Modeling Polyolefin Catalytic Cracking with Zeolite Catalysts Under Flow

The plastic feedstock for recycling is typically constituted by over 90% polyolefins. However, so far there have not been any attempts to explore low-temperature, catalytic reactive extrusion of polyolefin waste, despite its potential energy, cost savings and scalability potential. Our experimental and computational study addresses a novel recycling method of polyolefin incorporating zeolite catalyst within the extrusion process. Herein, using Machine Learning (ML) coupled with Dissipative Particle Dynamics (DPD), we develop a coarse-grained model to simulate the reaction-extrusion process of polyolefin with catalyst under flow. We associate bond breaking rate with polymer local velocity related to diffusivity and investigate the effect of catalyst structure and flow rates on polymer conformation and dynamics. We also track the evolution of polydispersity and average molecular weight and compare it successfully with experimental results.