Mechanistic Modelling and Parameter Optimisation of Polymer Pyrolysis

Abstract

The ever-growing crisis of solid plastic waste (SPW) accumulation in the natural environment has prompted significant industrial and academic enterprise. Pyrolysis has emerged as a principal recycling technology in this fight against SPW accumulation, demonstrating capacity to liberate high value chemical feedstocks from plastic wastes. Despite its conceptual maturation, there exists a sparsity of kinetic data and models describing the complex degradation networks involved in polymer pyrolysis. This lacuna is attributed to both the difficulties in constructing models that appropriately consider the extreme size of common polymers, and the functionally infinite number of ways these polymers decompose and rearrange via free-radical reactions. As a result, industrial-scale SPW pyrolysis is hindered by the costly and time-intensive construction of pilot plants to determine viability and optimise valuable product yield.

This thesis successfully produced highly detailed mechanistic models describing polystyrene and polyethylene pyrolysis. These stochastic models were validated against experimental yield data obtained from literature using a novel parameter optimisation methodology that utilised an artificial neural network to efficiently traverse a high dimensional parametric response surface. Parameter optimisation was enabled due to novel developments in the kinetic Monte Carlo (kMC) stochastic modelling framework, specifically the reduction of necessary simulated volume and the hybridisation of the kMC model with a Markov Chain model. This resulted in between a 4-7 order of magnitude reduction in simulation time, allowing for the rapid simulation of the decomposition of polymers with a high degree of polymerisation, while maintaining full accounting of all chain information. These models provided insight into outstanding questions in the literature regarding the role of competing highly degenerate reaction pathways on low molecular weight product yield.