Advances in plastic to fuel conversion: reactor design, operational optimization, and machine learning integration

Abstract

Plastic waste management is a pressing global problem that requires sustainable solutions to mitigate environmental harm. To this end, pyrolysis offers a practical method for converting waste plastics into valuable resources such as oil, gas, and char. This review comprehensively examines plastic pyrolysis, focusing on reactor diversity, operational variables, and the integration of machine learning (ML) techniques for process optimization. Understanding the reactor designs is crucial for tailoring pyrolysis processes to achieve specific product yield and composition targets. For example, a fluidized bed reactor offers continuous productivity and efficient mass transfer, whereas fixed bed pyrolysis reactors are suited for secondary pyrolysis reactions. Similarly, vacuum pyrolysis reactors operate under reduced pressure to minimize undesired reactions, and conical-spouted bed reactors display effective blending capabilities. Operational parameters such as residence time, temperature, and pressure significantly influence pyrolysis outcomes. Longer residence times and lower temperatures favor oil production, whereas higher temperatures promote gas formation. Optimal parameter settings can enhance pyrolysis efficiency and maximize product yields while ensuring environmental sustainability. ML emerges as a powerful tool for predictive modeling, interpretation, and optimization of pyrolysis processes. ML algorithms like neural networks and support vector regression techniques enable relatively accurate forecasting of product yields and properties, and can help researchers gain insights into complex pyrolysis kinetics for further tuning of process parameters to achieve desired outcomes. Overall, the synergistic integration of reactor design, operational parameters, and machine learning techniques can improve product yield and quality, minimize environmental impact, and advance sustainable plastic waste management efforts while promoting a circular economy model.