With the increasing demand for lithium-ion batteries, the supply of critical raw materials such as graphite, lithium and cobalt has become increasingly important. In response to environmental impact and geopo- litical reliance on Chinese exports, this study explores the recovery of these materials from spent lithium- ion batteries using computational modelling. Two methods are investigated: Magnetic Density Separa- tion (MDS) and hydrometallurgical leaching using the Shrinking Core model (SCM).
The first part focuses on MDS, in which a two-dimensional particle tracking model was developed to simulate the separation of graphite particles suspended in a paramagnetic MnCl2 solution subjected to a non-uniform magnetic field. The model incorporated experimentally determined magnetic suscep- tibility values, a magnetic field profile generated from COMSOL and a particle size distribution. The simulation reproduced key phenomena which were experimentally observed such as levitation height around 5-6 mm, settling dynamics and lateral particle accumulation.
In the second part a dimensionless SCM was developed to describe the leaching behaviour of LiCoO2 particles in sulfuric acid. The model includes mass transfer, diffusion and surface reaction mechanisms to describe the dissolution of the LiCoO2 particles. While the SCM captured general leaching trends, it overestimated leaching efficiencies due to assumptions of uniform lithium dissolution and neglecting increasing diffusion resistance during the leaching process and it the effect of H2O2 was not taken into account. Therefore a more advanced SCM with varying crust was developed, which included the formation of a Co3O4 crust on the LiCoO2 core. For this model, the alignment with experimental leaching data was improved across varying conditions of acid concentrations, H2O2 concentrations, pulp density, temperature and particle size.
These results demonstrate that both MDS and leaching models can be effectively described through computational modelling. Both methods offer a valuable insight into LIB recycling process and can support the design of more sustainable and efficient recovery systems.