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

doi:10.3969/j.issn.0438-1157.2012.09.036

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

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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).

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

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)。

Abstract

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

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

CLC Number:

TP273

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.

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.

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

基于随机学习因子混沌PSO算法的甲醇合成转化率测量

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