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Pyrolysis of Plastic Waste: The Emerging Role of Artificial Intelligence in Waste Valorization
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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.
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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.
Integrating Artificial Intelligence into Circular Strategies for Plastic Recycling and Upcycling
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This review shows how artificial intelligence — from ML-powered sorting to AI-assisted catalyst design and digital twins — is transforming plastic recycling and upcycling by improving material identification, process efficiency, and lifecycle transparency. Accelerating the circular economy for plastics is one of the most direct strategies for reducing the volume of plastic waste that ultimately fragments into microplastics in the environment.
Integrating Artificial Intelligence into Circular Strategies for Plastic Recycling and Upcycling
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This review shows that AI-integrated sensing (FTIR, Raman, hyperspectral imaging) and machine-learning tools are transforming plastic recycling by improving material sorting, kinetic modeling, enzyme design for depolymerization, and life cycle assessment — moving AI from a supporting tool to a structural enabler of circular plastic systems. These advances directly support reducing the plastic waste flows that generate microplastic pollution.
AI-Integrated Chemistry Solutions for Sustainable Plastic Waste Management
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This paper reviews how artificial intelligence is being applied to improve plastic waste recycling efficiency, expand recycling technologies, and support development of biodegradable plastic alternatives. Real-world case studies illustrate AI's potential to transform plastic waste management and advance sustainable solutions.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
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This paper examines how artificial intelligence and machine learning can help transition the plastics industry toward a circular economy. AI tools can optimize recycling processes, predict material degradation, and identify opportunities to reduce plastic waste before it enters the environment.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.