Melt Index Prediction of Polypropylene Based on Weighted Least Squares Support Vector Machines

Reliable estimation of melt index(MI) is crucial for the production of polypropylene.Considering the use of a SSE cost function without regularization,as in the case with LS-SVM,might lead to less robust estimates,the weighted LS-SVM soft-sensor model of propylene polymerization process is further presented.The research results show that the proposed method provides promising prediction reliability and accuracy.This method is supposed to have extensive application prospect in propylene polymerization processes.