Simulation-based optimisation for alloy-level sorting of aluminium scraps towards circularity

Objective

Aluminium is one of the most widely used metals and is theoretically infinitely recyclable, yet only about 70% of its material value is retained after a single use cycle, leading to an estimated annual economic loss of approximately €1 billion in Europe. Recycling aluminium scraps can reduce metal waste, promote circularity, and save up to 95% of the energy compared to primary production. However, current recycling technologies face significant challenges in accurately separating aluminium scraps according to alloy compositions in industrial conditions. This project aims to overcome these challenges by developing an advanced simulation-driven framework for enabling alloy-level aluminium scrap sorting. High-fidelity 3D scanned geometries will be employed to replicate scrap morphologies, while elastoplastic contact models will be implemented to capture realistic particle dynamics during handling. In parallel, real-time image analysis and adaptive process control will be integrated into a virtual-experiment model of the sorting line, enabling optimisation and validation at both laboratory and industrial scales. To enhance the alloy characterisation by laser-induced breakdown spectroscopy (LIBS), robust algorithms will be designed to account for three major sources of variability: surface flatness, contamination, and particle size, thereby improving the consistency and reliability of alloy identification in industrial conditions. The project will be carried out in close collaboration with an innovative recycling company, ensuring industrial relevance and accelerating technology transfer. By enabling precise alloy-level aluminium recycling, the outcomes will contribute to substantial energy savings, reduced greenhouse gas emissions, and Europe’s transition toward a more circular and sustainable economy.