Predictive geometallurgy

Geometallurgy is concerned with the optimization of processes along the entire value-added chain of raw materials. The discipline integrated detailed understanding of raw materials composition and microstructure with the impact of these materials characteristics on process efficiency.

With such a process-oriented view, we are able to predict more reliable recovery rates and optimum processing parameters; select optimal energy and resource efficient processing routes; predict the economic feasibility of new deposits; and develop near zero-waste mining concepts to minimize environmental footprint and increase social acceptance.

Our research focus is on:

• Integrative ore characterization
  • Drill core mineral mapping using hyperspectral and high-resolution mineralogical data fusion from an early exploration state on
  • Multi-scale characterization from bulk chemistry to 3D automated mineralogy
  • Process mineralogy via automated mineralogy and comprehensive data analysis
• Geometallurgical ore models
  • 3D microstructure modelling for grinding forecasts
  • Geostatistics beyond grade - from assays to geometallurgical parameters
  • Uncertainty based risk assessment - from geological to market risk
• Advanced forecasting of minerals processing
  • Complete use of quantitative particle information to predict a mineral processing outcome
  • Data mining and deep learning methodologies for process forecasting, easily adaptable to any commodity, from pre-feasibility studies to production
  • Integration of 3D particle characterization data for more robust process prediction
• Adaptive processing
  • Optimal design of dynamic processing routes, adapting on the fly to ore variability
  • Optimal, dynamic sensor selection for sensor-based sorting with automated mineralogy data
  • Real-time mining: updating resource models and dynamic mine planning and scheduling