Separation and Purification Technology 310 (2023) 123132

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Separation and Purification Technology

journal homepage: www.elsevier.com/locate/seppur

An intensified energy-saving architecture for side-stream extractive

distillation of four-azeotrope mixtures considering economic,
environmental and safety criteria simultaneously
Shirui Sun a, Liang Fu b, *, Ao Yang a, c, d, Weifeng Shen a, *
a
School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 400044, PR China
b
College of Materials Science and Engineering, Chongqing University, Chongqing 400045, PR China
c
Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, PR China
d
Chongqing Changyuan Group Limited, Chongqing 402460, PR China

A R T I C L E I N F O A B S T R A C T

Keywords: Side-stream extractive distillation is used in separating azeotropic mixtures due to its advantages in energy-
Side-stream extractive distillation saving. However, environmental and safety aspects should also be considered in most chemical industries.
Multi-objective optimization Thus, this work focuses on thermodynamic analysis, multi-objective optimization and multi-criteria decision
Multi-criteria decision making
making. The main contributions of this work are developing the intensified side-stream extractive distillation for
Conceptual design
complex ternary azeotropic mixture, obtaining the trade-offs between the economic, environmental and safety
impacts of SSED processes and determining the best sustainable SSED process according to objective mathe­
matical analysis. A systematic intensified architecture including conceptual design, multi-objective optimization
and multi-criteria decision making is proposed. Firstly, three strategies of side-stream extractive distillation (i.e.,
SSED-1, SSED-2, and SSED-3) are proposed via the thermodynamic analysis including quaternary phase diagram.
Subsequently, the three strategies are optimized via the multi-objective particle swarm algorithm simultaneously
using total annual costs (TAC), CO2 emission and process route index (PRI) as objective functions. Finally, the
optimal economic, environmental and safety performance of such a complex system is determined by the
technique for order of preference by similarity to ideal solution method with entropy weighting information. The
results indicate that the introduction of the side-stream for extractive distillation could decrease the exergy loss
and increase the thermodynamic efficiency. The SSED-1 process shows that the best performance in economic,
safety and environmental aspects, which reduce 19.21% of TAC, 5.24% of PRI and 16.80% of CO2 emission,
respectively.

extractive distillation (TCED) are receiving increasing attention [6].

1. Introduction For instance, Wang et al. [7] applied TCED for separating the N-hexane/
acetone/chloroform ternary mixture. Yang et al. [8] investigated TCED
The acetonitrile (ACN)/Ethanol (EtOH)/water mixture is inevitably for tetrahydrofuran/ethanol/methanol mixture containing two binary-
generated from the ACN purification column in the synthesis of ACN via azeotrope. Recently, the conventional TCED of the ACN/EtOH/water
the dehydrogenation of ethanol and ammonia [1]. The separation and ternary mixture has been studied [9,10]. Sun et al. [9] proposed TCED to
recovery of valuable organics components from industrial effluents separate such ternary mixture, and the suitable entrainer (i.e., DMSO)
could bring economic benefits [2]. Such ternary mixture has four and the separation sequence (i.e., the ACN first, then EtOH and water as
azeotropes (i.e., three binary azeotropes and a single ternary azeotrope) the last product) had been determined by thermodynamic insights. The
manifesting that it is difficult to separate such a complex system via energy-saving potential of such separation process should be further
conventional distillation. To achieve sustainable development and car­ explored under the country’s carbon peak and carbon neutrality goals.
bon emission reduction, an energy-saving intensified separation strategy To eliminate the remixing effect and reduce energy consumption, our
for such an azeotropic system should be well investigated [3–5]. previous work [11] developed an energy-saving side-stream extractive
The separation of ternary mixture via conventional triple-column distillation (SSED) combined with heat integration for the separation of

* Corresponding authors.
E-mail addresses: liangfu@cqu.edu.cn (L. Fu), shenweifeng@cqu.edu.cn (W. Shen).

https://doi.org/10.1016/j.seppur.2023.123132
Received 2 December 2022; Received in revised form 3 January 2023; Accepted 4 January 2023
Available online 7 January 2023
1383-5866/© 2023 Elsevier B.V. All rights reserved.
S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Nomenclature AOC annual operation cost, US$/y

PRI process route index
ACN acetonitrile NT1-3 total number of stages of main column, first side column
EtOH ethanol and second side column
DMSO dimethyl sulfoxide NF1 the feed locations of main column
SSED side-stream extractive distillation NFE feed locations of entrainer
TAC total annual cost, US$/y NV1-2 the first and second vapor withdrawn locations
TOPSIS technique for order preference by similarity to ideal D1-3 distillate rates of main column, first side column and
solution second side column, kmol/h
MOPSO multi objective particle swarm optimization VV1-2 the flowrate of first and second vapor withdrawn
REP the external repository RR1 reflux ratios of main column
gbest global best EE the flowrate of entrainer
pbest personal best ID diameter of main colunm, m
FCI fixed capital investment, US$

ethyl acetate-ethanol binary azeotropic mixture resulting in a 7.78 % chemical engineering industrial process [27,28]. Some researchers re­
reduction in total annual costs (TAC) and a 9.28 % reduction in CO2 gard the solution with the lowest TAC as the optimal scheme. However,
emission. The considerable benefits on the economy and environment it seems too subjective and one-sided. To make it objective, Luo et al.
promote us to extend this new strategy to the separation of a more [29] used the technique for order of preference by similarity to ideal
complex system (e.g., ternary mixtures with multi-azeotropes). More­ solution (TOPSIS) and Shannon entropy approach to select the optimal
over, the feasibility and essence of energy-saving of SSED for ternary solution of a distributed energy system. To the best of our knowledge,
mixtures should be further studied via thermodynamic analysis. To the the decision making using the Pareto front solution of the SSED process
best of our knowledge, the intensified SSED for separating ACN/EtOH/ has not been found in the recent open literature. Moreover, the selection
water mixture has not yet been reported in recent literatures. of the best intensified extractive distillation process is inherently a de­
The optimization of the intensified extractive distillation process cision problem with a finite set of alternatives indicating that the best
would bring much more energy saving [6,12]. A great number of studies process could be determined by a multi-criteria decision making
have been published on the single objective optimization of extractive approach.
distillation by conventional optimization methods [13–19]. For Based on the above-mentioned literature review, the research gaps in
instance, Wang et al. [20] optimized classic extractive distillation se­ the thermodynamic theoretical analysis, multi-objective optimization
quences for the separation of acetone and n-heptane through sequential and multi-criteria decision making for separating four-azeotrope
iterative optimization procedure with the minimum TAC as objective mixture could be concluded as follows: (1) the intensified separation
function. To investigate the operating pressure effects on the ternary strategies for the complex multi-azeotrope mixture (e.g., the ACN/
extractive distillation process, a two simulator-based sequential iterative EtOH/water) should be further investigated based on the comprehensive
optimization methodology is proposed and the objective function is the thermodynamic mechanism analysis including the study on how the
TAC [21]. Recently, Yang et al. [8] optimized the conventional extrac­ solvent breaking azeotropes. (2) Owing to the contradiction and con­
tive distillation processes via the multi-objective particle swarm opti­ sistency among each benefit, to simultaneously optimize the intensified
mization (MOPSO) using total operating cost and total capital cost as extractive distillation process considering multi-aspect performances (e.
objective functions and finally obtain a 15.61 % reduction in TAC. Such g., economy, environment and inherent safety) should be implemented.
optimization strategies ignore environmental and safety benefits or (3) As regards optimal solution in the intensified process, the decision
evaluates other important benefits after optimizing economic benefits making after obtaining Pareto front solutions should be based on
only. Moreover, conventional optimization methods for strong coupling objective mathematical analysis rather than subjective and groundless
and non-linear chemical industry processes might fall into the local speculation.
optimum [22,23]. Therefore, there are two aspects that could be To bridge the above-mentioned gap, the contribution of this work is
improved: (1) Economic, environmental, safety and other benefits to propose a systematic approach to develop intensified separation
should be simultaneously considered in the optimization of chemical strategies for azeotropic systems considering economic, environmental
industries. (2) Various intelligent evolutionary algorithms could be and safety performance at the same time, which consists of the con­
introduced to avoid falling into local optimum [8,24–26]. To the best of ceptual design of alternative configurations, the multi-objective opti­
our knowledge, few works focus on the optimization simultaneously mization and the multi-criteria decision making. The novelty is further
considering economic, environmental and safety benefits of the SSED explained as follows: (1) three intensified extractive distillation schemes
process because the high-dimensional objective space increases the (i.e., two single side-stream extractive distillation processes and one
difficulty of maintaining good diversity in the search process of the double side-stream extractive distillation process) are determined for
optimization algorithm. However, the optimization exploring trade-offs separating complex ternary azeotropic mixtures via the conceptual
between the economy, environment and safety could bring much saving design; (2) the trade-offs between the economic, environmental and
in investment and achieve the sustainable design of the SSED process. As safety impacts of SSED processes have been investigated; (3) the best
a whole, the multi-objective optimization considering economic, envi­ sustainable SSED process is determined via the TOPSIS based on the
ronmental and safety performance of the SSED process should be further objective weight. To validate the proposed framework, a case study of
investigated. To fill this research gap, this work attempts to MOPSO that separating the ternary mixture ACN, EtOH and water with four azeo­
improves the exploration capabilities of the algorithm by local and tropes was investigated. Three SSED processes for such mixture with
global search to solve the three-objective optimization of the SSED three binary azeotropes and a single ternary azeotrope were developed
process. via the thermodynamic analysis. The alternative configurations had
The decision making should be introduced after obtaining a set of been optimized via MOPSO to simultaneously achieve more economic,
nondominated solutions that are equally good in multi-objective opti­ environmental and safety benefits. Finally, the optimal economic,
mization because only one solution would be carried out in an actual environmental and safety performance for such an azeotropic system

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

was selected by the TOPSIS method with entropy weighting 2. Methodology

information.
A systematic architecture including conceptual design, multi-
objective optimization and multi-criteria decision making for alterna­
tive intensified strategies of extractive distillation is shown in Fig. 1. In

Fig. 1. The proposed systematic architecture for developping energy-saving extractive distillation considering ecomomy, enviroment and safety impact.

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

the first step, three intensified separation processes (i.e., three side- no stopping criteria are met; otherwise, it will be stopped and the Pareto
stream extractive distillation) had be proposed via a quaternary phase front solution will be output.
diagram. After that, the alternative separation process is optimized via (4) Update the pbest and gbest of particles.
evolutionary algorithms (i.e., MOPSO) with TAC, CO2 emission and The selection of the global best (gbest) and personal best (pbest) of the
process route index as objective functions. Finally, the optimal param­ particle swarm affects not only the convergence ability of the algorithm,
eter of the intensified process is determined via the TOPSIS method with but also the diffusion of non-dominated solutions. In this work, gbest is
entropy weighting information in that the conflicts between various selected from REP based on both the grid dominant ranking and a
objectives are fully considered. crowding distance [36]. The pbest would be updated when the objective
function of the current particle dominates that of the historical particle,
2.1. The conceptual design of extractive separation while the pbest is updated by the one-half probability when the indi­
vidual does not dominate each other.
The thermodynamic topological properties of the ternary azeotropic (5) Update the position and velocity of particles.
mixture ACN, EtOH and water are listed in Table 1. There are three After finding the two best values (i.e., gbest and pbest), the velocity
binary azeotropes and a ternary azeotrope. and the position of the particle should be updated. the velocity vector of
The conceptual design of SSED could be implemented via a quater­ particle i is calculated as follows,
nary phase diagram and material balance lines. The material balance vi (t) = wvi (t − 1) + C1 r1 (t)[pbest − xi (t)] + C2 r2 (t)[gbest − xi (t)]a (1)
lines of the distillation column are shown as a blue line in Fig. 2, which
connect by a straight line and the two endpoints represent the top and where w represents the inertial weight that controls the effect of the
bottom streams in a distillation column, respectively. It is worth noting previous velocity on the current velocity. C1 and C2 respectively
that the flow rates of bottoms and distillates conform to the inverse- represent the cognitive learning factor and social learning factor, which
lever-arm rule [30,31]. In other words, the compositions of feed, bot­ indicates the the attraction of particle to the succeed of its own and its
toms and distillates streams must be collinear. Moreover, the products at neighbors, respectively.
the top and bottom of the column must lie on the same residue curve The updated position (i.e., xi(t)) is calculated as follows,
(denoted as black dashed lines in Fig. 2) to ensure the feasibility of the
xi (t) = xi (t − 1) + vi (t) (2)
distillation process.
To implement the simulation-based optimization, the MOPSO algo­
2.2. The multi-objective optimization rithm code is programmed in a MATLAB environment. Aspen plus was
used to export the objective functions, which links MATLAB via Active
MOPSO could effectively avoid the local optima by simulating the X. And the calculator function compiled by Fortran in Aspen Plus was
evolutionary mechanism of swarm behavior in birds while foraging, thus used to calculate the objective functions.
guiding the particles for searching the global optimum [32]. The
calculation procedure of the MOPSO involves the following steps: 2.2.1. Objective function
generate initialization particles, evaluate the fitness of particles, check In the design process of chemical industries, TAC is frequently used
for stopping criteria, update the pbest and gbest of particles, and update to evaluate economic performance including annual operation cost
the position and velocity of particles (shown in Fig. 3), which minutely (AOC) and fixed capital investment (FCI), which is denoted as Eq. (3)
presented as follows: [2,37,38],
(1) Generate initialization particles. FCI
The current position of particle Xi is initialized by random real TAC = + AOC (3)
Payback period
numbers with a specified range of decision variables. It is noted that a set
of decision variables obtained by the sensitivity analysis function in Aspen In this work, FCI is the sum of the equipment costs of the column and
Plus has been incorporated into initial particles to guarantee the heat exchanger. More specifically, the costs of the column include col­
convergence of process simulation. umn shell and trays, and the costs of the heat exchanger main include
(2) Evaluate the fitness of particles. condensers, reboilers and cooler. The AOC includes the annual costs of
In multi-objective optimization, the conflicting objectives are hot and cold utilities (i.e., steam and cooling water) [39–41]. It is of note
simultaneously optimized obtaining a set of Pareto-optimal solutions that the costs of conveyor equipment (i.e., pipes and valves) are
that are equally-good [33]. According to the Pareto dominance princi­ neglected because it is relatively low [42]. The detail calculation of FCI
ple, non-dominated sorting mainly ranks the solutions in population. In and AOC are presented in Table S1 in Support information. The payback
this work, the external repository (REP) could be used to store a history period is set as three years in this work [42–44].
of non-dominant solutions, which would be updated after the evolution Inherent safety could be used to guide the minimization of hazard
of each generation. And the dominated solutions in the REP would be potential via a set of actions, which could be assessed by the process
eliminated and replaced by the dominating solutions if the non- route index (PRI) [43]. To comprehensively evaluate the inherent safety
dominated solutions found in a new generation [34,35]. of the chemical process, four properties parameters of all material
(3) Check for stopping criteria. streams are considered, and the parameters mainly include the mass
The optimization procedure would turn to the iterative update step if heating value, fluid density, pressure and combustibility as described by
Eq. (4) [45],
( )( )( )( )
Table 1 Average mass Average fluid Average Average
The thermodynamic topological properties of the ACN/EtOH/water at 1 atm. heating value density pressure combusibility
PRI =
Temperature Azeotropic composition Characterization 108
℃ mol% (4)
ACN 81.48 / stable node where the mass heating value (kJ/kg), fluid density (kg/m3), and
EtOH 78.31 / stable node
pressure (atm) of all streams could be obtained in the databanks and
Water 100.02 / stable node
ACN/Water 76.53 0.675/0.325 saddle nodes calculation result of Aspen Plus. Pressure denotes the average pressure
EtOH/Water 78.15 0.895/0.105 saddle nodes of all streams. The combustion limit of the mixture could be estimated
ACN/ EtOH 72.97 0.452/0.548 saddle nodes based on the lower and upper flammability limit of component i (i.e.,
ACN/EtOH/water 72.80 0.444/0.468/0.088 unstable node
LFLi and UFLi) and the mole fraction of component i in the mixture (i.e.,

4
S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Fig. 2. The quaternary phase diagram of ACN/EtOH/water with DMSO as entrainer.

Fig. 3. The multi-objective particle swarm optimization procedure.

yi) [46], the equation as follows, LFL25 and UFL25), the temperature of the stream and the heat of com­
bustion for component i (i.e., ΔHC), as shown in Eq. (6),
1
LFLmixture = ∑n yi [ ]
0.75 × (T − 25)
i=1 LFL LFLT = LFL25 × 1 −
i
(5) ΔHC
1 [ ] (6)
UFLmixture = ∑n 0.75 × (T − 25)
yi UFLT = UFL25 × 1 +
i=1 UFL
ΔHC
i
where T in ℃ and ΔHC in kcal/kmol could be found in the databank
The flammability limits at specified temperatures and pressure (LFLT
of Aspen Plus. And the LFL25 and UFL25 could be obtained from the
and UFLT) could be calculated by the standard flammability limit (i.e.,

5
S. Sun et al. Separation and Purification Technology 310 (2023) 123132

International Chemical Safety Cards (see on website http://icsc.brici.ac. According to the standardized decision matrix and the obtained en­
tropy weight, the weighted matrix of indicators value is calculated by
cn/).
the following formula,
CO2 emissions are a significant indicator to evaluate global warming,
⎡ ⎤ ⎡ ⎤
and could be used to judge the environmental impact of various chem­ ω1 r11 ⋯ ωn r1n υ11 ⋯ υ1n
ical processes [47]. The CO2 emissions of distillation system generates υ=⎣ ⋮ ⋱ ⋮ ⎦=⎣ ⋮ ⋱ ⋮ ⎦ (13)
due to the consumption of steam, which could be calculated as follows ω1 rm1 ⋯ ωn rmn υm1 ⋯ υmn
[48],
(4) Determine the positive ideal and negative ideal solution from
( ) ( )
Qfuel C% matrix v.
(CO2 )emissions = × α (7)
NHV 100 { } {( ⃒ ) ( ⃒ ) }
V + = υ+ + +
1 , υ2 , ⋯, υn = maxυij ⃒j ∈ J1 , minυij ⃒j ∈ J2 |i = 1, 2, ⋯, m
where α (set as 3.67) stands for the ratio of molecular weight be­ (14)
tween CO2 and C. The NHV represents the net heating with 39771 kJ/kg.
{ } {( ⃒ ) ( ⃒ ) }
The carbon content (C%) is set as 86.5. The fuel consumption Qfuel in kJ/ V − = υ−1 , υ−2 , ⋯, υ−n = minυij ⃒j ∈ J1 , maxυij ⃒j ∈ J2 |i = 1, 2, ⋯, m
h can be obtained via Eq. (8), (15)
( )
Qfuel =
Qseq ( )
× hseq − − 419 ×
TF − − T0
(8) (5) Obtain the distances of the existing alternatives from the positive
λseq TF − − TS ideal and negative ideal solutions.
where λseq in kJ/kg is the latent heat of fuel; the hseq in kJ/kg is the √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
∑n
( )2
enthalpy of steam; the Qseq in kJ/h is the energy consumption. The Li+ = υij − υ+j , i = 1, 2, ⋯, m (16)
flame, stack, and ambient temperatures are abbreviated as TF, TS, and i=1

T0, respectively [49]. √̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅

∑ n
( )2
Li = υij − υ−j , i = 1, 2, ⋯, m (17)
2.2.2. Decision variables and constraints i=1
Besides eight discrete variables and eight continuous variables in the
TCED process (i.e., the total numbers stages and feed stages of C1, C2 (6) Calculate the relative closeness to the ideal alternatives.
and C3; the feed stages of entrainer in C1 and C2; the reflux ratios of C1, Yi = Li/Li+ + Li (18)
C2 and C3; the distillate rates of C1, C2 and C3; the flowrate of entrainer;
the split ratio of entrainer), the SSED includes the liquid withdrawn Finally, the performance score would be calculated by the normali­
locations of C1 or C2 and the flowrate of side-liquid stream. The range of zation of Yi.
discrete and continuous variables are shown in Table S2 and Table S4 of The weight of the TOPSIS would be determined subjectively by
Supporting information, which obtained via the sensitivity analysis in policymakers in conventional TOPSIS. To make it objective, entropy
Aspen Plus. weighting has been introduced to the TOPSIS for analyzing the coordi­
Desired product purities of the SSED process are defined as Eq. (9), nation relationship of multiple objective functions [29]. Entropy weight
which are implemented as constraints in the MOPSO procedure. In this refers to the relative importance coefficient of each index in the
work, the mole purities of ACN, EtOH and water are set as 0.999. competition when making a decision under the condition of a given
evaluation object and evaluation index. In general, the smaller entropy
xproduct i ⩾xdesired i (9) is, the greater degree of variation of the index and the more information
it provides. Entropy weight can be calculated as follows,
2.3. TOPSIS method with entropy weighting information ∑
n
Qaj = (1 − Hj )/ (1 − Hj ) (19)
TOPSIS is a widely used multi-criteria decision analysis method that j=1

sorts a finite number of choices based on the shortest geometric distance [ ]

from the positive ideal solution and the longest geometric distance from 1 ∑ m
Hj = − pij lnpij (20)
the negative ideal solution [28]. The performance score could be lnm i=1
calculated as follows,
(1) All the original criteria receive tendency treatment. ∑
m

Assuming there are n evaluation indicators, and evaluation indicator pij = xij / xij (21)
sets have m subsets, the evaluation value of indicator j in subset i is αij.
i=1

The decision matrix of all subsets is represented as Eq. (10). Where Qaj is the objective weight, xij is a crisp value indicating the
⎡ ⎤ performance rating of i-th alternative with regard to j-th criterion, m is
α11 ⋯ α1n the number of Pareto solutions and n is the number of objective
A = (αij )m×n = ⎣ ⋮ ⋱ ⋮ ⎦ (10)
functions.
αm1 ⋯ αmn
The first step is to unify the different indicator types, that is, to 3. Computational results
transform cost criteria into benefit indicators, which is shown in detail as
follows, 3.1. The conceptual design of SSED process
( )
xij = max aij − aij , i = 1, 2, ⋯, m; j = 1, 2, ⋯, n (11)
To effectively eliminate the remixing phenomenon of the interme­
(2) Calculate the standardized decision matrix r. diate components, some research and our previous work have investi­
To solve the uniformity of the units of indicators, this work makes the gated the SSED process [7,50]. Based on the existing TCED process for
standardization on all indicators. ACN/EtOH/water mixture, three improved SSED processes including
√̅̅̅̅̅̅̅̅̅̅̅̅ two SSED with one liquid side-stream (i.e., SSED-1 and SSED-2) and one
∑ SSED with double liquid side-stream (i.e., SSED-3) are proposed in this
m
rij = xij / x2ij , i = 1, 2, ⋯, m; j = 1, 2, ⋯, n (12)
i=1
work. The SSED-1 and SSED-2 introducing one liquid side-stream want
to respectively reduce the ratios of feed and entrainer in C1 column and
(3) Determine the weighted decision matrix v.

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

C2 column to save energy consumption. The double liquid side-stream published work [9]. The NRTL is used as the thermodynamic model to
was introduced to investigate whether more side-stream result in more describe the phase equilibrium of the ACN/EtOH/water ternary
energy savings. mixture, as suggested by our previous study [9], which is given in
The quaternary phase diagram of the ACN/EtOH/water/Enerainer Table S5 of Supporting Information.
system and material balance lines of side-stream extractive distillation-1 The MOPSO procedure is implemented with the fixed parameters of
(SSED-1) are illustrated in Fig. 4a. The conceptual design flowsheet of 50 populations, C1 and C2 are 2, and the inertia weight of 0.5 with an
SSED-1 is shown in Fig. 4b. The SSED-1 process includes two columns inertia weight damping rate of 0.99 is chosen. The optimization pro­
with extractive section (i.e., C1 and C2) and one entrainer-recovery cedure stops after 600 generations without any significant improvement
column (i.e., C3). The feed stream (i.e., Feed) containing ACN/EtOH/ for objective functions. Fig. 7 and Table S6-S8 of Supporting Information
water enters the top section of C1 while the entrainer DMSO (i.e., EE1) present the final Pareto-optimal front solutions for the three SSED pro­
enters the bottom section of C1. The light components ACN (i.e., D1) cesses. As depicted in Fig. 7, CO2 emissions increase with the increase of
distillate at the top of the C1 column while the high-purity entrainer TAC. The main reason is that a high TAC infers high energy costs and
DMSO (i.e., B1) withdraws from the bottom of the C1. The liquid side- CO2 emissions increases consequently. There are two pairs of mutually
stream (i.e., S1) containing EtOH/water/DMSO withdraws from the exclusive objective functions (i.e., TAC and PRI, PRI and CO2 emissions).
C1 and then is sent to C2. Meanwhile, the entrainer (i.e., EE2) is fed to The performance score of three SSED processes using the TOPSIS
the top section of the C2 to alter the relative volatility of EtOH and method with entropy weighting information are presented in Table S9-
water. The light components EtOH (i.e., D2) distillate at the top of the C2 S11 of Supporting Information. The optimal solution recommended in
column and water with DMSO (i.e., B2) withdraws from the bottom each process by the TOPSIS method with entropy weighting information
section of the C2. The B2 is sent to C3 to separate water in the distillate is shown with a filled pink sphere in Fig. 7. The TAC, PRI and CO2
(i.e., D3) and DMSO (i.e., B3) withdraws at the bottom of the C3. Then, emissions of the optimal SSED-1 process are 0.9840 × 106 $, 6.1114 and
the bottom products of C1 and C2 are mixed and enter the cooler, which 0.9899 kt/h, respectively. The optimal flowsheet of SSED-2 with 1.0229
would recycle to C1 and C2 to alter the relative volatility between × 106 $ of TAC, 6.1624 of PRI, and 1.0432 kt/h of CO2 emissions. The
components. There is a makeup stream of entrainer to balance the losses TAC, PRI and CO2 emissions of the optimal SSED-3 process are 1.0233 ×
in the three distillates. 106 $, 6.0503 and 1.0761 kt/h, respectively.
Different from SSED-1, as shown in Fig. 5, the liquid side-stream (i.e., The optimal flowsheet of the SSED-1 process is shown in Fig. 8. The
S2) containing water and DMSO withdraws from the C2, and the bottom total numbers of stages of C1, C2 and C3 column are 40, 53 and 15,
stream of the C1 (i.e., B1) containing EtOH, water and DMSO is sent to respectively. The entrainer and feed location of C1 are 23th and 6th
C2 in side-stream extractive distillation-2 (SSED-2). The ACN, EtOH and stages, respectively. The feed and entrainer locations of C2 are 38th and
DMSO product meeting the purity specification are obtained at the top 4th stages, respectively. The feed location of C3 is the 6th stage. The
of C1, C2 and C3, respectively. The high-purity DMSO is obtained at the withdrawn locations and flowrate of S1 are 32th stage and 91.58 kmol/
bottom of C2 and C3. Similar to SSED-1, the high-purity DMSO (i.e., B2 h, respectively. The reflux ratio of C1, C2 and C3 are 0.349, 2.12 and
and B3) will be recycled to C1 and C2 after cooling. 0.360, respectively. The total flow rates of DMSO and makeup are
Based on SSED-1, a liquid side stream is added to the distillation 103.95 and 0.02 kmol/h. Moreover, the obtained rates of the distillate
column C2 in side-stream extractive distillation-3 (SSED-3) shown in D1, D2 and D3 of C1, C2 and C3 are 45.34, 13.06 and 41.62 kmol/h,
Fig. 6. In other words, there are two side-stream S1 containing EtOH/ respectively. The total numbers of stages of C1 and C2 colunm increases
water/DMSO and S2 containing water/DMSO which respectively with­ slightly while the total numbers of stages of C3 decreases slightly. The
draws from C1 and C2 column, and the high-purity DMSO is obtained at total flowrate of the entrainer is reduce by 70.58 kmol/h causing the
the bottom of C1, C2 and C3. reduction of duty of reboliers of C1, C2 and C3 column and the Cooler.
The condenser duty of C1 column increases slightly because the intro­
duction of side-stream increases the flowrate of liquid withdrawn of C1
3.2. The optimization results of SSED process colunm.
The optimal flowsheet of the SSED-2 process is shown in Fig. 9. The
In this study, Aspen Plus is used to conduct process simulations. The total numbers of stages of C1, C2 and C3 are 40, 48 and 16, respectively.
detailed feed parameter (i.e., flowrates and composition) is from the

Fig. 4. (a) the material balance lines of SSED-1 process, (b) the conceptual design flowsheet of SSED-1 process.

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Fig. 5. (a) the material balance lines of SSED-2 process, (b) the conceptual design flowsheet of SSED-2 process.

Fig. 6. (a) the material balance lines of SSED-3 process, (b) the conceptual design flowsheet of SSED-3 process.

The feed and entrainer locations of C1 are 29th and 6th stages, respec­ and 0.226, respectively. The total flow rates of DMSO and makeup are
tively. The feed and entrainer locations of C2 are 32th and 3th stages, 115.54 and 0.01 kmol/h. Moreover, the obtained rates of the distillate of
respectively. The feed location of C3 is the 8th stage. The withdrawn C1, C2 and C3 are 45.33, 13.06 and 41.62 kmol/h, respectively. The
locations and flowrate of S2 are 41th stage and 86.42 kmol/h, respec­ change of the total numbers of stages of C1, C2 and C3 column is same
tively. The reflux ratio of C1, C2 and C3 are 0.449, 3.992 and 0.227, with SSED-1 and SSED-2. The total flowrate of the entrainer is reduce by
respectively. The total flow rates of DMSO and makeup are 94.2 and 63.01 kmol/h causing the reduction of duty of reboliers of C1, C2 and C3
0.01 kmol/h. Moreover, the obtained rates of the distillate of C1, C2 and column and the Cooler. The introduction of side-stream in C1 column
C3 are 45.34, 13.05 and 41.62 kmol/h, respectively. The change trend of increases the condenser duty of C2 column decreased. Nevertheless, the
the total numbers of stages of C1, C2 and C3 column is same with SSED- condenser duty of decreases slightly because the feed flowrate of C2
1. Similarly, the introduction of side-stream in C2 column increases the column in SSED-3 is much less than that of TCED.
condenser duty of C2 column. The total flowrate of the entrainer is
reduce by 80.35 kmol/h causing the reduction of duty of reboliers of C1,
C2 and C3 column and the Cooler. It is of note is that the reflux ratio of 3.3. Process evaluations
C1 column is larger than that of TCED indicating the duty of the
condenser in C1 column increases slightly. The detailed economic evaluations including the FCI and AOC of
The optimal flowsheet of the SSED-3 process is shown in Fig. 10. The each equipment in the three alternative configurations and TCED pro­
total numbers of stages of C1, C2 and C3 are 42, 44 and 17, respectively. cess are shown in Table 2.
The feed and entrainer locations of C1 are 25th and 6th stages, respec­ It can be observed that the proposed three processes have domi­
tively. The feed and entrainer locations of C2 are 26th and 3th stages, nating advantages in economy and energy consumption than those of
respectively. The feed location of C3 is 8th stage. The withdrawn loca­ the conventional process. The TAC of three side-stream extractive
tions and flowrate of S1 are 35th stage and 94.62 kmol/h, respectively. distillation configurations (i.e., SSED-1, SSED-2 and SSED-3) are
The withdrawn locations and flowrate of S2 are 36th stage and 74.37 decreased by 19.21 %, 16.01 % and 15.98 %, respectively. The results
kmol/h, respectively. The reflux ratio of C1, C2 and C3 are 0.329, 2.735 demonstrated that the SSED-1 is the most promising process among the
proposed three separation processes in terms of economic benefits. It is

8
S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Fig. 7. The Pareto-optimal front for three alternative SSED processes.

Fig. 8. The optimal SSED-1 process for separating ACN/EtOH/water.

obvious that the flowrate of the entrainer of the SSED-1 is less than that of the heat exchanger. The duties of the reboilers of C1, C2 and C3 in
of the TCED indicating the duty of the reboilers of C1, C2 and C3 column SSED-1 process decrease by 0.3 MW, 0.41 MW and 0.33 MW, respec­
and the cooler of the entrainer would be cut down. In general, the tively. The cooler of the entrainer in SSED-1 decreases by 0.72 MW. In
smaller the duty of the heat exchanger, the smaller energy consumption other words, the costs of cooling water and steam would be reduced.

9
S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Fig. 9. The optimal SSED-2 process for separating ACN/EtOH/water.

Fig. 10. The optimal SSED-3 process for separating ACN/EtOH/water.

Thus, the AOC of SSED-1 cuts down by 16.69 %. In addition, a smaller existing TCED process have been shown in Fig. 11. The CO2 emissions of
duty heat exchanger means a smaller heat exchange area resulting in less the SSED-1 process decreased by 16.80 %, which is a greater reduction
equipment investment. Therefore, the FCI of SSED-1 reduces by 23.89 than other process. This significant reduction mainly attributes to the
%. reduction in the reboiler duty of the C2 column. Compared with the
The PRI index is used to evaluate the Inherent safety of different existing ED process, the CO2 emissions of the SSED-2 and SSED-3 con­
alternative separation configurations. Table 3 lists the inherent safety figurations are reduced by 12.32 % and 9.56 %, respectively.
performances of the SSED processes. Three SSED processes have good
safety performance compared with existing process because the average 3.4. The design-making considering economy, environment and safety
heating value, density and combustibility of proposed intensified pro­
cesses all reduced in varying degrees. The results illustrated that the Above mentioned analysis indicates the SSED-1 separation configu­
SSED-3 process has a much smaller PRI than that of the SSED-1, SSED-2 ration is the optimal separation scheme in terms of economic costs and
and TCED process. In other words, SSED-3 is preferred in terms of CO2 emissions aspect. However, the SSED-3 process is the best process in
inherent safety. inherent safety. Thus, the TOPSIS with entropy weights is introduced to
The CO2 emissions of the proposed alternative processes and the provide a comprehensive assessment of the three separation processes.

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Table 2 The results of TOPSIS are listed in Table 4. The weight value of TAC, PRI
The economic comparison of the three energy-saving SSED process. and CO2 emissions are 0.51, 0.25 and 0.24, respectively. According to
TCED SSED-1 SSED-2 SSED-3 the TOPSIS, the SSED-1 is recommended as the best alternative process
3 considering economy, environment and safety.
C1 Shell (10 $) 155.7 212.2 176.8 220.0
Trays (103 $) 8.106 12.30 9.435 12.85
HX (103 $) 213.2 134.8 174.1 134.4
AOC (103 $/y) 234.2 288.7 211.4 286.5 3.5. The thermodynamic efficiency and exergy analysis
C2 Shell (103 $) 181.5 170.9 238.1 199.9
Trays (103 $) 9.631 8.536 14.07 11.09 To further analyze the energy-saving mechanism of proposed SSED
HX (103 $) 208.2 199.5 145.9 124.9
processes, the exergy and thermodynamic efficiency analysis are carried
AOC (103 $/y) 223.4 138.4 272.6 225.2
C3 shell (103 $) 122.7 77.47 77.86 62.18 out. The the second-law thermodynamic efficiency (η) that depends on
Trays (103 $) 6.344 3.422 3.405 2.566 the minimum separation work and loss work is employed to assess the
HX (103 $) 213.2 112.8 105.9 104.1 energy efficiency, which calculated as follows,
AOC (103 $/y) 322.8 225.7 204.4 198.9
Cooler FCI (103 $) 160.7 41.75 39.31 43.90 Wmin
η= (22)
AOC (103 $/y) 11.18 6.686 6.135 7.282 Wmin + Exloss
Pump FCI (103 $) 0.2614 0.2237 0.2194 0.2065
AOC (103 $/y) 0.0170 0.0009 0.0008 0.0007 where Wmin and Exloss in kJ/h are the minimum separation and lost
total FCI (103 $)total AOC 1279.3 973.7 985.1 916.1 work, which could be calculated via the Eqs. (23)-(24) as follows,
(103 $/y) 791.5 659.4 694.6 717.9 ∑ ∑
TAC(103 $/y) 1218.0 984.0 1022.9 1023.3 Wmin = (h − T0 s) − (h − T0 s) (23)
TAC saving (%) 19.21 16.01 15.98 Out In

( ))
∑ ( T0
) ∑ (
T0
Exloss = QR 1 - − QC 1 - − Wmin (24)
Table 3 In
TS Out
TCW
The comparison of the PRI of the three energy-saving process.
where h in kJ/kmol and s in kJ/kmol-K are the enthalpy and entropy
Alternative Average Average Average Average PRI
processes heating pressure density combustibility
of inlet and output stream; QR and QC in kJ/h are denoted as the reboiler
value (kJ/ (atm) (kg/m3) (vol %) and condenser duty; T0, TS, and TCW [K] are the ambient, steam, and
kg) cooling water temperature.
TCED 19983.1947 1.2676 926.0214 27.2209 6.3852 The exergy and thermodynamic efficiency analysis results are shown
SSED-1 19771.1162 1.2654 923.8925 26.4399 6.1114 in Fig. 12. It can be seen that the SSED-1 has the lowest exergy loss and
SSED-2 19793.0597 1.2704 924.6836 26.5034 6.1624 highest thermodynamic efficiency, resulting in the lowest energy con­
SSED-3 19706.2411 1.2617 923.3041 26.3546 6.0503
sumption and operation investment. In terms of thermodynamic effi­
ciency, the SSED-1 provides improvement by about 16.48 % relative to
the TCED process in literature. The exergy loss of the SSED-1 is was
decreased by 14.64 %. The exergy loss reduction is mainly attributed to
the decrease of heat duty in the C2 column of the SSED-1 process.
Meanwhile, the low-pressure steam serves as the thermal medium in the
C2 column of the SSED-1 which also could decrease exergy loss. As
shown in Fig. 12(a), the exergy loss of the C1 column in the SSED-1 and
SSED-3 processes are both slightly increase due to the introduction of
high-pressure steam. For the same reason, the exergy loss of the C2
column in the SSED-2 and SSED-3 both take a slight increase. The exergy
losses of the C3 column in the proposed three processes are all decreased
to different degrees owing to the reduction of the feed stream of the C3
column with the decrease of duties in condense and reboiler.

4. Conclusions

In this work, a systematic architecture is proposed to develop

intensified separation process for multi-azeotrope mixture with much
economic, environmental and safety performance, which includes con­
ceptual design, multi-objective optimization and decision making. The
capability of such optimization approach is manifested by ACN/ EtOH/
Fig. 11. The CO2 emissions of the three SSED processes. water mixture with three binary-azeotrope and one ternary-azeotrope.
Three side-stream extractive distillation configurations (i.e., SSED-1,
SSED-2 and SSED-3) are investigated. The multi-objective particle
Table 4 swarm algorithm combined with the TOPSIS method with entropy
The decision making of the three SSED configurations. weighting information is carried out to optimize and determine the
optimum process. The results of multi-objective optimization and deci­
TAC (106 PRI CO2 emissions Performance Rank
$/y) (kt/h) score sion making demonstrate that the proposed three alternative processes
all have achieved certain economic, environmental and safety benefits.
SSED- 0.9840 6.1114 0.9899 0.6069 1
1 The SSED-1 separation configuration is the optimal separation scheme in
SSED- 1.0229 6.1624 1.0432 0.1300 3 terms of economic costs and environmental performance, which can
2 save 19.21 % of TAC and 16.80 % of CO2 emissions compared to TCED
SSED- 1.0233 6.0503 1.0761 0.2631 2 separation configurations. The SSED-3 which decreased by 5.24 % of
3
PRI performs best in safety benefits. With the improved multi-criteria

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S. Sun et al. Separation and Purification Technology 310 (2023) 123132

Fig. 12. The thermodynamics analysis of the SSED processes: (a) thermodynamic efficiency (b) exergy loss.

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conditions of a hybrid humidification-dehumidification and heat pump

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