Investigation of rock blast fragmentation based on specific explosives

In order to control and optimize a mining operation, it is important to assess the fragmentation caused by blasting and subsequent crushing  and grinding stages. Prediction of the mean size of a fragmented rock through the rock mass characteristics, the blasting geometry, the  technical parameters and the explosive properties is an important challenge for the blasting engineers. Some of the effective parameters on  rock fragmentation have been investigated in several empirical models. A model for fragmentation in bench blasting was developed using the  effective parameters on the existing empirical models to propose a simple applicable model for predicting the X50 value. The proposed model  was calibrated by nonlinear fits to 35 bench blasts in different sites from the Sungun copper mine, the Akdaglar quarry, the and Mrica quarry.  In order to validate the proposed model, the results were compared to the data obtained from six blast sites in the Chadormalu iron ore mine  and the Porgera gold mine. The results indicated a small variance in X50, which was calculated by the proposed model through the image  processing approach. The Comparison of the powers between the proposed and the Kuz-Ram models showed that the specific explosive  energy and the powder factor are almost the same. The advantage of the proposed model over the Kuz-Ram model is the specific explosive  energy, since this parameter includes the powder factor and the weight strength of an explosive. In addition, a sensitivity analysis was  conducted based on the artificial neural network. The results showed that the burden and the specific explosive energy were the most effective parameters in the proposed model.