Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization

doi:10.3969/j.issn.0438-1157.2012.09.019

CIESC Journal ›› 2012, Vol. 63 ›› Issue (9): 2794-2798.DOI: 10.3969/j.issn.0438-1157.2012.09.019

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Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization

JIANG Huaqin, ZHAO Chengye, LIU Xinggao

State Key Laboratory of Industrial Control Technology, Control Department, Zhejiang University, Hangzhou 310027, Zhejiang, China

Received:2012-06-13 Revised:2012-06-20 Online:2012-09-05 Published:2012-09-05
Supported by:
supported by the National Natural Science Foundation of China(U1162130),the High-tech Reasearch and Development Program of China(2006AA05Z226)and the Zhejiang Province Outstanding Youth Science Fund Project(R4100133).

群智能优化LSSVM最优聚丙烯熔融指数预报

蒋华琴, 赵成业, 刘兴高

浙江大学控制系, 工业控制技术国家重点实验室, 浙江杭州 310027

通讯作者: 刘兴高
作者简介:蒋华琴(1984-),女,硕士。
基金资助:
国家自然科学基金项目(U1162130);国家高技术研究发展计划项目(2006AA05Z226);浙江省杰出青年科学基金项目(R4100133)。

Abstract

Abstract: A novel swarm intelligence optimization AC_ICPSO(ant colony and immune clone particle swarm optimization)algorithm is proposed.It combines ACO(ant colony optimization)and PSO(particle swarm optimization)to conduct dynamic swarm query.According to introducing crossover and mutation operator,encoding repeatedly,iterative choice,etc.,it leads to widen data range,improves search precision and convergence efficiency,avoids premature convergence,and reduces complexity of the conventional ACO or PSO algorithm.Then AC_ ICPSO is used to optimize the parameters of LSSVM(least square support vector machines)to predict the melt index of polypropylene,so the best model AC_ICPSO_LSSVM is obtained.The detailed researches on the optimized model are carried out based on the data from a real plant,and the result shows that the proposed approach has great prediction accuracy and effectiveness.

Key words: swarm intelligence optimization, LSSVM, melt index prediction, parameter optimization

摘要： 提出了群智能优化AC_ICPSO(ant colony and immune clone particle swarm optimization)算法,融合蚁群算法与粒子群算法进行动态群体搜索,设计交叉算子和变异算子、群体多次编码、迭代选择等,来提高数据搜索的范围、精度和收敛的效率,避免早熟,降低算法的复杂度。然后利用AC_ICPSO方法对最小二乘支持向量机预报模型(LSSVM)进行参数寻优,得到最优的AC_ICPSO_LSSVM预报模型。以实际聚丙烯生产的熔融指数预报作为实例进行研究,结果表明所提出的AC_ICPSO_LSSVM方法有效,具有良好的预报精度。

关键词: 群智能优化, 最小二乘支持向量机, 熔融指数预报, 参数寻优

CLC Number:

TP273

JIANG Huaqin, ZHAO Chengye, LIU Xinggao. Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization[J]. CIESC Journal, 2012, 63(9): 2794-2798.

蒋华琴, 赵成业, 刘兴高. 群智能优化LSSVM最优聚丙烯熔融指数预报[J]. 化工学报, 2012, 63(9): 2794-2798.

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Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization

群智能优化LSSVM最优聚丙烯熔融指数预报

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