NSERC’s Awards Database
Award Details

Innovation mine optimization tailored to iron ore operations: long and short term planning with dynamic truck dispatching

Research Details
Application Id: 500016-2016
Competition Year: 2016 Fiscal Year: 2017-2018
Project Lead Name: Kumral, Mustafa Institution: McGill University
Department: Mining & Materials Engineering. Province: Québec
Award Amount: $44,000 Installment: 2 - 2
Program: Collaborative Research and Development Grants Selection Committee: RPP Internal Decision Cttee
Research Subject: Mining engineering Area of Application: Mineral resources ( prospecting, exploration, mining, extraction, processing)
Co-Researchers: No Co-Researcher Partners: Iron Ore Company of Canada
Award Summary

Canada has approximately 6.3 billion tonnes crude iron ore reserves, which is ranked eighth in the world. The slump in iron ore concentrate (62% Fe) prices (from USD 187 per tonne in February 2011 to USD 45 per tonne at early 2016) led to cessations of some operations and suspensions of many development projects in Canada. Mining industry makes significant contribution to local communities and governments. As such, dramatic price decline may have profound societal effects. Only way to respond to this negative economic aura is to find innovative ways such that efficiency, performance, productivity, sustainability and/or profitability of an operation are ensured. This research proposes efficient mine planning and optimization approaches addressing to specific characteristics of open pit iron ore operations using cutting-edge operation research, decision making, machine learning and uncertainty management tools. First of all, a multi-variate analysis will be implemented to understand relationships among iron ore variables. A new ore - waste discrimination model will be developed in such a way as to maximize ore tonnage considering that some low grade material can be included in ore at a blending cost. This model will be used in long-term planning as an input. Throughout the parameterization, this ore - waste scheme should be updated continuously. A Lagrengian relaxation approach will be investigated to find appropriate multipliers such that annual productions are compatible with capacity requirements. Then, a short-term planning and bed-blending design will be optimized in connection to Krige's relationship. When short-term planning addresses grade requirements of processing design through a computationally efficient meta-heuristic, bed-blending design will minimize grade fluctuations of processing input material. The success of short-term planning, a certain extent, depends upon equipment management. In this scope, an efficient truck dispatching strategy will be explored. Finally, sensitivities and risks associated with data uncertainties will be further investigated. Thus, mining industry will have new planning tools that focuses on specific characteristics of iron ore.