Optimizing Recycling Stream Sorting Systems Using Machine Learning to Minimize Contamination

Abstract-The efficiency of recycling systems is crucial for promoting sustainability and reducing environmental impact. However, contamination in recycling streams remains a significant challenge, often leading to decreased recycling effectiveness and increased operational costs. This paper investigates the potential of machine learning (ML) to optimize sorting systems in recycling plants, aiming to minimize contamination and improve material recovery rates. We explore the application of various ML algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVM), and Random Forests, for automating the detection and classification of contaminants in waste streams. By leveraging sensor data, image recognition, and real-time decision-making, our approach enhances sorting accuracy, reduces human error, and supports the efficient separation of recyclable materials. Experimental results from simulations and real-world case studies demonstrate that ML-driven sorting systems can achieve higher contamination reduction and sorting efficiency compared to traditional methods. This study highlights the promising role of machine learning in transforming recycling processes and proposes future directions for integrating AI technologies in waste management to create more sustainable and effective recycling solutions.