Automated Sorting and Catalytic Conversion of Mixed Plastic Waste Streams Using Predictive Machine Learning and Continuous Flow Reactors

**Abstract:** Current mechanical recycling of mixed plastic waste is limited by sorting inefficiencies and downcycling. This research proposes a novel system combining advanced near-infrared (NIR) spectroscopy with a predictive machine learning model and a continuous flow catalytic reactor to efficiently sort and valorize mixed plastic streams, achieving higher-value chemical upcycling of polyethylene (PE) and polypropylene (PP) into monomers. This approach offers a significant improvement over current practices, potentially reducing plastic landfill waste by 30-50% with an estimated market value of $5 billion annually. We detail an algorithm architecture, experimental validation, and scalability roadmap, demonstrating its immediate commercial readiness for implementation in existing waste management infrastructure.