MiningMath

MiningMath

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Increasing the sustainable value and facilitating the decision-making process of mining projects since 2010

Uncertainties at the Beginning

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One of the many possibilities offered by MiningMath’s approach is to have multiple overview scenarios to evaluate different project assumptions, before doing a more detailed work. It does not demand an arbitrary/automated trial-and-error cutoff definition, nor a fixed input in form of pushbacks that will guide further optimization steps within the boundaries of a simplified problem. A subtle but substantial implication is the possibility of seeing a totally different mine development throughout the mine life cycle for each project assumption change. This allows mine managers to have a clearer view of the decision-tree and the possibilities on their hands, to improve economic, technical, and socio-environmental performances.

Considering this context, mine managers can judge greenfield projects to know whether or not they should prioritize a geotechnical study. This could be done by running multiple scenarios, considering the expected variability for slope angles for a given deposit. For example, in a given deposit, benchmarks from similar deposits indicate the overall slope angles might vary between 35-45 degrees. Before reaching the conclusion using an in-depth geotechnical study, multiple scenarios can be used to estimate the economic impact of each possible assumption for the overall slope angle. The conclusion might, then indicate a low economic impact, that could postpone the need for a detailed study.

The same idea applies to any parameter, which ultimately represents a project assumption.

MiningMath conducted an illustrative example with 2000 simulations varying multiple parameters independently. The results produced the chart from Figure 1, showing the probability (Y-axis) and the Project’s Value (X-axis). In this case, a detailed geotechnical study might be postponed, as the Project’s Value varies between 700 to 1100 MU$, in function of the OSA.

Figure 1: What would 2000 simulations say about NPV distributions?
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Uncertainties at the Beginning

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