Development of the mineralogical path for geometallurgical modeling of iron ores

Abstract: The demands for more effective utilization of ore bodies and proper risk management in the mining industry have resulted in a new cross discipline 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 the industrial application of geometallurgy. It provides a way to map the variation in the ore body, to handling the data and giving metallurgical forecast on spatial level. Three different approaches are used in geometallurgical programs. These include the traditional way, which uses chemical elements, the proxy method, which applies geometallurgical tests, and the mineralogical approach using mineralogy. 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 method, quantitative mineralogical information is needed both on deposit and for 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 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 (samples, ore blocks, geometallurgical domains or blends prepared for the plant) and periods (from minutes via hourly and daily scale to week, monthly and annual production). 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) textural classification of the ore capable 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 tohandle all this information and transfer it to production model. This study focuses on solving the first and the third problem. A number of methods for obtaining mineral grades were evaluated with a focus on geometallurgical applicability, precision and trueness. The method survey included scanning electron microscopy based automated mineralogy, quantitative X-ray powder diffraction with Rietveld refinement, and element-to-mineral conversion. A new technique called combined method uses both quantitative X-ray 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 when used alone. Furthermore, various methods of obtaining modal mineralogy were compared and a model for evaluating precision and closeness of the methods was developed. Different levels of processing models can be classified in geometallurgy based on inwhich level the ore, i.e. the feed stream to the processing plant, is defined and what information subsequent streams carry. For mineral processing models the following five levels can be distinguished: particle size only level, elemental level, element by particle size level, mineral level, mineral by particle size level and mineral liberation (particle) level. The most comprehensive level of mineral processing models is the particle-based one which includes all necessary information for modeling unit operations. Within this study, as the first step, a unit operation model is built on particle level for wet low-intensity magnetic separation. The experimental data was gathered through a survey of the KA3 iron ore concentrator plant of Luossavaara-Kiirunavaara AB (LKAB) in Kiruna. The first wet magnetic separator of the process was used as the basis for the model development since the degree of liberation is important at this stage. Corresponding feed, concentrate and tailings streams were mass balanced on a mineral by size and liberation level. The mass balanced data showed that the behavior of individual particles in the magnetic separation is depending on their size and composition. The model, which has a size dependent by-pass parameter and a separation parameter dependent of the magnetic volume of the particle, is capable of forecasting the behavior of particles in magnetic separation. Modeling and simulation show the benefits that particle-based simulation provides compared to lower level process models which take into account only elemental or mineral grades.