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Improved design and optimization for separating azeotropes with heavy component as distillate through energy-saving extractive distillation by varying pressure Xinqiang YOU, Jinglian GU, Changjun Peng, Weifeng Shen, and Honglai Liu Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b00687 • Publication Date (Web): 21 Jul 2017 Downloaded from http://pubs.acs.org on July 23, 2017

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Industrial & Engineering Chemistry Research

Improved design and optimization for separating azeotropes with heavy component as distillate through energy-saving extractive distillation by varying pressure Xinqiang YOUa, Jinglian GUb,Changjun PENGa, Weifeng Shenb*, Honglai LIUa* a

State Key laboratory of Chemical Engineering, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China

b

School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, China

Abstract: With the aim of saving energy and capital cost, in this work, we proposed a novel extractive distillation strategy by varying pressure for the separation of pressure-sensitive azeotropic mixtures. Proceeding from the thermodynamic insight of ternary systems in extractive distillations, the considerable energy-saving potential by changing operating pressures is observed. The separation of the acetone – methanol with a minimum boiling azeotrope using a heavy entrainer chlorobenzene is chosen as an illustrative case, and it belongs to a scarce classification 1.0-1a-m2 where the component with higher boiling point is withdrawn as a product. Through the analysis of ternary residue curve map and isovolatility curves, it could be observed that the minimal amounts of entrainer feed and extractive feasible regions in ternary diagram are sensitive to pressures. A 3 atm pressure is preliminarily selected for the comparison with atmospheric one. And the optimal pressure is found through sensitivity analysis. The results showed that 33.9% and 30.1% reductions in energy consumption and TAC are achieved compared with that operated at atmosphere, respectively. The discussion is then carried out by analyzing residue curve map, relative volatility profile, and extractive efficiency indicators. Finally, the heat integration was conceded for further saving the cost. The methodology proposed in this work may provide some new theoretical guidance to design and optimize azeotrope separations through extractive distillation. Keywords: Extractive distillation; Process optimization; Energy saving; Thermodynamic insight; heat integration

*

E-mail addresses: [email protected](W.F. Shen), [email protected] (H.L. Liu)

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1 INTRODUCTION The close boiling or azeotropic mixtures are frequently encountered in chemical engineering processes. For their separations, advanced techniques such as pressure-swing distillation (PSD) and extractive distillation (ED) are required because it is hard or even impossible to separate close boiling or azeotropic mixtures via conventional distillation. PSD could be employed to separate pressure-sensitive minimum-boiling or maximum-boiling azeotropic mixtures without adding a third component entrainer.1,2 Thus there is no entrainer impurity in the product stream in addition to the energy-saving potential of heat integration. Recently, Li et al.3 extended the PSD process to the separation of pressure-insensitive phenol-cyclohexanone binary azeotropes by adding acetophenone as entrainer. Wang et al.4 studied the design and control of PSD process for separation of binary n-heptane and isobutanol mixture, which presents minimum-boiling and maximum-boiling azeotropes at different pressures. And they found that the conventional PSD is more economical but less controllable than the improved PSD process. ED is another promising alternative for separating azeotropic mixture. In ED process, the entrainer is fed at a different location than that of the main mixture, producing an additional extractive section between the usual stripping and/or the rectifying sections.5 In the extractive section, the entrainer interacts differently with the original azeotropic components, giving rise to an increase in the relative volatility of the original components.6 ED process is widely-used due to the low energy consumption and flexible selection of possible entrainers.7 The production of anhydrous ethanol by ED process is the most common case and ED process exhibits greater energy saving capability than that of PSD process.8 Correspondingly, the comparison between PSD and ED processes for the separation of pressure-sensitive azeotropic mixtures is frequently studied. Luyben9 investigated PSD and ED processes for acetone−methanol separation with water as entrainer, and found that the ED had a 15% total annual cost (TAC) saving compared with that through PSD, the similar conclusion is also obtained for acetone-chloroform system. Modla and Lang studied ED and PSD for acetone-methanol mixture by genetic algorithm and exhibited the advantage of ED process.10 On the contrary, Muñoz et al.11 studied the separation of isobutyl alcohol and isobutyl acetate mixture using both ED and PSD processes, and reported that PSD showed a 25% TAC reduction compared with that of ED. Lladosa et al.

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studied the separation system of di-n-propyl ether and n-propyl

2


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alcohol using entrainer 2-methoxyethanol, and the 29% TAC reduction in PSD process was observed compared with ED process. Therefore, it could be concluded that the advantages of ED and PSD processes are strongly depend on the effectiveness of entrainer or the pressure sensitivity to the azeotropic composition. In order to further improve energy efficiency, heat integration was implemented in ED and PSD processes by researchers. Knapp and Doherty 13 studied thermal integration of the ED process for acetone-methanol-water system, and found that the energy cost decreased but no big decrease in TAC. Similarly, Palacios-Bereche et al.

14

investigated the ethanol dehydration ED process with

heat integration and the results showed that there is no significant difference in TAC compared with the conventional process. Kravanja et al.

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also studied the heat integration of bio-ethanol process

and showed an improved design by modifying pressures and heat loads. They obtained a 15% reduction of the utility compared with that of base case. Unfortunately, the TAC of the related processes was not mentioned. On the other side, You et al.

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proposed a novel optimal partial heat

integration ED process for acetone-methanol with water system and found that the novel process contributed a 32.2% reduction in the energy cost and 24.4% decrease in the TAC compared with those of conventional ED process, respectively. Oppositely, Luo et al.

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studied the separation of

Diisopropyl ether and isopropyl alcohol mixture, and observed that the TAC of heat-integrated ED process increased rather than decreased compared with that of conventional ED process. They also revealed that fully heat-integrated PSD process gave a 5.75% reduction in the TAC and 7.97% savings in energy consumption as compared with that of ED process. Most noticeably, the heat integrated ED process mentioned above were heuristically implemented through only adjusting the operating pressure of regeneration columns for achieving temperature driving force between condenser in regeneration column and reboiler in extractive column. Therefore, the heat integrated ED process strongly relies upon the well-design level of the conventional process. Furthermore, to the best of our knowledge, there are few studies focusing on the design of class 1.0-1a-m2 5: minimum azeotropic mixture separation with the higher boiling point original component as product. In this study, we attempt to design an energy-efficient extractive distillation with varying pressure (EDVP) for the separation of pressure-sensitive azeotropic mixtures based on the thermodynamic insight of the ternary system in ED processes. The EDVP process could take 3



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advantages of both ED and PSD processes. The proposed systematic strategy for the design of an EDVP process in this work is preceded by three steps. Step 1 mainly involves the thermodynamic insight for investigating the energy-saving potential, and then the potential benefits of increasing the operating pressure for a homogeneous extractive distillation are illustrated through the ternary map with isovolatility lines. The second step is focused on the optimization of EDVP processes to demonstrate the profits compared with those of conventional ones. Notice that the optimization is done in open loop flowsheet and then rechecked in close loop flowsheet. In the third step, the explanations of the numerical results are carried out through the analysis of ternary composition profile maps, relative volatilities, and efficiency indicators, respectively. Finally, the condenser/reboiler kind of heat integration is considered for further saving the energy cost. The studied case that we chose is acetone-methanol with chlorobenzene as entrainer where methanol with a higher boiling point is withdrawn as distillate (class 1.0-1a-m2). In addition, during the second step, conventional strategies optimize two columns one after another but with a drawback of overlooking the interactions among two columns. In order to overcome this drawback, the two columns are optimized simultaneously with the aid of a new defined objective function represented by total energy consumption per product unit, which considers distillates, reboiler duty, and condenser duty of both columns. The total numbers of two columns stages are kept the same as that in base case for a fair comparison. Other 8 operational variables entrainer flow rate FE, reflux ratios R1 and R2, distillates D1 and D2, feed locations NFE, NFAB, and NFReg are optimized, as the notations indicated in Figure 1.

Cooler Makeup entrainer

Entrainer flowrate FE

P1=3 atm QC1

NFE

D1 R1

P2=1 atm QC2

NFReg

D2 R2

NFAB Feed 540 kmol/h 320 K acetone:methanol = 1:1 Nreg QR2

Next QR1

4


W2

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Figure 1. Flowsheet of the extractive distillation with the notation of variables.

2 PROCESS FEASIBILITY AND OBJECTIVE FUNCTIONS 2.1 General Feasibility Criterion of Extractive Distillation The general feasibility criterion for ED process under an infinite reflux ratio was reported by Rodriguez-Donis et al.,

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that is “homogeneous ED process of an A−B mixture with entrainer E

feeding is feasible if there exists a residue curve connecting E to A (B) following a decreasing or increasing temperature direction inside the region (condition 1) where A (B) is the most volatile (direct sequence) or the heaviest (indirect sequence) component of the mixture (condition 2)”. In other words, according to the general feasibility criterion, the ternary residue curve map along with the univolatility line determines the volatility order in possible regions. Accordingly, the following important information for a concept design could be concluded: Which component will be firstly withdrawn as the product? Will the column configuration be decided by a direct or an indirect sequence? Are there limitations for the operating parameters or not? 2.2 Thermodynamic Feasibility Analysis of Class 1.0-1a The features of the separation of a minimum boiling azeotropic mixture with a heavy entrainer (class 1.0-1a) in ED process involves the volatility order of relative regions are shown in Figure 2. (a)

B [Srcm]

(b)

residue curves

B [Srcm]

XYZ Volatility order (X possible distillate) αAB = 1

Tmin azeoAB [UNrcm]

ABE

Xp αAB = 1

XDB If P↓, then Situation 1 If P↓, then Situation 3 Tmin azeoAB [UNrcm] At 1 atm

ABE BAE E [SNrcm]

[SNextr,A] range

[SNextr,B] XDA range

A [Srcm]

Xp

E [SNrcm]

Distillate at R∞ in ABE: xDA if FE/V > FE/Vmin

If P↓, then Situation 2

BAE

A [Srcm]

Distillate at R∞ in BAE: xDB if FE/V > FE/Vmin

Figure 2. Thermodynamic features of 1.0-1a mixtures, separation of a minimum-boiling azeotrope with a heavy entrainer. (a) A is the distillate, (b) B is the distillate.

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The Serafimov’s class 1.0-1a occurrence amounts to 21.6% in nature. 19 When using the heavy entrainer E, which is a stable node of residue curve map (RCM), the original component A and B are both RCM saddles, the unstable node minimum boiling azeotrope Tmin

azeoAB

is formed

correspondingly. The univolatility curve αBA =1 switches the volatility order since the entrainer interacts differently with original components. Two sub-cases arise: class 1.0-1a-m1 (univolatility line reaches AE side) and class 1.0-1a-m2 (univolatility line reaches BE side). For class 1.0-1a-m2 (see Figure 2b) highlighted in this study, the αBA =1 curve intersects the binary side BE while the intersection point is called xP. Therefore, region BAE is the feasible region because it satisfies the two conditions of the general feasibility criterion under infinite reflux: B is connected to E by a residue curve following decreasing temperature direction from E to B (condition 1) and B is the most volatile one in this region (condition 2). Consequently, B will be the distillate product, the process will be a direct sequence and there is minimum entrainer-to-feed ratio following the location of xP. The entrainer flow over the vapor flow from the reboiler, FE/V, is the crucial parameter of the extractive composition profile for batch distillation. Below (FE/V)min, the extractive stable node SNext,B, lies on the univolatility line and the separation become unfeasible. Above (FE/V)min, SNext,B leaves the univolatility curve to come near the [xP; E] segment.20 Then the extractive profile could cross a rectifying profile and reaches the vicinity of the product. The ratio of the minimum entrainer flow rate FE over the vapor flow rate V produced in the reboiler in batch extractive distillation could be calculated by Eq. 1 and the ratio the minimum entrainer flow rate FE over the azeotropic mixture flow rate FAB in continuous extractive distillation could be further computed by Eq.2.21-22

( x − y *P )  FE  = P    V min, R∞ ( xE − xP )

(1)

 FE   FE   D    = ( R + 1) ⋅   ⋅    V   FAB   FAB 

(2)

Where y*P is the entrainer amount in the vapor phase in equilibrium with xP. The xE refers to entrainer compositions. Moreover, rectifying profiles approximately follow a residue curve near EB side. There are no need too many trays in this rectifying section, otherwise the rectifying section profile will turn off at the vertex B (saddle node) following the residue curve, and resulting in sub-quality product with A 6


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impurity.23 Besides, high recycling entrainer purity is needed for dragging SNext close enough to EB side where SNext could intersect a residue curve to achieve high purity product B. If the recycling entrainer purity is not that high, SNext can only intersect a residue curve apart from EB side which could not achieve the product with a high purity since the residue curves are not intersecting each other. On the other hand, region ABE in Figure 2b is an unfeasible region since there is no residue curve connecting E to A even though A is the most volatile one. For class 1.0-1a-m1 as shown in Figure 1a, region ABE satisfies the general criterion and A will be the distillate in a direct sequence with minimum entrainer flow rate as constraint.

This

replies the 3 questions we proposed in section 2.1. Region BAE fails to satisfy the general feasibility criterion and it is an unfeasible region. For pressure sensitive azeotropic mixture, the composition of azeotropic point Tazeo,AB and xp would move along with the change of operating pressures. Therefore, the moving trend of the two points indicates the energy saving potential of separating processes. Based on thermodynamic features, there are three situations when the operating pressure is reduced (see Figure 2b). Situation 1, both Tazeo,AB and xp approach to the desired product vertex. In this situation, the EDVP process is recommended since it would perform better than either ED process or PSD process. The example of situation 1 is the system of acetone-methanol with water. Situation 2, both Tazeo,AB and xp move away from the desired product vertex. In such situation, the operating pressure should be increased in order to reach the desired situation 1. The studied case in this work belongs to situation 2 and situation 2 is the main focuses of this work. Situation 3, one of the two point moves toward the desired product vertex and the other one deviates from the desired product vertex. For situation 1, the decrease of the pressure in the extractive column could lead to significant reduction of energy cost and TAC.24 For situation 2, the increase of the pressure in the extractive column may produce saving potentials of energy and TAC. Following the proposed methodology in this work, firstly the effects of pressure on isovolatility lines are shown in Figure 3. Then a preliminary optimization with increasing pressure (3 atm) is done to verify the energy-saving ability of the EDVP process and the results are explained via ternary map and relative volatility. Finally, the optimal pressure of the extractive column is determined and the heat integration is considered.

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For situation 3, the energy saving potentials of ED and pressure change counteracts each other, and a further detail investigation is needed for comparing ED, PSD and EDVP processes. For a given separating system, it belongs to one of the three situations and the EDVP will perform no worse than either single ED or single PSD process from the view of energy consumption and TAC. The studied case of acetone-methanol separation with heavy entrainer chlorobenzene in this work belongs to the situation 2 as shown in Figure 3 in section 3.1. 2.3 Objective Functions As we all know, there are two columns in ED processes and they affect each other through the non-product stream of extractive column and the recycling entrainer stream. The strategy of optimizing the two columns in sequence disregards their mutual effects. Therefore, we had proposed an objective function OF

24

for simultaneously taking both extractive columns and

regeneration columns into account. The following modified OF is employed in this work: min OF =

M ⋅ QR1 + m ⋅ QC1 + M ⋅ QR2 + m ⋅ QC2 D1 + k ⋅ D2

subject to : xmethanol,D1 ≥ 0.995 xmethanol,W 1 ≤ 0.001

(3)

xacetone,D 2 ≥ 0.995 xchlorobenzene,W 2 ≥ 0.9999

Where m = 0.036 represents energy price difference factor between condenser and reboiler, k=5.9 is the price differences of two distillates. In order to account steams at different pressures, a factor M is added: it may equal to 1, 1.065 or 1.280 while low, middle or high pressure steams are used, respectively. The utility cost is shown in Table S1 (in the Supporting Information). OF refers the energy consumption per unit product output flowrate (kJ/kmol) and it is sensitive to the operational variables FE/FF, R1, R2, D1, D2 and so on. The meanings of other variables have been indicated in Figure1. The optimization is carried out following the minimizing energy cost (OF). According to this optimization result, TAC is then calculated with the purpose of comparison among different separation sequences. TAC involves capital cost per year and operating costs, and it is computed from the following formula: TAC =

capital cost +operating cost payback period

(4) 8


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For computing the capital cost, Douglas’ cost formulas with CEPCI inflation index (see Capital cost formulas in the Supporting Information) were employed.

25, 26

The column shell, tray

and heat exchanger cost constitute the capital cost and their formulas are shown in the Capital cost formulas (in the Supporting Information). The effect of pressure on the capital cost is shown in Table S2 in Supporting Information. The CEPCI in 2013

26

equals to 567.3, and a three-year

payback period is taken for calculating the capital cost in TAC. The operating cost involves the energy cost in reboilers and condensers. The heat exchanger for cooling recycling entrainer is taken into account in order to emphasize the process effect. Other costs such as liquid delivery pumps, pipes, and valves are neglected.

3 RESULTS AND DISCUSSIONS 3.1 Effect of Pressure on the Isovolatility Lines Figure 3 displays the residue curve map (RCM) of ternary system acetone(A)–methanol(B) with chlorobenzene at 1 atm, along with isovolatility curves αBA = 1, 2 and 3 at 1 atm and 3 atm, respectively. For acetone-methanol-chlorobenzene system, αBA =1 intersection xP with the B-E edge, it therefore belongs to class 1.0-1a-m2 and the minimum entrainer content for breaking the azeotropic xp,E equals to 0.45 at 1 atm, while xp,E reduces dramatically up to 0.29 at 3 atm. Besides, the isovolatility curves in Figure 3 indicate that the methanol – acetone relative volatility αBA increases more quickly at higher pressures as the amount of entrainer increases. Therefore, the entrainer would contribute a higher αBA at a higher operating pressure under the condition of same amount of entrainer, the separation therefore becomes more facilitated and the energy cost will be cut more. Since both Tazeo,AB and xp in Figure 3 move towards to the desired product vertex at a higher pressure, the situation 2 mentioned in section 2.2 is satisfied. Therefore, the design of EDVP with P1 at 3 atm in the extractive column will be done afterward for preliminarily illustrating the energy saving potential.

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Methanol (B) (64.5°C) [Srcm]

Volatility order XYZ (X possible distillate)

1 0

xP = [SNext] @1atm for FE,min 0.8

αBA=2 @3atm

αBA=3 @3atm αBA=3 @1atm 0

0.2

αBA=1 @3atm 0.4

0.6

αBA=1 @1atm Tmin azeoAB 0.6 [UNrcm ] (55.2°C) @1atm

0.4

0.8

0.2

1

0.8

0.6

Chlorobenzene (E) (132.0°C) [SNrcm]

0.4

0.2

Residue curves Univolatility curves

αBA=2 @1atm 1 0

Acetone (A) (56.1°C) [Srcm]

Figure 3. Effects of different pressures on isovolatility curves for system acetone-methanol-chlorobenzene.

3.2 Process Optimization The design of ED process for acetone-methanol using entrainer chlorobenzene in the work of Luyben and Chien 27 is taken as a base case for the comparison and as initial values for our design. The UNIQUAC model is chosen for describing vapor-liquid equilibrium and the vapor phase is assumed as ideal gas. The Aspen plus V7.3 built-in binary parameters (see Table S2 in the Supporting Information) are employed and the Radfrac module with rigorous MESH equations is selected. The total numbers of trays of the extractive column (Next = 45) and of the entrainer regeneration column (Nreg = 18) are kept the same as that of base case.27 The same product purity constraints (0.995 molar fractions) for both acetone and methanol are specified. The equimolar feed (FAB=540 kmol/h) and the entrainer feed are set at 320K. The two step optimization procedure24 is employed for optimizing FE, R1, R2, D1, D2, NFE, NFAB, and NFReg.

3.2.1

Optimization of FE, R1, and R2

Table 1 displays the optimized FE, R1, and R2 while keeping the other variables the same as base case: Next=45, Nreg=18, NFE=17, NFAB=33, NFReg=10, D1=217.2, D2=271.2. Design 1 is the design with both P1 and P2 are 1 atm. 27 Design 2 represents our design with P1 at 3 atm and P2 at 1 atm. Notice that the tray number is counted from top to bottom of the column, and the condenser and reboiler are regarded as the first and last stage. Table 1.

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Optimized values of FE, R1, and R2 for the extractive distillation of acetone – methanol with chlorobenzene under reduced pressure variables

Design 1

Design 2

P1 atm

1

3

P2 atm

1

1

FAB kmol/h

540.0

540.0

FE kmol/h

1902.4

1028.2

R1

1.550

1.650

R2

3.520

2.852

OF kJ/kmol

50165.9

39344.0

From Table 1, it could be observed that increasing the pressure P1 up to 3 atm (design 2) enables a 21.6% reduction of the energy consumption represented by the OF decreases. The results verified the conclusions obtained from the analysis insight of the isovolatility curves at different pressures. Besides, the interaction of the two columns represented by the interrelation between FE, R1 and R2 could also be observed. Due to the increase of the operating pressure, the FE is dramatically reduced by 45.9%, and subsequently R1 increases to achieve the same product purity specification. Meanwhile the concentration of entrainer fed to the regeneration column decreases due to mass balance, and less energy is needed to recycle the entrainer, leading to a decrease of R2 following the target of minimizing OF value. This highlights the importance of simultaneously optimizing the regeneration column together with the extractive column.

3.2.2

Determination of distillates D1 and D2

It is generally known that the effects of D1 and D2 on the product purity are strongly non-linear and could be revealed by their interrelationships.28 Herein, D1 and D2 are varied with a discrete step of 0.1 kmol/h from 270 kmol/h to 271.3 kmol/h, corresponding to a nearly 100% recovery. A sensitivity analysis of D1 and D2 is implemented and the sequence quadratic program (SQP) optimization for FE, R1 and R2 are done based on the initial design from base case: Next=45, Nreg=18, NFE=17, NFAB=33, NFReg=10. The optimal values of D1 and D2 are found following the minimizing OF value. The effect of distillates D1 and D2 on the energy cost are illustrated in Figure 4.

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Figure 4. Effect of distillates D1 and D2 on the energy cost

Three important conclusions could be drawn from Figure 4. Frist of all, both D1 and D2 exhibit strong effects on the energy cost OF. The ability for optimizing the two columns simultaneously is therefore quantitatively proved better. Most importantly, the better OF value is found at D1=271.0 and D2=270.8 kmol/h, instead of D1=271.2 and D2=271.2 kmol/h. Besides, for a given D2 value, OF decreases until D1 reaches 271.0 kmol/h and then increases. It shows that there is a better D1 value for contributing lower OF and the effect of D1 on energy cost are nonlinear. At last, for a given D1 value, OF firstly climbs down quickly, then reduces smoothly, and finally increases dramatically following the increase of D2. Increasing D2 causes two aspects: first, OF declines since D2 is a part of the denominator in OF formula, second, QR2 increases as more distillate is obtained and consequently increasing OF because QR2 is at the numerator of OF formula. Therefore, when D2 increases from 270 to 270.1 kmol/h at D1=271.0 kmol/h, first aspect dominates the process and contributes a reduction of OF, then the two aspects counteract each other until D2 equals to 270.8 kmol/h; after that the second aspect governs the process and results in an increase of OF. It verifies again that it is better to optimize the two columns together. As we will optimize other variables such as NFE, NFAB, NFReg in the subsequent steps, we select the pair of D1=271.0 and D2=270.8 kmol/h following Figure 4. The related OF value is 37021.3 kJ/kmol with FE=900.0 kmol/h, R1=1.950 and R2=2.225, and this design is defined as design 3. It gives a further 5.9% reduction compared with design 2 in the previous step.

3.2.3

Optimization of feed locations

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The variables NFE, NFAB and NFReg are optimized with D1=271.0 and D2=270.8 kmol/h at the process design step. All the possible value ranges [5; 35] for NFE, [>NFE; 44] for NFAB, [3; 17] for NFReg are investigated through sensitivity analysis following the target of minimizing OF in order to avoid local minimum. 3 continuous variables FE, R1, R2 are optimized for each set of 3 feed locations while D1 and D2 are fixed at their optimal values. As shown in You et al.28 there is a strong incentive to shift the feed locations for reducing the process energy cost and for improving the process efficiency indicators. Table 2 shows some optimal results with three feed locations. Table 2. Some optimal results of FE, R1, R2, NFE, NFAB, NFReg under fixed D1 and D2 for the extractive distillation of acetone – methanol using entrainer chlorobenzene Design

NFE

NFAB

NFReg

P1

FE

R1

P2

R2

OF kJ/kmol

3

17

33

10

3

901.3

1.950

1

2.225

37021.3

4

16

33

10

3

890.0

2.210

1

2.175

37107.1

5

17

32

10

3

934.4

2.002

1

2.276

37775.3

6

17

33

9

3

894.8

1.999

1

2.018

36304.5

7

17

34

9

3

890.0

2.000

1

2.006

36213.5

8

17

35

8

3

849.9

1.749

1

1.820

33823.2

9

17

36

7

3

860.8

1.668

1

1.714

33024.3

10

18

36

5

3

842.9

1.735

1

1.786

33208.9

11

18

36

6

3

858.3

1.661

1

1.694

32881.0

12

18

36

7

3

850.9

1.687

1

1.683

32909.1

13

18

37

6

3

854.5

1.692

1

1.681

32952.5

14

19

36

6

3

885.2

1.588

1

1.753

33004.9

15

19

37

5

3

873.2

1.635

1

1.998

34235.3

From Table 2, the further reduction of entrainer flowrate is obtained due to the increase of operating pressure. When we only reduce NFReg by one tray as shown by design 6 compared with design 3, energy cost OF decreases significantly. It quantitatively demonstrates the effect of variable NFReg in the regeneration column on the extractive column and the entire process. It reminds us that for the design of ED processes, it is unbefitting to focus merely on the extractive column. Besides, there is a well-known fact that more trays used in the rectifying section enables to reduce the reflux ratio. This point is displayed by comparing the design 3 with design 11 as the feed location of entrainer NFE moves only one tray down the column from 17 to 18. What’s more， interestingly, two extra trays are employed in the extractive section in design 11 than design 3. On the other hand, three trays originally used in the stripping section in design 3 are employed in 13



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extractive section and rectifying section as compared with design 11. This number difference is related to the efficiency of the extractive section that is discussed in section 3.2.6. Finally and most importantly, the minimum OF value in design 11 is reduced further by 11.2% compared with design 3 in previous step.

3.2.4

Effect of entrainer purity on optimization process

The above optimization process is performed in an open loop flow sheet

20

where pure

chlorobenzene is used. Unfortunately, the real process should be implemented with a recycling entrainer stream bearing some impurities. Based on the optimized results design 11, NFE=18, NFAB=36, NFReg=6, FE=858.3 kmol/h, R1=1.661 and R2=1.694 for which OF = 32881.0 kJ/kmol, the ED process is simulated in the closed loop flowsheet,20 the Wegstein tear method built-in Aspen plus is applied for achieving process convergence. The results show that the process converges but at a xclorobenzene,w2＝0.99989 and the constraints condition xclorobenzene,w2＝0.9999 in Eq.3 couldn’t be met. Subsequently, it also affects that the purity of the extractive column distillate methanol goes only to 0.99488. Notice that the design 1 taken from Luyben and Chien 27 also couldn’t satisfy the product purity specifications in closed loop mode although it works well in open loop mode. The results demonstrate that, in ternary diagram, the composition at the entrainer feed stage should be as close as to the chlorobenzene-methanol edge for approaching high purity methanol product because the product vertex is a saddle node. This agrees with the statement obtained from the thermodynamic insight for class 1.0-1a, as mentioned in section 2.2. As shown in Figure 5 in section 3.2.5, the acetone content at the entrainer feed stage is so low that it is unnecessary to change FE. Therefore, we made the slight adjustments on R1 and R2 in the closed loop simulation to reach the process purity specifications on methanol product and the recycling entrainer. The obtained final design of the process is shown in Table 3 labeled design 16 with R1=1.710 and R2=1.702 for which OF = 33165.3 kJ/kmol.

3.2.5

Comparison of optimal design parameters

The design and operating variables are shown in Table 3, referring to the notations in section 2.3 and Figure 1. Table 4 provides the sizing parameters and the cost data of the design in literature labeled design 1 and our optimal design named design 16. The recovery of design 1 and design 16 is shown in Table S3 (in the Supporting Information) The temperature and composition profiles in 14


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the two columns of design 16 are shown in Figure 5 and Figure 6. Since the bottom temperature of the extractive column TR,ext in design 1 is 391.1 K, a low pressure steam and a temperature driving force ∆T = 41.9 K is used. Whereas TR,ext in design 16 is 422.5 K due to the increased operating pressure. Therefore, the low pressure steam could be employed for heating ∆T up to 10.5 K, or middle pressure steam with ∆T = 34.5 K. It could be concluded that using middle pressure steam for heating the extractive column results in a lower TAC.

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Table 3. Design parameters of the extractive distillation process for acetone – methanol with chlorobenzene Design 1

Percentage

Design 16

column

C1

C2

C1

Next

45

45

P1 / atm

1

3

FAB / kmol/h

540.0

540.0

W2 / kmol/h

1900.0

856.6

of saving C2

Emake-up / kmol/h

2.4

1.8

FE / kmol/h

1902.4

858.4

D1 / kmol/h

271.2

271.0

NFE

17

18

NFAB

33

36

R1

1.550

1.710

QC / MW

6.730

6.720

---

QR / MW

15.970

13.448

15.8%

54.9%

Nreg

18

18

P2 / atm

1

1

D2 / kmol/h

271.2

270.8

NFReg

10

6

R2

3.520

1.702

QC / MW

10.11

6.029

40.4%

QR / MW

9.50

3.310

65.1%

Methanol recovery

0.9994

0.9987

Acetone recoery

0.9994

0.9987

TAC / 10 $

6.885

4.812

30.1%

OF / kJ/kmol

50165.9

33165.3

33.9%

6

Table 4. Sizing parameters and cost data of extractive distillation process for acetone – methanol with chlorobenzene Design 1

Design 16

column

C1

C2

C1

C2

Diameter / m

3.619

3.498

3.006

2.111

Height / m

31.09

11.59

31.09

11.59

6

ICS / 10 $

1.408

0.615

1.155

0.359

2

231

459

121

274

2

672

703

687

245

IHE /10 $

0.974

1.177

0.873

0.700

Costcap / 106$

2.707

1.906

2.272

1.111

3.244

1.974

2.914

0.706

AC / m AR / m

6

6

Costope / 10 $

16


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CostCA / 106$

4.146

2.609

3.671

QHA / MW

8.290

3.559

CostHA /106$

0.130

0.065

1.076

Table 3 and Table 4 show that design 1 in the literature could be improved through increasing the operating pressure of the extractive column while keeping the same total number of trays in the extractive and regeneration columns. In summary, several conclusions could be drawn as follow: First, the FE could be reduced drastically from 1902.4 kmol/h (as in design 1) to 858.4 kmol/h (as in design 16). It verified the conclusion deduced from the analysis of thermodynamic insight in section 2.2 that the effective benefits of increasing operating pressure in the extractive column. Second, the energy consumption, OF value, in design 16 is saved by 33.9% compared with that in design 1. It is mostly attributed by the significant reduction in FE and R2 although R1 is increased. Third, Economic interestingly, TAC savings reach 30.1% due to the decrease in entrainer flow rate, column diameters, heat exchanger areas, and total reboiler duty. Furthermore, for extractive column, compared with that in design 1, the mainly capital cost: column shell cost ICS and total heat exchanger cost IHE in design 16 are reduced by 17.9% and 10.4%, respectively. It is because the decrease of column diameters and the increase of temperature driving forces in the condenser caused by the increase of operating pressure. Meanwhile, the operating cost Costope is saved by 10.2% due to the sharp reductions of reboiler duty. At last, for regeneration column operating at atmosphere, both capital cost and operating cost in design 16 are cut down by 41.7% and 64.2% with the dramatically reduction of entrainer usage comparing with that of design 1. Besides, the heat duty difference between reboiler and condenser of the extractive column is relatively large. The main reason is probably that the entrainer and main feed stream are subcooled liquids for the extractive column operating at 3 atm. Consistently, the reboiler heat duty of the regeneration column is much lower than that of condenser since the high pressure bottom liquid is fed into the regeneration column. Table 4 also suggests the importance of finding the suitable feed locations to reduce the process energy consumption while the pressure P1 is set as 3 atm. As explained in Luyben and Chien,

27

the feed tray locations of the extractive column NFE and NFF in design 1 are found

empirically by minimizing the reboiler duty QR1. It results in a large amount of entrainer flowrate FE and a small reflux ratio R1 since the two columns are designed separately. In this work, the two

17



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columns are taken into account simultaneously with the aid of objective function OF, and the feed locations are optimized by minimizing OF value. This statement is evidenced by comparing the total reboiler heat duty of design 1 (25.470 MW) and design (16.758 MW): a 34.2 % saving is obtained although R1 increases. The recoveries of methanol and acetone in both design 1 and design 16 are very high and could be nearly regarded as the same although the recovery values in design 1 is a little higher thatn that in design 16. On the other hand, the distillates of the two columns D1 and D2 in design 16 are reduced by 0.2 and 0.4 kmol/h compared with design 1, their reductions could contribute a 0.6 kmol/h decrease of make-up entrainer flow rate through mass balance.

Figure 5. Temperature and composition profiles of extractive column in extractive distillation of acetone – methanol with chlorobenzene (CHLOR-01)

18


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Figure 6. Temperature and composition profiles of regeneration column in extractive distillation of acetone – methanol with chlorobenzene (CHLOR-01)

Regarding the temperature and composition profile of the extractive column and regeneration column of design 16, the entrainer and main mixture feed temperature (specified 320K) is much lower than those of related feed tray temperature, resulting in the large heat duty difference between the reboiler and condenser. It indicates that preheating the entrainer and main feed is a better way to reduce the reboiler duty QR1. In the extractive section, as could be seen in Fig 5 from tray NFAB to tray NFE, the target product methanol content increases and non-product acetone content decreases quickly. It demonstrates that the much lower FE in design 16 could be able to achieve desired separation effectiveness. Most importantly, there are four trays being employed below NFE tray at the top region of the extractive section and thus acetone is prevented from entering the rectifying section. It agrees with the statement obtained from thermodynamic insight: the stable node of 19



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extractive section SNext should be as close to the entrainer-product edge as possible. In addition, the stripping section enables to prevent methanol from going to the bottom of extractive column (see Figure 5), and the remixing of acetone and chlorobenzene is observed.

3.2.6

Analysis based on ternary map, relative volatility and efficiency indicators

The ternary liquid composition profiles for design 1 and design 16 in the extractive column of acetone – methanol with entrainer chlorobenzene are shown in Figure 7. Meanwhile, Figure 8 displays the relative volatility of methanol vs acetone through the extractive column for design 1 and design 16.

Methanol (B) (64.5°C) [Srcm] 1 0

Design 1 Design 16

xD 0.2

0.8

xAB TminazeoAB[UNrcm] (89.0 oC) @ 3atm

0.4

0.6

NFE 0.4

NFAB

0.6

0.8

0.2

xFE 0

1

TminazeoAB[UNrcm] (55.2 oC) @ 1atm

0.8

0.6

0.4

Chlorobenzene(E) (132.0°C) [SNrcm]

1 0

0.2

Acetone (A) (56.1°C) [UNrcm]

Figure 7. Ternary liquid composition profiles for design 1 and design 16 in the extractive column of acetone – methanol with chlorobenzene.

Figure 8. Relative volatility of methanol vs acetone through the extractive column for design 1 and design 16, acetone – methanol with chlorobenzene. 20


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From Figure 7, we observe that the stable node of extractive section SNext in both design 1 and design 16 are very close to the BE side, and it demonstrates that the desired high purity recycling entrainer is achieved. It could also be observed that the separation of methanol and chlorobenzene in the rectifying section is difficult since many trays are used in the high methanol purity part. The reason is that there is a tangent azeotrope between methanol and chlorobenzene at methanol-rich side. Moreover, even though much lower FE is implemented, the effect of extractive section in design 16 is much larger than that of design 1 from the view of the extractive section between NFE and NFAB. It suggests the importance of determining suitable feed tray NFE and NFAB in addition to FE and R1. From Figure 8, αBA in the rectifying section is lower than 1 and thus acetone impurity for methanol product will be accumulated in the distillate. Therefore, high purity entrainer should be used to prevent acetone entering the rectifying section. This statement is proved by Figure 5 as there are 4 more trays used below entrainer feed tray. In the extractive section, αBAin design 16 is lower than that in design 1 because of massive reductions of entrainer FE, but αBA is still higher than 2, contributing enough separation driving force. This conclusion reminds us that only pursuing high relative volatility in the extractive section through adding superfluous entrainer is unreasonable. It also agrees with the conclusions that once FE is enough higher than its minimum value, changing other variables (e.g., reflux ratio and feed locations) is a better way to approach a suitable design.5 Table 5. Efficiency indicators for the extractive distillation of acetone – methanol with chlorobenzene Methanol concentration at NES

Eext /10-3

eext /10-3

0.1841

17

51.2

3.01

0.2501

19

154.2

8.12

Entrainer feed

Main feed

tray NFE

tray NFAB

Design 1

0.2353

Design 16

0.4043

The efficiency indicators Eext and eext, defined by You et al.,20 are computed for design 1 and design 16 (see Table 5). The efficiency indicators Eext and eext describe the ability of the extractive section to discriminate the desired product between the top and the bottom of the extractive section and that for each tray. The efficiency indicators Eext and eext in design 16 is more than 3 times and 2.7 times of that in design 1 although FE is greatly reduced. It quantitatively demonstrates that the 21



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separation effects of extractive section in design 16 are much better than that in design 1, which contributes the much lower energy consumption OF and total annual cost through finding more suitable FE, R1, R2, D1, D2，NFE, NFAB and NFReg.

3.2.7

Effects of extractive column operating pressure on the total process

Because the increase of the operating pressure P1 in the extractive column showed strong effects on the energy cost OF and TAC of the total process, the sensitivity analysis of P1 was carried out in order to find the optimal P1 value. The operating pressure P1 was varied from 1 atm to 10 atm with a step of one atm. For each value of P1, the above optimization process was employed while the tray numbers of the columns were kept the same. Following the computational formula of capital cost shown by Douglas (1988), the effects of pressure P1 on the capital cost of heat exchanger and column trays could be neglected. But the effect of pressure P1 on the capital cost of column shell has to be considered and it could be amended with the pressure factor Fp. The pressure factor is shown in Table S2 in Supporting Information. The results of the pressure sensitivity over the entrainer-main feed flowrate ratio, the process OF and TAC values are displayed in Figure 9.

22


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LP steam

MP steam

HP steam

Figure 9. Effects of pressure on the entrainer-main feed flowrate ratio, the process energy cost OF and TAC.

From Figure 9, we observe the expected results that entrainer-main feed flowrate ratio decreases drastically from 3.52 at P1=1 atm to 1.59 at P1=3 atm, and could be further reduced to 0.72 at P1=10 atm. Interestingly, when the heat source is changed from low pressure steam to middle pressure steam, OF value keeps decreasing due to the sharp decrease of FE. But OF value jumps up when the middle pressure steam is replaced by the high pressure steam. It demonstrates that the benefits of energy cost obtained by increasing pressure is overwhelmed by the penalty of using high pressure steam. TAC shows a similar trends following OF value. The lowest TAC (4.467

×10-6 $) was found at P1 =5 atm, a further 7.2% reduction compared with that at P1 as 3 atm. In summary, the results firstly verify the analysis from the thermodynamic insight displayed in Figure 3. The relative volatility increases faster following the increase of pressure. Secondly, our objective function OF could reflect the effects of different pressure steams with the use of M factor shown in section 2.3. The results shown in this section remind us that there are more choices for the operating pressure of the extractive column according to the different practical requirements. 23



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3.2.8

Heat integration

Since the extractive column is operated at a high pressure, it is possible to consider the reboiler/condenser heat integration in order to further save energy cost. Herein, the condenser of the extractive column is used to heat the reboiler of the regeneration column. The temperature in the condenser of the extractive column reaches the highest value (410.5 K) when P1 is at 10 atm. However, there is no enough temperature difference when P2 is 1 atm because the temperature in the reboiler of the regeneration column is 409 K at atmosphere. So P2 should be reduced. On the other hand, P2 could not be too low with the aim of employing the cooling water as cold source in the condenser of the regeneration column. Therefore, a sensitivity analysis on P2 is done and the results is shown in Figure 10.

Figure 10. Effects of P2 on the TAC of heat integration process.

From Figure10, the pressure of the regeneration column P2 at 0.8 atm gives the lowest TAC (4.297×10-6 $) for the heat integration process. It is a further 3.8% reduction comparing the optimal design without heat integration in section 3.2.7. When P2 is lower than 0.8 atm, TAC value increases because the temperature difference between the top of regeneration column and the cooling water decreases quickly, resulting in the sharp increase of the condenser cost in regeneration column. When P2 is higher than 0.8 atm, TAC value jumps up because the temperature difference for heat integration decreases sharply, leading to the increase of the heat exchanger for heat integration. In addition, the TAC of the process with heat integration exhibits a 37.6% reduction compared with that of design 1 with no heat integration.

24


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4 CONCLUSION A novel extractive distillation strategy by varying pressure for separating pressure-sensitive azeotropic mixtures is proposed in this work. Based on the thermodynamic insight of acetone – methanol with a minimum boiling azeotrope using a heavy entrainer chlorobenzene, the higher boiling component of original mixture is suggested to be withdrawn as distillate. Through the analysis of ternary residue curve maps and isovolatility curves, it could be observed that the minimal amounts of entrainer feed and extractive feasible regions in ternary diagram are sensitive to pressures. The considerable energy-saving potential by changing operating pressures as 3 atm is observed. Further, an optimal process with pressure as 5 atm was found for the proposed process compared with that at atmosphere. Through the optimization and economic evaluation study, the benefit of EDVP is demonstrated: 33.9% reductions in energy consumption OF and 30.1% reductions in TAC are achieved as compared with those of conventional ones. The results also verified the conclusion obtained from the analysis thermodynamic insight and isovolatility curves in the first part of this work. Lastly, the heat integration between the extractive column and the regeneration column was considered and it gave a further 3.8% reduction in TAC compared with the optimal process without heat integration. The much higher efficiency indicators Eext and eext in our design quantitatively demonstrate the separation effects of the extractive section and its contribution to a much lower energy cost and TAC. The strongly non-linear relationships between the two distillates are displayed and it verified the advantages of optimizing the two columns of extractive distillation simultaneously. The effect of entrainer purity on the process is considered by rigorous simulation in closed loop flow sheet. The ternary profile map illustrates that a suitable determination of feed tray locations could improve the separation efficiency even with much less entrainer. The novel methodology proposed in this work may provide some new theoretical guidance for the design and optimization of azeotropes separation through extractive distillation.

5 ACKNOWLEDGMENT This work was supported by the Doctoral Fund of Ministry of Education of China (No. 2016M601528); the National Natural Science Foundation of China (No. 21606026 and 21276073); 25



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the Natural Science Foundation of Chongqing, China (No.CSTC2016JCYJA0474); and the Fundamental Research Funds for the Central Universities (No.106112017CDJQJ228809).

Nomenclature AC

condenser heat transfer area [m2]

AR

reboiler heat transfer area [m2]

CEPCI

Chemical economic plant cost index

Costcap

capital cost [106 $]

Costope

operating cost [106 $]

CostCA

column annual cost [106 $]

CostHA

cost of heater for cooling recycling entrainer [106 $]

D

distillate flow [kmol/h]

D1

distillate flow of extractive column

D2

distillate flow of regeneration column

Diameter

diameter of column

E

entrainer

ED

extractive distillation

EPSD

extractive pressure-swing distillation

F

feed flow rate [kmol/h]

FABor FF

original azeotropic mixtures feed flow rate [kmol/h]

FE

entrainer feed flow rate [kmol/h]

FE/F

feed ratio, continuous process

Height

height of column

Ics

column shell investment cost [106 $]

IHE

heat exchanger investment cost [106 $]

K

product price factor for A vs B

m

energy price difference factor for condenser vs reboiler

M

energy price factor for different pressure steam

N

number of theoretical stage

Next

number of theoretical stages of extractive column 26


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NES

number of theoretical stages of extractive section

NFE

entrainer feed stage

NFAB

original mixture feed stage

NFReg

feed stage of regeneration column

Nreg

number of theoretical stages of regeneration column

OF

objective function (the energy consumption per product flow rate)

PSD

pressure-swing distillation

P1

pressure of extractive column [atm]

P2

pressure of regeneration column [atm]

QC1

condenser heat duty of extractive column [MW]

QC2

condenser heat duty of regeneration column [MW]

QHA

heat duty of heater for cooling recycling entrainer [MW]

QR1

reboiler heat duty of extractive column [MW]

QR2

reboiler heat duty of regeneration column [MW]

R

reflux ratio

R1

reflux ratio of extractive column

R2

reflux ratio of regeneration column

RCM

residue curve map

SNext

extractive stable node

SNext,A

extractive stable node with A as product

SNext,B

extractive stable node with B as product

SQP

sequential quadratic programming

TAC

total annual cost

Tmin azeoAB

azeotropic temperature of minimum-boiling azeotropic mixture AB [K]

TR,ext

bottom temperature of the extractive column

V

vapor flow rate [kmol/h]

W

bottom product flow rate [kmol/h]

xp

intersection point between univolatility line and side of ternary map

xE

entrainer liquid mole fraction

y*P

vapor phase composition in equilibrium with xp. 27



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Greek letters αij

relative volatility of component i to component j

28


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6 REFERENCES (1) Knapp, J. P.; Doherty, M. F. A New Pressure-swing-distillation Process for Separating Homogeneous Azeotropic Mixtures. Ind. Eng. Chem. Res.1992, 31, 346. (2) Luyben, W. L. Pressure-swing Distillation for Minimum-and Maximum-boiling Homogeneous Azeotropes. Ind. Eng. Chem. Res.2012, 51, 10881. (3) Li, W.; Shi, L.; Yu, B.; Xia, M.; Luo, J.; Shi, H.; Xu, C. New Pressure-swing Distillation for Separating Pressure-insensitive Maximum Boiling Azeotrope via Introducing a Heavy Entrainer: Design and Control. Ind. Eng. Chem. Res.2013, 52, 7836. (4) Wang, Y.; Zhang, Z.; Xu, D.; Liu, W.; Zhu, Z. Design and Control of Pressure-swing Distillation for Azeotropes with Different Types of Boiling Behavior at Different Pressures. J. Process Contr. 2016, 42, 59. (5) Gerbaud, V.; Rodriguez-Donis, I. Chapter 6. Extractive distillation, in: Gorak, A., Olujic, Z. (Eds.), Distillation: Equipment and Processes. Elsevier, Oxford, 2014, GB. 201. (6) Shen W.F.; Dong L.C.; Wei S.A.; Li J.; Benyounes H.; You, X.; Gerbaud V. Systematic Design of an Extractive Distillation for Maximum‐boiling Azeotropes with Heavy Entrainers. AIChE J. 2015, 61(11), 3898. (7) Mahdi, T.; Ahmad, A.; Nasef, M. M.; Ripin, A. State-of-the-art Technologies for Separation of Azeotropic Mixtures. Sep. Purif. Rev. 2015, 44, 308. (8) Kumar, S.; Singh, N.; Prasad, R. Anhydrous Ethanol: A Renewable Source of Energy. Renew. Sust. Energ. Rev. 2010, 14, 1830. (9) Luyben, W. L. Comparison of Extractive Distillation and Pressure-swing Distillation for Acetone-methanol Separation. Ind. Eng. Chem. Res. 2008, 47, 2696. (10) Modla, G.; Lang, P. Removal and Recovery of Organic Solvents From Aqueous Waste Mixtures by Extractive and Pressure Swing Distillation. Ind. Eng. Chem. Res. 2012, 51, 11473. (11) Muñoz, R.; Monton, J. B.; Burguet, M. C.; De la Torre, J. Separation of Isobutyl Alcohol and Isobutyl Acetate by Extractive Distillation and Pressure-swing Distillation: Simulation and Optimization. Sep. Purif. Technol. 2006, 50, 175. (12) Lladosa, E.; Montón, J. B.; Burguet, M. C. Separation of Di-n-propyl ether and n-propyl Alcohol by Extractive Distillation and Pressure-swing Distillation: Computer Simulation and Economic Optimization. Chem. Eng. Process. Proc. Intensification, 2011, 50, 1266. (13) Knapp, J. P.; Doherty, M.F. Thermal Integration of Homogeneous Azeotropic Distillation Sequences. AIChE J. 1990, 36, 969. (14) Palacios-Bereche, R.; Ensinas, A. V.; Modesto, M.; Nebra, S.A. Double-effect Distillation and Thermal Integration Applied to the Ethanol Production Process. Energy, 2015, 82, 512. (15) Kravanja, P.; Modarresi, A.; Friedl, A. Heat Integration of Biochemical Ethanol Production From Straw–A Case Study. Appl. Energ. 2013, 102, 32.

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(16) You, X.; Rodriguez-Donis, I.; Gerbaud, V. Reducing Process Cost and CO2 Emissions for Extractive Distillation by Double-effect Heat Integration and Mechanical Heat Pump. Appl. Energ. 2016, 166, 128. (17) Luo, H.; Liang, K.; Li, W.; Li, Y.; Xia, M.; Xu, C. Comparison of Pressure-swing Distillation and Extractive Distillation Methods for Isopropyl alcohol/Diisopropyl Ether Separation. Ind. Eng. Chem. Res. 2014, 53, 15167. (18) Rodriguez-Donis, I.; Gerbaud, V.; Joulia, X. Thermodynamic Insights on the Feasibility of Homogeneous Batch Extractive Distillation, 1. Azeotropic Mixtures with a Heavy Entrainer. Ind. Eng. Chem. Res. 2009, 48, 3544. (19) Hilmen, E. K.; Kiva, V. N.; Skogestad, S. Topology of Ternary VLE Diagrams: Elementary Cells. AIChE J. 2002, 48, 752. (20) Frits, E.R.; Lelkes, Z.; Fonyó, Z.; Rév, E.; Markót, M.C.; Csendes, T. Finding Limiting Flows of Batch Extractive Distillation With Interval Arithmetic. AIChE J. 2006, 52, 3100. (21) Lelkes, Z., Lang, P., Benadda, B., Moszkowicz, P. Feasibility of Extractive Distillation in a Batch Rectifier. AIChE J. 1998, 44, 810. (22) Shen, W., Benyounes, H.; Gerbaud, V. Extension of Thermodynamic Insights on Batch Extractive Distillation to Continuous Operation. 1. Azeotropic Mixtures with a Heavy Entrainer. Ind. Eng. Chem. Res. 2013, 52, 4606. (23) You, X.; Rodriguez-Donis, I.; Gerbaud, V. Investigation of Separation Efficiency Indicator for the Optimization of the Acetone–Methanol Extractive Distillation with Water. Ind. Eng. Chem. Res. 2015, 54, 10863. (24) You, X.; Rodriguez-Donis, I.; Gerbaud, V. Improved Design and Efficiency of the Extractive Distillation Process for Acetone–methanol with Water. Ind. Eng. Chem. Res. 2015, 54, 491. (25) Douglas, J. M. Conceptual Design of Chemical Processes; McGraw-Hill: New York, 1988. (26) Chemical economic plant cost index. CEPCI index for year 2013. Chem. Eng. 2016, 123, 92. (27) Luyben, W. L.; Chien, I. L. Design and Control of Distillation Systems for Separating Azeotropes. John Wiley & Sons, 2011. (28) You, X.; Rodriguez-Donis, I.; Gerbaud, V. Low Pressure Design for Reducing Energy Cost of Extractive Distillation for Separating Diisopropyl Ether and Isopropyl Alcohol. Chem. Eng. Res. Des. 2016, 109, 540.

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Methanol (B) (64.5°C) [Srcm] 1 0

Design 1 Design 16

xD 0.2

0.8

xAB TminazeoAB[UNrcm] (89.0 oC) @ 3atm

0.4

0.6

NFE 0.4

NFAB

0.6

0.8

0.2

xFE 0

1

TminazeoAB[UNrcm] (55.2 oC) @ 1atm

0.8

Chlorobenzene(E) (132.0°C) [SNrcm]

0.6

0.4

0.2

1 0

Acetone (A) (56.1°C) [UNrcm]


Improved Design and Optimization for Separating ... - ACS Publications

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