基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

doi:10.11949/j.issn.0438-1157.20141473

化工学报 ›› 2015, Vol. 66 ›› Issue (1): 326-332.DOI: 10.11949/j.issn.0438-1157.20141473

基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

胡蓉, 杨明磊, 钱锋

华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237

收稿日期:2014-09-28 修回日期:2014-10-08 出版日期:2015-01-05 发布日期:2015-01-05
通讯作者: 钱锋
基金资助:
国家重点基础研究发展计划项目(2012CB720500);国家自然科学基金项目(U1162202, 21206037);国家高技术研究发展计划项目(2013AA040701);中国博士后基金项目(2013M531143);中央高校基本科研业务费专项资金;上海市“科技创新行动计划”研发平台建设项(13DZ2295300)。

Optimization of xylene adsorption separation process based on multi-objective teaching-learning-based optimization algorithm

HU Rong, YANG Minglei, QIAN Feng

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2014-09-28 Revised:2014-10-08 Online:2015-01-05 Published:2015-01-05
Supported by:
supported by the National Basic Research Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202, 21206037), the National High Technology Research and Development Program of China (2013AA040701), the Postdoctoral Science Foundation of China (2013M531143), the Fundamental Research Funds for the Central Universities and the Shanghai“Technology Innovation Action Plan”Development Platform for Building Projects (13DZ2295300).

摘要/Abstract

摘要：

以C8芳烃混合物的吸附分离过程作为研究对象, 应用多目标教学优化算法(multi-objective teaching-learning-based optimization algorithm, MOTLBO)对模拟移动床多目标优化问题进行求解。采用TMB方法, 建立了模拟移动床模型, 并对两个典型的模拟移动床多目标操作优化问题进行了优化设计。通过与NSGA-Ⅱ算法的比较, 证明了多目标教学优化算法在求解模拟移动床多目标优化问题上的有效性和优势。此外, 还分析了抽出液流量、抽余液流量以及步进时间等对多目标优化非劣解的影响, 优化结果为模拟移动床分离过程的工艺设计和操作提供了依据。

关键词: 模拟移动床, 多目标教学优化算法, 多目标, 优化设计

Abstract:

Multi-objective teaching-learning-base optimization (MOTLBO) algorithm has been employed to investigate the multi-objective optimization problem of simulated moving bed chromatography separation for the recovery of p-xylene from a mixture of C8 aromatics. The separation process was simulated using true moving bed (TMB) modeling strategy. Based on the MOTLBO algorithm, the optimal operation conditions are designed for two typical multi-objective optimization problems. Comparing with NSGA-Ⅱ, the MOTLBO algorithm has been verified to be more efficient in solving the multi-objective optimization problem of simulated moving bed. In addition, The influences of the extract flow rate, the raffinate flow rate and the switching time on the pareto optimal solutions were also analyzed. The optimization can facilitate the design and operation of simulated moving bed.

Key words: simulated moving bed, MOTLBO algorithm, multi-objective, optimization design

中图分类号:

TQ028

胡蓉, 杨明磊, 钱锋. 基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用[J]. 化工学报, 2015, 66(1): 326-332.

HU Rong, YANG Minglei, QIAN Feng. Optimization of xylene adsorption separation process based on multi-objective teaching-learning-based optimization algorithm[J]. CIESC Journal, 2015, 66(1): 326-332.

参考文献 20

[1]	Ruthven D M, Ching C B. Counter-current and simulated counter-current adsorption separation process [J]. Chem. Eng. Sci., 1989, 44(5): 1011-1038
[2]	Pais L S, Loureiro J M, Rodrigues A E. Chiral separation by SMB chromatography [J]. Separation and Purification Technology, 2000, 20(1): 67-77
[3]	Minceva M, Rodrigues A E. Understanding and revamping of industrial scale SMB units for p-xylene separation [J]. AIChE J., 2007, 53(1): 138-149
[4]	Yu H W, Ching C B. Optimization of a simulated moving bed based on an approximated langmuir model [J]. AIChE J., 2002, 48(10): 2240-2246
[5]	Migliorini C, Mazzotti M, Morbidelli M. Robust design of counter-current adsorption separation process(5): Nonconstant selectivity [J]. AIChE J., 2000, 46(7): 1384-1399
[6]	Minceva M, Rodrigues A E. Modeling and simulation of a simulated moving bed for the separation of p-xylene [J]. AIChE J., 2007, 53(1): 138-149
[7]	Storti G, Mazzotti M, Morbidelli M. Robust design of binary countercurrent adsorption separation processes [J]. AIChE J., 1994, 40(11): 1825-1842
[8]	Kawajiri Y, Biegler L T. Optimization strategies for simulated moving bed and powerfeed processes [J]. AIChE J., 2006, 52(4): 1343-1350
[9]	Zhang Ziyang, Mazzotti M, Morbidelli M. Multiobjective optimization of simulated moving bed and varicol processes using a genetic algorithm [J]. J. Chromatogr. A, 2003, 989(1): 95-108
[10]	Kurup A S, Hidajat K, Ray A K. Optimal operation of an industrial-scale parex process for the recovery of p-xylene from a mixture of C8 aromatics [J]. Ind. Eng. Chem. Res., 2005, 44(15): 5703-5714
[11]	Wu Xiandong(吴献东), Jin Xiaoming(金晓明), Su Hongye(苏宏业). Multi-objective optimization of simulated moving bed chromatography separation based on NSGA-Ⅱ algorithm [J]. Journal of Chemical Industry and Engineering (China)(化工学报), 2007, 58(8): 2038-2044
[12]	Xiao Di(肖迪), Ge Qicheng(葛启承), Lin Jinguo(林锦国), Cheng Ming(程明). Double populations genetic and particle swarm algorithm and its application in SMB optimization [J]. Journal of Nanjing University of Science and Technology(南京理工大学学报), 2012, 36(1): 31-36
[13]	Huang Liang, Sun Lei, Wang Ning, Jin Xiaoming. Multiobjective optimization of simulated moving bed by tissue P system [J]. Chin. J. Chem. Eng., 2007, 15(5): 683-690
[14]	Nayak M R, Nayak C K, Rout P K. Application of multi-objective teaching learning based optimization algorithm to optimal power flow problem [J]. Procedia Technology, 2012, 6: 255-264
[15]	Pais L S, Loureiro J M, Rodrigues A E. Modelling strategies for enantiomers separation by SMB chromatography [J]. AIChE J., 1998, 44(3): 561-569
[16]	Yang Minglei(杨明磊), Wei Min(魏民), Hu Rong(胡蓉), Ye Zhencheng(叶贞成), Qian Feng(钱锋). Modeling of the simulated moving bed for xylene separation [J]. CIESC Journal (化工学报), 2013, 64(12): 4335-4341
[17]	Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems [J]. Computer-Aided Design, 2011, 43(3): 303-315
[18]	Rao R V, Savsani V J, Vakharia D P. Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems [J]. Information Sciences, 2012, 183(1): 1-15
[19]	Niknam T. A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems [J]. Eng. Appl. Artif. Intell., 2012, 25(8): 1577-1588
[20]	Rao R V, Patel V. Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm [J]. Applied Mathematical Modeling, 2013, 37(3): 1147-1162

基于多目标教学优化算法在二甲苯吸附分离过程优化中的应用

Optimization of xylene adsorption separation process based on multi-objective teaching-learning-based optimization algorithm

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