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Advancements in Plastic Waste Sorting: A Review of Techniques and Applications
Summary
This study applied artificial intelligence and computer vision techniques to detect marine debris in Black Sea environments from aerial or satellite imagery. The AI system demonstrated high detection accuracy for floating plastic debris, offering a scalable remote sensing approach for marine litter monitoring.
The widespread utilization of plastic materials across various industrial sectors drives a continuous increase in global polymer demand. The exponential production growth generates severe environmental challenges regarding municipal solid waste management, as substantial fractions of post-consumer residuals enter landfills due to limited recycling infrastructure. Mitigating the global environmental burden requires the implementation of advanced recovery strategies to transition polymer waste into viable secondary feedstocks. Consequently, deploying efficient sorting techniques constitutes a fundamental requirement to integrate plastic materials into formal waste management protocols and optimize recycling yields. Technological innovations currently drive the transition from traditional manual segregation towards highly sophisticated automated sensor-based sorting architectures, maximizing separation efficiency. In this context, the present study comprehensively reviews pretreatment classification techniques engineered to fractionate heterogeneous waste streams into high-purity material flows. Rather than restricting the analysis to polyolefins, this review encompasses a broad spectrum of commodity polymers predominantly found in urban solid waste environments.