Rock fragmentation control in opencast blasting

The blasting operation plays a pivotal role in the overall economics of opencast mines.The blasting subsystem affects all the other associated sub-systems,i.e.loading,transport,crushing and milling operations.Fragmentation control through effective blast design and its effect on productivity are the challenging tasks for practicing blasting engineer due to inadequate knowledge of actual explosive energy released in the borehole,varying initiation practice in blast design and its effect on explosive energy release characteristic.This paper describes the result of a systematic study on the impact of blast design parameters on rock fragmentation at three mines in India.The mines use draglines and shoveledumper combination for removal of overburden.Despite its pivotal role in controlling the overall economics of a mining operation,the expected blasting performance is often judged almost exclusively on the basis of poorly defined parameters such as powder factor and is often qualitative which results in very subjective assessment of blasting performance.Such an approach is very poor substitutes for accurate assessment of explosive and blasting performance.Ninety one blasts were conducted with varying blast designs and charging patterns,and their impacts on the rock fragmentation were documented.A high-speed camera was deployed to record the detonation sequences of the blasts.The efficiency of the loading machines was also correlated with the mean fragment size obtained from the fragmentation analyses.

Analysis of key technologies and development of integrated digital processing system for cast blasting design

Casting blast can greatly reduce the stripping cost and improve the production capacity of opencast coal mines. Key technologies including high bench blasting, inclined hole, millisecond blasting, pre-splitting blasting and casting blast parameters determination which have influence on the effect of casting blast have been researched with the combination of the ballistic theory and experience in mines. The integrated digital processing system of casting blast was developed in order to simplify the design process of casting blast, improve working efficiency and veracity of design result and comprehensively adopt the software programming method and the theory of casting blast. This system has achieved five functions, namely, the 3D visualization graphics management, the intelligent management of geological information, the intelligent design of casting blast, the analysis and prediction of the blasting effect and the automatic output of the design results. Long-term application in opencast coal mines has shown that research results can not only reduce the specific explosive consumption and improve the blasting effect, but also have high value of popularization and application.

Prediction of blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.