Performance evaluation of a new stochastic network flow approach to optimal open pit mine design - application at a gold mine

Abstract

The optimal design of production phases and ultimate pit limit foran open pit mining operation may be generated using conventionalor stochastic approaches. Unlike the conventional approach, thestochastic framework accounts for expected variability anduncertainty in metal content by considering a set of equallyprobable realizations (models) of the orebody. This paper evaluatesthe performance of a new stochastic network flow approach for thedevelopment of optimal phase design and ultimate pit limit using agold deposit as the case study. The stochastic and conventionalframeworks as considered here utilize the maximum flow andLerchs-Grossman (LG) algorithms, respectively. The LG algorithm isrestricted to considering an estimated (average-type) orebodymodel, while the stochastic maximum flow algorithm is developed tosimultaneously use a set of simulated orebody realizations as aninput. The case study demonstrates that, when compared to theconventional LG algorithm as used in the industry, the stochasticapproach generates a 30 per cent increase in discounted cash flow, a21 per cent larger ultimate pit limit, and about 7 per cent moremetal, while it maintains a consistency in phase size.