Kinetic model optimization of n-butane isomerization by improved biogeography optimization algorithm

doi:10.11949/j.issn.0438-1157.20171083

Abstract

Abstract:

To overcome the problem that biogeography-based optimization (BBO) algorithm easily falls into precocity in optimization process, a three-dimensional variation biogeography-based optimization (Tdv-BBO) was proposed. With introduction of 3D variation into BBO algorithm, the improved algorithm solved issue of lack of searching power in late stage of BBO algorithm and accelerated optimization speed of BBO algorithm. Further, Tdv-BBO was applied for optimizing kinetic model and settling model parameters of n-butane isomerization. The simulation results show that Tdv-BBO algorithm improves diversity of individual population, enhances searching capacity of algorithm, and accelerates optimization speed. The optimized kinetic model by Tdv-BBO has advantages of high precision and good generalization ability. Hence, Tdv-BBO provides an effective technique for modeling n-butane isomerization.

Key words: three-dimensional variation, Tdv-BBO algorithm, n-butane isomerization, kinetic modeling

摘要：

针对生物地理学优化（biogeography-based optimization，BBO）算法在寻优过程中容易陷入早熟的现象，提出了一种基于三维变异的生物地理学优化（three-dimensional variation biogeography-based optimization，Tdv-BBO）算法。该算法是在BBO算法的基础上，引入了三维变量的变异，解决了BBO算法后期搜索动力不足的问题，加快了BBO算法的寻优速度。同时，提出将改进的Tdv-BBO算法应用到正丁烷异构反应动力学模型的优化中，对反应动力学模型的参数进行了优化和整定。仿真实验表明：改进的Tdv-BBO算法提高了个体种群的多样性，增强了算法的搜索能力，加快了寻优速度。用该方法优化得到的反应动力学模型，模型精度较高，泛化能力强；可为正丁烷异构反应的建模提供一种有效的方法。

关键词: 三维变异, Tdv-BBO优化算法, 正丁烷异构反应, 动力学建模

CLC Number:

TE65

LUO Ruihan, CHEN Juan, WANG Qi. Kinetic model optimization of n-butane isomerization by improved biogeography optimization algorithm[J]. CIESC Journal, 2018, 69(3): 1158-1166.

罗锐涵, 陈娟, 王齐. 改进生物地理学算法对正丁烷异构反应模型的优化[J]. 化工学报, 2018, 69(3): 1158-1166.

References 30

[1]	刘娜娜, 张励, 张晓光. 正丁烷异构化与烷基化联合对汽油调合的积极作用[J]. 炼油技术与工程, 2013, 43(9):1-4. LIU N N, ZHANG L, ZHANG X G. Significance of integration of normal butane isomerization with alkylation for gasoline blending[J]. Refining Technology and Engineering, 2013, 43(9):1-4.
[2]	尹佳音. 关于煤基碳四实际生产MTBE的技术改造[J]. 当代化工研究, 2017, (3):11-12. YIN J Y. About MTBE, coal-based carbon four practical production technical innovation[J]. Contemporary Chemical Research, 2017, (3):11-12.
[3]	王鹏飞. 焦炉煤气二级加氢脱硫反应动力学及反应器模拟研究[D]. 北京:北京化工大学, 2013. WANG P F. Secondary hydrodesulfurization reaction kinetics of coke oven gas and study of reactor simulation[D]. Beijing:Beijing University of Chemical Technology, 2013.
[4]	NIEMINEN V, KANGAS M, TAPIO S A, et al. Kinetic study of n-butane isomerization over Pt-H-mordenite.[J]. Eng. Chem. Res., 2005, 44(3):471-484.
[5]	SIMON D. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6):702-713.
[6]	GAN L, DUAN H. Chemical reaction optimization for feature combination in bio-inspired visual attention[J]. International Journal of Computational Intelligence Systems, 2015, 8(3):530-538.
[7]	ISHAQUE K, SALAM Z, AMJAD M, et al. An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation[J]. IEEE Transactions on Power Electronics, 2012, 27(8):3627-3638.
[8]	NILNAM T, AMIRI B. An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis[J]. Applied Soft Computing, 2010, 10(1):183-197.
[9]	陈道君, 龚庆武, 乔卉, 等. 采用改进生物地理学算法的风电并网电力系统多目标发电调度[J].中国电机工程学报, 2012, 32(31):150-158. CHEN D J, GONG Q W, QIAO H, et al. Multi-objective generation dispatching for wind power integrated system adopting improved biogeography-based optimization algorithm[J].Proceedings of the CSEE, 2012, 32(31):150-158.
[10]	YANG G P, LIU S Y, ZHANG J K, et al. Control and synchronization of chaotic systems by an improved biogeography-based optimization algorithm[J]. Applied Intelligence, 2013, 39(1):132-143.
[11]	MA H, SIMON D. Blended biogeography-based optimization for constrained optimization[J]. Engineering Applications of Artificial Intelligence, 2011, 24(3):517-525.
[12]	BHATTACHRYA A, CHATTOPADHYAY P K. Solving complex economic load dispatch problems using biogeography-based optimization[J]. Expert Systems with Applications, 2010, 37(5):3605-3615.
[13]	GONG W, CAI Z, LING C X, et al. A real-coded biogeography-based optimization with mutation[J]. Applied Mathematics & Computation, 2010, 216(9):2749-2758.
[14]	王玉梅, 程辉, 钱锋. 改进生物地理学优化算法及其在汽油调合调度中的应用[J]. 化工学报, 2016, 67(3):774-778. WANG Y M, CHEN H, QIAN F. Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling[J]. CIESC Journal, 2016, 67(3):774-778.
[15]	李知聪, 顾幸生. 改进的生物地理学优化算法在混合流水车间调度中的应用[J]. 化工学报, 2016, 67(3):751-757. LI Z C, GU X S. Improved biogeography-based optimization algorithm used in solving hybrid flow shop scheduling problem[J]. CIESC Journal, 2016, 67(3):751-757.
[16]	MA H P. Analysis of the behavior of migration models for biogeography-based optimization[J]. Information Sciences, 2010, 180(18):3444-3464.
[17]	马海平, 李雪, 林东升. 生物地理学优化算法的迁移率模型分析[J]. 东南大学学报(自然科学版), 2009, 39(S1):16-21. MA H P, LI X, LIN D S. Analysis of migration rate models for biogeography based optimization[J]. Journal of Southeast University (Nature Science Edition), 2009, 39(S1):16-21.
[18]	WANG Y, LI H X, HUANG T, et al. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting[J]. Applied Soft Computing, 2014, 18(1):232-247.
[19]	YAO X, LIU Y, LIN G. Evolutionary programming made faster[J]. IEEE Transactions on Evolutionary. Computation, 2002, 3 (2):82-102.
[20]	ERGEZER M, SIMON D, DU D. Oppositional biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation, 2014, 47(10):1009-1014.
[21]	鲁宇明, 王彦超, 刘嘉瑞, 等. 一种改进的生物地理学优化算法[J]. 计算机工程与应用, 2016, 52(17):146-151. LU Y M, WANG Y C, LIU J R, et al. Improved biogeography-based optimization algorithm[J]. Computer Engineering and Applications, 2016, 52(17):146-151.
[22]	张新明, 尹欣欣, 冯梦清, 等. 改进的生物地理学算法及其在图像分割中的应用[J]. 电光与控制, 2015, 22(12):24-28. ZHANG X M, YIN X X, FENG M Q, et al. Improved biogeography-based optimization and its application in image segmentation[J]. Electro-Optical and Control, 2015, 22(12):24-28.
[23]	CHEN X, TIANFIELD H, DU W, et al. Biogeography-based optimization with covariance matrix based migration[J]. Applied Soft Computing, 2016, 45:71-85.
[24]	FENG Q, LIU S, ZHANG J, et al. Improved biogeography-based optimization with random ring topology and Powell's method[J]. Applied Mathematical Modelling, 2017, 41:630-649.
[25]	KHADEMI G, MOHAMMADI H, SIMON D. Hybrid invasive weed/biogeography-based optimization[J]. Engineering Applications of Artificial Intelligence, 2017, 64:213-231.
[26]	潘文超. 果蝇最佳化演算法-最新演化式计算技术[M]. 台湾:沧海书局, 2011:11-12. PAN W C. Duo Fly Optimization Algorithm-The Latest Evolutionary Computing Technology[M]. Taiwan:Canghai Bookstore, 2011:11-12.
[27]	LIN S M. Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network[J]. Neural Computing & Applications, 2013, 22(3/4):783-791.
[28]	张勇, 夏树发, 唐冬生. 果蝇优化算法对多峰函数求解性能的仿真研究[J]. 暨南大学学报(自然科学与医学版), 2014, 35(1):82-87. ZHANG Y, XIA S F, TANG D S. Simulation of multi-peak function based on the fly optimization algorithm[J]. Journal of Jinan University (Natural Science &Medicine Edition), 2014, 35(1):82-87.
[29]	王凌. 智能优化算法及其应用[M]. 北京:清华大学出版社, 2001:10-15. WANG L. Intelligent Optimization Algorithm and Its Application[M]. Beijing:Tsinghua University Press, 2001:10-15.
[30]	CHEN J, HUANG X Y, QI Y L, et al. Process optimization of ultrasonic extraction of puerarin based on support vector machine[J]. Chinese Journal of Chemical Engineering, 2014, 22(7) 735-741.