Prediction of Rock Fragmentation in Open Pit Mines, using Neural Network Analysis

Abstract

Loading and transport costs constitute up to 50% of the total operational costs in open pit mines. Fragmentation of the rock after blasting is an important determinant of the cost associated with these two components of mine development. In this paper, fragmentation of the rock after blasting is estimated analytically by the use of neural network method. The results obtained here, are compared with those predicted by Kuz-Ram and image analysis methods. All these have then been tested using real data gathered from Gol Gohar iron ore mine of Iran. It is shown that neural network method can be used efficiently in such cases and the final results can be expected to have a high degree of accuracy. The results obtained in this study and the methodology introduced, can assist the mining design engineer to decide on a drilling and blasting pattern that produces the most suitable fragmentation of the blasted ore and hence minimize the total cost of the mining operations.