Metal recovery and recycling using machine learning and eddy current inspection

Industry globally is moved to a circular economy model for the supply of materials and products driven by the imperative to reduce waste, conserve valuable raw materials and minimise green-house gas emissions. Metals are very well suited to the cyclic use and this is supported by a mature metal recycling industry. However, the recyclers face a significant challenge in separating the different non-ferritic metals from mixed-metal waste streams (known as ZORBA). These have typically been sorted by hand in developing countries or by expensive and potentially environmentally damaging float-sink systems. The situation is unsustainable both financially and ethically and a cost-effective dry technological solution is needed.

This PhD project will investigate new approaches to classify metals in ZORBA waste streams using machine learning algorithms, computer vision and magnetic induction spectroscopy. The project will conduct research, design, and implement new sensors, and explore new feature-sets and analysis of induction measurements and fragment physical geometry to create efficient and cost-effective solutions to improve recovery and purity rates.

This PhD project is responsive to industrial needs and UKRI strategies. It cuts across priorities such as the EPSRC 'Sustainable Industries' priority, 'Waste' and 'Non-renewable resources' challenges identified by the IUK Horizons toolkit, and 'Manage resources efficiently and sustainably' ambitions under the prosperity outcomes.