Electrowinning of Printed Circuit Boards (PCBs) waste – an AI-based approach for process optimization

Printed circuit boards (PCBs) are among the most significant types of e-waste, presenting recycling challenges due to their complexity. However, they contain valuable metals like gold, silver, and copper. While machine learning (ML) and AI have improved e-waste recycling, their focus on metal recovery is limited.

This proposed project aims to explore the potential of ML algorithms in optimizing electrowinning by monitoring parameters. The objectives include ML model development to predict optimal parameters for metal recovery. Developing an ML model for electrowinning advances circular economy practices by increasing metal recovery, reducing primary extraction, and conserving resources. This aligns with circular economy principles, promoting responsible material use and waste reduction.