Catalytic pyrolysis of HDPE for enhanced hydrocarbon yield: A boosted regression tree assisted kinetics study for effective recycling of waste plastic

Abstract

Kinetic study is crucial in digital chemical engineering as a foundation for understanding and optimizing chemical processes. By analyzing reaction rates and mechanisms, kinetic models provide essential data for designing reactors, scaling processes, and predicting performance under various conditions. This study is part of a broader research series focused on efficiently converting waste plastics into hydrocarbons through catalytic pyrolysis. As the first study from the series, it investigates the thermal degradation of pristine high-density polyethylene (HDPE), aiming to understand its reaction kinetics under catalytic and non-catalytic conditions. The research employs iso-conventional methods to estimate the activation of energy HDPE and leverages a machine learning algorithm, specifically the BRT model, to effectively predict activation energy and optimize pyrolysis parameters. The activation energy (323 kJ/mol) of non-catalytic pyrolysis of HDPE was reduced to 164 kJ/mol during catalytic pyrolysis. Thermodynamic parameters such as change in activation enthalpy ($\Delta H^{‡}$), activation Gibbs free energy ($\Delta G^{‡}$) and, activation entropy ($\Delta S^{‡}$) were also significantly reduced during catalytic reaction. The statistical and machine-learning approaches were used in the kinetic analyses. Boosted regression trees (BRT) were used to predict the$E_a$ during conversion at different heating rates for non-catalytic and catalytic processes. The liquid and gas fractions obtained from HDPE at different temperatures were characterized. The increase in yield of hydrocarbons at elevated temperatures indicated the reuse potential of plastic waste. The comprehensive analysis of HDPE exhibited 86 % of carbon and 14 % of hydrogen contributing to high heating value (HHV) of 44.41 MJ/kg.