Towards an integrated, self-learning stochastic mining complex framework and new digital technologies for the sustainable development of mineral resources

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Over the past decade, the concept of a mining complex or mineral value chain was introduced to reflect an integrated system that manages the extraction of materials from a group of mines, followed by the treatment of the extracted materials through different interconnected processing facilities. This system generates sellable products delivered to customers and/or the spot market. Mining complexes and related research contribute to the sustainable development of mineral resources and reserves that ensures the continued supply of raw materials and metals we rely upon, while managing environmental aspects. Technical aspects and components of a mining complex are substantially affected by uncertainties (stochasticity) stemming from multiple sources. These range from the valuation of assets to their operational performance and include their ability to adapt to endogenous and exogenous changes. Uncertainty-related effects are compounded with decision-making for a multitude of decisions from extraction to transportation. Critical sources of uncertainty of this integrated system include the quality and quantity of materials produced from the mines (supply uncertainty) and the metal's spot market price (demand uncertainty). Given new technological developments, it is important to address these uncertainties and assimilate new information collected as a mining complex operates, including from various sensors. This needs to be evaluated and used to update models, forecasts and further support complex decision-making. The applicant's ongoing research has generated substantial new developments to address these challenges. This has shifted the related paradigm and contributed to a new framework termed simultaneous stochastic optimization of mining complexes (SSOMC). It has also generated new stochastic optimization - simulation methods that integrate and manage uncertainty, including but not limited to geological uncertainty, while maximizing productivity, extending life of asset, and increasing return on investment. The new framework jointly optimizes components of a mining complex, including mine production schedules, blending, stockpiling, aspects of material processing streams and capital investments that define the critical bottlenecks in the mining system. These contributions support the development of a new generation of intelligent risk-management technologies. The proposed 5-year research program aims to build upon our previous work to develop the next level of state-of-the-art approaches. It intends to increase understanding of a new stochastic self-learning, modelling and optimization framework and related technologies by: developing an all-inclusive supply-meets-demand approach for SSOMC to respond to unveiling information; extending high-order quantification of spatial uncertainty to include statistical learning; and exploring intelligent approaches for jointly optimizing different time scales, while addressing multi-source data assimilation.