Abstract
Production planning decisions in the mining industry are affected by geological, geometallurgical, economic and operational information. However, the traditional approach to address this problem often relies on simplified models that ignore the variability and uncertainty of these parameters. In this paper, two main sources of uncertainty are combined to obtain multiple simulated block models in an iron ore deposit that include the rock type and seven quantitative variables (grades of Fe, SiO2, S, P and K, magnetic ratio and specific gravity). To assess the effect of integrating these two sources of uncertainty in mine planning decision, stochastic and deterministic production scheduling models are applied based on the simulated block models. The results show the capacity of the stochastic mine planning model to identify and minimize risks, obtaining valuable information in ore content or quality at early stages of the project, and improving decision-making with respect to the deterministic production scheduling. Numerically speaking, the stochastic mine planning model improves 6% expected cumulative discounted cash flow and generates 16% more iron ore than deterministic model.
Highlights
Mineral resources evaluation and long-term mine planning are two of the most important steps in a mining project
This work compared stochastic and deterministic mine planning under geological uncertainty
This work compared stochastic and deterministic mine planning under geological uncertainty The stochastic mine planning model allows for deviations, but controls the associated cost of using a real iron ore deposit as a case study
Summary
Mineral resources evaluation and long-term mine planning are two of the most important steps in a mining project. Geological block modeling: data are obtained from the geological logging and analytical assays of drill hole samples taken at different locations and depths in the deposit, and are used to delineate lithological and mineralogical domains and to interpolate the grades of elements of interest (products, by-products and contaminants) and other attributes, such as the specific gravity and the metal recovery. The steps described above are the traditional approach to generating a long-term open-pit mine production plan, which suffer from two drawbacks [8,9] They only use a single block model of the deposit and, as a result, do not consider uncertainty in the geological properties such as the metal grades.
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