AI Supercharges Europe’s Plastic Recycling Race

AI-powered solvent recycling boosts yields, cuts costs, and helps Europe meet strict recycled-content targets

11 Feb 2026

Europe’s effort to tackle plastic waste is gaining support from artificial intelligence, as researchers and industry seek more efficient ways to meet stricter recycling targets.

At Eindhoven University of Technology, researchers have developed AI-STRAP, a system designed to improve advanced plastic recycling by making it faster and more precise. The project comes as EU policymakers enforce higher recycled-content requirements and consumer goods groups step up sustainability commitments.

Conventional mechanical recycling has struggled to process multilayer packaging and mixed plastics, often producing lower-quality output. AI-STRAP focuses instead on solvent-based recycling, which uses tailored liquids to separate polymers and recover material of higher purity. While the method can deliver better results, it is sensitive to changes in the composition of incoming waste.

Waste streams can vary significantly from day to day, affecting efficiency and raising operating costs. AI-STRAP addresses this by analysing real-time plant data to predict how different plastic mixtures will behave during treatment. A digital replica, or digital twin, of the facility then adjusts operating conditions automatically to maintain performance.

Researchers say the approach can increase recovery rates, reduce energy use and stabilise output quality, with less manual oversight.

Feedstock variability remains one of the main economic challenges for advanced recycling plants. The ability to anticipate disruptions before they affect production could improve margins and attract further investment. Industry bodies, including European Bioplastics, have argued that reliable supplies of high-quality recycled material are essential if companies are to comply with evolving packaging rules.

The regulatory backdrop is becoming more demanding. Europe’s Packaging and Packaging Waste Regulation requires higher levels of recycled content in certain products, while investors continue to fund new recycling infrastructure. Digital process control is increasingly viewed as a competitive advantage.

Adoption, however, carries costs. AI-based systems require sensors, data infrastructure and specialist skills. Smaller operators may find implementation difficult, and solvent-based methods remain under scrutiny over their environmental footprint.

As recycling plants become more data-driven, digital capabilities are likely to play a larger role in determining performance and cost. Whether systems such as AI-STRAP can scale across the industry will shape the next phase of Europe’s plastics strategy.