Particle-based Process Modelling
Crushing, milling and minerals separation are energy-intensive processing procedures. The Department of Modelling and Evaluation is therefore developing process models for the prediction of energy-efficient processing routes. Optimum process parameters are simulated and derived from particle structure data or the behavior of particle flows under different process conditions. This addionally allows for a better estimate of operating costs.
For developing process models, in which the Departments of Analytics and Processing are integrated, physically based models as well as statistical "particle tracking" models are used. The former are based on theoretical assumptions about process physics itself. Particle tracking models identify different particle classes from MLA datasets and track them through the processing route. Process models are coupled with microstructure models to quantify the distribution of feed particles in the different output streams. The goal is not just to simulate the particle behavior in a single processing machine, but for the entire processing route - thus, particle-based process modelling is a cornerstone of adaptive processing.
- van den Boogaart, K. G.; Tolosana-Delgado, R.; Templ, M.
"Regression with compositional response having unobserved components or below detection limit values", Statistical Modelling, 2015
- Teichmann, J.; van den Boogaart, K. G.
"Cluster models for random particle aggregates-Morphological statistics and collision distance", Spatial Statistics, 2015
- Matos Camacho, S.; Leißner, T.; Bachmann, K.; van den Boogaart, K. G.
"Inference of phase properties from sorting experiments and MLA data", Contribution to proceedings IAMG, 2015