Roussos Dimitrakopoulos

Professor of the Department of Mining and Materials Engineering, McGill University

Roussos Dimitrakopoulos is a professor of the Department of Mining and Materials Engineering at McGill University. He holds a Canada Research Chair (Tier I) in Sustainable Mineral Resource Development and Optimisation under Uncertainty, and is director of the COSMO - Stochastic Mine Planning Laboratory (http://cosmo.mcgill.ca/). Roussos holds a PhD from École Polytechnique de Montréal, and an MSc from the University of Alberta in Edmonton. He works on geostatistical simulation and stochastic optimisation as well as artificial intelligence applications in mine planning and production scheduling, along with the simultaneous optimisation of industrial mining complexes and mineral value chains under uncertainty. He has published extensively, maintaining large competitive grants from the National Science and Engineering Research Council of Canada and a long-standing partnership with AngloGold Ashanti, BHP, AngloAmerican/De Beers, Agnico Eagle, IAMGOLD, Kinross Gold, Newmont, Vale SA and Vale BaseMetals (COSMO Consortium) who support this research. He has taught and worked in Australia, North America, South America, Europe, the Middle East, South Africa and Japan.

Professor Roussos Dimitrakopoulos keynote title:

From simultaneous stochastic optimisation to self-learning mining complexes: Advances and Challenges

The presentation outlines the simultaneous stochastic optimisation of mining complexes, considered integrated engineering systems, where raw materials extracted from mineral deposits are transformed into a set of sellable products. This framework manages uncertainty in material types and grades from mines and demand/market uncertainty for strategic mine planning. As new digital technologies enable mining complexes to acquire information on performance of different components and flow of materials from mines to products, the extension of the above framework leads to the self-learning mining complexes. These learn from their own experiences to adapt short-term production scheduling decisions and respond to incoming new information.