Research progress on simulated moving bed separation process and its optimization methods

doi:10.16085/j.issn.1000-6613.2022-1321

Abstract

Abstract:

The simulated moving bed technology has the advantages of high yield, high purity and process continuity, which makes it applicable for multi-component system and complicated system containing components with similar properties. However, the process design and optimization are always the important and challenging works for its research and application. In this paper, the separation mechanism and various modifications of simulated moving bed were firstly introduced, and then the widely used sequential simulated moving bed technology was emphatically described due to its unique separation mode and superior performance. Based on this background, diverse optimization methods and research progress of simulated moving bed area were summarized and analyzed. At first, the conventional triangle theory was investigated and its important applications and limitations were summarized. Furthermore, several optimization methods, such as sequence quadratic programming algorithm, standing wave design, separation volume analysis and multi-objective optimization method based on NSGA-Ⅱ algorithm were reviewed, respectively. The literature analysis revealed that in addition to multi-objective optimization, both the basic triangle theory and other optimization methods showed many limitations in parameter selection and experiment design. The multi-objective optimization method was proved to perform better and suitable for most simulated moving bed modes, which would have great development potential and application prospects in the future.

Key words: simulated moving bed, sequential simulated moving bed, multi-objective optimization, genetic algorithm

摘要：

模拟移动床技术具有产率高、纯度高、过程连续性等优点，适用于多组分体系及各组分性质比较接近的难分离体系，其过程设计和优化工作一直是研究的重点和难点。本文首先对模拟移动床分离机理及其各种变型进行了介绍，其中重点阐述了目前应用广泛的顺序式模拟移动床技术的分离模式和优越性能。在此基础上，总结并分析了模拟移动床领域的各种优化方法和研究进展。从传统的三角形理论开始，总结归纳了三角形理论应用体系，指出其重要地位及局限性。之后依次介绍了基于三角形理论衍生而出的序列二次规划算法、应用较为广泛的驻波设计、体积分离分析和基于遗传算法的过程模拟、多目标优化等多种优化方法。分析结果表明，除多目标优化以外，无论是最传统的三角形理论，还是其他几种优化方法，在参数选择和实验设计等方面都存在诸多局限性，而多目标优化方法已被证明表现更为优异，可以适用于模拟移动床操作模式的各种变型，在未来将具有极大的发展潜力和应用前景。

关键词: 模拟移动床, 顺序式模拟移动床, 多目标优化, 遗传算法

CLC Number:

TQ02

LING Shan, LIU Juming, ZHANG Qiancheng, LI Yan. Research progress on simulated moving bed separation process and its optimization methods[J]. Chemical Industry and Engineering Progress, 2023, 42(5): 2233-2244.

凌山, 刘聚明, 张前程, 李艳. 模拟移动床分离过程及其优化方法研究进展[J]. 化工进展, 2023, 42(5): 2233-2244.

Figures/Tables 7

References 103

1	MARáZ A, KOVáCS Z, BENJAMINS E, et al. Recent developments in microbial production of high-purity galacto-oligosaccharides[J]. World Journal of Microbiology and Biotechnology, 2022, 38(6): 1-10.
2	JUZA M, MAZZOTTI M, MORBIDELLI M. Simulated moving-bed chromatography and its application to chirotechnology[J]. Trends in Biotechnology, 2000, 18(3): 108-118.
3	ZHANG Y, HIDAJAT K, RAY A K. Multi-objective optimization of simulated moving bed and Varicol processes for enantio-separation of racemic pindolol[J]. Separation and Purification Technology, 2009, 65(3): 311-321.
4	SONG Mingkai, CUI Linlin, KUANG Han, et al. Model-based design of an intermittent simulated moving bed process for recovering lactic acid from ternary mixture[J]. Journal of Chromatography A, 2018, 1562: 47-58.
5	YOUNGJIN K, TAEJONG K, CHANHO P, et al. Development of novel flow distribution apparatus for simulated moving bed to improve degree of mixing[J]. Computers & Chemical Engineering, 2022, 156: 107553.
6	BREVEGLIERI F, OTGONBAYAR T, MAZZOTTI M. Optimizing the yield of a pure enantiomer by integrating chiral SMB chromatography and racemization. part 2: Theory[J]. Industrial & Engineering Chemistry Research, 2021, 60(29): 10720-10735.
7	RAJENDRAN A, PAREDES G, MAZZOTTI M. Simulated moving bed chromatography for the separation of enantiomers[J]. Journal of Chromatography A, 2009, 1216(4): 709-738.
8	BROUGHTON D B. Production-scale adsorptive separations of liquid mixtures by simulated moving bed technology[J]. Separation Science and Technology, 1984, 19(11/12): 723-736.
9	MINCEVA M, RODRIGUES A E. Influence of the transfer line dead volume on the performance of an industrial scale simulated moving bed for p-xylene separation[J]. Separation Science and Technology, 2003, 38(7):1463-1497.
10	HONG S B, CHOI J H, PARK H, et al. Simulated moving bed purification of fucoidan hydrolysate for an efficient production of fucose with high purity and little loss[J]. Journal of the Taiwan Institute of Chemical Engineers, 2019, 99: 29-37.
11	LEE J W, KIENLE A, SEIDEL-MORGENSTERN A. On-line optimization of four-zone simulated moving bed chromatography using an equilibrium-dispersion model: II. Experimental validation[J]. Chemical Engineering Science, 2020, 226: 115808.
12	LIU Lina, HE Yingying, WANG Kai, et al. Metagenomics approach to the intestinal microbiome structure and function in high fat diet-induced obesity in mice fed with conjugated linoleic acid (CLA)[J]. Food & Function, 2020, 11(11): 9729-9739.
13	JO C Y, KANG H J, MUN S. Improving the performances of a simulated-moving-bed reactor for the synthesis of methyl acetate ester by using partial port-closing strategies[J]. Chemical Engineering Journal, 2022, 435: 134887.
14	姚传义，郑震玮，涂志贤，等. 模拟移动床色谱分离吴茱萸碱和吴茱萸次碱[J]. 化工学报, 2021, 72(7): 3728-3737.
	YAO Chuanyi, ZHENG Zhenwei, TU Zhixian, et al. Separation of evodiamine and rutaecarpine with simulated moving bed chromatography[J]. CIESC Journal, 2021, 72(7): 3728-3737.
15	ERDEM G, ABEL S, MORARI M, et al. Automatic control of simulated moving beds[J]. Industrial & Engineering Chemistry Research, 2004, 43(2): 405-421.
16	TOUMI A, ENGELL S, DIEHL M, et al. Efficient optimization of simulated moving bed processes[J]. Chemical Engineering and Processing: Process Intensification, 2007, 46(11): 1067-1084.
17	BORGES DA SILVA E A, RODRIGUES A E. Design of chromatographic multicomponent separation by a pseudo-simulated moving bed[J]. AIChE Journal, 2006, 52(11): 3794-3812.
18	GONG Rujin, LIN Xiaojian, LI Ping, et al. Experiment and modeling for the separation of guaifenesin enantiomers using simulated moving bed and Varicol units[J]. Journal of Chromatography A, 2014, 1363: 242-249.
19	LIN Xiaojian, GONG Rujian, LI Jiaxu, et al. Enantioseparation of racemic aminoglutethimide using asynchronous simulated moving bed chromatography[J]. Journal of Chromatography A, 2016, 1467: 347-355.
20	YAO Chanyi, CHEN Jinliang, LU Yinghua, et al. Construction of an asynchronous three-zone simulated-moving-bed chromatography and its application for the separation of vanillin and syringaldehyde[J]. Chemical Engineering Journal, 2018, 331: 644-651.
21	CALDERON SUPELANO R, BARRETO JR A G, ANDRADE NETO A S, et al. One-step optimization strategy in the simulated moving bed process with asynchronous movement of ports: A varicol case study[J]. Journal of Chromatography A, 2020, 1634: 461672.
22	CALDERÓN SUPELANO R, BARRETO A G, SECCHI A R. Optimal performance comparison of the simulated moving bed process variants based on the modulation of the length of zones and the feed concentration[J]. Journal of Chromatography A, 2021,1651: 462280.
23	MATOS J, FARIA R P V, LOUREIRO J M, et al. Design and optimization for simulated moving bed: varicol approach[J]. IFAC-PapersOnLine, 2021, 54(3): 542-547.
24	ANICETO J P S, SILVA C M. Simulated moving bed strategies and designs: From established systems to the latest developments[J]. Separation & Purification Reviews, 2015, 44(1): 41-73.
25	YU Yueying, WOOD K R, LIU Y A. Simulation and comparison of operational modes in simulated moving bed chromatography[J]. Industrial & Engineering Chemistry Research, 2015, 54(46): 11576-11591.
26	YANG Ying, LU Kai, GONG Rujin, et al. Separation of guaifenesin enantiomers by simulated moving bed process with four operation modes[J]. Adsorption, 2019, 25(6): 1227-1240.
27	DENET F, HAUCK W, NICOUD R M, et al. Enantioseparation through supercritical fluid simulated moving bed (SF-SMB) chromatography[J]. Industrial & Engineering Chemistry Research, 2001, 40(21): 4603-4609.
28	CâMARA L D T. Modifier mass transfer kinetic effect in the performance of solvent gradient simulated moving bed (SG-SMB) process[J]. Journal of Physics: Conference Series, 2015, 633(1): 012104.
29	蒋晓霄. 温度梯度模拟移动床拆分酮洛芬对映体的过程研究[D]. 温州: 温州大学, 2017.
	JIANG Xiaoxiao. The study of separation process of ketoprofen enantiomers on temperature gradient of simulated moving bed[D]. Wenzhou: Wenzhou University, 2017.
30	WEI Feng, SHI Licheng, WANG Qiang, et al. Fast and accurate separation of the paclitaxel from yew extracum by a pseudo simulated moving bed with solvent gradient[J]. Journal of Chromatography A, 2018, 1564: 120-127.
31	JIANG Xiaoxiao, ZHU Lei, YU Bei, et al. Analyses of simulated moving bed with internal temperature gradients for binary separation of ketoprofen enantiomers using multi-objective optimization: Linear equilibria[J]. Journal of Chromatography A, 2018, 1531: 131-142.
32	REINALDO CALDERÓN S, AMARO GOMES B, JR, ARGIMIRO RESENDE S. Evaluation of the optimal performance of ModiCon and ModiCon+VariCol simulated moving bed variants[J]. Journal of Chromatography A, 2022, 1675: 463182.
33	LI Yan, YU Weifang, DING Ziyuan, et al. Equilibrium and kinetic differences of XOS2-XOS7 in xylo-oligosaccharides and their effects on the design of simulated moving bed purification process[J]. Separation and Purification Technology, 2019, 215: 360-367.
34	李良玉，孙蕊，李朝阳，等. 顺序式模拟移动色谱纯化木糖醇母液[J]. 天然产物研究与开发, 2015, 27(10): 1789-1793.
	LI Liangyu, SUN Rui, LI Chaoyang, et al. Purification of xylitol mother liquid using sequential simulated moving bed chromatography[J]. Natural Product Research and Development, 2015, 27(10): 1789-1793.
35	LI Yan, DING Ziyuan, WANG Jian, et al. A comparison between simulated moving bed and sequential simulated moving bed system based on multi-objective optimization[J]. Chemical Engineering Science, 2020, 219: 115562.
36	LI Yan, XU Jin, YU Weifang, et al. Multi-objective optimization of sequential simulated moving bed for the purification of xylo-oligosaccharides[J]. Chemical Engineering Science, 2020, 211: 115279.
37	SANTOS DA SILVA F V, SEIDEL-MORGENSTERN A. Evaluation of center-cut separations applying simulated moving bed chromatography with 8 zones[J]. Journal of Chromatography A, 2016, 1456: 123-136.
38	SREEDHAR B, KAWAJIRI Y. Multi-column chromatographic process development using simulated moving bed superstructure and simultaneous optimization—Model correction framework[J]. Chemical Engineering Science, 2014, 116: 428-441.
39	YU Weifang, HIDAJAT K, RAY A K. Optimal operation of reactive simulated moving bed and Varicol systems[J]. Journal of Chemical Technology & Biotechnology, 2003, 78(2/3): 287-293.
40	WONGSO F, HIDAJAT K, RAY A. Improved performance for continuous separation of 1,1'-bi-2-naphthol racemate based on simulated moving bed technology[J]. Separation and Purification Technology, 2005, 46(3): 168-191.
41	TARAFDER A, RANGAIAH G P, RAY A K. A study of finding many desirable solutions in multiobjective optimization of chemical processes[J]. Computers & Chemical Engineering, 2007, 31(10): 1257-1271.
42	PARK H, KIM J W, LEE K B, et al. Comparison of the process performances of a tandem 4-zone SMB and a single-cascade 5-zone SMB for separation of galactose, levulinic acid, and 5-hydroxymethylfurfural in agarose hydrolyzate[J]. Separation and Purification Technology, 2020, 237: 116357.
43	PEDEFERRI M, ZENONI G, MAZZOTTI M, et al. Experimental analysis of a chiral separation through simulated moving bed chromatography[J]. Chemical Engineering Science, 1999, 54(17): 3735-3748.
44	KASPEREIT M, SEIDEL-MORGENSTERN A, KIENLE A. Design of simulated moving bed processes under reduced purity requirements[J]. Journal of Chromatography A, 2007, 1162(1): 2-13.
45	KHATTABI S, CHERRAK D E, MIHLBACHLER K, et al. Enantioseparation of 1-phenyl-1-propanol by simulated moving bed under linear and nonlinear conditions[J]. Journal of Chromatography A, 2000, 893(2): 307-319.
46	LEE K. Effects of mass transfer on simulated moving bed process[J]. Korean Journal of Chemical Engineering, 2009, 26(2): 468-474.
47	LEE J W, KIENLE A, SEIDEL-MORGENSTERN A. Numerical short-cut design of simulated moving bed chromatography for multicomponent nonlinear adsorption isotherms: Nonstoichiometric Langmuir model[J]. Industrial & Engineering Chemistry Research, 2021, 60(29): 10753-10763.
48	SONG I H, LEE S B, RHEE H K, et al. Identification and predictive control of a simulated moving bed process: Purity control[J]. Chemical Engineering Science, 2006, 61(6): 1973-1986.
49	KHAN H. Simulation assessment of continuous simulating moving bed chromatography process with partial feed and new strategy with partial feed[J]. Brazilian Journal of Chemical Engineering, 2009, 26(3): 595-610.
50	SOEPRIATNA N, WANG N H L, WANKAT P C. Standing wave design of a four-zone thermal SMB fractionator and concentrator (4-zone TSMB-FC) for linear systems[J]. Adsorption, 2014, 20(1): 37-52.
51	MUN S. Effect of adsorbent particle size on the relative merits of a non-triangular and a triangular separation region in the optimal design of a three-zone simulated moving bed chromatography for binary separation with linear isotherms[J]. Journal of Chromatography A, 2016, 1452: 36-46.
52	YOON T H, CHUNG B H, KIM I H. A novel design of simulated moving bed(SMB) chromatography for separation of ketoprofen enantiomer[J]. Biotechnology and Bioprocess Engineering, 2004, 9(4): 285-291.
53	JERMANN S, MEIJSSEN M, MAZZOTTI M. Three column intermittent simulated moving bed chromatography: 3. Cascade operation for center-cut separations[J]. Journal of Chromatography A, 2015, 1378: 37-49.
54	ANICETO J P S, AZENHA I S, DOMINGUES F M J, et al. Design and optimization of a simulated moving bed unit for the separation of betulinic, oleanolic and ursolic acids mixtures: Experimental and modeling studies[J]. Separation and Purification Technology, 2018, 192: 401-411.
55	WEI Bofeng, WANG Shaoyan. Separation of eicosapentaenoic acid and docosahexaenoic acid by three-zone simulated moving bed chromatography[J]. Journal of Chromatography A, 2020, 1625: 461326.
56	HOUWING J, BILLIET H A H, VAN DER WIELEN L A M. Optimization of azeotropic protein separations in gradient and isocratic ion-exchange simulated moving bed chromatography[J]. Journal of Chromatography A, 2002, 944(1/2): 189-201.
57	PARK B J, LEE C H, KOO Y M. Development of novel protein refolding using simulated moving bed chromatography[J]. Korean Journal of Chemical Engineering, 2005, 22(3): 425-432.
58	MUELLER I, SEIDEL-MORGENSTERN A, HAMEL C. Simulated-moving-bed technology for purification of the prebiotics galacto-oligosaccharides[J]. Separation and Purification Technology, 2021, 271: 118829.
59	ANICETO J P S, CARDOSO S P, SILVA C M. General optimization strategy of simulated moving bed units through design of experiments and response surface methodologies[J]. Computers & Chemical Engineering, 2016, 90: 161-170.
60	HE Q, LIERES E V, SUN Z, et al. Model-based process design of a ternary protein separation using multi-step gradient ion-exchange SMB chromatography[J]. Computers & Chemical Engineering, 2020, 138: 106851.
61	URIBE SANTOS D L, DELGADO DOBLADEZ J A, ÁGUEDA MATé V I, et al. Recovery and purification of acetic acid from aqueous mixtures by simulated moving bed adsorption with methanol and water as desorbents[J]. Separation and Purification Technology, 2020, 237: 116368.
62	DELGADO J A, ÁGUEDA V I, UGUINA M Á, et al. Modeling of the separation of lactic acid from an aqueous mixture by adsorption on polyvinylpyridine resin and desorption with methanol[J]. Separation and Purification Technology, 2018, 200: 307-317.
63	GILL P E, ROBINSON D P. A globally convergent stabilized SQP method[J]. SIAM Journal on Optimization, 2013, 23(4): 1983-2010.
64	BOGGS P T, TOLLE J W. Sequential quadratic programming[J]. Acta Numerica, 1995, 4(1): 1-51.
65	SHEN Yuanhui, FU Qiang, ZHANG Donghui, et al. A systematic simulation and optimization of an industrial-scale p-xylene simulated moving bed process[J]. Separation and Purification Technology, 2018, 191:48-60.
66	SHAHMORADI A, KHOSRAVI-NIKOU M R, AGHAJANI M, et al. Mathematical modeling and optimization of industrial scale ELUXYL simulated moving bed (SMB)[J]. Separation and Purification Technology, 2020, 248: 116961.
67	Y-I LIM. An optimization strategy for nonlinear simulated moving bed chromatography: Multi-level optimization procedure (MLOP)[J]. Korean Journal of Chemical Engineering, 2004, 21(4): 836-852.
68	WU D-J, XIE Y, MA Z, et al. Design of simulated moving bed chromatography for amino acid separations[J]. Industrial & Engineering Chemistry Research, 1998, 37(10): 4023-4035.
69	HRITZKO B J, XIE Y, WOOLEY R J, et al. Standing-wave design of tandem SMB for linear multicomponent systems[J]. AIChE Journal, 2002, 48(12): 2769-2787.
70	HARVEY D, WEEDEN G, WANG N-H L. Speedy standing wave design and simulated moving bed splitting strategies for the separation of ternary mixtures with linear isotherms[J]. Journal of Chromatography A, 2017, 1530: 152-170.
71	LEE C-G, JO C Y, LEE K B, et al. Optimization of a simulated-moving-bed process for continuous separation of racemic and meso-2,3-butanediol using an efficient optimization tool based on nonlinear standing-wave-design method[J]. Separation and Purification Technology, 2021, 254: 117597.
72	SOEPRIATNA N, WANG N H L, WANKAT P C. Standing wave design and optimization of nonlinear four-zone thermal simulated moving bed systems[J]. Industrial & Engineering Chemistry Research, 2015, 54(42): 10419-10433.
73	KIM Seul-Gi, Hee-Geun NAM, KIM Jin-Hyun, et al. Optimal design of a four-zone simulated moving bed process for separation of homoharringtonine and harringtonine[J]. The Canadian Journal of Chemical Engineering, 2011, 89(2): 304-313.
74	HARVEY D, DING Y, WANG N-H L. Standing-wave design of Three-Zone, open-loop non-isocratic SMB for purification[J]. BMC Chemical Engineering, 2019, 1(1): 1-18.
75	XIE Y, FARRENBURG C A, CHIN C Y, et al. Design of SMB for a nonlinear amino acid system with mass-transfer effects[J]. AIChE Journal, 2003, 49(11): 2850-2863.
76	XIE Y, WU D, MA Z, et al. Extended standing wave design method for simulated moving bed chromatography: Linear systems[J]. Industrial & Engineering Chemistry Research, 2000, 39(6): 1993-2005.
77	LEE C-G, J-H CHOI, PARK C, et al. Standing wave design and optimization of a simulated moving bed chromatography for separation of xylobiose and xylose under the constraints on product concentration and pressure drop[J]. Journal of Chromatography A, 2017, 1527: 80-90.
78	MUN S. Optimization of production rate, productivity, and product concentration for a simulated moving bed process aimed atfucose separation using standing-wave-design and genetic algorithm[J]. Journal of Chromatography A, 2018, 1575: 113-121.
79	PARK H, JO C Y, LEE K B, et al. Standing wave design and optimization of a tandem size-exclusion simulated moving bed process for high-throughput recovery of neoagarohexaose from neoagarooligosaccharides[J]. Separation and Purification Technology, 2021, 276: 119039.
80	LEE H-J, XIE Y, Y-M KOO, et al. Separation of lactic acid from acetic acid using a four-zone SMB[J]. Biotechnology Progress, 2004, 20(1): 179-192.
81	H-G NAM, S-H JO, MUN S. Comparison of Amberchrom-CG161C and Dowex99 as the adsorbent of a four-zone simulated moving bed process for removal of acetic acid from biomass hydrolyzate[J]. Process Biochemistry, 2011, 46(10): 2044-2053.
82	NAM H G, HAN M G, YI S C, et al. Optimization of productivity in a four-zone simulated moving bed process for separation of succinic acid and lactic acid[J]. Chemical Engineering Journal, 2011, 171(1): 92-103.
83	AZEVEDO D C S, RODRIGUES A E. Design of a simulated moving bed in the presence of mass-transfer resistances[J]. AIChE Journal, 1999, 45(5): 956-966.
84	SILVA V M T, MINCEVA M, RODRIGUES A E. Novel analytical solution for a simulated moving bed in the presence of mass-transfer resistance[J]. Industrial & Engineering Chemistry Research, 2004, 43(16): 4494-4502.
85	ZABKA M, MINCEVA M, Sá GOMES P, et al. Chiral separation of R,S-α-tetralol by simulated moving bed[J]. Separation Science and Technology, 2008, 43(4): 727-765.
86	URIBE SANTOS D L, DELGADO J A, ÁGUEDA V I, et al. Recovery of a succinic, formic, and acetic acid mixture from a model fermentation broth by simulated moving bed adsorption with methanol as a desorbent[J]. Industrial & Engineering Chemistry Research, 2022, 61(1): 672-683.
87	AZEVEDO D C S, RODRIGUES A E. Fructose–glucose separation in a SMB pilot unit: Modeling, simulation, design, and operation[J]. AIChE Journal, 2001, 47(9): 2042-2051.
88	BORGES DA SILVA E A, PEDRUZZI I, RODRIGUES A E. Simulated moving bed technology to improve the yield of the biotechnological production of lactobionic acid and sorbitol[J]. Adsorption, 2011, 17(1): 145-158.
89	MINCEVA M, RODRIGUES A E. Modeling and simulation of a simulated moving bed for the separation of p-xylene[J]. Industrial & Engineering Chemistry Research, 2002, 41(14): 3454-3461.
90	MINCEVA M, RODRIGUES A E. Two-level optimization of an existing SMB for p-xylene separation[J]. Computers & Chemical Engineering, 2005, 29(10): 2215-2228.
91	LEHOUCQ S, VERHèVE D, VANDE WOUWER A, et al. SMB enantioseparation: Process development, modeling, and operating conditions[J]. AIChE Journal, 2000, 46(2): 247-256.
92	RODRIGUES A E, PAIS L S. Design of SMB chiral separations using the concept of separation volume[J]. Separation Science and Technology, 2005, 39(2): 245-270.
93	GOLDBERG D E. Genetic algorithm in search, optimization, and machine learning[M]. Reading, Mass: Addison-Wesley Publishing Co., 1989.
94	REED P, MINSKER B S, GOLDBERG D E. A multiobjective approach to cost effective long-term groundwater monitoring using an elitist nondominated sorted genetic algorithm with historical data[J]. Journal of Hydroinformatics, 2001, 3(2): 71-89.
95	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
96	KASAT R B, GUPTA S K. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit(FCCU) using genetic algorithm (GA) with the jumping genes operator[J]. Computers & Chemical Engineering, 2003, 27(12): 1785-1800.
97	吴献东，金晓明，苏宏业.基于NSGA-Ⅱ的模拟移动床色谱分离过程多目标操作优化[J]. 化工学报, 2007, 58(8): 2038-2044.
	WU Xiandong, JIN Xiaoming, SU Hongye. Multi-objective optimization of simulated moving bed chromatography separation based on NSGA- Ⅱ algorithm[J]. Journal of Chemical Industry and Engineering(China), 2007, 58(8): 2038-2044.
98	WANG J, TIAN Y M, LI Y, et al. Multi-objective optimization of non-isothermal simulated moving bed reactor: methyl acetate synthesis[J]. Chemical Engineering Journal, 2020, 395: 125041.
99	朱磊，徐进，孙玉高，等.逆向法测定酮洛芬对映体在Chiralpak AD柱上的吸附等温线[J]. 化工学报, 2012, 63(8): 2469-2476.
	ZHU Lei, XU Jin, SUN Yugao, et al. Determination of adsorption isotherms of ketoprofen enantiomers on Chiralpak AD column by inverse method[J]. CIESC Journal, 2012, 63(8): 2469-2476.
100	WAGNER N, HAKANSSON E, WAHLER S, et al. Multi-objective optimization for the economic production of D-psicose using simulated moving bed chromatography[J]. Journal of Chromatography A, 2015, 1398: 47-56.
101	KIM P H, NAM H G, PARK C, et al. Simulated moving bed separation of agarose-hydrolyzate components for biofuel production from marine biomass [J]. Journal of Chromatography A, 2015, 1406: 231-243.
102	ZHANG Z, LIAO C J, CHAI H, et al. Multi-objective optimization of controllable configurations for bistable laminates using NSGA-Ⅱ[J]. Composite Structures, 2021, 266: 113764.
103	路艳雪，赵超凡，吴晓锋，等. 基于改进的NSGA-Ⅱ多目标优化方法研究[J]. 计算机应用研究, 2018, 35(6): 1733-1737.
	LU Yanxue, ZHAO Chaofan, WU Xiaofeng, et al. Multi-objective optimization method research based on improved NSGA-Ⅱ[J]. Application Research of Computers, 2018, 35(6): 1733-1737.

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[7]	Gongchu SHI, Yalong LIAO, Bowen SU, Yu ZHANG, Jiajun XI. Multi-objective optimization of pressure oxidative selective leaching of copper smelting slag by response surface methodology [J]. Chemical Industry and Engineering Progress, 2020, 39(S1): 270-280.
[8]	Jiangwei XIE, Chunli LI, Guoming HUANG. Structural optimization of dividing wall column using response surface methodology coupled with NSGA-Ⅱ algorithm [J]. Chemical Industry and Engineering Progress, 2020, 39(8): 2962-2971.
[9]	Jingkang ZHANG, Haiqing WANG, Weiwei JIANG, Xinge QI. Optimal placement of gas detector based on unavailability and voting logic [J]. Chemical Industry and Engineering Progress, 2020, 39(6): 2503-2509.
[10]	ZHANG Shen, GAO Wei, QI Ming, YU Wenhao, WANG Honghai. A review of optimization rectification systems based on multi-objective [J]. Chemical Industry and Engineering Progress, 2019, 38(s1): 1-9.
[11]	Hongshan BAI,Dongya ZHAO,Qunhong TIAN,Qi WANG,Shijian LU,Zhongde YANG,Jianping YANG. Stochastic optimization of the whole process of CO₂ capture, transportation, utilization and sequestration [J]. Chemical Industry and Engineering Progress, 2019, 38(11): 4911-4920.
[12]	Ning JIANG,Xiaodong XIE,Wei FAN,Yingjie XU. Data-driven optimization retrofit method with fixed topology structure for heat exchanger network [J]. Chemical Industry and Engineering Progress, 2019, 38(10): 4452-4460.
[13]	Ning JIANG, Fengyuan GUO, Wenqiao HAN, Huajing LIU, Lu LIN. 3E Optimization of heat exchanger network system based on non-counterflow heat transfer [J]. Chemical Industry and Engineering Progress, 2019, 38(02): 761-771.
[14]	JIANG Ning, HAN Wenqiao, GUO Fengyuan, XU Yingjie. Optimization of heat exchanger network retrofit based on actual heat load distribution [J]. Chemical Industry and Engineering Progress, 2018, 37(08): 2935-2941.
[15]	JIANG Ning, YU Hangsheng, HAN Wenqiao. Retrofit of heat exchanger network based on exchanger reassignment and genetic algorithm (GA) [J]. Chemical Industry and Engineering Progress, 2017, 36(08): 2830-2837.