- Industry 4.0
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.
Affiliated research axes
Axis 2: Planning Optimization2.1 – Support the development and use of tools to analyze and monitor the circular economy
2.4 – Plan and optimize the production of products and delivery of services in the context of the circular economy
Axis 3: Resource and Product Maximization3.1 – Map the knowledge and potential of product circularization
3.3 – Identify models for product circularization strategies