Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic

Abstract

Abstract Usually, the rock fragmentation is used in the mining industry as an index to estimate the effect of bench blasting. However, a good fragmentation is a concept that it mainly depends on the downstream process characteristics i.e. mucking equipment, processing plant, mining goal etc. As a matter of fact, the fragmentation has a direct effect on the costs of drilling and blasting as well as economics of the subsequent operations. Using regression analysis and fuzzy inference system (FIS), the present paper tries to develop predictive models in order to predict fragmentation caused by blasting at Gol-E-Gohar iron mine. It is worth mentioning that the rock fragmentation is influenced by various parameters such as rock mass properties, blast geometry and explosive properties. With regard to the aforementioned fuzzy system, the paper prepares a database of the blasting operations, which includes burden, spacing, hole-depth, specific drilling, stemming length, charge-per-delay, rock density and powder factor as input parameters and fragmentation as output parameter. Since the explosive was unchanged in all the blasts, therefore, it cannot be considered. To validate and compare the obtained results, determination coefficient (R2) and root mean square error (RMSE) index are chosen and calculated for both the models. It is observed that the fuzzy predictor performs, significantly, better than the statistical method. For the fuzzy model, R2 and RMSE are equal to 0.96 and 3.26, respectively, whereas for regression model, they are 0.80 and 6.83, respectively.