Sirvan Moradi; Seyed Davoud Mohammadi; Abbas Aghajani Bazzazi; Ali Aali Anvari; Ava Osmanpour
Abstract
Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since ...
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Feasibility studies of mining and industrial investment projects are usually associated with uncertain parameters; hence, these investigations rely on prediction. In these particular conditions, simulation and modelling techniques remain the most significant approaches to reduce the decision risk. Since several uncertain parameters are incorporated in the modelling process, distribution functions are employed to explain the parameters. However, due to the usual constrain of limited data, these functions cannot significantly explain the variation of those uncertain parameters. Support vector machine, one of the efficient techniques of artificial intelligence, provides the appropriate results in the classification and regression tasks. The principal aims of this research work are to integrate the simulation and artificial intelligence methods to manage the risk prediction of an economic system under uncertain conditions. The financial process of the Halichal mine in the Mazandaran province, Iran, is considered a case study to prove the performance of the support vector machine technique. The results show that integrating the simulation and support vector machine techniques can provide more realistic results, especially when including uncertain parameters. The correlation between the net present value obtained from the simulation and the net present value is about 0.96, which shows the capability of artificial intelligence methods and the simulation process. The root mean square error of the support vector machine prediction is about 0.322, which indicates a low error rate in the net present value estimation. The values of these errors prove that this method has a high accuracy and performance for predicting a net present value in the Halichal granite mine.
M. Shenavar; M. Ataee-pour; M. Rahmanpour
Abstract
The uncertainty-based mine evaluation and optimization have been regarded as a critical issue. However, it has received less attention in the underground mines than in the open-pit mines due to the diversity of the underground mining methods, and the underground mining parameters' complexity. The grade ...
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The uncertainty-based mine evaluation and optimization have been regarded as a critical issue. However, it has received less attention in the underground mines than in the open-pit mines due to the diversity of the underground mining methods, and the underground mining parameters' complexity. The grade and commodity price uncertainties play essential roles in mining projects. Mine planning by not incorporating these uncertainties is accompanied by risks. The evaluation and risk assessment of the mine plans is possible through evaluating the mineable reserve in the presence of such uncertainties. In the present work, we evaluate the effects of grade and commodity price uncertainties on the underground mining stope optimization and the resultant mineable reserve. In this regard, the stope boundary is studied both deterministically and stochastically in the presence of the grade and price uncertainties. For this purpose, in this work, we implement the conditional simulation in order to generate equally probable ore reserve models. Furthermore, we optimize the stope boundary using the floating-stope algorithm in each realization. Several decision support criteria including the 'mineable reserve,' 'metal-content,' 'profit,' and 'value-at-risk' are defined to assist the decision-maker in uncertain conditions. Finally, a procedure is defined in order to consider two types of uncertainty sources simultaneously in underground mining. It will guide the decision-maker toward the most appropriate stope boundary that best fits the mining company's requirements. The procedure is implemented in a bauxite mine, and the optimal stope boundary is determined concerning the different criteria.
