Recent advances in plastic waste pyrolysis for liquid fuel production: Critical factors and machine learning applications

Abstract

It is urgent to promote the technology development of plastic waste (PW) valorization to mitigate the present serious plastic pollution. Pyrolysis has been widely used to convert PW into high-value-added products, especially liquid fuels, in a sustainable manner. However, achieving a high yield of liquid fuel with good quality remains challenging due to the difficulty in optimizing pyrolysis process and synthesizing outstanding catalysts to narrow the product distribution. This work comprehensively reviewed PW pyrolysis from both technical and computational (machine learning modeling) aspects, with a critical discussion of recent challenges to find new insights for improving the conversion efficiency and promoting commercialization. Results indicated that the impacts of various factors, including PW type, process condition, catalyst, and reactor type, on the PW pyrolysis were extensively investigated by the research community. Machine learning methods have also been frequently applied to predict, interpret, and optimize the PW pyrolysis. However, more efforts can be made in the future regarding catalyst synthesis, selection of co-pyrolysis additives, mechanism of catalyst deactivation, and design of renewable energy supply system for PW pyrolysis plants. Additionally, more attention should be paid to enlarging the data size, improving model interpretability, and exploring innovative ways of machine learning application (e.g., active learning) in process optimization and catalyst design for PW pyrolysis.