Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

doi:10.1016/j.cjche.2017.03.022

›› 2017, Vol. 25 ›› Issue (8): 983-991.DOI: 10.1016/j.cjche.2017.03.022

• Article • 下一篇

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Lili Tao¹, Bin Xu², Zhihua Hu¹, Weimin Zhong³

1 College of Engineering, Shanghai Polytechnic University, Shanghai 201209, China;
2 School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
3 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2016-09-16 修回日期:2016-10-22 出版日期:2017-08-28 发布日期:2017-09-11
通讯作者: Lili Tao, Weimin Zhong
基金资助:
Supported by the Shanghai Second Polytechnic University Key Discipline Construction-Control Theory&Control Engineering (No.XXKPY1609),the National Natural Science Foundation of China (61422303),Shanghai Talent Development Funding (H200-2R-15111),and 2017 Shanghai Second Polytechnic University Cultivation Research Program of Young Teachers (02).

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Lili Tao¹, Bin Xu², Zhihua Hu¹, Weimin Zhong³

1 College of Engineering, Shanghai Polytechnic University, Shanghai 201209, China;
2 School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;
3 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2016-09-16 Revised:2016-10-22 Online:2017-08-28 Published:2017-09-11
Supported by:
Supported by the Shanghai Second Polytechnic University Key Discipline Construction-Control Theory&Control Engineering (No.XXKPY1609),the National Natural Science Foundation of China (61422303),Shanghai Talent Development Funding (H200-2R-15111),and 2017 Shanghai Second Polytechnic University Cultivation Research Program of Young Teachers (02).

摘要/Abstract

摘要： The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simultaneously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization problems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

关键词: p-Xylene oxidation, Operation condition optimization, Multi-objective optimization, Self-adaptive differential evolution

Abstract: The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simultaneously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization problems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Key words: p-Xylene oxidation, Operation condition optimization, Multi-objective optimization, Self-adaptive differential evolution

Lili Tao, Bin Xu, Zhihua Hu, Weimin Zhong. Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm[J]. , 2017, 25(8): 983-991.

参考文献

[1] R.A. Tomás, J.C. Bordado, J.F. Gomes, p-Xylene oxidation to terephthalic acid:A literature review oriented toward process optimization and development, Chem. Rev. 113(10) (2013) 7421-7469.
[2] W. Sun, Y. Pan, L. Zhao, X. Zhou, Simplified free-radical reaction kinetics for p-xylene oxidation to terephthalic acid, Chem. Eng. Technol. 31(10) (2008) 1402-1409.
[3] F. Qian, L. Tao, W. Sun, W. Du, Development of a free radical kinetic model for industrial oxidation of p-xylene based on artificial network and adaptive immune genetic algorithm, Ind. Eng. Chem. Res. 51(4) (2011) 3229-3237.
[4] W. Sun, M. An, W. Zhong, L. Zhao, Kinetics of COx formation in the homogeneous metal/bromide-catalyzed aerobic oxidation of p-xylene, In. J. Chem. Kinet. 44(5) (2012) 277-283.
[5] N. Srinivas, K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2(3) (1994) 221-248.
[6] C.A. Coello Coello, M.S. Lechuga, MOPSO:A proposal for multiple objective particle swarm optimization, Evolutionary Computation, Proceedings of the 2002 Congress on 2002, pp. 1051-1056.
[7] B.V. Babu, P.G. Chakole, J.H.S. Mubeen, Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor, Chem. Eng. Sci. 60(2005) 4822-4837.
[8] R. Storn, K. Price, Differential Evolution-A simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, International Computer Science Institute, Berkeley, CA, 1995.
[9] H.A. Abbass, R. Sarker, C. Newton, PDE:A Pareto-frontier differential evolution approach for multi-objective optimization problems, Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, IEEE 2001, pp. 971-978.
[10] W. Gong, Z. Cai, An improved multiobjective differential evolution based on Paretoadaptive ε-dominance and orthogonal design, Eur. J. Oper. Res. 198(2009) 576-601.
[11] B.-y. Qu, P.-N. Suganthan, Multi-objective differential evolution with diversity enhancement, J. Zheijang Univ. Sci. C 11(2010) 538-543.
[12] Y.-N. Wang, L.-H. Wu, X.-F. Yuan, Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure, Soft. Comput. 14(2010) 193-209.
[13] F. Qian, B. Xu, R. Qi, H. Tianfield, Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization, Soft. Comput. 16(8) (2012) 1353-1372.
[14] B.V. Babu, P.G. Chakole, J.H.S. Mubeen, Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor, Chem. Eng. Sci. 60(2005) 4822-4837.
[15] M.J. Reddy, D.N. Kumar, Multiobjective differential evolution with application to reservoir system optimization, J. Comput. Civ. Eng. 21(2007) 136-146.
[16] A.M. Gujarathi, B. Babu, Improved multiobjective differential evolution (MODE) approach for purified terephthalic acid (PTA) oxidation process, Mater. Manuf. Process. 24(2009) 303-319.
[17] L. Tao, X. Kong, W. Zhong, F. Qian, Modified self-adaptive immune genetic algorithm for optimization in the combustion side reaction of p-xylene oxidation, Chin. J. Chem. Eng. 20(6) (2012) 1047-1052.
[18] X. Yan, W. Du, F. Qian, Development of a kinetic model for industrial oxidation of p-xylene by RBF-PLS and CCA, AIChE J 50(2004) 1169-1176.
[19] Lili Tao, Zhihua Hu, Feng Qian, et al., Design and control of a p-xylene oxidation process, Chin. J. Chem. Eng. 23(12) (2015) 1935-1944.
[20] K.V. Pontes, R. Maciel, M. Embirucu, Hartwich, W. Marquardt, Optimal operating policies for tailored linear polyethylene resins production, AIChE J. 54(9) (2008) 2346-2365.
[21] C.A.C. Coello, D.A.V. Veldhuizen, G.B. Lamont, Evolutionary Algorithms for Solving Multi-objective Problems, Kluwer Academic Publishers, New York, 2002.
[22] B.Y. Qu, P.N. Suganthan, Constrained multi-objective optimization algorithm with an ensemble of constraint handling methods, Eng. Optim. 43(2010) 403-416.
[23] G.P. Rangaiah, Multi-objective Optimization:Techniques and Applications in Chemical Engineering, (With Cd-rom) (Advances in Process Systems Engineering), World Scientific Publishing Company, Singapore, 2009.
[24] V. Huang, A. Qin, K. Deb, E. Zitzler, P. Suganthan, J. Liang, M. Preuss, S. Huband, Problem definitions for performance assessment of multi-objective optimization algorithms, Tech. Rep, Nanyang Technological University, Singapore, 2007.
[25] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm:NSGA-Ⅱ, IEEE Trans. Evol. Comput. 6(2002) 182-197.

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	Lukuan Yang, Wenqi Zhong, Li Sun, Xi Chen, Yingjuan Shao. Dynamic optimization oriented modeling and nonlinear model predictive control of the wet limestone FGD system[J]. 中国化学工程学报, 2020, 28(3): 832-845.
[2]	Weijun Li, Jakob Kjøbsted Huusom, Zhimao Zhou, Yi Nie, Yajing Xu, Xiangping Zhang. Multi-objective optimization of methane production system from biomass through anaerobic digestion[J]. Chinese Journal of Chemical Engineering, 2018, 26(10): 2084-2092.
[3]	Ke Yang, Yijun He, Zifeng Ma. Multi-objective steady-state optimization of two-chamber microbial fuel cells[J]. , 2017, 25(8): 1000-1012.
[4]	陶莉莉, 孔祥东, 钟伟民, 钱锋. Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1047-1052.
[5]	焦云强, 苏宏业, 廖祖维, 侯卫锋. Modeling and Multi-objective Optimization of Refinery Hydrogen Network[J]. , 2011, 19(6): 990-998.
[6]	成有为, 彭革, 王丽军, 李希. Kinetics of Burning Side Reaction in the Liquid-phase Oxidation of p-Xylene[J]. , 2009, 17(2): 181-188.
[7]	孙力, Helen H. Lou. A Strategy for Multi-objective Optimization under Uncertainty in Chemical Process Design[J]. , 2008, 16(1): 39-42.
[8]	牟盛静, 苏宏业, 古勇, 褚健. 工业PTA氧化过程的多目标优化[J]. , 2003, 11(5): 536-541.
[9]	高瑛石磊姚平经. 废物最小化的过程集成方法及多目标优化[J]. , 2001, 9(3): 267-272.