基于随机学习因子混沌PSO算法的甲醇合成转化率测量

doi:10.3969/j.issn.0438-1157.2012.09.036

化工学报 ›› 2012, Vol. 63 ›› Issue (9): 2899-2903.DOI: 10.3969/j.issn.0438-1157.2012.09.036

基于随机学习因子混沌PSO算法的甲醇合成转化率测量

林蔚天¹, 许伟², 顾幸生¹

1. 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;
2. 浙江大学工业控制技术国家重点实验室, 浙江杭州 310027

收稿日期:2012-06-14 修回日期:2012-06-21 出版日期:2012-09-05 发布日期:2012-09-05
通讯作者: 顾幸生
作者简介:林蔚天(1962-),男,副教授。
基金资助:
国家自然科学基金项目(61174040);国家高技术研究发展计划项目(2009AA04Z141)。

Measurement of methanol conversion rate based on random learning factor PSO with chaos

LIN Weitian¹, XU Wei², GU Xingsheng¹

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237;
2. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, Zhejiang, China

Received:2012-06-14 Revised:2012-06-21 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the National Natural Science Foundation of China(61174040)and High-tech Research and Development Program of China(2009AA04Z141).

摘要/Abstract

摘要： 粗甲醇转化率不仅是粗甲醇的主要技术指标,也是直接影响粗甲醇经济指标的重要因素。在之前工作的基础上,提出了两类随机学习因子混沌粒子群优化算法(RLFPSOC)。两种新算法分别从种群进化初期和后期两个方面引入混沌遍历性的特点,有效提高了算法的全局寻优能力。典型测试函数的仿真实验验证RLFPSOC算法的有效性。最后,将提出的RLFPSOC算法用于神经网络参数的优化,并建立甲醇合成塔转化率预测模型。实验结果表明,基于RLFPSOC的神经网络模型能够较好地预测甲醇合成转化率,并进一步验证了RLFPSOC算法的全局收敛性能。

关键词: 粒子群优化算法, 混沌, 甲醇转化率

Abstract: The conversion rate of the crude methanol is the primary indicator of methanol production,but also the key factor to influence the economic target.Based on the previous work,two random learning factor particle swarm optimizations with chaos are proposed.In the algorithms,the ergodicity of chaos is introduced respectively at early and late stage of evolution.The simulation of test functions evaluates the effectiveness of RLFPSOC.Finally,the proposed RLFPSOC,which is employed to optimize the parameter of neural network,is integrated with neural network to measure the methanol conversion rate.The results indicate that RLFPSOC-based neural network model can predict the methanol conversion rate well,which further verifies the global convergence of RLFPSOC.

Key words: particle swarm optimization, chaos, methanol conversion rate

中图分类号:

TP273

林蔚天, 许伟, 顾幸生. 基于随机学习因子混沌PSO算法的甲醇合成转化率测量[J]. 化工学报, 2012, 63(9): 2899-2903.

LIN Weitian, XU Wei, GU Xingsheng. Measurement of methanol conversion rate based on random learning factor PSO with chaos[J]. CIESC Journal, 2012, 63(9): 2899-2903.

参考文献 13

[1]	Kennedy J,Eberbart R C.Particle swarm optimization//Proceedings of the IEEE International Conference on Neural Networks.Perth,WA,1995,4:1942-1948
[2]	Song Weiduan(宋维端),Xiao Renjian(肖任坚),Fang Dingye(房鼎业).Methanol Technology(甲醇工艺)[M].Beijing:Chemical Industry Press,1999
[3]	Shi Y,Eberhart R C.Fuzzy adaptive particle swarm optimization//Proceedings of the 2001 Congress on Evolutionary Computation.Seoul,South Korea,2001:101-106
[4]	Chen Guochu(陈国初),Yu Jinshou(俞金寿).Hill-climbing particle swarm optimization algorithm with variable width neighborhood and its application[J].Journal of Chemical Industry and Engineering(China)(化工学报),2005,56(10):1928-1931
[5]	Chen C,Yang B,Yuan Jun,Wang Z,Wang L. Establishment and solution of eight-lump kinetic model for FCC gasoline secondary reaction using particle swarm optimization[J].Fuel,2007,86(15):2325-2332
[6]	Prata D M,Schwaab M,Lima E L,Pinto J C.Nonlinear dynamic data reconciliation and parameter estimation through particle swarm optimization:application for an industrial polypropylene reactor[J].Chem. Eng. Sci.,2009,64(18):3953-3967
[7]	Eberhart R C,Kennedy J.A new optimizer using particle swarm theory//Proceedings of the Sixth International Symposium on Micro Machine and Human Science.Nagoya,Japan,1995:39-43
[8]	Shi Y,Eberhart R C.Modified particle swarm optimizer//IEEE World Congress on Computation Intelligence.Piscatauay,NJ,1998:69-73
[9]	Lin W,et al.Based on random learn factor particle swarm optimization algorithm and its application//Proceeding of 2011 AASRI Conference on Artificial Intelligence and Industry Application.Male,Maldives,2011,2:261-263
[10]	Lorenz E N.The Essence of Chaos[M].Washington:University of Washington Press,1993
[11]	Zhang Shunfu(张顺福).Optimal control analysis on the Synthesis of methanol[J].Yunnan Chemical Technology(China)(云南化工),2008,35(6):28-32
[12]	Zhang Gu(张固).Probe on optimization method of ratio of hydrogen and carbon of fresh synthesis gas for methanol[J].Chemical Engineering Design(China)(化工设计),2004,14(5):6-10
[13]	Liu Y,et al.Center particle swarm optimization[J].Neurocomp.,2007,70(4/5/6):672-679

[1]	徐百平, 刘彪, 刘尧, 谭寿再, 杜遥雪, 刘春太. 不同错列角三角形转子作用下的分布混合数值模拟[J]. 化工学报, 2020, 71(8): 3545-3555.
[2]	刘作华,杨林荣,熊黠,陶长元,王运东,程芳琴. 多层刚柔组合桨诱发流场界面失稳强化非牛顿流体混沌混合行为[J]. 化工学报, 2020, 71(12): 5470-5478.
[3]	刘作华,魏红军,熊黠,陶长元,王运东,程芳琴. 长短叶片复合型刚柔桨强化搅拌槽内流体混沌混合行为[J]. 化工学报, 2020, 71(11): 5080-5089.
[4]	刘作华, 孙伟, 熊黠, 陶长元, 王运东, 程芳琴. 脉冲射流-刚柔组合桨强化流体混沌混合及传质性能研究[J]. 化工学报, 2020, 71(10): 4632-4641.
[5]	李钰冰, 杨茉, 陆廷康, 戴正华. 具有质热源的方腔内对流传热传质及其非线性特性[J]. 化工学报, 2019, 70(S2): 130-137.
[6]	张丽, 由钢, 乔霄峰, 许光文, 刘国桢, 刘云义. 氯碱电解槽内压力波动的混沌分析及流型识别[J]. 化工学报, 2019, 70(S1): 35-44.
[7]	熊黠, 刘作华, 谷德银, 邱发成, 王靓, 陶长元, 王运东. 刚柔组合桨强化粉煤灰酸浸搅拌槽内固液混沌混合[J]. 化工学报, 2019, 70(5): 1693-1701.
[8]	谢昭明, 邓容锐, 刘作华, 邓丽, 李千文, 张柱, 殷兆迁, 陶长元. 钠化焙烧转炉钒渣粉体分形生长的演化行为[J]. 化工学报, 2019, 70(5): 1904-1912.
[9]	白竣仁, 易军, 李倩, 吴凌, 陈雪梅. 面向反应再生过程的量子粒子群多目标优化[J]. 化工学报, 2019, 70(2): 750-756.
[10]	周新志, 邵伦, 李荣昆, 赵成萍, 董晨龙. 基于小波去噪和改进型PSO-SVM的微波加热温度预测模型研究[J]. 化工学报, 2018, 69(S2): 291-299.
[11]	夏大海, 宋诗哲, 王吉会. 基于混沌理论的电化学噪声谱数据解析及其在局部腐蚀检测中的应用[J]. 化工学报, 2018, 69(4): 1569-1577.
[12]	谷德银, 刘作华, 张姬一哲, 邱发成, 李军, 陶长元, 王运东. 搅拌槽内气液两相混沌混合及分散特性[J]. 化工学报, 2018, 69(2): 625-632.
[13]	邱发成, 刘作华, 刘仁龙, 全学军, 陈家兴, 谷德银, 李兵, 王运东. 偏心射流-刚柔组合桨搅拌器内混沌混合行为研究[J]. 化工学报, 2018, 69(2): 618-624.
[14]	肖清泰, 黄峻伟, 潘建新, 刘赟, 徐建新, 王华. 直接接触式沸腾换热过程气泡混合均匀度评价[J]. 化工学报, 2017, 68(8): 3049-3055.
[15]	刘作华, 许传林, 何木川, 谷德银, 许恢琴, 何海先, 陶长元, 王运东. 穿流式刚-柔组合搅拌桨强化混合澄清槽内油-水两相混沌混合[J]. 化工学报, 2017, 68(2): 637-642.

基于随机学习因子混沌PSO算法的甲醇合成转化率测量

Measurement of methanol conversion rate based on random learning factor PSO with chaos

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 13

相关文章 15

编辑推荐

Metrics

本文评价