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Prediction of rock fragmentation in a fiery seam of an open-pit coal mine in India

Journal of Rock Mechanics and Geotechnical Engineering 2024年第8期16卷 2879-2893页

作者： Mukul Sharma Bhanwar Singh Choudhary autar k.raina Manoj khandelwal Saurav Rukhiyar Department of Mining Engineering Indian Institute of Technology(ISM)Dhanbad826004India CSIR-Central Institute of Mining and Fuel Research Nagpur Research Center(Mining Technology)Nagpur440006India Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC3350Australia

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading *** condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded ***,it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of ***,it will help achieve the desired fragment *** study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery *** on blast design,rock mass,and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational *** coefficient of determination(R^(2)),root mean square error(RMSE),mean absolute error(MAE),mean square error(MSE),variance account for(VAF)and coefficient of efficiency in percentage(CE)were calculated to validate the *** indicates that the random forest algorithm(RFA)outperforms the artificial neural network(ANN),response surface method(RSM),and decision tree(DT).The values of R^(2),RMSE,MAE,MSE,VAF,and CE for RFA are 0.94,0.034,0.027,0.001,93.58,and 93.01,*** parametric sensitivity analyses(MPSAs)of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment *** analysis was finally used to define the best blast design variables to achieve optimum fragment size from *** optimum factor values for RFA of S/B,ld/B and ls/ld are 1.03,1.85 and 0.7,respectively.

关键词： Fiery seam Rock fragmentation Response Surface Method(RSM) Artificial Neural Network(ANN) Random Forest Algorithm(RFA) Multiple Parametric Sensitivity Analysis (MPSA)

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