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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.
The plastic industry and consumer demand have both exploded since the 1950s. Plastic waste in the ocean has also skyrocketed, growing by a factor of 10 since 1980. Many animal species can't survive this kind of pollution. This is probably bad for people. Impacts plankton, which in turn modifies the carbon cycle. Effects global warming by adding to it. This list, by the way, is not comprehensive. When scarce resources are used inefficiently and without good planning, a great deal of waste is generated, which has a negative impact on the natural world. The notion of a circular economy (CE) has shown encouraging signs of being adopted at industrial and governmental levels as an alternative for the conventional but wasteful linear manufacturing lines. Through careful planning and subsequent reuse, recycling, and remanufacturing, CE strives to maximise the value of raw materials over a product's entire life cycle. Two cutting-edge technologies that can considerably aid in the widespread acceptance and application of CE in actual practises are artificial intelligence (AI) and machine learning (ML). This research delves into how AI applications are being included into CE.
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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.
Transitioning to a circular economy for plastics with an artist in residence (TRACE-P)
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Researchers applied artificial intelligence to model how transitioning to a circular economy for plastics could reduce pollution and resource use at scale. The study offers a data-driven roadmap for policymakers aiming to close plastic loops and cut the flow of new plastic into the environment.
AI Techniques Aid for Optimizing the Collection System of Industrial Plastic Waste
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This study applied artificial intelligence techniques to optimize collection routes and predict demand for industrial plastic waste pickup. AI methods outperformed traditional statistical approaches in accuracy and route efficiency. Smarter collection systems could significantly reduce costs and improve recovery rates for industrial plastic waste.
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.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
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This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
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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.