Grade estimation is one of the important stages for mining assessment. Grade values have a significant effect on scheduling, designing and management of the mine. Therefore, it is important to apply a method, which is able to estimate the necessary parameters with a high accuracy. One of the direct methods for figuring out the grades is to use exploration wells, which, because of their high costs, usually it is impossible to use them extensively. In this study, a novel method based on fuzzy logic, neural networks and genetic algorithm is presented. This algorithm, by applying genetic algorithms for optimization of the architecture and the neuro-fuzzy parameters, is able to achieve better results as compare with other traditional methods for grade estimation. This is because of using different artificial intelligent methods. Foe this aim, Darrehzar copper porphyry was selected as the case study. According to the results obtained our proposed algorithm able to capture the existing pattern between the inputs and outputs finely and to estimate the grade with a high precision.
Tahmasebi, P., & Hezarkhani, A. (2011). Application of Neural-Fuzzy-Genetic Network for Grade Estimation of
Darrehzar Copper Porphyry-Kerman. Journal of Mining Engineering, 6(12), 1-9.
MLA
P Tahmasebi; A Hezarkhani. "Application of Neural-Fuzzy-Genetic Network for Grade Estimation of
Darrehzar Copper Porphyry-Kerman". Journal of Mining Engineering, 6, 12, 2011, 1-9.
HARVARD
Tahmasebi, P., Hezarkhani, A. (2011). 'Application of Neural-Fuzzy-Genetic Network for Grade Estimation of
Darrehzar Copper Porphyry-Kerman', Journal of Mining Engineering, 6(12), pp. 1-9.
VANCOUVER
Tahmasebi, P., Hezarkhani, A. Application of Neural-Fuzzy-Genetic Network for Grade Estimation of
Darrehzar Copper Porphyry-Kerman. Journal of Mining Engineering, 2011; 6(12): 1-9.
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