Hybrid modeling approach for polymer melt index prediction

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. Hybrid modeling approach that combines the mechanistic modeling and machine learning is proposed to predict the melt index of a thermoplastic polymer during the polymerization process. The results of this research suggest that the prediction performance of the proposed hybrid model is greater than that of the traditional data‐only machine learning model.