Embracing Step-Changes in Geoscientific Information for Effective Implementation of Geometallurgy

"Geometallurgy aims to improve mining project value by predicting the impact that ore and waste characteristics will have on mining and metallurgical processes. This requires integration of rich spatial models of orebody characteristics with validated process response models. These models have, until recently, been constrained by the spatial coverage and representativity of relevant data and the ability to validate predictions made.The revolution in the diversity and volume of data and computational power that is now becoming available for integrated geoscientific modelling of orebodies, and stochastic simulation of mining and mineral processes is accelerating. By embracing emergent integrated data analysis and simulation techniques, geoscientists and engineers can lead a transformation in the way the mining value chain, from orebody to recovery, can be conceived, evaluated, and operated by using the geometallurgical paradigm.This paper describes a methodology that is applied to an existing diamond operation. Analysis of spatial and process data is used to build an integrated geometallurgical value chain model (IGVCM). This IGVCM is used to generate geometallurgical options and evaluate their potential outcomes. The model facilitates the use of flexible, highly configurable, and potentially automated intelligent approaches to evaluate mining and mineral process configuration, and results in more robust design outcomes. The approach described here, and its successful implementation has potential to deliver step-changes in value. IntroductionGeometallurgy aims to identify treatability risks – and opportunities – that arise from the interaction of orebody characteristics with mining and treatment. Improved understanding of the rock/process relationships allows project planners and production teams to predict performance, identify potential shortfalls, and thus enable the proactive creation and evaluation of strategies to mitigate these risks. These advanced insights also create opportunities for continually improving operational performance through dynamically adapting and improving the process response to the changing properties of the ore (Figure 1)."