Samer Hmoud, MSc., P.Geo., MAusIMM (CP) - CSA Global
Join us for a networking reception sponsored by CSA Global following the talk!
Orebody Intelligence is a rapidly evolving interdisciplinary field of research that combines geology, mining engineering, mineral processing, metallurgy, environmental science and engineering, mineral economics, and data science to optimize the Net Present Value of an orebody while minimizing technical, environmental, and operational risks. The Orebody Intelligence approach is a comprehensive concept that extends beyond the traditional Orebody Knowledge framework, which primarily focuses on creating predictive models for metallurgical indicators based on geoscientific data and observations. Orebody Intelligence employs all the knowledge gained during the Orebody Knowledge stage to make informed decisions that enhance mine performance by forecasting process-plant performance, energy and water usage, non-ore material characteristics, and other aspects of the entire mining value chain. It is a vital component of the mine of the future, ensuring optimal resource utilization, improving environmental management, and promoting stewardship. This presentation will outline a generalized workflow for constructing predictive machine learning models for geometallurgical performance indicators, encompassing feature engineering, material characterization, metallurgical sample selection, geostatistical modeling, and mineral processing flowsheet simulation. To illustrate the suggested workflow, a case study from an open pit mine will be presented.
Samer is an accomplished Principal Geostatistician at CSA Global in Toronto with a diverse background in the mining industry, spanning over 12 years of experience in Canada and internationally. He has worked as a geostatistician, exploration geologist, and geometallurgist, and specializes in using advanced geostatistics and machine learning to optimize the mining value chain from early exploration to mine closure. Samer is highly skilled in developing innovative algorithms and workflows tailored to specific projects and deposit types. His expertise includes multivariate and non-linear geostatistics, as well as conditional simulations for risk analysis and drill hole spacing studies. In addition, he is an expert in applying advanced geostatistics to build conceptual site models as part of site characterization studies, providing valuable insights to support decision-making. He also has a deep understanding of the transition from resources to reserves and can provide tailored resource estimation approaches to meet specific project requirements. As a current PhD candidate, Samer is researching the implications of blast movement on dig limit optimization in open pit mines under geological uncertainty. His research involves extensive data analysis and modeling, aimed at improving operational efficiency and productivity in the mining industry.
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