Annual Meeting and February tech talk: Advances in geometallurgical applications

Join the Central Victoria Branch for their Annual Meeting and February tech talk: Advances in geometallurgical applications.

About this event

The Central Victoria Branch has traditionally held its Annual Meeting at Harcourt, Victoria for many years now. The Henry of Harcourt establishment, affably named “Henry’s” provides a peaceful and serene setting to enjoy a catered BBQ dinner prior to the branch’s annual formalities.

Nominations for the 2026 Committee Members are now open. Being part of the committee is an extremely rewarding experience and is valuable for your professional development and connectivity in the industry.

AusIMM Members that wish to be considered to participate on the Branch Committee in 2026 must complete and email the Branch Committee Nomination Form to centralvic@ausimm.com prior to the Annual Meeting or in-person on the day.

Advances in geometallurgical applications

Traditionally, metallurgical sampling and the results of metallurgical test work form the quantitative basis for prediction of the processing behaviour of ore. These predictions are critical inputs for the design, capital and operating cost estimates for the ore processing plant, the economic evaluation of the project and the final investment decision.

Once mining commences and the plant is operational, the metallurgical test work used to train predictive geometallurgical models can be superseded by actual processing data from the plant. The process data provides a new opportunity to build predictive geometallurgical models that can be used for short-term predictions of recoveries from the assays of the feed. Additional ore characteristics such as quantitative mineralogy can also be used where measured with sufficient frequency. These models can be updated periodically as feed material changes.

We demonstrate predictive geometallurgical modeling applied to process data for two different examples, one Ni-Cu sulfide operation and one Cu-Ag sulfide operation. In both cases, the composition of the gangue mineralogy was known colloquially to impact metal recoveries and concentrate quality but a clear mathematical relationship could not be determined. Multivariate data analysis, using machine learning algorithms, provided new insights into the controls on the ore behaviour and informed the identification of predictive relationships. Successful predictive geometallurgical models were trained and implemented in the day-to-day operation and optimisation of the operations.