A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

doi:10.1016/S1004-9541(13)60531-5

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (5): 537-543.DOI: 10.1016/S1004-9541(13)60531-5

• 过程系统工程与过程安全 • 上一篇下一篇

A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

年笑宇, 王振雷, 钱锋

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:2012-07-30 修回日期:2012-10-29 出版日期:2013-05-28 发布日期:2013-05-31
通讯作者: WANG Zhenlei
基金资助:
Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202), the National Natural Science Foundation of China (61174118), the National High Technology Research and Development Program of China (2012AA040307), Shanghai Key Technologies R&D program (12dz1125100) and the Shanghai Leading Academic Discipline Project (B504).

A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

NIAN Xiaoyu, WANG Zhenlei, 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:2012-07-30 Revised:2012-10-29 Online:2013-05-28 Published:2013-05-31
Supported by:
Supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202), the National Natural Science Foundation of China (61174118), the National High Technology Research and Development Program of China (2012AA040307), Shanghai Key Technologies R&D program (12dz1125100) and the Shanghai Leading Academic Discipline Project (B504).

摘要/Abstract

摘要： To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online, a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed, which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold, it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy, global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization, the yield of ethylene and propylene is increased remarkably, which provides the proper operational condition of the ethylene cracking furnace.

关键词: group search optimization, differential evolution, ethylene and propylene yields, cracking furnace

Abstract: To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online, a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed, which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold, it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy, global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization, the yield of ethylene and propylene is increased remarkably, which provides the proper operational condition of the ethylene cracking furnace.

Key words: group search optimization, differential evolution, ethylene and propylene yields, cracking furnace

年笑宇, 王振雷, 钱锋. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. Chinese Journal of Chemical Engineering, 2013, 21(5): 537-543.

NIAN Xiaoyu, WANG Zhenlei, QIAN Feng. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. Chin.J.Chem.Eng., 2013, 21(5): 537-543.

参考文献

1 Liu, S.T., Wang, H.G., Qian, F., Hu, G.H., "Coupled simulation of combustion with heat transfer and cracking reaction in SL-II industrial ethylene pyrolyzer", Journal of Chemical Industry and Engineering (China), 62 (5),1309-1317 (2011). (in Chinese)

2 Li, P., Li, Q.A., Lei, R.X., Chen, A.J., Ren, L.L., Cao, W., "Development and application of advanced process control system for ethylene cracking heaters", Journal of Chemical Industry and Engineering (China), 62 (8),2217-2220 (2011). (in Chinese)

3 Wu, Y.L., Lu, J.G., Sun, Y.X., "An improved differential evolution for optimization of chemical process", Chin. J. Chem. Eng., 16 (2),228-234 (2008).

4 Qian, Y., Pan, M., Huang, Y.C., "Modeling and optimization for scheduling of chemical batch processes", Chin. J. Chem. Eng., 17 (1),1-7 (2009).

5 Wu, Y.L., Lu, J.G., Sun, Y.X., "An improved differential evolution for optimization of chemical process", Chin. J. Chem. Eng., 16 (2),228-234 (2008).

6 Huang, F.Z., Wang, L., "A hybrid differential evolution with double population for constrained optimization", In: IEEE Congress on Evolutionary Computation, Hong Kong,18-25 (2008).

7 He, S., Wu, Q.H., Saunders, J.R., "A novel group search optimizer inspired by animal behavioural ecology", In: IEEE Congress on Evolutionary Computation, Vancouver1272-1278 (2006).

8 He, S., Wu, Q.H., Saunders, J.R., "Group search optimizer: An optimization algorithm inspired by animal searching behavior", In: IEEE Transactions on Evolutionary Computation, 13 (5),973-990 (2009).

9 He, S., Wu, Q.H., Saunders, J.R., "A group search optimizer for neural network training", Computer Science, 3982,934-943 (2006).

10 He, S., "Training artificial neural networks using Lévy group search optimizer", J. Multiple-Valued Logic Soft Comput., 16,527-545 (2010).

11 He, S., Li, X., "Application of a group search optimization based artificial neural network to machine condition monitoring", In: ETFA2008 IEEE International Conference,1260-1266 (2008).

12 Shen, H., Zhu, Y., Niu, B., Wu, Q.H., "An improved group search optimizer for mechanical design optimization problems", Prog. Natur., 19,91-97 (2009).

13 Wu, Q.H., Lu, Z., Li, M.S., Ji, T.Y., "Optimal placement of FACTS devices by a group search optimizer with multiple producer", In: CEC2008 IEEE World Congress on Computational Intelligence, Hong Kong,1033-1039 (2008).

14 Qin, G., Liu, F., Li, L., "A quick group search optimizer with passive congregation and its convergence analysis", In: Computational Intelligence and Security, Beijing,249-253 (2009).

15 Storm, R., Price, K., "Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces", J. Glob. Optim., 11 (4),341-359 (1997).

16 Storm, R., Price, K., "Differential evolution for multi-objective optimization", Evol. Comput., 4,8-12 (2003).

17 Tasgetiren, M.F., Suganthan, P.N., Chua, T.J., AI-Hajri, A., "Differential evolution algorithms for the generalized assignment problem", In: IEEE Congress on Evolutionary Computation, Trondheim,2606-2613 (2009).

18 Aslantas, V., Tunckanat, M., "Differential evolution algorithm for segmentation of wound images", In: IEEE International Symposium on Intelligent Signal Processing, Alcala de Henares,1-5 (2007).

19 Sum-Ia, T., Taylor, G.A., Irving, M.R., Song, Y.H., "A differential evolution algorithm for multistage transmission expansion planning", In: Universities Power Engineering Conference2007, Brighton,357-364 (2007).

20 Zhang, W.G., Chen, H.P., Lu, D., Hao, S., "A novel differential evolution algorithm for a single batch-processing machine with non-identical job size", In: The Fourth International Conference Natural Computation, Jinan,447-451 (2008).

21 Zhu, X.B., Yu, Q., Wang, X.J., "A hybrid differential evolution algorithm for solving nonlinear bilevel programming with linear constraints", In: Proc.5th IEEE Int. Conf. on Cognitive Informatics (ICCI'06), Beijing,126-131 (2006).

22 Van Geem, K.M., Hudebine, D., Reyniers, M.F., Wahl, F., Verstraete, J.J., Marin, G.B., "Molecular reconstruction of naphtha steam cracking feedstocks based on commercial indices", Computers & Chemical Engineering, 31 (9),1020-1034 (2007).

23 Pyl, S.P., Van Geem, K.M., Reyniers, M.F., Guy, B.M., "Molecular reconstruction of complex hydrocarbon mixtures: An application of principal component analysis", AIChE Journal, 56 (12),3174-3188 (2010).

[1]	Peng Jiang, Wenli Du. Multi-objective modeling and optimization for scheduling of cracking furnace systems[J]. , 2017, 25(8): 992-999.
[2]	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.
[3]	Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms[J]. Chinese Journal of Chemical Engineering, 2016, 24(11): 1600-1608.
[4]	Xiaoyu Nian, Zhenlei Wang, Feng Qian. Strategy of changing cracking furnace feedstock based on improved group search optimization[J]. Chinese Journal of Chemical Engineering, 2015, 23(1): 181-191.
[5]	赵超, 许巧玲, 林思铭, 李学来. Hybrid Differential Evolution for Estimation of Kinetic Parameters for Biochemical Systems[J]. Chinese Journal of Chemical Engineering, 2013, 21(2): 155-162.
[6]	孙帆, 钟伟民, 程辉, 钱锋 . Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes[J]. Chinese Journal of Chemical Engineering, 2013, 21(1): 64-71.
[7]	阎兴頔, 杨文, 马贺贺, 侍洪波. Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1184-1190.
[8]	摆亮, 王钧炎, 江永亨, 黄德先. Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1074-1080.
[9]	吕文祥, 朱鹰, 黄德先, 江永亨, 金以慧. A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process[J]. , 2010, 18(1): 66-79.
[10]	胡春平, 颜学峰. An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation[J]. , 2009, 17(2): 232-240.
[11]	吴燕玲, 卢建刚, 孙优贤. An Improved Differential Evolution for Optimization of Chemical Process[J]. , 2008, 16(2): 228-234.

A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价