Abstract:

Reliable prediction of drainage flow and chemistry from waste rock storages is important to the flood control and contaminant treatment at many mine sites. At this time, the state-of-art predictive models generally require identifications of the physical processes inside waste rock storages, and also intensive characterization. However, the prediction accuracy is not satisfied sometimes as many simplification and assumptions are adopted in the prediction. In the big data era, various sensors have been put in the field to monitor mine sites, which enable advanced machine learning technology to investigate those monitoring data and study the underlying patterns behind them. This presentation will introduce how National Research Council Canada use artificial neural network to predict the drainage flow and chemistry from waste rock storages based on historical monitoring data. The advantage of this technology is that no further costly characterization are required to make a reliable prediction, as most geo-bio-chemical mechanisms involved in waste rock storages have been naturally implied in the monitoring data and they can be captured through machine learning process.

Biography of the presenter: Dr. Liang Ma is a research officer working in the ARD-ML team from Energy, Mining and Environment Research Center, National Research Council (NRC) Canada. He received his B.Eng. (Mechanical Engineering) in 2005, M.Eng. (Mechanical Engineering) in 2007 and PhD degree in Mechanical Engineering in 2010. He joined NRC in 2011 and has undertaken multiple mining related projects. His current research activities focus on evaluating the design of waste rock and tailing storage facilities through advanced numerical modeling, and developing machine learning toolbox for automatically analyzing monitoring data.

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Zhong-Sheng Liu