Title : AI-Optimized catalytic pyrolysis of Nigerian waste plastics for sustainable fuel production: Review
Abstract:
The escalating accumulation of plastic waste and the persistent energy deficit in developing economies, particularly in Nigeria, present a dual environmental and socio-economic challenge that requires innovative and sustainable technological interventions. This review examines the integration of catalytic pyrolysis and artificial intelligence (AI) as an emerging hybrid framework for converting heterogeneous plastic waste into high-value liquid fuels and petrochemical feedstocks, with a specific emphasis on locally relevant solutions, such as Nigerian kaolin-derived zeolite catalysts.
Globally, managing plastic waste remains a pressing issue due to the rapid increase in single-use plastics and inadequate recycling infrastructure. This situation leads to severe environmental pollution, greenhouse gas emissions, and ecosystem degradation. In Nigeria, these challenges worsen due to inefficient waste collection, open dumping, and widespread burning practices. Waste plastics, mainly composed of polyethene (PE), polypropylene (PP), and polystyrene (PS), remain a largely untapped resource for energy recovery and chemical production. Catalytic pyrolysis offers a thermochemical route to convert these polymers into liquid hydrocarbons, syngas, and aromatic compounds under oxygen-deficient conditions. This process typically occurs within the temperature range of 300–800°C.
The incorporation of zeolite-based catalysts, particularly ZSM-5, HZSM-5, and USY, significantly enhances reaction selectivity, reduces wax formation, and improves the yield of fuel-range hydrocarbons (C5–C23). However, the high cost of commercial catalysts limits large-scale implementation in developing regions. Recent advances demonstrate that kaolin-rich clay deposits in Nigeria can be transformed into zeolitic catalysts with comparable physicochemical properties, offering a cost-effective and sustainable alternative for decentralised waste-to-energy systems.
Furthermore, the emergence of artificial intelligence and machine learning techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and XGBoost, has introduced a paradigm shift in catalytic pyrolysis optimisation. These models enable accurate prediction of product yields, identification of key operational parameters, and real-time process optimisation under highly nonlinear and multivariate conditions. The integration of AI with catalytic systems facilitates the development of intelligent pyrolysis reactors capable of adaptive control, improved efficiency, and enhanced product quality.
This review highlights the synergistic potential of AI-optimised catalytic pyrolysis as a transformative approach for addressing Nigeria’s plastic waste crisis while simultaneously contributing to sustainable fuel production and circular economy development. The study further identifies critical research gaps, including limited region-specific datasets, catalyst stability issues, and the absence of pilot-scale AI-integrated reactors. Generally, this integrated framework provides a scalable and economically viable pathway for converting plastic waste into strategic energy resources, aligning with global sustainability and decarbonization goals.
