Artificial Intelligence Applications in E-Waste Sorting and Recycling Processes

The growing global challenge of electronic waste (e-waste) disposal has driven the exploration of advanced technologies for efficient sorting, recycling, and recovery of valuable materials. Among these, Artificial Intelligence (AI) has emerged as a promising solution to address the complexities associated with e-waste management. This review paper consolidates recent research on the application of AI in e-waste sorting and recycling, drawing insights from multiple studies across different methodologies and technological approaches. AI, particularly machine learning (ML), deep learning, computer vision, and robotics, has demonstrated significant potential in automating various stages of e-waste processing, from the identification of recyclable materials to the separation of valuable and hazardous components. Notably, AI-based systems have enhanced the precision of material classification, increased throughput in sorting processes, and improved the overall efficiency of recycling operations. Computer vision algorithms, for instance, have been successfully employed for the automatic recognition and classification of components such as metals, plastics, and printed circuit boards (PCBs), enabling a more effective extraction of valuable materials like gold, copper, and rare earth elements. Furthermore, AI-driven robotics play a critical role in handling hazardous substances and reducing human exposure to toxic materials. Despite the considerable promise, several challenges hinder the widespread adoption of AI in ewaste recycling. These include issues related to data availability and quality, the high cost of AI system implementation, and the complexity of integrating AI technologies with existing recycling infrastructures. Moreover, the sustainability of AI models remains a concern, as they often require significant energy consumption and extensive computational resources. This review also examines the potential of AI to contribute to the circular economy by facilitating closed-loop recycling systems, where products are designed for easier reuse and material recovery. In addition, the paper highlights the need for interdisciplinary collaboration between AI researchers, environmental engineers, and waste management professionals to develop more effective and scalable solutions. Finally, it outlines future research directions, including the need for more robust AI models that can adapt to the dynamic nature of e-waste composition, as well as the exploration of AI's role in enhancing public awareness and policy frameworks for sustainable e-waste management. Overall, while AI technologies hold transformative potential to revolutionize e-waste recycling, continued research and innovation are essential to overcoming the technical, economic, and environmental barriers that currently exist. The matter presented in this article is the review of group of papers and is the summary of those papers which was submitted in the form of an assignment or as an alternate assessment tool / case study.