Estimation of Optimum Burden for Blasting of Different Rock Strata in an Indian Iron Ore Mine

Abstract

Burden movement plays very important role in the bench blasting for the effective utilization of the explosive energy. The optimum burden for a blast face varies with the nature of the strata and explosive quantity & quality. There are various experimental and empirical approaches for the estimation of optimum burden. However, there is a need for the site-specific estimation suited with the strata condition for estimation of optimum burden at the blast faces having huge variations in the rock types. Two different approaches have been used in this paper for computation of optimal burden of different rock strata of an Indian iron ore mine. The first approach is based on assessment of rock mass properties and explosive properties for the blast. The assessed properties are put in different empirical equations of Kuz-Ram model. To achieve optimal burden desired fragment size has been computed by back calculation from the empirical model. The second approach is based on the assessment of critical vibration limit for different rock strata. The determination of critical vibration is done using the assessment of rock mass properties. The prediction of distance up to which critical vibration limit will be achieved using the designed charging parameters, gives the optimum burden. The computed burden has been compared for both the approaches, which gives a good correlation with correlation coefficient of 0.78. The experimental blasts of different rock strata were conducted using the computed optimal burden. The blast output with optimal burden and charging parameters have been improved in terms of fragmentation, blast face movement and reduction of back-break.

Prediction and Analysis Method of Mine Blasting Quality Based on GA-BP Neural Network

“…e two important steps of quantification and regional segmentation were studied, and a blasting rock distribution model with strong applicability and high processing efficiency was established, and a software package for automatic determination of ore block degree was developed [13][14][15]. However, the above research has not solved the corresponding problems well, so this paper puts forward the following innovations: ① Combining genetic algorithm and BP algorithm, a computer image recognition algorithm based on GA-BP depth neural network is proposed.…”