Predicting Polyolefin Microstructure: A Parallelized Multidimensional Model for Metallocene-Catalyzed Copolymerization of Propylene and 1-Decene

Abstract

This work explores the copolymerization of propylene and 1-decene using homogeneous metallocene catalysts to optimize polyolefin functionalization. A detailed mathematical model is developed with experimental validation employing the method of moments and probability generating functions to predict average molecular properties, the molecular weight distribution, the copolymer composition distribution, and the joint molecular weight distribution-copolymer composition distribution. To efficiently handle computational resources, the model code is parallelized. This comprehensive model allows for explaining in detail the copolymer's microstructure under various semibatch reactor conditions. Moreover, the model is a powerful tool for selecting reaction conditions to synthesize materials with desired properties.