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Artificial intelligence for plastic pyrolysis to produce fuels and chemicals: From reaction kinetics and reactor design to intelligent agent development
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This review paper looks at how artificial intelligence can help turn waste plastic into fuel and useful chemicals through a heating process called pyrolysis, which is a promising way to tackle plastic pollution instead of letting it pile up in landfills or oceans. While this technology doesn't directly address microplastics already in our bodies, reducing overall plastic waste through smarter recycling could mean less plastic breaking down into the environment over time. The paper notes AI tools are still limited by insufficient data, so this remains a developing area of research rather than a ready-to-use solution.
Given the escalating challenge of global plastic pollution, pyrolysis has emerged as an important technology for converting waste plastics into high-value chemicals and liquid fuels. However, due to complex reaction networks, variable feedstock compositions, and multiphase flow characteristics, traditional methods face significant bottlenecks in process optimization and mechanistic exploration. Artificial intelligence (AI) offers a novel perspective for overcoming these complexities with its inherent advantages in processing high-dimensional data involving nonlinear interactions among variables. Against this backdrop, this review comprehensively surveys and analyzes the latest progress and limitations of applying AI to plastic pyrolysis, focusing on the identification of pyrolysis kinetic parameters using intelligent optimization algorithms and AI methods, the prediction of plastic pyrolysis reaction outcomes using AI, the exploration of intrinsic relationships between key process features and product distribution, and pyrolysis mechanisms using interpretability tools, and reactor optimization combining physical models such as computational fluid dynamics (CFD) with AI-based surrogate models. Furthermore, emerging applications of natural language processing (NLP) and large language models (LLMs) in automating the construction of standardized pyrolysis databases and knowledge graphs are explored. While summarizing technical advantages, current weaknesses such as data scarcity, insufficient model generalization, and the lack of physical information coupling are also analyzed and discussed. Finally, a conceptual architecture for an LLM-driven pyrolysis agent is proposed to integrate mechanism retrieval, process simulation, and experimental support. This review provides theoretical insights and future perspectives for intelligent process optimization and potential engineering applications of plastic pyrolysis for the conversion of waste plastics into fuels and chemicals.
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