Geometallurgy aims to improve resource efficiency by creating a spatial model of an orebody in order to forecast ore processing behaviour. This enables calculation of the economic value of each ore block and the use of this information for production optimisation and hence an improved economic return. Modelling uses different approaches to link geological information with mineral processing: traditional approach uses chemical elements; proxy approach - small-scale geometallurgical tests; and mineralogical approach - minerals._x000D_
A geometallurgical model is obtained during execution of a geometallurgical program encompassing the entire value chain of ore processing. Available methods for establishing a geometallurgical model are in many cases limited and not yet comprehensively validated. Some of the limitations include: sampling density, assaying methods and quality, forecast accuracy block model content, geostatistical solutions for non-additive components, blending and assessment of positive impact of geometallurgy on the project. Synthetic ore models combined with process simulations and production scheduling, based on strong case studies, present a viable way to address these shortcomings. Such synthetic ore models will have realistic elemental and mineral distribution, textural attributes and 3D variability for input into the design of an appropriate geometallurgical program._x000D_
A synthetic orebody was created for an apatite-bearing iron deposit. Variability in modal mineralogy was described by geostatistical parameters combined by a set of linear and trigonometric functions. The generated synthetic deposit model was used to develop a mine production plan to generate a simulated process feed. The process feed had detailed information required for beneficiation process simulation: modal mineralogy, chemical composition of minerals, grain size and specific gravity. The synthetic system was used to measure benefits that a geometallurgical program developed for real cases could provide, compared to production where only head grade is optimised. These include a reliable sampling strategy and confidence in the feed-quality forecast for economic evaluation._x000D_
CITATION: Lishchuk, V, Lund, C and Lamberg, P, 2016. Development of a Synthetic Ore Deposit Model for Geometallurgy, in Proceedings The Third AusIMM International Geometallurgy Conference (GeoMet) 2016, pp 275-286 (The Australasian Institute of Mining and Metallurgy: Melbourne).