Hybrid modeling approach for polymer melt index prediction

Abstract

This research paper presents a hybrid modeling approach that combines mechanistic modeling and machine learning to predict the melt index (MI) of an industrial styrene-acrylonitrile (SAN) polymerization process. MI is one of the important quality variables of a thermoplastic polymer and is measured offline infrequently. The accurate prediction of MI is necessary for monitoring and quality control of the process. The proposed hybrid model consists of two parts: a white-box submodel and a black-box submodel. First, the white-box submodel based on the process knowledge such as reaction kinetics predicts the polymerization-related variables such as average molecular weights and rate of polymerization from measurement data. Then, the black-box submodel which is a machine learning soft sensor model is trained to predict MI of the polymer product from both the output of the white-box submodel and measurement data. The proposed approach is used to compare the MI prediction performance of hybrid models to that of data-only machine learning soft sensor models and mechanistic models. As a result, the results indicate that the proposed hybrid model has an increased prediction accuracy and generalizability for MI prediction in an industrial polymerization process.