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Machine learning based prediction of flyrock distance in rock blasting:A safe and sustainable mining approach

Green and Smart Mining Engineering 2024年第3期1卷 346-361页

作者： Blessing Olamide Taiwo Yewuhalashet Fissha Shahab Hosseini Mohammad Khishe Esma Kahraman Babatunde Adebayo Mohammed Sazid Patrick Adeniyi Adesida Oluwaseun Victor Famobuwa Joshua Oluwaseyi Faluyi adams abiodun akinlabi Department of Mining Engineering Federal University of Technology AkureAkure 340001Nigeria HNF Global Resources Limited Akoko EdoIgarra 312101Nigeria Department of Geosciences Geotechnology and Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita 010-8502Japan Department of Mining Engineering Aksum UniversityAksum 7080TigrayEthiopia Faculty of Engineering Tarbiat Modares UniversityTehran 14115-111Iran Department of Electrical Engineering Imam Khomeini Naval Science University of NowshahrNowshahr 46517Iran Applied Science Research Center Applied Science Private UniversityAmman 11937Jordan Department of Mining Engineering Cukurova UniversityAdana 01250Turkey Department of Mining Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Mining West Virginia UniversityMorgantown 26506USA

Flyrock is a significant environmental and safety concern in mining and *** arises from various geological and blast design factors,posing risks to workers,machinery,and nearby *** study examined how these factors affect the rate and distance of flyrock projections caused by *** address this issue,advanced machine learning(ML)models were used to predict flyrock distances in the Akoko Edo dolomite *** models examined included bidirectional recurrent neural networks(BRNNs),support vector regression(SVR)with different kernels(SVR-S,SVR-RBF,SVR-L,SVR-P),long short-term memory(LSTM)networks,and random forest(RF)algorithms.A case study was conducted using 258 blasting data samples to develop these *** factors influencing flyrock were identified:blast hole burden distance,maximum instantaneous charge,and rock brittleness *** these factors,a flyrock possibility assessment chart was created to enhance the safety of small-scale mining *** model’s prediction accuracy was evaluated using correlation coefficients and four performance *** LSTM model stood out,achieving the highest coefficient of correlation(R2=0.99)for both training and testing *** indicates that the LSTM model accurately predicts blast-induced flyrock *** study also revealed that the Gaussian-RBF kernel SVR has high prediction accuracy when compared to other SVR variants(SVR-S,SVR-L,and SVR-P).In conclusion,the study compared various ML models for flyrock reduction and found that the LSTM model was the most effective in estimating blast-induced flyrock distances.

关键词： Blasting Flyrock Safety chart Mine production reliability Long short-term memory networks

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