Regression Models of the Impact of Rockmass and Blast Design Variations on the Effectiveness of Iron Ore Surface Blasting

摘要

The desired economics of hard rock surface mining is mainly determined by the parameters of process design which minimize the overall cost per tonne of the rock mined in drilling, blasting, handling and primary crushing in given rockmass conditions. The most effective parameters of process design could be established based on the regression models of the cumulative influence of rockmass and mine design parameters on the overall cost per tonne of the rock drilled, blasted, handled and crushed. These models could be developed from the huge data accumulated worldwide on the costs per tonne of hard rock surface mining in drilling, blasting, handling and primary crushing vs the parameters of rockmass and mine design. This paper only dwelt on the development of regression models for oversize generation, blasthole productivity and blasting cost for iron ore surface mines, whose data is available. The SPSS standard statistical correlation - regression analysis software was used in the analysis. Interpretation of the models generated shows that the individual effects of the determinant rockmass and blast design parameters on oversize generation, blasthole productivity and blasting cost are all in compliance with the findings of other researchers and the theory of explosive rock fragmentation and could be used for the estimation of oversize generation, blasthole productivity and blasting cost in rockmass and blast design conditions similar to those of the iron ore surface mines examined in this study. However, the regression models obtained here could not be used alone for the optimization of blast design because most of the determinant parameters also have conflicting effect on the other processes of drilling, handling and primary crushing the blasted rock. Also, the quality and content of the regression models could be enhanced further by increasing the content of rockmass and blast design parameters and the volume of data considered in the regression analysis.