The demands for efficient utilization of ore bodies and proper risk management in the mining industry have resulted in a new cross-disciplinary subject called geometallurgy. Geometallurgy connects geological, mineral processing and subsequent downstream processing information together to provide a comprehensive model to be used in production planning and management. A geometallurgical program is an industrial application of geometallurgy. Various approaches that are employed in geometallurgical programs include the traditional way, which uses chemical elements, the proxy method, which applies small-scale tests, and the mineralogical approach using mineralogy or the combination of those. The mineralogical approach provides the most comprehensive and versatile way to treat geometallurgical data. Therefore it was selected as a basis for this study.

For the mineralogical approach, quantitative mineralogical information is needed both for the deposit and the process. The geological model must describe the minerals present, give their chemical composition, report their mass proportions (modal composition) in the ore body and describe the ore texture. The process model must be capable of using mineralogical information provided by the geological model to forecast the metallurgical performance of different geological volumes and periods. A literature survey showed that areas, where more development is needed for using the mineralogical approach, are: 1) quick and inexpensive techniques for reliable modal analysis of the ore samples; 2) ore textural characterization of the ore to forecast the liberation distribution of the ore when crushed and ground; 3) unit operation models based on particle properties (at mineral liberation level) and 4) a system capable of handling all this information and transferring it to production model. This study focuses on developing tools in these areas.

A number of methods for obtaining mineral grades were evaluated with a focus on geometallurgical applicability, precision, and trueness. A new technique developed called combined method uses both quantitative X-ray powder diffraction with Rietveld refinement and the Element-to-Mineral Conversion method. The method not only delivers the required turnover for geometallurgy but also overcomes the shortcomings if X-ray powder diffraction or Element-to-Mineral Conversion were used alone.

Characterization of ore texture before and after breakage provides valuable insights about the fracture pattern in comminution, the population of particles for specific ore texture and their relation to parent ore texture. In the context of the mineralogical approach to geometallurgy, predicting the particle population from ore texture is a critical step to establish an interface between geology and mineral processing. A new method called Association Indicator Matrix developed to assess breakage pattern of ore texture and analyze mineral association. The results of ore texture and particle analysis were used to generate particle population from ore texture by applying particle size distribution and breakage frequencies. The outcome matches well with experimental data specifically for magnetite ore texture.

In geometallurgy, process models can be classified based on in which level the ore, i.e. the feed stream to the processing plant and each unit operation, is defined and what information subsequent streams carry. The most comprehensive level of mineral processing models is the particle-based one which includes practically all necessary information on streams for modeling unit operations. Within this study, a particle-based unit operation model was built for wet low-intensity magnetic separation, and existing size classification and grinding models were evaluated to be used in particle level. A property-based model of magnetic beneficiation plant was created based on one of the LKAB operating plants in mineral and particle level and the results were compared. Two different feeds to the plant were used. The results revealed that in the particle level, the process model is more sensitive to changes in feed property than any other levels. Particle level is more capable for process optimization for different geometallurgical domains.