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Pyrolysis of Plastic Waste: The Emerging Role of Artificial Intelligence in Waste Valorization
Summary
This review explores how artificial intelligence and machine learning are being applied to plastic waste pyrolysis, a thermal process that converts plastics into fuels and chemicals. AI tools are helping researchers optimize reaction conditions, predict product yields, and accelerate the development of more efficient recycling pathways. The paper highlights the growing role of data-driven methods in advancing plastic waste valorization technology.
The integration of Artificial Intelligence (AI) into plastics waste pyrolysis technology presents a transformative approach to sustainable waste-to-energy conversion. This study reviews the latest AI-driven methodologies in optimizing pyrolysis processes, focusing on machine learning-based feedstock classification, real time monitoring via IoT, reinforced learning for reactor control, AI-assisted life cycle assessment (LCA). AI enhances efficiency by predicting optimal pyrolysis conditions, reducing emissions, and improving produce yields. Deep learning and computer vision enable precise plastic waste sorting, while reinforcement learning optimizes process parameters dynamically. Additionally, AI driven market forecasting models support economic decision-making in plastic-to-fuel technologies. Despite challenges in data availability, model selection, and system integration, AI-powered solutions offer a promising path toward circular economy goals. The literature review highlights the need for further research in multi-modal AI frameworks to maximize sustainability and commercial viability in pyrolysis-based plastic waste valorization.