With today’s increasingly complex ore bodies, it is no longer sufficient to use grade and tonnes alone to manage risk and optimise an operation. In response to this, Geometallurgy is being increasingly employed; seeking to allow a block model of an ore deposit to be developed based on key metallurgical parameters and the predicted response of the rock during mining, processing and subsequent environmental management.
Left – Figure 1. Geometallurgy is applied from exploration, through mine planning, blending strategy, flowsheet design, plant optimisation and tailings management.
What is Geometallurgy?
Rather than reacting to a spike in losses to tails, imagine a situation where plant operators know exactly how to process the next 24 hours of feed ore that is sitting on the conveyor belt ahead of time. How much risk would this remove from an operation? Now imagine plant operators being able to walk along this ‘virtual’ conveyor belt and plan for exactly what will be coming in tomorrow and how it will behave. Continue to extend the vision, where the operator can continue to walk along the side of this ‘virtual’ conveyor and stop at any point representing next week, next month, next year… through to the end of the life of the mine: and at any given point in time, know what the ore will be, how it will process, how to operate the plant to; maximize recovery, optimise cost efficiency, and effectively manage the environmental impact. This knowledge will subsequently help drive mine planning, blending strategy and tailings management. This is Geometallurgy.
MinAssist’s Geometallurgy capabilities
MinAssist has a strong range of capabilities in geometallurgy and ore body modelling. Our focus has been on strategic ore body knowledge linking the mine plan with metal produced to ensure that planning and processing decisions increase the total value of the process.
We approach geometallurgy from the processing perspective, working closely with geologists and mine planners to create robust geometallurgical models.
Key capabilities include:
- Predictive algorithm development for extrapolation of geometallurgical models.
- Domain characterisation and ore typing based on mineralogy and process behaviour.
- Development or extension of geometallurgical models for existing operations.
- QA/QC of geometallurgical inputs.
- Geometallurgical model audits.
- Implementation of geometallurgical models and procedures.
- Training and workshops.
Who have we helped?
Detailed mineralogical characterisation of variability grade control samples was used to identify relevant variables and cluster material based on ore type. Regression analysis of historic processing data in both lead-zinc concentrator and smelter was completed to identify predicted processing behaviour of each ore type. Predictive algorithms were developed and applied to short term mine planning to assess impacts of ore variability on planned process performance. The resulting ore typing was implemented in ongoing planning for the operation.
Detailed characterisation of gold and silver deportment was undertaken to understand variability of gold association with copper minerals. The data were then used to develop a predictive model for gold and silver recovery to copper concentrates. This predictive model formed a ‘payability’ model for precious metals and was integrated into mine planning, resulting in an appreciable increase in total Net Present Value (NPV) of the SGO Project.
MinAssist undertook QA/QC audits of mineralogical data used in the operational geometallurgical model.
Developed a geometallurgical model of the Tiris Uranium Project as an input to the Definitive Feasibility Study. The model was used to define processing parameters based on processing clusters and was combined with steady state process simulation to integrate mine planning with project financial modelling.
Dr Will Goodall led a team of academic researchers and industry representatives to develop multidisciplinary and multi-institution research program as an extension of the P843B GEMIII program.