January 2020
Volume 72    Issue 1

Pit optimization on clustered realizations: Identifying functional scenarios

Wilson, Brandon; Acorn, Tyler; Boisvert, Jeff

ABSTRACT:
Full-text paper: Mining, Metallurgy & Exploration, https://doi.org/10.1007/s42461-019-0101-7
Keywords: Mine planning, Decision-making, Stochastic modeling, Clustering, Mining
 
Project-scale decisions in surface mining operations rely on the optimization and subsequent evaluation of pit limits. This process ignores the ability for mine plans to adapt as more information is gathered. Stochastic methods, which consider uncertainty in the pit optimization, generally act to refine the boundaries of the pit on a fine scale. This paper extends uncertainty-based methods to consider unique scenarios through a clustering of the realizations that are being optimized. This results in the generation of multiple sets of similar realizations. A separate pit is optimized on each set of realizations, resulting in the production of a series of planning scenarios. This accounts for the adaptability of mine planning as more information is gained. A decision-making framework is then applied to the resulting scenario plans to select the optimal choice. The improvements stem from the joint uncertainty considered when realizations are grouped that is not considered when the probability of a block being in any optimized pit is considered. Mining operations today can benefit from considering additional planning options as identified and evaluated in this workflow.

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