(183d) Understanding Chemical Recycling of Step-Growth Polymers Using Kinetic Monte Carlo Approach | AIChE

(183d) Understanding Chemical Recycling of Step-Growth Polymers Using Kinetic Monte Carlo Approach


SriBala, G. - Presenter, Northwestern University
Morais, A. R. C., University of Kansas
Rorrer, N. A., National Renewable Energy Laboratory
Buss, B. L., National Renewable Energy Laboratory
Beckham, G., National Renewable Energy Laboratory
Allen, R. D., UOP LLC
Broadbelt, L., Northwestern University
Step-growth polymers like polyethylene terephthalate (PET) find their applications in the production of various materials. As post-consumer plastic waste poses a serious environmental threat, efforts to design novel bio-based materials and efficient recycling strategies are underway. Towards this end, it is crucial to understand the depolymerization chemistry to optimize the recycling process. The existing kinetic studies provide little information on the influence of properties like structure, degree of polymerization, or molecular weight on the monomer recovery.

The current study aims at developing a detailed mechanistic model to unravel depolymerization pathways of PET using the kinetic Monte Carlo (kMC) approach. The model comprises three steps, viz. polymer reconstruction, reaction network generation, and obtaining product distributions using the kMC framework. For simple polymers like PET, the polymer chains are comprised of several ester bond linkages. Automatic network generator creates the list of possible reaction events based on a set of defined reaction families that characterize the types of bond cleavage events and any possible side reactions, which allow the formation of intermediate and LMWP species. Finally, the kMC framework simulates these reactions as single events occurring at discrete time steps.

First, Schulz-Flory distribution method was used to determine the maximum chain length and the corresponding number of PET chains in the feed. Further, studies in the absence of a catalyst were used to provide a baseline, fitting rate coefficients for the key bond cleavage events. The neat chemistry was then combined with catalyzed pathways to evaluate the catalyst’s effect on the molecular weight decay and the temporal yield of LMWP. Molecular weight information as a function of time in a batch reactor was modeled, and the yield of low molecular weight products was monitored. Finally, the mechanistic model was validated using the experimental data available from the literature on glycolysis of PET.