This lab focuses on how AI can revolutionise secondary extraction – the recovery of valuable materials from waste – and how it drives efficiency, sustainability and innovation. Scientists interested in joining this lab develop thinking, research and projects on how AI-based recycling equipment and machine vision systems can enable intelligent sorting to accurately identify materials such as metals and plastics for more efficient recovery. Predictive maintenance algorithms minimise downtime by predicting potential machine problems. Scholars explore how machine learning contributes to resource mapping, helping to identify unutilised waste streams and monitor material flows. This lab also focuses on how AI optimises extraction processes such as bioleaching to increase yields while reducing the use of energy and chemicals. It also supports strategic decision-making by evaluating economic and environmental trade-offs and providing insights into consumption and reuse patterns to strengthen the circular economy.

Contact: Nidhin Madankara Kottayi, Director of Technology of Secondary Extraction Association