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

JIANG Huaqin; ZHAO Chengye; LIU Xinggao

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

更新时间：2025-12-10

- Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization
- CIESC Journal Issue 9, Pages: 2794-2798(2012)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.019
  CLC： P273
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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JIANG Huaqin, ZHAO Chengye, LIU Xinggao. Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization[J]. CIESC Journal, 2012, (9): 2794-2798.
DOI：

JIANG Huaqin, ZHAO Chengye, LIU Xinggao. Melt index prediction of propylene polymerization based on LSSVM using swarm intelligence optimization[J]. CIESC Journal, 2012, (9): 2794-2798. DOI： 10.3969/j.issn.0438-1157.2012.09.019.

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。

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.

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