Application of inverse theory in open pit blasting

Abstract

Blasting is a crucial part in mining operation. Proper blasting practice not only reduces the adverse effects like peak particle velocity but also improve the production and productivity. The blasting in open pit mine is controlled by number of parameters like spacing, burden, quantity of explosive etc. The production and productivity are measured by power factor, throw, and drop. Numerous models have been developed to calculate the powder factor, drop, and throw using spacing, burden, hole depth and explosive charges etc. as input variables, which means that for known input variables, powder factor, throw, and drop can be calculated. However, field mining engineers are more interested about what should be the suitable values of input parameters to get a specific factor, throw, and drop. In most cases this has been done on a trial and error basis to fix the values of input parameters. A technique was developed to predict a nearly optimum set of blast design parameters from the nine variable design parameters (drill penetration rate, bench height, burden, spacing, hole depth, sub-grade drilling, stemming, blast round, and length to width ratio) that largely affect the shape (throw and drop) and the PF values of blasted muck piles. A stepwise forward multivariate regression algorithm was applied to generate optimum forward equation for powder factor, drop, and throw parameters. A linear inverse theory was applied to develop linear equations for independent variables. The developed method was applied in a limestone case study mine and results revealed that the this approach could be a good methods for selection of blasting parameters to get desire outputs.