Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

2 Publications
Property-based Modelling and Simulation of Mechanical Separation Processes using Dynamic Binning and Neural Networks
Hannula, J.; Kern, M.; Luukkanen, S.; Roine, A.; van den Boogaart, K. G.; Reuter, M. A.;
To fully understand 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 and shows the benefits of property based binning over for example liberation based binning. The proposed approach can be integrated into present simulation platforms such as HSC Sim.
Keywords: Particle Tracking, Particle-based model, Modelling, Simulation, Circular Economy
  • Minerals Engineering 126(2018), 52-63
    DOI: 10.1016/j.mineng.2018.06.017
  • Lecture (Conference)
    Sustainable Minerals '18, 15.06.2018, Windhoek, Namibia

Publ.-Id: 26739 - Permalink