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 s…