An optimization framework for block-cave mine planning

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The Canadian mining industry is one of the fastest growing sectors in North America. According to Canada's Economic Action Plan the energy and mining sector provides over $30 billion a year in revenue to governments- money that supports critical social programs such as health care, education and public pensions. Canada has a global responsibility to develop and utilize its mineral resources sustainably and optimally. The economics of today’s mining industry are such that the major mining companies are increasing the use of massive mining methods. They expect that, approximately 50 percent of the world’s copper production will come from underground mines by 2020. It is a step change for the industry, from the traditional open-pit to a move underground. Among the mining methods available, caving methods are favored because of their low-cost and high-production rates. Block-cave mining is one of the best solutions for continuing the operation after shutting down the mine in deep open-pit mines. The proposed research program builds on our past findings to develop an optimization framework for block-cave mine planning. This research directly contributes to the creation of new knowledge, and innovative technologies for block-cave production planning. This research is focused on applications of operations research in the context of mine planning. It is anticipated that the outcome of this research will contribute to responsible resource development by transferring knowledge to practitioners and end-users through prototype open-source software applications in the resources industry. The importance of this study can be examined from different perspectives. A production scheduling methodology that is based on a limited number of influential parameters will lead to optimistic production schedules. Also, relying only on manual planning methods or computer software that is based on heuristic algorithms will lead to mine schedules that are not the optimal global solution. Unfortunately, common methodologies and tools used in mining industry for block-cave scheduling are not adequate in dealing with the complexity of the optimal production scheduling of mineable deposits. Moreover, decisions related to block-cave planning are based on models that predict the behavior of rock masses and the recovery of ore. Rarely do these models account for variation in metallurgical characteristics or the influence of cave fragmentation on mill performance. Development of new technologies, optimization, and uncertainty quantification in long-term planning play a vital role in reducing environmental footprint and financial risk of block-cave projects. The outcome of the research is expected to result in sustainable optimal block-cave plans. This research will also result in training HQP with key capabilities in block-cave mine design and optimization.