A tree-based
kinetic Monte Carlo (kMC) model is presented that
differentiates between 38 end-group pairs for isothermal degradation
of poly(styrene peroxide) (PSP). The binary trees allow for fast and
accurate calculation of reaction probabilities, with mass-weighted
binary trees for the accurate sampling of peroxide bond fissions and
hydrogen abstractions along chains. The kinetic parameters are tuned
via artificial neural networks (ANNs) to successfully predict literature
experimental data, among other lumped product yields. ANNs are also
utilized for sensitivity analysis to unravel the effects of individual
reactions on the time evolution of experimental responses and other
simulation outputs, including the variations of the chain length distributions
of the macrospecies. PSP degradation is characterized by three stages
of degradation considering both instantaneous and time-averaged concentrations.
The first stage features rapid unzipping and results in the fast production
of major products benzaldehyde and formaldehyde, the second stage
features the most significant level of hydrogen abstractions involving
PSP and other macrospecies types, and the third stage exhibits the
consumption of the remaining peroxide bonds toward oligomeric species
in a wide time frame until the degradation process is finalized by
the depletion of peroxide bonds. This proof-of-concept study based
on unprecedentedly detailed analyses of the chemistry via kinetic
Monte Carlo paves the way to further improve our understanding of
chemical recycling of solid plastic waste.