A review published in ENGINEERING Energy systematically evaluates how artificial intelligence is transforming waste plastic pyrolysis. By integrating kinetic parameter identification, machine learning product prediction, model interpretability, and AI-driven reactor design, researchers outline an advanced roadmap toward the autonomous, intelligent chemical recycling of global plastic waste.
With global plastic consumption projected to nearly triple by 2060 and less than 10% currently recycled, thermal conversion via plastic pyrolysis offers a promising industrial solution for transforming post-consumer waste polymers into valuable liquid fuels and raw chemical feedstocks. However, scaling up plastic pyrolysis has long been bottlenecked by variable feedstock streams, complex non-linear degradation mechanisms, and severe heat and mass transfer limitations inside reactors.
In a comprehensive review published in ENGINEERING Energy , researchers from Shanghai Jiao Tong University, Guangdong Technion-Israel Institute of Technology, and the Technion-Israel Institute of Technology systematically evaluate how artificial intelligence (AI) is solving these foundational challenges—from fundamental reaction kinetics to scale-up reactor design and fully automated, agent-driven workflows.
Traditional laboratory and computational methods, such as density functional theory (DFT) or pure Computational Fluid Dynamics (CFD), often struggle with high computational costs and exponential complexity when applied to real-world, mixed plastic streams. AI models bypass these traditional computation bottlenecks by directly processing high-dimensional, non-linear experimental and physical data.
To overcome the challenges of process optimization and fundamental exploration, the review highlights several key scientific breakthroughs and methodologies across five critical areas:
Despite these significant technical advancements, the review highlights critical gaps that must be addressed before wide industrial implementation. Current models suffer from small-sample data limitations, lack standardization across literature datasets, and frequently fail to account for real-world reactor dynamics such as catalyst deactivation, vapor residence time, dynamic coke formation, and heat-and-mass transfer limitations inherent to scale-up.
To address these hurdles, the research team emphasizes the urgent need to build standardized, high-quality open-access databases, integrate thermodynamic and physical constraints into hybrid machine learning models, and advance AI-CFD digital twin technologies for physical reactor optimization.
About the Research
Journal: ENGINEERING Energy
Read the full article for free: https://rdcu.be/EHQmzCeNJQ5a
Cite this article: Li, G., Huang, J., Gao, X., Zhou, Y.-N., & Zhu, L.-T. Artificial intelligence for plastic pyrolysis to produce fuels and chemicals: From reaction kinetics and reactor design to intelligent agent development. ENGINEERING Energy , 2026, 20(5): 10958. https://doi.org/10.1007/s11708-026-1095-8
ENGINEERING Energy
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Artificial intelligence for plastic pyrolysis to produce fuels and chemicals: From reaction kinetics and reactor design to intelligent agent development
31-Aug-2026