Application of Neural-Fuzzy-Genetic Network for Grade Estimation of Darrehzar Copper Porphyry-Kerman

Grade estimation is one of the important stages for mining assessment. Grade values have asignificant effect on scheduling, designing and management of the mine. Therefore, it is importantto apply a method, which is able to estimate the necessary parameters with a high accuracy. One ofthe direct methods for figuring out the grades is to use exploration wells, which, because of theirhigh costs, usually it is impossible to use them extensively. In this study, a novel method based onfuzzy logic, neural networks and genetic algorithm is presented. This algorithm, by applying geneticalgorithms for optimization of the architecture and the neuro-fuzzy parameters, is able to achievebetter results as compare with other traditional methods for grade estimation. This is because ofusing different artificial intelligent methods. Foe this aim, Darrehzar copper porphyry was selectedas the case study. According to the results obtained our proposed algorithm able to capture theexisting pattern between the inputs and outputs finely and to estimate the grade with a highprecision.