Mineral processing simulation models can be classified based on the level that feed stream to the plant and unit models are described. The levels of modelling in this context are: bulk, mineral or element by size, and particle. Particle level modelling and simulation utilises liberation data in the feed stream and is more sensitive to the variations in ore quality, specifically ore texture. In this paper, simulations for two texturally different magnetite ores are demonstrated at different modelling levels. The model parameters were calibrated for current run-of-mine ore and then in the simulation applied directly to the other ore. For the second ore, the simulation results vary between the different levels. This is because, at the bulk level, the model assumes minerals do not change their behaviour if ore texture or grinding fineness are changed. At the mineral by size level, the assumption is that minerals behave identically in each size fraction even if the ore texture changes. At the particle level, the assumption is that similar particles behave in the same way. The particle level approach gives results that are more realistic and it can be used in optimisation, thus finding the most optimal processing way for different geometallurgical domains.

Reconciliation of geological, mining and mineral processing information is a costly and time demanding procedure with high uncertainty due to incomplete information, especially during the early stages of a project, i.e., pre-feasibility, feasibility studies. Lack of information at those project stages can be overcome by applying synthetic data for investigating different scenarios. Generation of the synthetic data requires some minimum sparse knowledge already available from other parts of the mining value chain, i.e., geology, mining, mineral processing. This paper describes how to establish and construct a synthetic testing environment, or “synthetic ore body model” by integrating a synthetic deposit, mine production, constrained by a mine plan, and a simulated beneficiation process. The approach uses quantitative mineralogical data and liberation information for process simulation. The results of geological and process data integration are compared with the real case data of an apatite iron ore. The discussed approach allows for studying the implications in downstream processes caused by changes in upstream parts of the mining value chain. It also opens the possibility of optimising sampling campaigns by investigating different synthetic drilling scenarios including changes to the spacing between synthetic drill holes, composite length, drill hole orientation and assayed parameters.

Geometallurgy as a link connecting geological features of deposit with metallurgical performance of a concentrator have found broad utilization in metals mining as well as for industrial minerals and black sands mining. However, coal industry yet stays uncovered by successful applications of geometallurgical approach due to certain specifics of a commodity. Production of coal preparation plant in terms of quality and quantity can be forecasted knowing behavior of coal bearing particles in process that is controlled by petrological and mineralogical properties. Application of process mineralogical tools together with comprehensive metallurgical testwork helps to acquire essential information for a simulation of coal preparation operations. Being combined with geological, geochemical and geotechnical data available for a deposit, outcome of process simulation will form holistic geometallurgical model. Once implemented, such models will become a powerful instrument for efficient utilization of resources and proper risk management, e.g. adaptation of the process to variations in run-of-mine coal quality, "what-if" analysis of alternative production strategies, forecasting of financial results, assessment of environmental impact.

Comminution modeling aims to predict the size and liberation distribution of mineral particles and the required comminution energy. The current state-of-the-art comminution models provide a calculation of neither particle size distribution, grinding energy and throughput dependency with neither a broad understanding of how the mineral grade varies by size nor the liberation distribution of the product. The underlying breakage mechanisms affect the liberation of mineral grains and are dependent on modal mineralogy and mineral texture (micro structure). It has also been a challenge to model comminution systems to predict the optimal energy and size for better mineral liberation because of the variability of the mineral particle properties i.e. grains arrangement and composition. A detailed mineralogical study was carried out in order to broaden the understanding of the nature and distribution of comminuted particles in a ball mill product. Focusing on iron ore samples the study showed how the particle breakage rate decreases when the particles reach the grain size of the main mineral component. Below that size, comminution does not increase mineral liberation and therefore in most of the cases passing over that boundary is only a waste of energy. The study involving iron ores from Malmberget and Kiruna, Northern Sweden, showed that certain shortcuts can be applied to empirically model the mineral liberation distribution of the particles in a ball mill based on the mineral grade-by-size pattern from a geometallurgical program. In Malmberget and Kiruna the mineral grade-by-size pattern is depending on the mineral distribution and grain size of gangue as well as magnetite or hematite minerals. A significant difference between mineral breakage of the same grade and gangue minerals can be observed due to texture differences.

Based on the requirements and available sample amounts in geometallurgical studies of ore variability, a small scale batch grindability test has been developed, the Geometallurgical Comminution Test (GCT). The test requires 220 g of sample material and can be conducted within 2.5–3 h. Test results are evaluated using a modified Bond equation together with a linear correlation factor. The test and evaluation method have been validated against several ore types.

Early integration of sustainability decisions and mineralogical attributes into the design of minerals processing units offers potential for reducing environmental impacts at mining and processing sites. The objective of this study is to demonstrate how the integration of sustainability indicators and mineralogical attributes could be achieved in developing an integrated modelling framework of a magnetic separator. A magnetic separator unit model based on existing literature was developed to include process stream mineralogical data and to output sustainability indicators. The overall sustainability of processing three ore types (low, medium and high grade iron ore) was evaluated using the developed model. Novel measures for evaluating magnetic separation (Grade Recovery Deviation Index (GRDI)) and energy efficiency (Rotational Energy Transfer Efficiency (RETE)) that incorporate the use of ore characteristics were developed in this study. These measures were used to calculate the separation and energy efficiency sustainability indicator ratings. In total eleven magnetic separator sustainability indicators were identified. Each indicator was assigned a weighting value out of 10 based on its importance. Of the 11 sustainability indicators identified; safety, reliability, Carbon dioxide (CO₂) emissions, water use, noise and job creation ratings did not vary with changing mineralogical attributes of the feed ore. GRDI, RETE, electricity cost, particle emissions and waste generation ratings were observed to be dependent on the ore characteristics and therefore their values varied with different feed ore grades. The Analytic Hierarchy Process (AHP) and Weighted Sum Method (WSM) methods were applied to the sustainability indicator ratings and weightings to evaluate an overall sustainability cardinal score of processing a particular ore feed. Results of this study demonstrate the dependence of overall process sustainability indicators on feed ore mineralogical attributes. The results also provide an indication of the effect of ore variability (typical within a single deposit) on sustainability indicators.

The polymetallic Cu–Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu–Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated based on bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpected high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit.

Process models in mineral processing can be classified based on the level of information required from the ore, i.e. the feed stream to the processing plant. Mineral processing models usually require information on total solid flow rate, mineralogical composition and particle size information. The most comprehensive level of mineral processing models is the particle-based one (liberation level), which gives particle-by-particle information on their mineralogical composition, size, density, shape i.e. all necessary information on the processed material for simulating unit operations. In flowsheet simulation, the major benefit of a particle-based model over other models is that it can be directly linked to any other particle-based unit models in the process simulation. This study aims to develop a unit operation model for a wet low intensity magnetic separator on particle property level. The experimental data was gathered in a plant survey of the KA3 iron ore concentrator of Luossavaara-Kiirunavaara AB in Kiruna. Corresponding feed, concentrate and tailings streams of the primary magnetic separator were sampled, assayed and mass balanced on mineral 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 developed model involves a size and composition dependent entrapment parameter and a separation function that depends on the magnetic volume of the particle and the nature of gangue mineral. The model is capable of forecasting the behavior of particles in magnetic separation with the necessary accuracy. This study highlights the benefits that particle-based models in simulation offer whereas lower level process models fail to provide.

Monitoring of mineral beneficiation processes is difficult due to lack of reliable measurements and hazardous environment. Therefore robust models describing steady-state and dynamic behaviour of the processes are needed when aiming for improved monitoring and control. Specific characteristics of models used in mineral processes are that they require spatial mineralogical information of raw material. In this study, a dynamic simulator combining ore characteristic physical data, process operating parameters and mineralogical properties is developed for the Oulu Mining School (OMS) mini-pilot scale mineral beneficiation plant. The mini-pilot process, theoretical part of the model and the model development are described. Open and closed flotation circuit experiments were carried out in mini-pilot research environment for model identification. Experimental and simulated results of ore type variation and pH change are presented. Based on the results the effects of the aforementioned factors on flotation performance are predicted.