Sustainable development and utilization of mineral resources: A stochastic mine planning optimization framework with uncertain metal supply and market demand

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The sustainable development and utilization of mineral resources and reserves ensures the continued supply of raw materials, metals and energy we rely upon. Sustainable development is a critical global problem, particularly given the fast growth and demand of emerging economies and increasing environmental concerns. Several sources of uncertainty impact sustainable mineral resource development: technical, financial, and environmental. Technical and economic uncertainties include the ability of orebodies to supply raw materials, operational mining uncertainties, fluctuating market demand for raw materials and related commodity prices. To date our ongoing research has shifted the paradigm for mine design and production scheduling (MDPS) through new technological advances founded on models of uncertainty (stochastic models) and optimization. These quantify uncertainty and manage risk, maximize productivity and mine life, and increase return on investment. During the past 5 years the new stochastic mine planning optimization framework has included several novel methods for risk-based MDPS, as well as modelling the spatial aspects of pertinent attributes of mineral deposits. Field testing shows that these methods can increase metal production for the same assets by about 15%, increase life of mine, and add about 25% of economic value compared to best conventional practices (differences are based on identical data and production characteristics). These improvements are only due to the new technologies being developed and contribute to the sustainable utilization of mineral resources and reserves through better financial performance, and technically defendable and objective decision-making. Based upon our research and learning to date, a new five year research program is proposed to explore and further develop the above new stochastic MDPS paradigm, through: new computationally efficient stochastic optimization methods, new high-order stochastic models, and risk-based financial models. This will include expanding the field of research, addressing new problems encountered in our past research, full-field testing of new methods, and increasing our understanding of the new stochastic framework and related technologies.