Property-based modelling and simulation of mechanical separation processes using dynamic binning and neural networks

To fully understand the possibilities and the limits of the Circular Economy (CE), a comprehensive model taking into account its different stages (product design, mechanical pre-processing, metallurgy, etc.) is required. A crucial aspect is to understand the inevitable losses at different stages of recycling. The complexity of the material streams in mechanical separation processes requires a detailed description of particles and their properties to successfully simulate unit processes. This paper presents a new approach that connects measurement-based particle properties to statistical modelling and simulation of mechanical separation processes. The proposed approach combines particle tracking with the generalization ability of neural networks. Above all, it advances the present particle binning and tracking methods utilizing property-based binning rather than liberation-based binning for modelling purposes of complex systems. In order to demonstrate the new approach, this paper uses Mineral Liberation Analysis (MLA) data from magnetic and gravity separation processes of a complex ore. The proposed approach can be integrated into present simulation platforms such as HSC Sim.