1
MSc. of Mining Engineering, Dept. of Mining Engineering, Sahand University of Technology, Iran
2
Assistant Professor, Dept. of Mining Engineering, Sahand University of Technology, Iran
Abstract
The accurate estimation of ore grade plays an important role for the mine evaluation, planning and designing. According to some existing problems when using conventional methods such as Kriging for grade estimation of deposit. In this research, the performance of intelligent estimators such as multilayer perceptron neural network, adaptive neuro-fuzzy inference system and support vector regression were investigated for grade estimation in Masjeddaghy porphyry copper (gold) deposit located in East-Azerbaijan province. For this purpose since divided assay data achieved from 31 exploratory boreholes into training and test subsets, optimum structure and designing parameters value of the mentioned methods were determinated by using the genetic algorithm and based on the training dataset. Finally the validation indicators calculated for estimation grades of testing dataset for used estimators. According to the results, support vector regression method showed higher generalization capability and computational efficiency in copper grade estimation. Also close and better results of this method than ordinary Kriging indicate that support vector regression method can be used as rapid, accurate approaches and better than other intelligent estimator for grade in same problems.
valizadeh, N., & Sharghi, Y. (2014). Performance Comparison of Estimators Based on Artificial Intelligence for Ore Grade Estimation in Masjed Daghi Copper Deposit. Journal of Analytical and Numerical Methods in Mining Engineering, 4(8), 29-37.
MLA
N valizadeh; Y Sharghi. "Performance Comparison of Estimators Based on Artificial Intelligence for Ore Grade Estimation in Masjed Daghi Copper Deposit", Journal of Analytical and Numerical Methods in Mining Engineering, 4, 8, 2014, 29-37.
HARVARD
valizadeh, N., Sharghi, Y. (2014). 'Performance Comparison of Estimators Based on Artificial Intelligence for Ore Grade Estimation in Masjed Daghi Copper Deposit', Journal of Analytical and Numerical Methods in Mining Engineering, 4(8), pp. 29-37.
VANCOUVER
valizadeh, N., Sharghi, Y. Performance Comparison of Estimators Based on Artificial Intelligence for Ore Grade Estimation in Masjed Daghi Copper Deposit. Journal of Analytical and Numerical Methods in Mining Engineering, 2014; 4(8): 29-37.
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