A review of extractive distillation from an azeotropic phenomenon for dynamic control

Accepted Manuscript A review of extractive distillation from an azeotropic phenomenon for dynamic control

Yixin Ma, Peizhe Cui, Yongkun Wang, Zhaoyou Zhu, Yinglong Wang, Jun Gao PII: DOI: Reference:

S1004-9541(18)30587-1 doi:10.1016/j.cjche.2018.08.015 CJCHE 1241

To appear in:

Chinese Journal of Chemical Engineering

Received date: Revised date: Accepted date:

4 May 2018 23 July 2018 7 August 2018

Please cite this article as: Yixin Ma, Peizhe Cui, Yongkun Wang, Zhaoyou Zhu, Yinglong Wang, Jun Gao , A review of extractive distillation from an azeotropic phenomenon for dynamic control. Cjche (2018), doi:10.1016/j.cjche.2018.08.015

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Special Issue of SCP

A review of extractive distillation from an azeotropic phenomenon for dynamic control☆ Yixin Ma1, Peizhe Cui2, Yongkun Wang2, Zhaoyou Zhu2, Yinglong Wang2*, Jun Gao1* 1

College of Chemical and Environmental Engineering, Shandong University of Science and

Technology, Qingdao 266590, China 2

College of Chemical Engineering, Qingdao University of Science and Technology, Qingdao

PT

266042, China Corresponding author.Yinglong Wang(E-mail: [email protected]); Jun Gao([email protected])

RI

Abstract

Extractive distillation is an effective method for separating azeotropic or close boiling point

SC

mixtures by adding a third component. Various technologies for performing the extractive distillation process have been explored to protect the environment and save resources. This paper

NU

focuses on the improvement of these advanced technologies in recent years. Extractive distillation is retrieved and analyzed from the view of phase equilibrium, selection of solvent in extractive

MA

distillation, process design, energy conservation, and dynamic control. The quantitative structure-property relationship used in extractive distillation is discussed, and the future

D

development of extractive distillation is proposed to determine how the solvent affects the relative

PT E

volatility of the separated mixture. In the steady state design, the relationship between the curvature of the residue curve and parameters of the optimal steady state is also highlighted as another field worthy of further study to simplify the distillation process.

CE

Keywords: Thermodynamic; Quantitative structure-property relationship; Solvent Selection; Process design; Energy conservation; Dynamic control.

AC

1. Introduction

Exhaustion of energy, which is a resource problem, is a critical issue that should be considered in all fields. In the chemical industry, energy consumption is needed to ensure the production of enterprises. In the late 1990s, hard-working scientists promoted the vigorous development of the distillation industry in China [1]. High energy consumption and serious pollution also appeared. Thus, the appropriate use of energy has attracted the attention of global researchers. Separation is an important process in the chemical industry, particularly in energy ☆

Supported by the National Natural Science Foundation of China (21676152). 1

ACCEPTED MANUSCRIPT consumption during distillation, which accounts for 10%-15% [2] of the total energy consumption in chemical plants. Despite its high energy consumption, distillation is the preferred method for separating a mixture into pure components. These mixtures are typically azeotropic. The existence of azeotropes increases the difficulty of separating mixtures, and simple distillation cannot meet the separation requirements. The present study is a review of the development of the extractive

PT

distillation of azeotropes via dynamic control. The quantitative structural property relationship was proposed for solvent selection and to investigate the effect of a solvent on extractive

RI

distillation to improve synthesis.

Special distillations, such as azeotropic distillation [3], extractive distillation [4],

SC

pressure-swing distillation [5, 6] and reactive distillation [7-9], are commonly used method to separate azeotropic mixtures. Azeotropic distillation consumes a great deal of energy because

NU

azeotrope mixtures consist of an entrainer, and a component needs to evaporate from the top of the column. Pressure-swing distillation is often used to separate azeotropic mixtures with

MA

pressure-sensitive properties. Reactive distillation is a complex process due to the separation of products by distillation while performing the reaction. Extractive distillation has been widely

D

studied and applied in industry. To achieve extractive distillation, a sufficient change of relative

PT E

volatility must occur within the separated components when the solvent is added. The effect of the solvent on azeotropic mixtures has been studied in theory and experimentally [10-12]. Extractive distillation was first proposed in the 1930s [13]. Extractive distillation can be

CE

divided into three types according to the operating mode: continuous extractive distillation, batch extractive distillation and semi-continuous extractive distillation [4]. Continuous extractive

AC

distillation utilizes the primary method according to the literature, as shown in Table 1. Continuous extractive distillation is a flowsheet containing two columns; the first column is called the extractive column, and the second column is called the solvent recovery column. The mixture to be separated is fed into the extractive column in the lower section, while the solvent is fed into the recovery column in the upper section. Two products are obtained at the top of the two columns, and the solvent is recycled onto the extractive column from the bottom of the solvent recovery column. Compared with continuous extractive distillation, batch extractive distillation [14-16] is used to separate mixtures with frequently changing compositions into several products. The mixture to be separated and the solvent are simultaneously charged in the reboiler, and products 2

ACCEPTED MANUSCRIPT are obtained at the top of the column. To reach maximum productivity, it is crucial to determine the appropriate ratio of feed and solvent. Semi-continuous extractive distillation is another operating mode. The difference between batch extractive distillation and semi-continuous extractive distillation is the feed location of the solvent. The solvent is added in the boiler and at the middle and top[17, 18] of the column in a semi-continuous mode. The two operating strategies

PT

in semi-continuous extractive distillation in terms of the charge of the initial feed are full charge and fractional charge [19]. Full charge means that the feed is charged into the reboiler to reach the

RI

maximum capacity of the column at a fixed condenser vapor load; the column is then flooded without careful control of the reflux ratio and solvent feed rate. Fractional charge is a suitable

SC

operation mode to separate many azeotropic mixtures using a fixed column.

There is another classification method that is based on the type of solvent. Extractive

NU

distillation can be divided into four types: extractive distillation with a solid salt [20-21], extractive distillation with a liquid solvent [24-28] , extractive distillation with a combination of a

MA

liquid solvent and a solid salt [27] and extractive distillation with an ionic liquid [28-32]. A suitable solvent should decrease the consumption of solvent needed to meet the requirement of the

D

product purity and be easy to operate. Extractive distillation with a liquid solvent is a traditional

PT E

method compared with other types of solvent. The combination of a liquid solvent and solid salt used in extractive distillation has superior characteristics, such as ease of operation and high separation ability; this combined solvent is also suitable for both polar systems and non-polar

CE

systems. Furter[33] noted that although the use of salt as a solvent has the advantage of a high separation ability, salts corrode equipment, which limits their industrial application. Ionic liquids

AC

have increasingly been used as solvents in extractive distillation and have attracted the attention of researchers [30, 34, 35] . Because ionic liquids have high costs and research is mainly limited to the laboratory, this review primarily focuses on liquid solvents without ionic liquids. Although extractive distillation has some advantages over other special distillation strategies, there are disadvantages that limit its application in some mixtures. First, a third component needs to be introduced during extractive distillation, resulting in the appearance of impurities in the products. Second, the solvent used in extractive distillation can affect energy consumption and capital investment due to the different properties of the solvent used, such as its selectivity, capacity and boiling point. Third, the use of three components increases the difficulty of 3

ACCEPTED MANUSCRIPT controlling the process[36]. Fourth, it is difficult to select an effective solvent to separate mixtures to meet the separation requirement [37]. Selection of an appropriate solvent is a crucial factor and is the basis of extractive distillation. As Laroche reported in 1991 and Lelkes (1998) and Rodriguez-donis (2009) showed, the general feasibility criteria are based on the topology of the residue curve map and the location of the

PT

univolatility curve, which are both driven by the properties of the solvent and its interactions with the mixture to be separated. Studies on solvent selection are based on a computer-aided molecular

RI

design. A molecular simulation method is important to enhance the understanding of interaction between entrainer and separated mixtures. Li et al. [38] simulated binary mixtures of esters with

SC

alcohols or organic acids based on the TraPPE-UA force and investigated DFT as molecular simulation method to optimize geometries of ILs and molecules in the next year [39]. Yin et al.

NU

[40] studied the selection of co-solvents to separate a benzene-cyclohexane system. Van Dyk and Nieuwoudt [41] proposed a method that combined a genetic algorithm and the UNIFAC model for

MA

computer-aided molecular design for extractive distillation. Recent studies have examined the characteristics of mixtures based on a model established according to the quantitative

D

structure-property relationship (QSPR) [42-45]. QSPR has been used in extractive distillation to

PT E

predict the suitability of solvents. Kang et al. [46, 47] established a quantitative structure relative volatility relationship based on a multiple linear regression model and artificial neural network model to predict the suitability of ethylbenzene as a solvent in a p-xylene system.

CE

The theoretical design of extractive distillation can be improved using QSPR. The molecular information includes many aspects, such as topological descriptors, dipoles, jurs descriptors of the

AC

strain energy, and so on. The ability of the solvent to change the relative volatility of the separated mixture and the relatedness between the molecular structure of the solvent and the relative volatility are important to study. The nature of the solvent can be determined using some descriptors. The ternary phase diagram is a common tool that is used to determine the feasibility of extractive distillation, but there are no studies focusing on the inner link between the curvature change of the residue curve in the ternary phase diagram and the parameters of the separation process. The difficulty in performing extractive distillation is quantitatively analyzing the nature of the solvent when the relative volatility changes and describing the relationship between the curvature change of the residue curve and the parameters of the separating process using QSPR. 4

ACCEPTED MANUSCRIPT Some reviews and books have studied extractive distillation for separating azeotropes in terms of theoretical calculation of the column and application of extractive distillation [48, 49]. For example, Luyben and Chien [50] contributed to the knowledge of extraction by analyzing a phase diagram, the operating mode, and dynamic control. Lei et al. [4] reviewed extractive distillation and classified extractive distillation according to several aspects, such as the number of

PT

columns and types of solvents used. This analysis provided a detailed illustration of a specific case. Gerbaud V et al. [51] studied extractive distillation using a mathematical calculation of the

RI

equipment phase diagram to theoretically account for the establishment of extractive distillation in a published book. Mahdi et al. [52] reviewed the type of solvent used in extractive distillation and

SC

analyzed the superiority of different solvents. The aim of the present study is to review each step of extractive distillation in recently published papers and the future development of extractive

NU

distillation according to thermodynamic analysis, solvent selection, process design, and dynamic control. Fig. 1 shows the classification and domain of extractive distillation.

MA

2. Phase equilibrium

The azeotropic phenomenon is when the composition of a separated mixture in the vapor and

D

liquid phases is the same. Relative volatility is typically used to describe the azeotropic

PT E

phenomenon and is a criterion to assess the feasibility of extractive distillation. The expression of relative volatility is defined as Eq. 1

yi / xi γ P0  i i0 y j / x j γ j Pj

1

CE

αij 

where x is the molar fraction in the liquid phase, y is the molar fraction in the vapor phase, i, j

AC

represent the components in the separated mixture, γ is the activity coefficient, and P0 is the vapor pressure of the pure component. At the azeotropic point, αij is equal to 1, and this equation is unable to meet the requirement of separation of a mixture via single distillation [53]. Vapor liquid equilibrium (VLE) data are used to judge whether the property package used in a simulation is correct. Before the simulation and modeling of extractive distillation, the first step is to obtain VLE data. Although thermodynamic models, such as UNIQUAC, Wilson and NRTL, can be used to regress VLE data, the results typically show some deviation from experimental data. Thus, thermodynamic regression is necessary to determine the binary interaction parameters and reduce the gap between the predicted data and experimental data. The root-mean-square deviation 5

ACCEPTED MANUSCRIPT (RMSD) is usually used as a criterion for judging the gap between experimental data and predicted data by thermodynamic regression. When the RMSD value is generally considered to be less than 1%, the thermodynamic model is considered to be of high accuracy and the binary interaction parameters obtained by the regression of the model are also considered to be reliable. COSMO-RS is a model that is based on quantum computation of the molecular structure, and compared with

PT

other models, this model requires fewer parameters and, as a promising model, is used to screen solvents in extractive distillation [54]. The published literature studying the VLE of separated

RI

mixtures with solvents and the thermodynamic models are listed in Table 1. According to the separation scale of extractive distillation, the binary azeotrope mixtures, which can be separated

SC

with extractive distillation, are divided into three types in terms of the T-x y diagrams, and Fig. 2 shows the three types. Examples of extractive distillation focusing on the separation of azeotropes

NU

are listed in Table 1.

Azeotropes have been analyzed in various studies, and analysis of the azeotrope type can lead

MA

to the development of extractive distillation. The minimum boiling azeotropes and maximum boiling azeotropes are shown in Figs. 2 (a) and 2 (b), respectively; these azeotropes are often used

D

in industry and have been broadly studied using extractive distillation. Separation of mixtures with

PT E

similar boiling relies on extractive distillation. The remarkable characteristics of a mixture is that the vapor line and liquid line in the T-x diagram become closer as the component composition changes; the mixture cannot form an azeotrope, but the relative volatility is very close to 1. The

CE

relevant T-x diagrams are shown in Fig. 2 (c). The mixtures shown in Fig. 2 (c) can theoretically be separated via single distillation, but a large column is required to achieve separation, so

AC

extractive distillation is the first choice for separating the type of mixture described above. 3. Selection of solvent in extractive distillation The solvent is the third component added to the separated mixture and is used to break the azeotropic phenomenon and change the relative volatility of the mixture to be separated. The selected solvent must possess certain qualities, such as it being stable, noncorrosive, inexpensive, selective, having a low boiling point and toxicity, and so on, and these qualities have been previously discussed in the literature [55]. It is difficult to select a suitable solvent from a large number of candidates. The initial method of solvent selection is to examine the primary properties of the solvent and separated mixture, such as the distribution coefficients, selectivities, solvent 6

ACCEPTED MANUSCRIPT losses, and so on. Research on solvent selection has attracted the attention of many scholars, and Computer-Aided-Molecular-Design (CAMD) and QSPR are widely used for solvent selection [37]. 3.1 CAMD The CAMD method was proposed in the 1980s because the introduction of CAMD provided

PT

a new method for solvent selection, and this method has slowly expanded to other processes, such as gas absorption, liquid-liquid extraction, extractive distillation and so on [56, 57]. CAMD is

RI

used to generate molecular structures with certain rules, and the newly formed molecule is tested and its molecular properties are matched, which is essential in extractive distillation. Kossack et

SC

al.[58] selected a solvent for separating acetone and methanol by using CAMD; after primary selection, the solvent was selected based on its selectivity at an infinite dilution via the

NU

rectification body method, and then, a rigorous mixed-integer optimization of the entire extractive flowsheet for the remaining entrainer candidates was performed to fix the remaining design

MA

degrees of freedom and determine the best entrainer. The application of CAMD is based on the contribution of UNIFAC, and all of the work on CAMD has been conducted either by using

D

software or mathematic programming using special solution techniques, such as the support grid

PT E

methodology [59] or branch-and-bound algorithm [60] , to improve the accuracy of the CAMD method. A single solvent is commonly used in extractive distillation, and many systems have been separated with the use of a solvent containing a single component [61-63], but considering the cost,

CE

solvents containing two or more components are now considered for use in extractive distillation [64-66]. In batch extractive distillation, researchers have tried to analyze the feasibility of using

AC

light and intermediate solvents from the point of thermodynamics, and the type of the solvents used in extractive distillation are listed in Table 2. 3.2 QSPR

Dong et al. [67] conducted a multiscale study on ionic liquids and found that the scale promted the organic solvent, improving the development of extractive distillation. The change in the relative volatility of a binary mixture typically depends on the solvent added to the mixture; using the separation of isobutyl acetate/isobutyl alcohol as an example [68], the components N,N-dimethylformamide, 1-hexanol, and butyl propionate were selected as alternative solvents. According to the x-y diagram drawn from the VLE data, the change of the relative volatility is 7

ACCEPTED MANUSCRIPT different based on the different solvents used; butyl propionate and 1-hexanol increased the relative volatility of isobutyl acetate and isobutyl alcohol, but the effect of butyl propionate was superior to that of the 1-hexanol. By contrast, N,N-dimethylformamide decreased the relative volatility of isobutyl acetate and isobutyl alcohol. What are the differences among the alternative solvents? Further research on the component structure and component property is needed to

PT

provide a clear explanation. QSPR is used to study and determine the quantitative relationship between the activity of a compound and its molecular structure or its physical and chemical

RI

properties based on the method of mathematical statistics. The outcome of the method is the generation of a model that connects the material properties and material structure; the established

SC

model is used to predict the molecular properties of a new component or a nonexistent component with specific molecular groups. The process of model building typically occurs through four steps:

NU

data collection, calculation and selection of molecular descriptors, model building, and validation of the model. The crucial step in building the model is to select molecular descriptors related to

MA

the material properties from various alternative molecular descriptors. The method of descriptor selection occurs via three types [69]: classical methods [70, 71], artificial intelligence-based

D

methods [72-74] and miscellaneous methods [75-79]. Although the methods described above can

PT E

provide a fast strategy for molecular descriptor selection, every method has disadvantages; for example, the disadvantage of the forward selection method included in the classical methods is that several descriptors will lead to a good prediction, while the use of single descriptors leads to

CE

poor prediction; in this case, none of the molecular descriptors will be selected. The existing modeling method has two parts: a linear modeling method, such as multiple linear regression, and

AC

a nonlinear modeling method, such as artificial neural network. Validation of the QSPR model is typically via internal and external cross validation. Early in the development of QSPR technology, QSPR was used in drug research [80, 81], and with further development, QSPR has been used to study the material properties of single components [82, 83] and binary component [84-86]. Recently, QSPR has been used in extractive distillation to predict the relative volatility of a separated mixture with a solvent added [46, 47]. The models study a specific mixture and provide a relatively accurate prediction of the relative volatility. Nevertheless, no papers have been published on general factors that can explain the changes in the relative volatility applied to all systems. Fig. 3 shows the contents of the current 8

ACCEPTED MANUSCRIPT study and the future development of extractive distillation. QSPR might be able to explain the nature of the effect of the solvent on the mixture to be separated and to select a solvent for extractive distillation. A suitable solvent can promote the separation of the mixture via extractive distillation. If a relationship between the molecular descriptors and solvent is built, then this model will provide a solid foundation for developing the extractive distillation process.

PT

5. Process design of extractive distillation 5.1 Pressure selection

RI

Pressure is the primary factor that should be confirmed before beginning extractive distillation. Increasing the relative volatility in the extractive distillation column can facilitate

SC

separation and reduce TAC. Many scholars have conducted extensive research on pressure selection for the extractive distillation column [87-89]. Luyben [90] increased the pressure of the

NU

two columns in extractive distillation to separate acetone and chloroform, and the aim of that paper was to guarantee the use of cooling water in the condenser. In some cases, the boiling point

MA

of the component distilled in the top of the column is lower, so inexpensive cooling water cannot be used as the heat sink. The pressure of the column should be increased to ensure that the

D

temperature of the reflux-drum is approximately 323 K. To reduce the consumption of steam, it is

PT E

necessary to reduce the pressure of the column. However, there are some limitations that restrict pressure selection in some systems. If the components to be separated are thermally unstable, there will be a maximum temperature in the column based on the purity of the product. The pressure of

CE

the column can be fixed if the pressure drop is known; then, the pressure of the condenser can also be obtained by considering the purity of the distillate, and the temperature can then also be fixed.

AC

If the temperature is lower than the temperature needed to use water as a cold sink, then expensive refrigeration is required. Another limitation focuses on the light component; if the pressure needed to meet the requirement of 323 K in the reflux-drum is near the critical pressure of the separated component and the density difference of the vapor and fluid phase is small, then hydraulic problems restrict the high pressure in the column; therefore, refrigeration or vapor recompression is essential for these systems. 5.2 Determination of the distillation sequence for continuous extractive distillation The separating configuration has a fatal effect on the subsequent economics and dynamic control. The type of solvent plays an essential role in confirming the separating configuration. The 9

ACCEPTED MANUSCRIPT ternary phase diagram is a useful tool for analyzing phase behavior [91-93]. Kiva et al. [91] used a ternary phase diagram to classify different ternary mixtures, and using ternary phase diagram can improve extractive distillation by determining solvent selection and the feasibility of the separation process. Rodriguez-Donis et al.[94] noted that the 0.0-1, 1.0-1a, 1.0-1b and 1.0-2 classes can meet all of the situations that occur in extractive distillation: the 0.0-1 class represents

PT

a low relative volatility mixture, the 1.0-1a class explains the separation of a minimum boiling temperature (maximum boiling temperature) azeotropic mixture with a heavy (light) solvent, the

RI

1.0-2 class can explain the separation of a maximum boiling temperature (minimum boiling temperature) azeotropic mixture with a heavy (light) solvent, and the 1.0-1b class can explain

SC

either the maximum or minimum boiling temperature of an azeotropic mixture with an intermediate solvent. Fien and Liu [95] stated that the split strategy can be divided into direct and

NU

indirect methods. The direct method indicates that the low-boiling component is separated at the top of the first column. By contrast, the indirect method indicates that the high-boiling component

MA

is initially separated at the bottom of the first column. Many studies have examined the common situation of the 0.0-1 class [37, 96]. Separation of a minimum boiling point azeotropic mixture

D

with a light solvent has rarely been studied and is not considered in this review. In Fig. 4,

PT E

separation of a minimum boiling temperature azeotropic mixture with an intermediate boiling point solvent is selected as an example, and the solid line in the diagram represents the material balance line, which graphically shows the separation process. Two separating configurations can

CE

be used to separate the mixture. The first split configuration is called direct split; the configuration uses two columns that are called the extractive and solvent recovery columns. Feed stream F1 is

AC

fed into the extractive distillation column, and the recycled solvent from the top of the solvent recovery column is fed into the extractive distillation at a higher location. Light-boiling component A is distilled at the top of the extractive column, the mixture at the bottom of the column with trace A is fed into the solvent recovery column, the solvent reaches the top of the solvent recovery column, and pure B is obtained at the bottom of the recovery column. Another configuration used is that described above, except that component B is obtained at the bottom of the first column, component A is obtained at the top of the second column, and the solvent is recycled from the bottom of the second column to the first column. The final decision of which separation configuration to use depends on the minimal TAC. The residue curve is an effective 10

ACCEPTED MANUSCRIPT tool for identifying the separation tendency, analyzing the feasibility of the technology and predicting the separation efficiency. TAC is widely used in the distillation process [97, 98]. Timoshenko et al [99] designed an extractive distillation process by analyzing the feature of the residue curve. Wahnschafft et al. [100] confirmed the separation and products purities by analyzing the direction of the residue curve. In a previous study [101] of triple-column

PT

pressure-swing distillation, we showed that a change of the residue curve at different pressures can lead to a change of the separation configuration and parameters. In extractive distillation,

RI

introduction of the solvent can obviously change the direction of the residue curve. Thus, if the relationship between the curvature of the residue curve and separation parameters is determined, it

SC

can decrease the time required and increase the efficiency of the distillation process, promoting the development of extractive distillation.

NU

6. Process intensification

For extractive distillation, optimization of the flowsheet and hybridization of extractive

MA

distillation with other technologies are the two main methods used to reduce energy consumption and capital costs. Fig. 5 shows a schematic diagram of the optimization and improvement of

PT E

6.1 Process optimization

D

extractive distillation.

Optimization involves the identification of the optimal values of the final variables, and the final result is to maximize or minimize the objective function while meeting the specified

CE

constraints. Under normal conditions, the steady state of extractive distillation is simulated using process simulators, such as Aspen Plus, PRO//Ⅱ, and ChemCAD, among others. The equilibrium

AC

model and rate-based model are the two most commonly used models for simulation; the relevant publications are listed in Table 1. The variables, such as the feed location, number of theoretical plates, quantity of the solvent, and reflux ratio, should be optimized to obtain the minimal TAC. Douglas [102] provided a description of the parameters and an equation for the capital and energy costs in TAC. To reduce the optimization time and provide a clear picture of optimization, optimization methods were proposed in some previously published papers to adjust the variables and obtain the minimum energy consumption and equipment costs [103-105]. The sequential iterative optimization method is widely used in continuous extractive distillation to obtain the optimal parameters, and some published papers have accepted the method and provided the 11

ACCEPTED MANUSCRIPT optimization steps [106, 107]. The simulated annealing algorithm (SAA) and colony optimization method can be used to optimize the distillation process [108]. Introducing these methods to extractive distillation can also lead to optimization of the process. In the optimized process, the quantity of solvent and the number of theoretical plates are the outer iterative loop variables, enabling the optimization of the number of stages. The locations of the feed and the reflux ratio

PT

can be selected as inner iterative loop variables. The minimal TAC can be obtained after a continually iterative calculation. In the simulation of recovering of organic solvents from aqueous

RI

waste mixtures, the genetic algorithm is used to optimize the process[105]. Kossack et al. used the SR-MINLP technique to further optimize extractive distillation after primary optimization. This

SC

technique was efficient for optimizing extractive distillation according to both continuous and discrete variables [58]. The main optimization factors of the batch extractive distillation

NU

concentrate on the number of separated components, and the influence of some parameters, such as the operation policy and quantity of the solvent, should be accounted for in the final TAC;

MA

optimization of batch extractive distillation, as has been previously studied [103, 109]. 6.2 Improvement of ED

D

Optimization is used to reduce the TAC by adjusting the parameters of the column settings.

PT E

To obtain a more effective reduction of the TAC, it is necessary to improve extractive distillation and simplify the separation process. Some methods have been implied in extractive distillation, which can be clarified into different aspects. Some techniques rely on the reuse of heat, such as

CE

thermal coupling, while some methods focus on improving the separating method, such as by using an extractive dividing wall, and Table 3 lists some published papers that studied various

AC

extractive distillation methods to reduce the cost of the separating process. Due to the energy and capital advantages of using a dividing wall column, this technique began to be used in industrial application a long time ago, and the first dividing wall column was used in 1985 [110]; due to the energy saving in extractive distillation [64, 111, 112], extractive dividing-wall columns (EDWCs) were further studied [113-116]. In recent studies, EDWCs have been shown to have two different schemes: single column and double columns. The single column scheme has the right and left sections divided by a wall; mixture A/B to be separated is fed into the column, and solvent S is fed into a higher location of the column than the mixture. Solvent S interacts with the component to be separated and reduces 12

ACCEPTED MANUSCRIPT the vapor pressure of the heavy component B; hence, the volatility of B decreases and the light component A is obtained at the top of the left part. Then, the heavy component B is distilled from the top of the right part and pure solvent S is recycled back onto the column [114, 117, 118]. The obvious characteristic of EDWCs is that the two columns used for conventional continuous extractive distillation are integrated into one column and the left and right part operate in the same

PT

section and use a single reboiler, which avoids the use of unnecessary columns and reboilers and directly reduces the capital costs and consumption of steam. The two columns scheme consists of

RI

a main column and a rectifying column; the rectifying column does not use a reboiler. Xia et al. [115] studied the separation of methylal and methanol and compared the TAC of the optimum

SC

EDWC scheme with conventional continuous extractive distillation. EDWC saved 11.6% of energy, which indicated that EDWCs are superior to other columns.

NU

7. Dynamic control

Extractive distillation is a process that contains various input and output variables [119].

MA

Some characteristics involving distillation control make it difficult to meet the requirements of industry. The interaction between the input and output variables makes extractive distillation

D

difficult to control, and many researchers have studied the dynamic control of extractive

PT E

distillation to generate an optimal control strategy for different systems using dynamic simulation software[120,121]. The selection of the pairing should satisfy two criteria. First, the interaction between manipulated variables and control variables should be large enough. Second, the pairing

CE

should be able to reduce the time constants of the controllers. In view of the point discussed above, some methods have been developed based on

AC

arithmetical operation to select the pairing. Bristol [122] proposed the relative gain array for multivariable control to solve the theoretical and practical deficiencies of matrix representation for the design of the system. Hovd and Skogestad [123] extended the relative gain array method to open-loop unstable plants to choose pairings, but the method neglected realistic dynamic information and the interaction loop pairing was often incorrect. The dynamic relative gain array method was proposed to solve the problem mentioned above by Witcher and McAvoy [124]; the method used the transfer function model to calculate the relative gain array instead of the steady-state gain matrix, but this method is tedious and difficult to apply. An effective relative gain array method was presented by Xiong et al. [125], and the method combined the two means. The 13

ACCEPTED MANUSCRIPT dynamic information of the distillation process can be reflected by the effective relative gain array method using a simple and effective method. Fig. 6 shows a case that involved the control scheme of extractive distillation, tuning condition and disturbance response curve, which are criteria that can be used to assess the control

effect.

The

conventional

control

method

in

extractive

distillation

is

the

PT

proportional-integral-derivative (PID) control method. A case describing the optimal control scheme and controllers is shown in Fig. 6. In the basic control scheme, the flow rate controllers,

RI

proportion controllers of the solvent to the fresh feed, liquid level controllers, pressure controllers and temperature controllers are involved; the basic control scheme can achieve optimal control

SC

under disturbances in most cases and is widely used in industrial production. For some separation systems, more complex controllers, such as a composition-temperature cascade control, the

NU

reboiler heat duty/fresh feed flow rate (QR/F) scheme, and the fixed reflux ratio (RR) scheme, are needed to improve the control strategy.

MA

The tuning method is used to obtain the tuning parameters, which have an important effect on the controllers; the result of tuning within the composition and temperature is crucial for setting

D

the control strategy. The Ziegler-Nichols [126] and Tyreus-Luyben [127] methods are widely used

PT E

for tuning, but they are limited for calculating the controller, so the tuning method of PID controllers has attracted the attention of researchers [128-131]. PID controllers based on various models, such as the genetic algorithm [132] and the so-called n-th order lag process [133], were

CE

proposed to achieve the optimal control scheme. A key point that determines the control result is the final robust performance of the process

AC

when facing a feed disturbance, such as feed flow and feed composition. Robust performance is typically demonstrated by the purity of the product, temperature of the sensitive stage, distillation and bottom flow rate. Fig. 6 shows a common trace of a robust performance. To determine whether a trace is robust, the standards used are traces of the temperature and composition, which show a slight deviation and take little to time return to the steady state. The PID controller is a widely used method in extractive distillation and is more suitable for industrial processes because of its simplicity and low requirements. However, the conventional PID controller has some disadvantages on some occasions, and some PID controller improvements have been made. Most chemical processes, such as multi-input variable and 14

ACCEPTED MANUSCRIPT multi-output variable processes, are multivariable, and the interaction between them is non-negligible. The decoupling PID controller can solve the problem of multi-input and multi-output [119]. The fuzzy-PID controller method has also been studied because of its fast execution compared to the conventional control method and because it can be used to establish a real-time control system [134].

PT

Dynamic control in extractive distillation is complex due to the addition of a third component. The solvent flow rate/fresh feed flow rate (S/F) scheme is used to ensure that there is sufficient

RI

solvent in extractive distillation, but the optimal control scheme is difficult to obtain. The third component can occasionally be the key point in process control. Luyben [135] investigated the

SC

effect of three solvents on the controllability of the separation of acetone and methanol using extractive distillation, and the results showed that the three solvents all achieved stabilization, but

NU

the product purity was poor with chlorobenzene because the solvent was facing a feed disturbance. To solve the problem of the lack of stable dynamic control with a specific solvent, Wang et al.

MA

[136] presented a method to improve the amount of solvent used and sacrifice the TAC to obtain robust control. This method is also discussed in the separation of ethanol and water, and the same

D

conclusion was drawn [137].

PT E

8. Conclusions

Six aspects of extractive distillation were reviewed herein: thermodynamic analysis, QSPR, the solvent used, process design, process intensification, and dynamic control. Extractive

CE

distillation is a common technology for separating azeotropes, which are non-pressure-sensitive or close-to-boiling-point mixtures. Finding a suitable solvent is the key aspect of extractive

AC

distillation. The role of QSPR in extractive distillation is becoming more important. A published paper reported the QSPR model, which relates the molecular structure and melting point of compounds, from which we can confirm that the information of microscopic molecular structure can be used to represent and predict some properties of matters. Some established models can predict the ternary azeotropic temperature with highly accuracy; in other words, it is feasible to identify the properties of a mixture with molecular descriptors. So, the phenomenon that it can be easier to separate an azeotropic mixture with the addition of solvent can be explained from the microcosmic aspect; in addition, QSPR is a common technology to explain the quantitative relationship between a compound structure and its various physicochemical properties; therefore, 15

ACCEPTED MANUSCRIPT QSPR can be a useful method to select a solvent in extractive distillation. Due to its high energy consumption and high investment, optimization of extractive distillation and improvements based on traditional extractive distillation should be further explored. Process intensification can help to reduce energy consumption and capital costs while protecting the environment. Dynamic control could promote the development of industry in the direction of intelligence and security. We

PT

propose that the most important aspects of extractive distillation to explore are the natural relationship between the solvent and separated mixture and the factors that lead to changes in

RI

relative volatility.

QSPR

Quantitative structure-property relationship

VLE

Vapor liquid equilibrium Computer-Aided-Molecular-Design

αij

Relative volatility

Pi0 Pj0

Vapor pressure of the pure component

γi, γj

Activity coefficient

TAC

Total annual cost

D

MA

NU

CAMD

Extractive dividing-wall columns

PID

Proportional-integral-derivative

QR/F

Reboiler heat duty/fresh feed flow rate

RR

Fixed reflux ratio

PID

Proportional-integral-derivative

S/F

Solvent flow rate/fresh feed flow rate

AC

CE

PT E

EDWCs

SAA

SC

Nomenclature

Simulated annealing algorithm

16

ACCEPTED MANUSCRIPT References

AC

CE

PT E

D

MA

NU

SC

RI

PT

[1] H Li, Y Wu, X Li, et al. State-of-the-Art of Advanced Distillation Technologies in China. Chem Eng Technol., 2016, 39(5): 815-833. [2] S David, Lively, P Ryan. Seven chemical separations to change the world. Nature, 2016, 532(7600): 435. [3] S Widagdo, W D Seider. Journal review. Azeotropic distillation. AlChE J., 1996, 42(1): 96-130. [4] Z Lei, C Li, B Chen. Extractive Distillation: A Review. Sep. Purif. Rev., 2003, 32(2): 121-213. [5] S Liang, Y Cao, X Liu, et al. Insight into pressure-swing distillation from azeotropic phenomenon to dynamic control. Chem. Eng. Res. Des., 2017, 117: 318-335. [6] G Modla, P Lang. Separation of an Acetone−Methanol Mixture by Pressure-Swing Batch Distillation in a Double-Column System with and without Thermal Integration. Ind. Eng. Chem. Res., 2010, 49(8): 3785-3793. [7] W Huang, H Li, R Wang, et al. Application of the aldolization reaction in separating the mixture of ethylene glycol and 1,2-butanediol: Kinetics and reactive distillation. Chem Eng Process., 2017, 120: 173-183. [8] X Li, R Wang, J Na, et al. Reversible Reaction-Assisted Intensification Process for Separating the Azeotropic Mixture of Ethanediol and 1,2-Butanediol: Reactants Screening. Ind. Eng. Chem. Res., 2018, 57(2): 710-717. [9] H Li, Z Zhao, J Qin, et al. Reversible Reaction-Assisted Intensification Process for Separating the Azeotropic Mixture of Ethanediol and 1,2-Butanediol: Vapor–Liquid Equilibrium and Economic Evaluation. Ind. Eng. Chem. Res., 2018, 57(14): 5083-5092. [10] J Prausnitz, R Anderson. Thermodynamics of solvent selectivity in extractive distillation of hydrocarbons[J]. AlChE J., 1961, 7(1): 96-101. [11] H Matsuda, H Takahara, S Fujino, et al. Selection of entrainers for the separation of the binary azeotropic system methanol+dimethyl carbonate by extractive distillation. Fluid Phase Equilib., 2011, 310(1–2): 166-181. [12] A Y Sazonova, V M Raeva, T V Chelyuskina, et al. Choice of extractive agents for separating benzene-perfluorobenzene biazeotropic mixture based on thermodynamic criterion. Theor. Found. Chem. Eng., 2014, 48(2): 148-157. [13] Y Wang, P Cui, Y Ma, et al. Extractive distillation and pressure‐swing distillation for THF/ethanol separation. J. Chem. Technol. Biotechnol., 2015, 90(8): 1463-1472. [13] M Randall, W A Webb. Separation Process. Ind.eng.chem, 1939. [14] P D G Kortüm, D-C A Bittel. Die Trennung primärer, sekundärer und tertiärer aromatischer Amine durch extraktive Destillation. I. Entwicklung und Prüfung einer Laboratoriumskolonne. Chem. Ing. Tech., 1956, 28(1): 40–44. [15] H Yatim, P Moszkowicz, M Otterbein, et al. Dynamic simulation of a batch extractive distillation process. Comput. Chem. Eng., 1992, 17(1): S57–S62. [16] R Düssel, J Stichlmair. Separation of azeotropic mixtures by batch distillation using an entrainer. Comput. Chem. Eng., 1995, 19(1): 113-118. [17] P Lang, Z Lelkes, P Moszkowicz, et al. Different operational policies for the batch extractive distillation. Comput. Chem. Eng., 1995, 19(1): 645-650. [18] V Varga, E R Frits, V Gerbaud, et al. Separation of azeotropes in batch extractive stripper with intermediate entrainer. Computer Aided Chemical Engineering., 2006, 21(06): 17

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

793-797. [19] I M Mujtaba. Optimization of Batch Extractive Distillation Processes for Separating Close Boiling and Azeotropic Mixtures. Chem. Eng. Res. Des., 1999, 77(7): 588-596. [20] R A Cook, W F Fvrter. Extractive distillation employing a dissolved salt as separating agent. Can. J. Chem. Eng., 1968, 46(2): 119-123. [21] M A M Hussain, P H Pfromm. Reducing the Energy Demand of Cellulosic Ethanol through Salt Extractive Distillation Enabled by Electrodialysis. Sep. Sci. Technol., 2013, 48(10): 1518-1528. [22] X Wang, L Xie, P Tian, et al. Design and control of extractive dividing wall column and pressure-swing distillation for separating azeotropic mixture of acetonitrile/ N -propanol. Chem. Eng. Process., 2016, 110: 172-187. [23] O A Deorukhkar, B S Deogharkar, Y S Mahajan. Purification of tetrahydrofuran from its aqueous azeotrope by extractive distillation: Pilot plant studies. Chem. Eng. Process., 2016, 105: 79-91. [24] S Navarrete-Contreras, M Sánchez-Ibarra, F O Barroso-Muñoz, et al. Use of glycerol as entrainer in the dehydration of bioethanol using extractive batch distillation: Simulation and experimental studies. Chem. Eng. Process., 2014, 77: 38-41. [25] Z Lei, R Zhou, Z Duan. Separating 1-Butene and 1, 3-Butadiene with DMF and DMF with Salt by Extractive Distillation. J. Chem. Eng. Jpn., 2002, 35(2): 211-216. [26] M T G Jongmans, B Schuur, A B de Haan. Ionic Liquid Screening for Ethylbenzene/Styrene Separation by Extractive Distillation. Ind. Eng. Chem. Res., 2011, 50(18): 10800-10810. [27] Y Dong, C Dai, Z Lei. Extractive distillation of methylal/methanol mixture using the mixture of dimethylformamide (DMF) and ionic liquid as entrainers. Fuel., 2018, 216: 503-512. [28] Y Dong, C Dai, Z Lei. Extractive distillation of methylal/methanol mixture using ethylene glycol as entrainer. Fluid Phase Equilib., 2018, 462: 172-180. [29] I Díaz, J Palomar, M Rodríguez, et al. Ionic liquids as entrainers for the separation of aromatic–aliphatic hydrocarbon mixtures by extractive distillation. Chem. Eng. Res. Des., 2016, 115: 382-393. [30] C Dai, Z Lei, X Xi, et al. Extractive Distillation with a Mixture of Organic Solvent and Ionic Liquid as Entrainer. Ind. Eng. Chem. Res., 2014, 53(40): 15786-15791. [31] J Han, Z Lei, Y Dong, et al. Process intensification on the separation of benzene and thiophene by extractive distillation. AlChE J., 2015, 61(12): 4470-4480. [32] Z Zhu, X Geng, W He, et al. Computer-Aided Screening of Ionic Liquids as Entrainers for Separating Methyl Acetate and Methanol via Extractive Distillation. Ind. Eng. Chem. Res., 2018. (10.1021/acs.iecr.8b01355) [33] W F Furter. Extractive Distillation by Salt Effect. Chem. Eng. Commun., 1992, 116(1): 35-40. [34] Z Zhu, Y Ri, H Jia, et al. Process Evaluation on the Separation of Ethyl acetate and Ethanol using Extractive Distillation with Ionic Liquid. Sep. Purif. Technol., 2017, 181: 44-52. [35] H-H Chen, M-K Chen, B-C Chen, et al. Critical Assessment of Using Ionic Liquid as Entrainer via Extractive Distillation. Ind. Eng. Chem. Res., 2017, 56(27): 7768-7782 [36] G Fieg. Distillation Design and Control Using Aspen Simulation. Von W. L. Luyben (page 312). Chem. Ing. Tech., 2015, 87(3): 312-312. [37] P Lek-utaiwan, B Suphanit, P L Douglas, et al. Design of extractive distillation for the separation of close-boiling mixtures: Solvent selection and column optimization. Comput. 18

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

Chem. Eng., 2011, 35(6): 1088-1100. [38] H Li, J Zhang, D Li, et al. Monte Carlo simulations of vapour–liquid phase equilibrium and microstructure for the system containing azeotropes. Molecular Simulation., 2017, 43(13-16): 1125-1133. [39] H Li, P Zhou, J Zhang, et al. A theoretical guide for screening ionic liquid extractants applied in the separation of a binary alcohol-ester azeotrope through a DFT method. J. Mol. Liq., 2018, 251: 51-60. [40] W Yin, S Ding, S Xia, et al. Cosolvent selection for benzene− cyclohexane separation in extractive distillation. J. Chem. Eng. Data., 2010, 55(9): 3274-3277. [41] B Van Dyk, I Nieuwoudt. Design of solvents for extractive distillation. Ind. Eng. Chem. Res., 2000, 39(5): 1423-1429. [42] T Gaudin, P Rotureau, G Fayet. Mixture Descriptors toward the Development of Quantitative Structure–Property Relationship Models for the Flash Points of Organic Mixtures. Ind. Eng. Chem. Res., 2015, 54(25): 6596-6604. [43] A A Oliferenko, P V Oliferenko, J S Torrecilla, et al. Boiling Points of Ternary Azeotropic Mixtures Modeled with the Use of the Universal Solvation Equation and Neural Networks. Ind. Eng. Chem. Res., 2012, 51(26): 9123-9128. [44] A Abbasi, R Eslamloueyan. Determination of binary diffusion coefficients of hydrocarbon mixtures using MLP and ANFIS networks based on QSPR method. Chemom. Intell. Lab. Syst., 2014, 132(6): 39-51. [45] M Shahlaei. Descriptor Selection Methods in Quantitative Structure–Activity Relationship Studies: A Review Study. Chem. Rev., 2013, 113(10): 8093-8103. [46] Y-M Kang, Y Jeon, S B Hwang, et al. Quantitative Structure-Relative Volatility Relationship Model for Extractive Distillation of Ethylbenzene/p-Xylene Mixtures: Application to Binary and Ternary Mixtures as Extractive Agents. Bull. Korean Chem. Soc., 2016, 37(4): 548-555. [47] Y-M Kang, Y Jeon, G Lee, et al. Quantitative Structure Relative Volatility Relationship Model for Extractive Distillation of Ethylbenzene/p-Xylene Mixtures. Ind. Eng. Chem. Res., 2014, 53(27): 11159-11166. [48] A B Pereiro, J M M Araújo, J M S S Esperança, et al. Ionic liquids in separations of azeotropic systems – A review. J. Chem. Thermodyn., 2012, 46(3): 2-28. [49] H J Huang, S Ramaswamy, U W Tschirner, et al. A review of separation technologies in current and future biorefineries. Sep. Purif. Technol., 2008, 62(1): 1-21. [50] W L Luyben, I-L Chien. Design and control of distillation systems for separating azeotropes. John Wiley & Sons, Hoboken New Jersey, 2011. [51] A Górak, Z Olujić. Distillation: Equipment and Processes. Academic Press., 2014, UK. [52] T Mahdi, A Ahmad, M M Nasef, et al. State-of-the-Art Technologies for Separation of Azeotropic Mixtures. Sep. Purif. Rev., 2014, 44(4): 308-330. [53] J Gmehling, B Kolbe, M Kleiber, et al. Chemical Thermodynamics for Process Simulation. Wiley-VCH, 2012. [54] F Eckert, A Klamt. Fast solvent screening via quantum chemistry: COSMO‐RS approach[J]. AlChE J., 2002, 48(2): 369–385. [55] I D Gil, D C Botía, P Ortiz, et al. Extractive Distillation of Acetone/Methanol Mixture Using Water as Entrainer. Ind. Eng. Chem. Res., 2009, 48(10): 4858-4865. 19

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[56] N C And, L E K Achenie. Novel Mathematical Programming Model for Computer Aided Molecular Design. Ind. Eng. Chem. Res., 1996, 35(10): 3788-3794. [57] P M Harper, R Gani, P Kolar, et al. Computer-aided molecular design with combined molecular modeling and group contribution. Fluid Phase Equilib., 1999, 158-160(5): 337-347. [58] S Kossack, K Kraemer, R Gani, et al. A systematic synthesis framework for extractive distillation processes. Chem. Eng. Res. Des., 2008, 86(7): 781-792. [59] A I Papadopoulos, P Linke. A decision support grid for integrated molecular solvent design and chemical process selection. Comput. Chem. Eng., 2009, 33(1): 72-87. [60] G M Ostrovsky, L E Achenie, M Sinha. On the solution of mixed-integer nonlinear programming models for computer aided molecular design. Comput. Chem., 2002, 26(6): 645-660. [61] M A S S Ravagnani, M H M Reis, R M Filho, et al. Anhydrous ethanol production by extractive distillation: A solvent case study. Process Saf. Environ. Prot., 2010, 88(1): 67-73. [62] B Marrufo, S Loras, E Lladosa. Phase Equilibria Involved in the Extractive Distillation of Cyclohexane + Cyclohexene Using Diethyl Carbonate as an Entrainer. J. Chem. Eng. Data., 2011, 56(12): 4790-4796. [63] M T G Jongmans, A Londoño, S B Mamilla, et al. Extractant screening for the separation of dichloroacetic acid from monochloroacetic acid by extractive distillation. Sep. Purif. Technol., 2012, 98: 206-215. [64] X Dai, Q Ye, J Qin, et al. Energy-saving dividing-wall column design and control for benzene extraction distillation via mixed entrainer. Chem. Eng. Process., 2016, 100: 49-64. [65] A Y Sazonova, V M Raeva, A K Frolkova. Design of extractive distillation process with mixed entrainer. Chem Pap., 2015, 70(5): 594-601. [66] Y Zhao, T Zhao, H Jia, et al. Optimization of the composition of mixed entrainer for economic extractive distillation process in view of the separation of tetrahydrofuran/ethanol/water ternary azeotrope. J. Chem. Technol. Biotechnol., 2017, 92(9): 2433-2444. [67] K Dong, X Liu, H Dong, et al. Multiscale Studies on Ionic Liquids. Chem. Rev., 2017, 117(10): 6636-6695. [68] R Munoz, J Monton, M Burguet, et al. Separation of isobutyl alcohol and isobutyl acetate by extractive distillation and pressure-swing distillation: simulation and optimization. Sep. Purif. Technol., 2006, 50(2): 175-183. [69] M Shahlaei. Descriptor selection methods in quantitative structure-activity relationship studies: a review study. Chem. Rev., 2013, 113(10): 8093-8103. [70] S P Yang, S T Song, Z M Tang, et al. Optimization of antisense drug design against conservative local motif in simulant secondary structures of HER-2 mRNA and QSAR analysis. Acta Pharmacol. Sin., 2003, 24(9): 897-902. [71] Shushen Liu, Hailing Liu, A Chunsheng Yin, et al. VSMP:  A Novel Variable Selection and Modeling Method Based on the Prediction. J. Chem. Inf. Comput. Sci., 2003, 43(3): 964-969. [72] J H Wikel, E R Dow. The use of neural networks for variable selection in QSAR. Bioorg. Med. Chem. Lett., 1993, 3(4): 645-651. 20

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[73] M Jung, J Tak, Y Lee, et al. Quantitative structure-activity relationship (QSAR) of tacrine derivatives against acetylcholinesterase (AChE) activity using variable selections. Bioorg. Med. Chem. Lett., 2007, 17(4): 1082. [74] F R Burden, M G F And, D C Whitley, et al. Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks. J. Chem. Inf. Comput. Sci., 2000, 40(6): 1423. [75] P R Duchowicz, E A Castro, F M Fernández, et al. A new search algorithm for QSPR/QSAR theories: Normal boiling points of some organic molecules. Chem. Phys. Lett., 2005, 412(4): 376-380. [76] P R Duchowicz, M Fernández, J Caballero, et al. QSAR for non-nucleoside inhibitors of HIV-1 reverse transcriptase. Biorg. Med. Chem., 2006, 14(17): 5876-5889. [77] W Z And, A Tropsha. Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k-Nearest-Neighbor Principle. J. Chem. Inf. Comput. Sci., 2000, 40(1): 185. [78] M C U Araújo, T C B Saldanha, R K H Galvão, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom. Intell. Lab. Syst., 2001, 57(2): 65-73. [79] M Daszykowski, I Stanimirova, B Walczak, et al. Improving QSAR models for the biological activity of HIV Reverse Transcriptase inhibitors: Aspects of outlier detection and uninformative variable elimination. Talanta., 2005, 68(1): 54-60. [80] H Kubinyi. QSAR and 3D QSAR in drug design Part 1: methodology. Drug Discovery Today., 1997, 2(11): 457-467. [81] H Kubinyi. QSAR and 3D QSAR in drug design Part 2: applications and problems. Drug Discovery Today., 1997, 2(12): 538-546. [82] A Guendouzi, S M Mekelleche. Prediction of the melting points of fatty acids from computed molecular descriptors: a quantitative structure-property relationship study. Chem. Phys. Lipids., 2012, 165(1): 1-6. [83] E Pourbasheer, R Aalizadeh, J S Ardabili, et al. QSPR study on solubility of some fullerenes derivatives using the genetic algorithms-Multiple linear regression. J. Mol. Liq., 2015, 204: 162-169. [84] V P Solov’Ev, I Oprisiu, G Marcou, et al. Quantitative Structure–Property Relationship (QSPR) Modeling of Normal Boiling Point Temperature and Composition of Binary Azeotropes. Ind. Eng. Chem. Res., 2015, 50(24): 14162-14167. [85] A R Katritzky, I B Stoyanovaslavova, K Tämm, et al. Application of the QSPR Approach to the Boiling Points of Azeotropes. J. Phys. Chem. A., 2011, 115(15): 3475-3479. [86] S Ajmani, S C Rogers, M H Barley, et al. Application of QSPR to Mixtures. J. Chem. Inf. Model., 2006, 46(5): 2043-2055. [87] W L Luyben. Distillation column pressure selection. Sep. Purif. Technol., 2016, 168: 62-67. [88] X You, I Rodriguez-Donis, V Gerbaud. Low pressure design for reducing energy cost of extractive distillation for separating diisopropyl ether and isopropyl alcohol. Chem. Eng. Res. Des., 2016, 109: 540-552. [89] S Yang, Y Wang, G Bai, et al. Design and Control of an Extractive Distillation System for Benzene/Acetonitrile Separation Using Dimethyl Sulfoxide as an Entrainer. Ind. Eng. Chem. Res., 2013, 52(36): 13102-13112. 21

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[90] W L Luyben. Comparison of Extractive Distillation and Pressure-Swing Distillation for Acetone−Methanol Separation. Comput. Chem. Eng., 2013, 50(8): 1-7. [91] V N Kiva, E K Hilmen, S Skogestad. Azeotropic phase equilibrium diagrams: a survey. Chem. Eng. Sci., 2003, 58(10): 1903-1953. [92] Z Lelkes, P Lang, B Benadda, et al. Feasibility of extractive distillation in a batch rectifier. AlChE J., 1998, 44(4): 810-822. [93] J P Knapp, M F Doherty. Minimum entrainer flows for extractive distillation: A bifurcation theoretic approach. AlChE J., 1994, 40(2): 243-268. [94] I Rodriguez-Donis, V Gerbaud, X Joulia. Thermodynamic Insights on the Feasibility of Homogeneous Batch Extractive Distillation. 4. Azeotropic Mixtures with Intermediate Boiling Entrainer. Ind. Eng. Chem. Res., 2012, 51(18): 6489-6501. [95] G J A F Fien, Y A Liu. Heuristic Synthesis and Shortcut Design of Separation Processes Using Residue Curve Maps: A Review. Ind. Eng. Chem. Res., 1994, 33(11): 2505-2522. [96] I Rodriguez-Donis, V Gerbaud, X Joulia. Thermodynamic insights on the feasibility of homogeneous batch extractive distillation, 2. Low-relative-volatility binary mixtures with a heavy entrainer. Ind. Eng. Chem. Res., 2009, 48(7): 3560-3572. [97] L Jiménez, O M Wanhschafft, V Julka. Analysis of residue curve maps of reactive and extractive distillation units. Comput. Chem. Eng., 2001, 25(4-6): 635-642. [98] V Gerbaud, J Xavier, R D Ivonne, et al. Practical residue curve map analysis applied to solvent recovery in non-ideal binary mixtures by batch distillation processes. Chem. Eng. Process., 2006, 45(8): 672-683. [99] A V Timoshenko, E A Anokhina, A V Morgunov, et al. Application of the partially thermally coupled distillation flowsheets for the extractive distillation of ternary azeotropic mixtures. Chem. Eng. Res. Des., 2015, 104: 139-155. [100] O M Wahnschafft, J W Koehler, E Blass, et al. The product composition regions of single-feed azeotropic distillation columns. Ind. Eng. Chem. Res., 1992, 31(10): 2345-2362. [101] Z Zhu, D Xu, X Liu, et al. Separation of acetonitrile/methanol/benzene ternary azeotrope via triple column pressure-swing distillation. Sep. Purif. Technol., 2016, 169: 66-77. [102] J M Douglas. Conceptual Design of Chemical Processes. McGraw-Hill, 1988, New York. [103] I M Mujtaba. Optimization of Batch Extractive Distillation Processes for Separating Close Boiling and Azeotropic Mixtures. Chem. Eng. Res. Des., 1999, 77(7): 588-596. [104] P García-Herreros, J M Gómez, I n D Gil, et al. Optimization of the Design and Operation of an Extractive Distillation System for the Production of Fuel Grade Ethanol Using Glycerol as Entrainer. Ind. Eng. Chem. Res., 2011, 50(7): 3977-3985. [105] G Modla, P Lang. Removal and Recovery of Organic Solvents from Aqueous Waste Mixtures by Extractive and Pressure Swing Distillation. Ind. Eng. Chem. Res., 2012, 51(35): 11473-11481. [106] Q Wang, B Yu, C Xu. Design and Control of Distillation System for Methylal/Methanol Separation. Part 1: Extractive Distillation Using DMF as an Entrainer. Ind. Eng. Chem. Res., 2016, 51(3): 1281-1292. [107] Y C Chen, B Y Yu, C C Hsu, et al. Comparison of Heteroazeotropic and Extractive Distillation for the Dehydration of Propylene Glycol Methyl Ether. Chem. Eng. Res. Des., 2016, 111: 184-195. 22

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[108] Y Wang, G Bu, Y Wang, et al. Application of a simulated annealing algorithm to design and optimize a pressure-swing distillation process. Comput. Chem. Eng., 2016, 95: 97-107. [109] A A Barreto, I Rodriguez-Donis, V Gerbaud, et al. Optimization of Heterogeneous Batch Extractive Distillation. Ind. Eng. Chem. Res., 2011, 50(9): 5204-5217. [110] G. Parkinson. Dividing-wall columns find greater appeal, Chem. Eng. Prog., 2007, 103: 8-11. [111] Y C Wu, H C Hsu, I Chien. Critical Assessment of the Energy-Saving Potential of an Extractive Dividing-Wall Column. Ind. Eng. Chem. Res., 2013, 52(15): 5384-5399. [112] L Sun, Q Wang, L Li, et al. Design and Control of Extractive Dividing Wall Column for Separating Benzene/Cyclohexane Mixtures. Ind. Eng. Chem. Res., 2014, 53(19): 8120– 8131. [113] G Modla. Energy saving methods for the separation of a minimum boiling point azeotrope using an intermediate entrainer. Energy., 2013, 50: 103-109. [114] M Xia, B Yu, Q Wang, et al. Design and Control of Extractive Dividing-Wall Column for Separating Methylal-Methanol Mixture. Ind. Eng. Chem. Res., 2012, 51(49): 16016-16033. [115] Y C Wu, P H-C Hsu, I L Chien. Critical Assessment of the Energy-Saving Potential of an Extractive Dividing-Wall Column. Ind. Eng. Chem. Res., 2013, 52(15): 5384-5399. [116] Y Tavan, S Shahhosseini, S H Hosseini. Design and simulation of ethane recovery process in an extractive dividing wall column. J. Clean. Prod., 2014, 72: 222-229. [117] H Zhang, Q Ye, J Qin, et al. Design and Control of Extractive Dividing-Wall Column for Separating Ethyl Acetate–Isopropyl Alcohol Mixture. Ind. Eng. Chem. Res., 2013, 53(3): 1189-1205. [118] M Xia, Y Xin, J Luo, et al. Temperature Control for Extractive Dividing-Wall Column with an Adjustable Vapor Split: Methylal/Methanol Azeotrope Separation. Ind. Eng. Chem. Res., 2013, 52(50): 17996-18013. [119] S Wu. Multivariable PID Control Using Improved State Space Model Predictive Control Optimization. Ind. Eng. Chem. Res., 2015, 54(20): 5505-5513. [120] Y Cao, J Hu, H Jia, et al. Comparison of pressure-swing distillation and extractive distillation with varied-diameter column in economics and dynamic control. J. Process Contr., 2017, 49: 9-25. [121] I Patraşcu, C S Bildea, A A Kiss. Dynamics and control of a heat pump assisted extractive dividing-wall column for bioethanol dehydration. Chem. Eng. Res. Des., 2017, 119: 66-74. [122] E Bristol. On a new measure of interaction for multivariable process control. IEEE Transactions on Automatic Control., 2003, 11(1): 133-134. [123] M Hovd, S Skogestad. Pairing Criteria for Decentralized Control of Unstable Plants. Ind. Eng. Chem. Res., 1994, 33(9): 2134-2139. [124] M F Witcher, T J Mcavoy. Interacting control systems: steady state and dynamic measurement of interaction. ISA transactions., 1977, 16(3): 35-41. [125] Q Xiong, W J Cai, M J He. A practical loop pairing criterion for multivariable processes. J. Process Contr., 2005, 15(7): 741-747. [126] J G Ziegler, N B Nichols. Optimum Setting for Automatic Controllers. J Dyn Syst Meas Contro., 1993, 115(2B): 759-768. 23

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[127] W L Luyben. Tuning proportional− integral− derivative controllers for integrator/deadtime processes. Ind. Eng. Chem. Res., 1996, 35(10): 3480-3483. [128] S Bouallègue, J Haggège, M Ayadi, et al. PID-type fuzzy logic controller tuning based on particle swarm optimization. Eng. Appl. Aryif. Intel., 2012, 25(3): 484-493. [129] J C Jeng, W L Tseng, M S Chiu. A one-step tuning method for PID controllers with robustness specification using plant step-response data. Chem. Eng. Res. Des., 2014, 92(3): 545-558. [130] D Pavković, S Polak, D Zorc. PID controller auto-tuning based on process step response and damping optimum criterion. ISA Trans., 2014, 53(1): 85. [131] S Zheng, X Tang, B Song. A graphical tuning method of fractional order proportional integral derivative controllers for interval fractional order plant. J. Process Contr., 2014, 24(11): 1691-1709. [132] J C Shen. New tuning method for PID controller. IEEE International Conference on Control Applications, 2001, 459-464. [133] D Pavkovic, S Polak, D Zorc. PID controller auto-tuning based on process step response and damping optimum criterion. ISA Trans., 2014, 53(1): 85-96. [134] K D Badgujar, S T Revankar. Design of fuzzy-PID controller for hydrogen production using HTPBR. International Conference on Nuclear Engineering, 2013, V006T016A001-V006T016A001. [135] W L Luyben. Effect of solvent on controllability in extractive distillation. Ind. Eng. Chem. Res., 2008, 47(13): 4425-4439. [136] Y Wang, S Liang, G Bu, et al. Effect of Solvent Flow Rates on Controllability of Extractive Distillation for Separating Binary Azeotropic Mixture. Ind. Eng. Chem. Res., 2015, 54(51): 12908-12919 [137] W B Ramos, M F Figueirêdo, K D Brito, et al. Effect of Solvent Content and Heat Integration on the Controllability of Extractive Distillation Process for Anhydrous Ethanol Production. Ind. Eng. Chem. Res., 2016, 55(43): 11315-11328. [138] Z Bao, W Zhang, X Cui, et al. Design, Optimization and Control of Extractive Distillation for the Separation of Trimethyl Borate-Methanol. Ind. Eng. Chem. Res., 2014, 53(38): 14802-14814. [139] F Lastari, V Pareek, M Trebble, et al. Extractive distillation for CO2-ethane azeotrope separation. Chem. Eng. Process., 2012, 52: 155-161. [140] E Lladosa, J B Montón, M Burguet. 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., 2011, 50(11-12): 1266-1274. [141] G Li, Y Yu, P Bai. Batch extractive distillation of mixture methanol-acetonitrile using aniline as a asolvent. Pol. J. Chem. Technol., 2012, 14(3): 48-53. [142] Z Fan, X Zhang, W Cai, et al. Design and Control of Extraction Distillation for Dehydration of Tetrahydrofuran. Chem. Eng. Technol., 2013, 36(5): 829-839. [143] I D Gil, J M Gómez, G Rodríguez. Control of an extractive distillation process to dehydrate ethanol using glycerol as entrainer. Comput. Chem. Eng., 2012, 39: 129-142. [144] J Qin, Q Ye, X Xiong, et al. Control of Benzene-Cyclohexane Separation System via Extractive Distillation Using Sulfolane as Entrainer. Ind. Eng. Chem. Res., 2013, 52(31): 10754-10766. 24

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[145] J Á Pacheco-Basulto, D Hernández-McConville, F O Barroso-Muñoz, et al. Purification of bioethanol using extractive batch distillation: Simulation and experimental studies. Chem. Eng. Process., 2012, 61: 30-35. [146] P Kittisupakorn, K Jariyaboon, W Weerachaipichasgul. Optimal high purity acetone production in a batch extractive distillation column. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2013, 1: 143-147. [147] H Luo, K Liang, W Li, et al. Comparison of Pressure-Swing Distillation and Extractive Distillation Methods for Isopropyl Alcohol/Diisopropyl Ether Separation. Ind. Eng. Chem. Res., 2014, 53(39): 15167-15182. [148] S Tututi-Avila, A Jiménez-Gutiérrez, J Hahn. Control analysis of an extractive dividing-wall column used for ethanol dehydration. Chem. Eng. Process., 2014, 82: 88-100. [149] H Yu, Q Ye, H Xu, et al. Comparison of alternative distillation processes for the maximum-boiling ethylenediamine dehydration system. Chem. Eng. Process., 2015, 97: 84-105. [150] J Pla-Franco, E Lladosa, S Loras, et al. Approach to the 1-propanol dehydration using an extractive distillation process with ethylene glycol. Chem. Eng. Process., 2015, 91: 121-129. [151] Y-C Chen, B-Y Yu, C-C Hsu, et al. Comparison of heteroazeotropic and extractive distillation for the dehydration of propylene glycol methyl ether. Chem. Eng. Res. Des., 2016, 111: 184-195. [152] W L Luyben. Comparison of extractive distillation and pressure-swing distillation for acetone- methanol separation. Ind. Eng. Chem. Res., 2008, 47(8): 2696-2707. [153] U M García-Ventura, F O Barroso-Muñoz, S Hernández, et al. Experimental study of the production of high purity ethanol using a semi-continuous extractive batch dividing wall distillation column. Chem. Eng. Process., 2016, 108: 74-77. [154] K-M Lo, I L Chien. Efficient separation method for tert -butanol dehydration via extractive distillation. J. Taiwan. Inst. Chem. E., 2017, 73: 27-36. [155] I V Ivanov, V A Lotkhov, K A Moiseeva, et al. Mass transfer in a packed extractive distillation column. Theor. Found. Chem. Eng., 2016, 50(5): 667-677. [156] S Pradhan, A Kannan. Simulation and analysis of extractive distillation process in a valve tray column using the rate based model. Korean J. Chem. Eng., 2005, 22(3): 441-451. [157] E Quijada-Maldonado, T A M Aelmans, G W Meindersma, et al. Pilot plant validation of a rate-based extractive distillation model for water–ethanol separation with the ionic liquid [emim][DCA] as solvent. Chem. Eng. J., 2013, 223(3): 287-297. [158] W L Luyben. Control of the Maximum-Boiling Acetone/Chloroform Azeotropic Distillation System. Ind. Eng. Chem. Res., 2008, 47(16): 6140-6149. [159] L Li, L Guo, Y Tu, et al. Comparison of different extractive distillation processes for 2-methoxyethanol/toluene separation: Design and control. Comput. Chem. Eng., 2017, 99: 117-134. [160] W L Luyben. Improved design of an extractive distillation system with an intermediate-boiling solvent. Sep. Purif. Technol., 2015, 156: 336-347. [161] I Rodriguez-Donis, V Gerbaud, X Joulia. Thermodynamic Insights on the Feasibility of Homogeneous Batch Extractive Distillation. 3. Azeotropic Mixtures with Light Entrainer. Ind. Eng. Chem. Res., 2012, 51(12): 4643-4660. 25

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

[162] W Shen, V Gerbaud. Extension of Thermodynamic Insights on Batch Extractive Distillation to Continuous Operation. 2. Azeotropic Mixtures with a Light Entrainer. Ind. Eng. Chem. Res., 2013, 52(12): 4623-4637. [163] W Shen, H Benyounes, V Gerbaud. Extension of Thermodynamic Insights on Batch Extractive Distillation to Continuous Operation. 1. Azeotropic Mixtures with a Heavy Entrainer. Ind. Eng. Chem. Res., 2013, 52(12): 4606-4622. [164] N Van Duc Long, M Lee. Optimal retrofit design of extractive distillation to energy efficient thermally coupled distillation scheme. AlChE J., 2013, 59(4): 1175-1182. [165] A A Kiss, R M Ignat. Innovative single step bioethanol dehydration in an extractive dividing-wall column. Sep. Purif. Technol., 2012, 98: 290-297. [166] C E Torres-Ortega, J G Segovia-Hernández, F I Gómez-Castro, et al. Design, optimization and controllability of an alternative process based on extractive distillation for an ethane– carbon dioxide mixture. Chem. Eng. Process., 2013, 74: 55-68. [167] M Errico, B-G Rong. Synthesis of new separation processes for bioethanol production by extractive distillation. Sep. Purif. Technol., 2012, 96: 58-67. [168] Y An, W Li, Y Li, et al. Design/optimization of energy-saving extractive distillation process by combining preconcentration column and extractive distillation column. Chem. Eng. Sci., 2015, 135: 166-178. [169] M Errico, B-G Rong, G Tola, et al. Optimal Synthesis of Distillation Systems for Bioethanol Separation. Part 1: Extractive Distillation with Simple Columns. Ind. Eng. Chem. Res., 2013, 52(4): 1612-1619. [170] A A Kiss, R M Ignat. Optimal Economic Design of an Extractive Distillation Process for Bioethanol Dehydration. Energy Technology., 2013, 1(2-3): 166-170. [171] S-J Wang, C-C Yu, H-P Huang. Plant-wide design and control of DMC synthesis process via reactive distillation and thermally coupled extractive distillation. Comput. Chem. Eng., 2010, 34(3): 361-373. [172] A Avilés Martínez, J Saucedo-Luna, J G Segovia-Hernandez, et al. Dehydration of Bioethanol by Hybrid Process Liquid-Liquid Extraction/Extractive Distillation. Ind. Eng. Chem. Res., 2012, 51(17): 5847-5855. [173] G Li, P Bai. New Operation Strategy for Separation of Ethanol–Water by Extractive Distillation. Ind. Eng. Chem. Res., 2012, 51(6): 2723-2729. [174] L Sun, K He, Y Liu, et al. Analysis of Different Pressure Thermally Coupled Extractive Distillation Column. Open Chem. Eng. J., 2014, 8: 12-18. [175] K Liang, W Li, H Luo, et al. Energy-Efficient Extractive Distillation Process by Combining Preconcentration Column and Entrainer Recovery Column. Ind. Eng. Chem. Res., 2014, 53(17): 7121-7131. [176] K D Brito, G M Cordeiro, M F Figueirêdo, et al. Economic evaluation of energy saving alternatives in extractive distillation process. Comput. Chem. Eng., 2016, 93: 185-196. [177] H Luo, C S Bildea, A A Kiss. Novel Heat-Pump-Assisted Extractive Distillation for Bioethanol Purification. Ind. Eng. Chem. Res., 2015, 54(7): 2208-2213. [178] L Li, Y Tu, L Sun, et al. Enhanced Efficient Extractive Distillation by Combining Heat-Integrated Technology and Intermediate Heating. Ind. Eng. Chem. Res., 2016, 55(32): 8837-8847. [179] G Genduso, A Amelio, E Colombini, et al. Retrofitting of extractive distillation columns 26

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

RI

PT

with high flux, low separation factor membranes: A way to reduce the energy demand.? Chem. Eng. Res. Des., 2016, 109: 127-140. [180] X You, I Rodriguez-Donis, V Gerbaud. Reducing process cost and CO2 emissions for extractive distillation by double-effect heat integration and mechanical heat pump. Appl. Energ., 2016, 166: 128-140.

27

ACCEPTED MANUSCRIPT

Dynamic system

VLE

Solvent

Theory

optimization

or Model

Process type

reference

control

expriment

T P

equilibrium

[55]

theory

equilibrium

[138]

theory

equilibrium

[139]

continuous

theory

equilibrium

[140]

batch

theory+ experiment

equilibrium

[141]

continuous

theory

equilibrium

[142]

yes

continuous

theory

equilibrium

[89]

yes

continuous

theory

equilibrium

[143]

yes

yes

continuous

theory

equilibrium

[144]

yes

yes

continuous

theory

equilibrium

[106]

tetraethylene-glycol

yes

no

continuous

theory

equilibrium

[61]

ethylene glycol

no

no

semi-continuous

theory

equilibrium

[145]

yes

water

no

yes

semi-continuous

theory

equilibrium

[146]

yes

2-methoxyethanol

yes

yes

continuous

theory

equilibrium

[147]

Acetone + Methanol

yes

water

yes

no

continuous

Trimethyl borate + Methanol

yes

dimethyl sulfoxide

yes

yes

continuous

CO2+Ethane

yes

Nc5

no

no

Di-n-propyl ether + n-propyl alcohol

yes

N,N-dimethylformamide

yes

no

methanol+acetonitrile

yes

aniline

yes

no

tetrahydrofuran+water

yes

ethylene glycol

yes

yes

benzene+acetonitrile

yes

demethyl sulfoxide

yes

ethanol+water

yes

glycerol

no

benzene+cyclohexane

yes

sulfolane

methylal+methanol

yes

dimethylformamide

ethanol+water

yes

ethanol+water

yes

Acetone/Methanol diisopropyl ether+ isopropyl alcohol

PT

E C

C A

D E

28

I R

C S U continuous

N A

M

theory

ACCEPTED MANUSCRIPT

ethanol+water

yes

ethylene glycol

yes

yes

continuous

theory

equilibrium

[148]

Ethylenediamine +water

yes

1,4-butanediol

yes

yes

continuous

theory

equilibrium

[149]

ethanol+water

yes

glycerol

no

no

continuous

theory+ experiment

equilibrium

[26]

1-propanol+water

yes

ethylene glycol

yes

no

continuous

theory+ experiment

equilibrium

[150]

propylene glycol methyl ether+water

yes

Sulfolane /N-methyl2-pyrrolidone

yes

no

theory

equilibrium

[151]

Acetone/Methanol

yes

water

no

no

continuous

theory

equilibrium

[152]

ethanol+water

no

glycerol

no

no

semi-continuous

experiment

equilibrium

[153]

tert-butanol+water

yes

glycerol

yes

no

continuous

theory

equilibrium

[154]

benzene+heptane

yes

N-methylpyrrolidone

no

no

batch

experiment

equilibrium

[155]

yes

1,2-propanediol/ dimethyl sulfoxide

no

no

batch/semi-batch/ continuous

experiment

equilibrium

[25]

yes

N-methyl pyrrolidone

yes

yes

continuous

theory

equilibrium

[24]

Methanol+ acetone

no

water

yes

no

continuous

theory

rate-based

[156]

Water+ethanol

no

ethyl-3-methylimi dazoliumdicyanamide

no

no

continuous

theory+ experiment

rate-based

[157]

Tetrahydrofuran +water acetoneitrile+ N-propanol

T P E

C C

A

D E

N A

M

Table 1 List of the study of the azeotrope with extractive distillation separation

29

I R

C S U continuous

T P

ACCEPTED MANUSCRIPT

Type of azeotrope

System

phase state

Extractant type

Reference

Maximum-boiling Minimum-boiling Minimum-boiling

Acetone+chloroform 2-methoxyethanol+toluene Methanol+toluene Acetone+heptane Methanol+toluene Methyl acetate+cyclohexane Dichloromethane+ethanol Ethyl acetate+heptane

Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous

[158]

Chloroform +ethyl acetate

Homogeneous

Dimethyl sulfoxide (Heavy) Dimethyl sulfoxide (Heavy) Triethylamine (Intermediate) Benzene (Intermediate) Triethylamine (Intermediate) Carbon tetrachloride (Intermediate) Acetone (Intermediate) Benzene (Intermediate) 2-chlorobutane (Intermediate) Isobutylchloride (Intermediate) Bromoporpane (Intermediate) Bromochloromethane (Intermediate) Methanol (Light) Acetone (Light) Acetone (Light) Methanol (Light) Dichlomethane (Light) Methyl isobutyl ketone (Light) Acetone (Light) Phenol (Heavy) Phenol (Heavy) Chlorobenzene (Heavy) Methyl isobutyl ketone (Light)

Minimum-boiling

Maximum-boiling

Minimum-boiling

Maximum-boiling Low relative volatility Low relative volatility

D E

T P E

A

M

Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous

30

I R

C S U

N A

Ethanol+water Ethanol+toluene Methyl ethyl ketoe+benzene Water+ethylenediamine Acetone +chloroform Propanoic acid+dimethyl formamide Ethyl acetate+benzene n-heptane+toluene Benzene+toluene n-heptane+ toluene Propanoic acid+dimethyl formamide

C C

T P

[159] [160]

[94]

[94]

[161]

[161]

[161]

[96]

ACCEPTED MANUSCRIPT

Maximum-boiling

Acetone+chloroform Water+ethylene diamine Ethanol+water Methul ethyl ketone+benzene Acetone+heptane Acetone+methanol Chloroform+vinyl acetate Acetone+chloroform

Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous Homogeneous

Maximum-boiling

Ethylenediamine+water

Homogeneous

Minimum-boiling Minimum-boiling

Chloroform+methanol Methanol+toluene

Homogeneous Homogeneous

Minimum-boiling Minimum-boiling Maximum-boiling

T P

M

D E

T P E

C C

A

31

I R

C S U

N A

Table 2 List of different type of binary mixture separated with different type of solvent

Dichloromethane (Light) Acetone (Light) Methanol (Light) Acetone (Light) Toluene (Heavy) Chlorobenzene (Heavy) Butyl acetate (Heavy) Benzene (Heavy) 1,4-butanediol (Heavy) n-propyl acetate (Light) Water (Heavy) Triethylamine (Intermediate)

[162]

[162]

[163]

[164]

[149] [109] [113]

ACCEPTED MANUSCRIPT

system

Operate model

Classified method

Method of calculation

reference

Acetone+methanol Ethanol+water Methanol+toluene CO2+Ethane Ethanol+water Methylal+methanol N-propanol+water Ethanol+water Ethanol+water Dimethyl carbonate+methanol Ethanol+water Ethanol+water Acetone +methanol Propylene+propane

Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous Continuous

Thermally coupled Extractive dividing-wall column Extractive dividing-wall column Cryogenic extractive distillation with side recifier Thermally coupled Extractive dividing-wall column Extractive distillation with preconcentration Extractive distillation with partial condenser Preconcentrated the feedstock

Reboiler duty Reboiler duty TAC TAC TAC TAC TAC TAC TAC

[164]

Continuous

Thermally coupled

Reboiler duty

[171]

Continuous Continuous Continuous Continuous

Hybrid of extraction and extractive distillation Preconcentrated the feedstock Extractive dividing-wall column Pressure thermally coupled extractive distillation

[172]

CO2+Ethane

Continuous

Extractive dividing-wall column

TAC Reboiler duty TAC TAC Energy demand+ CO2 removal + CO2 emission

Combine preconcentration/entrainer recovery column

TAC

[175]

Continuous

Thermal integration Extractive dividing-wall +Thermal integration

TAC

[176]

Continuous Continuous Continuous

Heat-pump-assisted extractive dividing-wall column Heat-integrated technology and intermediate heating Extractive dividing-wall column with mixed solvent

TAC TAC TAC

[177]

Alcohol +water Acetonitrile+water Ethanol+water Tetrahydrofuran+water Acetone+methanol Ethanol+water Benzene+cyclohexane Benzene+cyclohexane

T P

C S U

N A

D E

M

PT

E C

Continuous

C A

32

I R

[165] [113] [166] [166] [114] [168] [169] [170]

[173] [115] [174] [116]

[178] [64]

ACCEPTED MANUSCRIPT

Methyl acetate+methanol

Continuous

Acetone+methanol

Continuous

Retrofitted hybrid pervaporation-distillation Double-effect heat integration and mechanical heat pump technique

Power requirement of cooling and heating

[179]

TAC

[180]

T P

Table 3 Ways to reducing energy consumption in published papers

I R

C S U

N A

D E

M

T P E

C C

A

33

AC

CE

PT E

D

MA

NU

SC

RI

Fig. 1. The classification and study content of extractive distillation

PT

ACCEPTED MANUSCRIPT

34

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

PT E

Fig. 2. T-xy diagrams of different types of binary separated by extractive distillation: (a) minimum

AC

CE

boiling azeotrope;(b) maximum boiling azeotrope;(c) low relative volatility mixture.

35

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

Fig. 3. The current and future study domain of extractive distillation

36

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

Fig. 4. Ternary phase diagram of extractive distillation and the split method

37

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

Fig. 5. Optimization and extractive dividing-wall column of extractive distillation

38

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

PT E

D

Fig. 6. Flowsheet of the typical control system for continuous extractive distillation

AC

CE

Graphic Abstract

39