The Predictive Geometallurgy Research Group is a new unit in The Robert M. Buchan Department of Mining with plans to collaborate with a variety of professors in Mining as well as researchers in the Department of Geological Sciences and Geological Engineering. 

Ore bodies are variable in nature, and their locations can only be accessed through sampling, leading to uncertainty about their properties. The rapid development of artificial intelligence, however, presents an opportunity for the minerals industry by significantly improving the performance of mining projects through the reduction of resource consumption (water, energy, equipment, and labour), thereby improving mining sustainability (see Figure 1). Machine learning is being incorporated into Earth science problems, but it is not yet fully integrated into the geometallurgical workflows that model and optimize the performance of physical and chemical processes involved in the extraction (mining) and recovery (mineral processing and metallurgy) of minerals and metals. As well, deep learning is rapidly developing, but has not yet been explored in the mining field. In times of increasing water and energy scarcity, this research provides a unique and novel approach to significantly impact the decision-making processes in mining by offering systems workflows that predict and optimize the mining, mineral processing, and metallurgical processes. 

Advanced statistical analysis and geostatistic
Figure 1: Advanced statistical analysis and geostatistics are used to model the subsurface characteristics of ore deposits and the behaviour of materials through mineral processing and metallurgical recovery to optimize the sustainable extraction of raw materials.

Members of this group were awarded a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Accelerator Supplement, chaired the 2008 International Geostatistics Congress, edited the proceedings of major international conferences (Geostats 2008, International Conference on Innovations in Mine Operations 2004 and 2006), and have led major research grants funded by BHP Billiton (2011–2014) and Commonwealth Scientific and Industrial Research Organisation Australia (2013–2015). 

Researchers in this group: