Distribution Changes during Thermal Degradation of Poly(styrene peroxide) by Pairing Tree-Based Kinetic Monte Carlo and Artificial Intelligence Tools

Abstract

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.

Multi-Scale Modeling of Plastic Waste Gasification: Opportunities and Challenges

“…The reactive part of PWG was demonstrated in Section 2. The kinetic modeling of these reactions is complicated due to various reasons such as the inherent complexity of free-radical chemistry, the chain-length distribution of the polymer molecules [94], presence of the liquid, gas, and solid phases [95], the effect of interaction between different PW types [96], and also the effect of impurities [97], among others.…”

“…Diverse chain length distribution of polymers from different sources increases these complexities. Consequently, defining a fixed initial condition for the composition and chain length distribution for modeling the gasification of PW is challenging [94]. Other polymer micro-scale characteristics, such as backbone structure and the pendant groups, also affect their degradation behavior [32].…”

Procedures and Guidelines for Inputting and Output Smoothening of Kinetic Monte Carlo Distributions

“…A second input method is based on an (approximate) analytical formula to represent the initial CLD. [26][27][28][29][30] Hernandez-Ortiz et al, [26], e.g., used a log-normal distribution for the starting polyolefin for a grafting process, considering a virgin polymer possessing a high dispersity (Ð) of 4.0. Wu et al [27] and Figueira et al [28] in turn used a two parameter Schulz-Zimm distribution [31][32][33] to represent the initial polybutadiene number CLD (Ð = 1.9) for single phase grafting of PB with styrene.…”

“…Wu et al [27] and Figueira et al [28] in turn used a two parameter Schulz-Zimm distribution [31][32][33] to represent the initial polybutadiene number CLD (Ð = 1.9) for single phase grafting of PB with styrene. Furthermore, Dogu et al [29] used a Schulz-Flory distribution to study the isothermal degradation of polystyrene peroxide (Ð = 1.5). Pladis et al [30] used the two-parameter Wesslau distribution to reconstruct the MMD in the study of long-chain branching in free radical polymerizations.…”

Application of computational approach in plastic pyrolysis kinetic modelling: a review

“…To address these gaps, "data-driven" tools, based on machine learning (ML) and quantum mechanics (QM) have been widely applied to unravel reaction mechanisms and kinetics triplets [15][16][17][18][19]. For this reason, the following sections of this review explore the recent advances of computational tools, such as ML and QM, applied to obtain kinetics and mechanisms information of plastic pyrolysis.…”