Bayesian tuned kinetic Monte Carlo modeling of polystyrene pyrolysis: Unraveling the pathways to its monomer, dimers, and trimers formation

The current kinetic models for polystyrene (PS) pyrolysis contain many simplifications to reduce their size and the corresponding simulation time. Moreover, they are often based on rate coefficients determined using non -ideal experimental data featuring ambiguous process conditions with respect to mixing and temperature uni-formity. The practical interest of PS pyrolysis is the production of styrene monomer to be reused as a feedstock in the polymerization of styrene. In the present work, a lab-scale tree-based kinetic Monte Carlo (kMC) model is presented that differentiates between 18 reaction families and 26 end-group pairs to study the product yield variations for thermal degradation of PS. Model parameters follow from Bayesian optimization to experimental data recorded with an in-house micro-pyrolysis unit coupled with comprehensive two-dimensional gas chro-matography. Low chain length (CL) anionic-made PS is specifically considered to gain an understanding of the role of specific end-groups. The experimental yields of the major products (monomer: 74.7-80.8 wt%, dimer: 5.1-5.5 wt%, trimer: 1.6-7.7 wt%) are well-predicted with the fine-tuned parameters. The main reaction pathway in the formation of styrene monomer is end-chain beta-scission, while mid-chain beta-scission is primarily involved in the formation of the styrene dimer and trimer. Our model shows that the pyrolysis of low CL anionic-made PS leads to better rate coefficients than those obtained from state-of-the-art pyrolysis of long CL PS, in which end-groups play a much smaller role.