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.
Exploitation
F. Sotoudeh; M. Ataei; R. Kakaie; Y. Pourrahimian
Abstract
In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most ...
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In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods used for an entire ore body. In this work, all the algorithms developed by numerous researchers for optimization of the underground stope layout are reviewed. After that, a computer program called stope layout optimizer 3D is developed based on a previously proposed heuristic algorithm in order to incorporate the influence of grade variability in the final stope layout. Utilizing the sequential gaussian conditional simulation, 50 simulations and a kriging model are constructed for an underground copper vein deposit situated in the southwest of Iran, and the final stope layout is carried out separately. It can be observed that geostatistical simulation can effectively cope with the weakness of the kriging model. The final results obtained show that the frequency of economic value for all realizations varies between 6.7 M$ and 30.7 M$. This range of variation helps designers to make a better and lower risk decision under different conditions.
K. Tolouei; E. Moosavi; A.H. Bangian Tabrizi; P. Afzal; A. Aghajani Bazzazi
Abstract
It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to ...
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It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole profits of the project in a specific period. There is a requirement for improving the scheduling methodologies to get a good solution since the production scheduling problems are non-deterministic polynomial-time hard. The current paper introduces the hybrid models so as to solve the LTPS problem under the condition of grade uncertainty with the contribution of Lagrangian relaxation (LR), particle swarm optimization (PSO), firefly algorithm (FA), and bat algorithm (BA). In fact, the LTPS problem is solved under the condition of grade uncertainty. It is proposed to use the LR technique on the LTPS problem and develop its performance, speeding up the convergence. Furthermore, PSO, FA, and BA are projected to bring up-to-date the Lagrangian multipliers. The consequences of the case study specifies that the LR method is more influential than the traditional linearization method to clarify the large-scale problem and make an acceptable solution. The results obtained point out that a better presentation is gained by LR–FA in comparison with LR-PSO, LR-BA, LR-Genetic Algorithm (GA), and traditional methods in terms of the summation net present value. Moreover, the CPU time by the LR-FA method is approximately 16.2% upper than the other methods.
Exploitation
M. Jamshidi; M. Osanloo
Abstract
The block economic value (BEV) of a single-metal deposit is calculated based on the metal content and the related costs. The common methods available for calculating BEV are just based upon the profitable elements, and the effects of undesirable elements on BEV are not considered. However, in multi-element ...
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The block economic value (BEV) of a single-metal deposit is calculated based on the metal content and the related costs. The common methods available for calculating BEV are just based upon the profitable elements, and the effects of undesirable elements on BEV are not considered. However, in multi-element deposits, the effects of other elements existing in the blocks on BEV should be considered with the purpose of optimizing the blending. These elements and blending methods have considerable effects on the quality of the final product. In this paper, a new approach is introduced to determine BEV in multi-element deposit with two types of profitable and penalty elements by considering the effect of blending on BEV. Consequently, the ultimate pit limits (UPLs) will be determined based on these conditions. The developed model is tested in the Gol-e-Gohar No.2 iron-ore mine, and the mine UPLs is determined. The results obtained showed that the mineable reserve of the pit increased by 3% when the effects of both types of elements are considered. In order to investigate the effect of grade uncertainty on BEV, twenty realizations of the ore block are generated using the sequential Gaussian simulation approach. The UPLs of all the realizations are determined using the developed BEV-calculation method, and the pit limits with different probabilities of occurrence are determined. The total mineable reserve varied between 20,380 and 46,410 million tons. The exploitation of mine should start with the smallest pit (100% probability). The largest pit should be considered as a guide for surface-facility locating.
