Melt index predict by radial basis function network based on principal component analysis

Authors:
Xinggao Liu;Zhengbing Yan
Affiliations:
National Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou, P.R. China;National Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou, P.R. China
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Abstract

Melt index is considered important quality variable determining product specifications. Reliable prediction of melt index (MI) is crucial in quality control of practical propylene polymerization processes. In this paper, a radial basis function network (RBF) model based on principal component analysis (PCA) and genetic algorithm (GA) is developed to infer the MI of polypropylene from other process variables. Considering that the genetic algorithm need long time to converge, chaotic series are explored to get more effective computation rate. The PCA-RBF model is also developed as a basis of comparison research. Brief outlines of the modeling procedure are presented, followed by the procedures for training and validating the model. The research results confirm the effectiveness of the presented methods.