Data for predictive geometallurgy
One of our institute ́s strongest competences is “Predictive Geometallurgy” which uses an holistic and interdisciplinary approach to optimize processes along the entire value chain of raw materials. The approach is based on the complete understanding of all geoscience-related properties of that value chain and therefore requires a combination of all our competences and data on raw materials and processes.
The role of raw materials exploration and characterization(1) is to feed our geometallurgical models with data from:
- mineral mapping(2) of drill-cores using hyperspectral and high-resolution mineralogical data;
- bulk properties such as chemistry, mineralogy, and particle size distribution;
- 2D/3D spatially resolved automated mineralogy and mineral chemistry for rocks, feed materials, and products of beneficiation processes;
- process analytics such as online and inline data properties of the from process streams and from the operative conditions of processing machinery
From Mineral maps to deportment results. Deportment analysis help to locate and describe metal-containing particles in order to determine the metal speciation, grain size and mode of occurrence as well as to generally characterize the mineralogical composition of the ore. Deportment analysis are of particular relevance for metal recovery.
Source: HZDR/HIF
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