much.

6. Conclusion In this work, an energy-efficient HI-EWDC is proposed for the separation of heterogeneous multiazeotropes methanol/toluene/water system, and we built a systematic approaches involves the study on residue curve maps and univolatility curve to find suitable separation constraints, complete optimization based on a proposed CPOM, and a global control strategy to better maintain the desired product purities with a faster response. Four processes, ODCED-D, HI-ODCE-D, EDWC, and HI-EDWC have been optimized via the proposed CPOM using OF as objective function. The computational results show that the TAC of HI-EWDC is reduced by 15.14% compared with that of the ODCED-D process. One basic and two improved control strategies for the HI-EDWC featuring five temperature controllers, adjustable vapor split ratio αV, and temperature/(S/F) cascade are presented. It proved that the purity specifications of methanol, toluene, and water are impossible to achieved by the conventional basic control structure CS1 in face of ± 10% feed flowrate and composition disturbances. The dynamic 43 / 50



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results in Aspen Dynamics illustrated that the proposed CS3 with fixed QR/F and temperature/(S/F) cascade control structure is able to provide a better dynamic control for the proposed HI-EWDC process. Furthermore, additional ±20% feed flowrate and composition disturbances are introduced in CS3 to evaluate the stability and controllability of the proposed HI-EDWC sequence. The computational results illustrate that this sequence can handle as well as that of ±10% feed flowrate and composition disturbances in the real situation when the feed composition of waste water varies too much. It is worth mentioned that the proposed systematic approach could be widely applied for separating other complex heterogeneous multiazeotropes mixtures to recover the valuable resources and pursue sustainable development. Moreover, the proposed effective control strategy is able to provide a theoretical guideline for the control of EWDC, HI-EWDC, DWCs, and basic distillation columns.

Supporting Information The Supporting Information is available free of charge via the Internet at http://pubs.acs.org/.

Acknowledgment We acknowledge the financial support provided by the National Natural Science Foundation of China (No. 21606026); the Natural Science Foundation of Chongqing, China (No. CSTC2016JCYJA0474);

the

National

Key

Research

and

Development

Program

(No.

2017YFB0603105); the Fundamental Research Funds for the Central Universities (No. 106112017CDJQJ228809); and Hundred Talents Program at Chongqing University. Helpful comments and suggestions from the paper’s reviewers are also gratefully acknowledged.

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Nomenclature FEHE = feed-effluent-heat-exchanger ED = extractive distillation DWCs = dividing-wall columns EDWC = extractive dividing-wall column HI-EDWC = heat integration-extractive dividing-wall column OF = objective function [kJ/kmol] CPOM = complete the optimization method TAC = total annualized cost [106 $/y] DEG = diethylene glycol NMP = n-methyl-2-pyrrolidone DCED-D = double-column extractive distillation with a decanter TCED = triple-column extractive distillation ODCED-D = optimal double-column extractive distillation with a decanter HI-ODCED-D = heat integration-ODCED-D MC = main column of the EDWC RC = rectifying column of the EDWC TC = temperature control HX = total capital cost of heat transfer [106 $/y] EC = energy cost [106 $/y] CC = capital cost [106 $/y] R/F = reflux-to-feed ratio QR/F = reboiler duty-to-feed ratio [GJ/kmol] F = fresh feed stream D = distillate stream VR = vapour flowrate of sidestream NF1 = fresh feed locations NF2 = feed location of entrainer NT1 = total number stages of MC in the EDWC NT2 = total number stages of RC in the EDWC NVR = withdraw location of sidestream FE = flowrate of entrainer [kmol/h] R1 = reflux ratio of EDWC QR1 = heat duty of reboiler [MW] QC1 = heat duty of condenser 1 [MW] QC2 = heat duty of condenser 2 [MW] 48 / 50


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KC = gains τ1 = integrate time [min] OR = organic phase AQ = aqueous phase FW = mass flowrate of the fresh feed [kg/h] SW = mass flowrate of the solvent [kg/h] AC = area of the condenser [m2] AR = area of the reboiler [m2] RCMs = residue curve maps

49 / 50



Table of Contents (TOC)

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Residue curve maps of ternary mixtures (a) methanol/toluene/NMP and (b) toluene/water/NMP 87x43mm (300 x 300 DPI)


Energy-Saving Optimal Design and Effective Control of Heat

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