A comparison between open-pit ramp designs obtained by varying design characteristics and through linear optimization

Authors: Moreno, R; Reyes, M; Nancel-Penard, P

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This paper is hosted with the kind permission of the Universidad de Chile, Eighth International Conference & Exhibition on Mass Mining, 2020.


DOI https://doi.org/10.36487/ACG_repo/2063_110

Cite As:
Moreno, R, Reyes, M & Nancel-Penard, P 2020, 'A comparison between open-pit ramp designs obtained by varying design characteristics and through linear optimization', in R Castro, F Báez & K Suzuki (eds), MassMin 2020: Proceedings of the Eighth International Conference & Exhibition on Mass Mining, University of Chile, Santiago, pp. 1442-1450, https://doi.org/10.36487/ACG_repo/2063_110

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Abstract:
The problem of strategic open pit planning involves several stages, including (1) ultimate pit, (2) pushback design, (3) production scheduling. To address stages (1) and (2), mine planners rely on different optimization software to assist them in making the best choices, within the boundaries of these tools. However, for (2), there is no optimization tool and therefore this task, which consists of designing the actual ramps and slopes of the pit, is mostly manual, hence time-consuming, and highly dependent on the expertise of the planner. The methodology proposed in this paper selected some design parameters such as the starting position, the direction of spinning, and the presence of switchbacks and an orthogonal combination of these parameters to generate multiple scenarios that were later economically evaluated. We present a model that relates those design parameters and the pit value with ramps to predict the economic evaluation of different design. Scenarios with random design characteristics were created to evaluate the proposed linear model estimations. The model predicts the trends of the economic valuations in each case thus it could aid the mine planer providing estimated information regarding the resulting economic variability in the different possible designs and establish the bases for the development of a global model that obtains optimal results.

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