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

作　　者：Mukul Sharma Bhanwar Singh Choudhary Autar K.Raina Manoj Khandelwal Saurav Rukhiyar 

机构地区：[1]Department of Mining Engineering,Indian Institute of Technology(ISM),Dhanbad,826004,India [2]CSIR-Central Institute of Mining and Fuel Research,Nagpur Research Center(Mining Technology),Nagpur,440006,India [3]Institute of Innovation,Science and Sustainability,Federation University Australia,Ballarat,VIC,3350,Australia

出　　处：《Journal of Rock Mechanics and Geotechnical Engineering》2024年第8期2879-2893,共15页岩石力学与岩土工程学报（英文版）

摘　　要：Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes.This condition,known as hot-hole blasting,is dangerous due to the increased possibility of premature explosions in loaded blastholes.Thus,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 blastholes.Additionally,it will help achieve the desired fragment size.This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam.Data 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 techniques.The 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 results.It 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,respectively.Multiple 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 size.The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting.The 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) 

分 类 号：TU45[建筑科学—岩土工程]