Abstract
For brownfield operations with limited geometallurgical understanding, delivering effective long term plant debottlenecking and optimization solutions can be challenging. A geometallurgical driven process model (geomet model) can be rapidly developed utilizing the block model, mine plan, ore routeing, and plant process data. The objective of the geomet model is to de-risk and optimize the process, driven by an understanding of ore variability and its impact on circuit stability.
This work aims to use available data and build an understanding of geometallurgical parameters controlling plant performance metrics to provide an optimized strategy for the complete operation from mine to metal.
Process data verification and filtering are conducted using in-house developed analytics software, Ausenco InSite, to prepare the data for analysis and identify the performance variance. Metallurgical models and plant simulations are then completed to benchmark and differentiate the control variables affecting the performance. The simulation is calibrated through targeted plant surveys, test work and process control diagnostics. Process performance metrics including throughput, metal recovery, product grade, OPEX and control setpoints are reconciled with the ores’ origin through simulated ore tracking and integrated within the block model.
Geometallurgical relationships are modelled within the processed blocks to identify common geologic controls on metallurgical response and similar process control strategy and used for domain classification. Once established, the overall plant performance for the life of mine is then determined for these spatially mapped ore types.
Case studies are provided describing the development, calibration and application of this modelling approach coupled with plant process data to reconcile with spatial ore source.