Abstract
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. The aim of the paper is to describe how to establish and construct a synthetic testing environment, or “synthetic ore body model” for data integration by using 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. A synthetic deposit model can be a suitable tool for testing different scenarios for implementation of geometallurgical programs and also an educational tool for universities and companies.
Highlights
Geometallurgy aims to create a predictive model for mine-to-metal production chain through the combination of geological, processing and economic models
In a high-fidelity geometallurgical program, the critical methodology selection, such as sampling design, components included in the block model and their assaying methods, and geostatistical solutions should be based on scientific principles
The liberation voxel information is retrieved from the particles, and process simulator is capable of handling information and multiphase synthetic ore body model and the time aspect is controlled by the mining model
Summary
Geometallurgy aims to create a predictive model for mine-to-metal production chain through the combination of geological, processing and economic models. Minerals 2018, 8, 536 simulation and production scheduling, backed by validated case studies, offers an approach to improve the design, and cost-effectiveness of geometallurgical programs. A mining project emulator called “Challenge Geometallurgy!” developed for educational purposes at Luleå University of Technology, has demonstrated that the geometallurgical program can give up to 25% shorter payback time compared to cases when no geometallurgical information is available. Comparison between realisations and reference case give an idea about additional sampling needed and uncertainties in processing and mining related to the voxels location in the ore body. A synthetic orebody was created with models for the ore utilisation; i.e., mining, and processing Such an ore body model may provide an environment where different strategies for geometallurgical programs can be numerically tested in an effective way, with consideration of impact from all the upstream and downstream processes. Using mineralogical and liberation information for the spatial and process modelling makes the approach more realistic at higher level of process details
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