Geostatistics-block-based characterization of the relationship between rock mass quality and powder factor and its application on open-pit limit optimization

Abstract

Accurately predicting the powder factor during blasting is essential for sustainable production planning in low-grade mines. This research presents a method for predicting powder factor based on the heterogeneity of rock mass rating (RMR). Considering a low-grade metal mine as an example, this study exploited geostatistical methods to obtain independent RMR for each block unit. A three-dimensional spatial distribution model for the powder factor was developed on the basis of the relationships between the RMR and the powder factor. Subsequently, models for blasting cost and mining value were built and employed to optimize the open-pit limit. The multi-variable model based on the RMR performed well in predicting the powder factor, achieving a correlation coefficient of 0.88 (root mean square error of 4.3) and considerably outperforming the uniaxial compressive strength model. After model optimization, the mean size and standard deviation of the fragments in the blast pile decreased by 8.5% and 35.1%, respectively, whereas the boulder yield and its standard deviation decreased by 33.3% and 58.8%, respectively. Additionally, optimizing the open-pit limit using this method reduced the amount of rock, increased the amount of ore, and lowered blasting costs, thereby enhancing the economic efficiency of the mine. This study provides valuable insights for blasting design and mining decisions, demonstrating the advantages and potential applications of powder factor prediction based on the heterogeneity of rock mass quality.

Keywords

Geostatistics method / Powder factor / Open-pit limit optimization / Blasting cost / Rock mass quality

Acknowledgments

This work was supported by the National Key Research and Development Program of China (No. 2022YFC2903902), the National Natural Science Foundation of China (Nos. 52204080 and 52174070), and the Fundamental Research Funds for the Central Universities of China (No. 2023GFYD17).

References

[1] Yang TH, Wang H, Dong X, Liu FY, Zhang PH, Deng WX. Current situation, problems and countermeasures of intelligent evaluation of slope stability in open pit. J China Coal Soc 2022; 45(6):2277-95. in Chinese.

[2] Wang Q, Ren FY, Gu XW, Chen QK, Tu XL. Mining science in Chinese. In: Tan XY, editor. Perforation and blasting operations. Beijing: Metallurgical Industry Press; 2014. p. 425.

[3] Vennes I, Mitri H, Chinnasane DR, Yao MK. Large-scale destress blasting for seismicity control in hard rock mines: A case study. Int J Min Sci Technol 2020; 30(2):141-9.

[4] Singh PK, Roy MP, Paswan RK, Sarim M, Kumar S, Ranjan JaR. Rock fragmentation control in opencast blasting. J Rock Mech Geotech Eng 2016; 8(2):225-37.

[5] Rezaei M, Monjezi M, Matinpoor F, Mohammadi Bolbanabad S, Habibi H. Simulation of induced flyrock due to open-pit blasting using the PCA-CART hybrid modeling. Simul Model Pract Theory 2023; 129:102844.

[6] Dehghani H, Pourzafar M, Asadizadeh M. Prediction and minimization of blastinduced flyrock using gene expression programming and cuckoo optimization algorithm. Environ. Earth Sci 2021; 80(1):12.

[7] Lawal AI, Olajuyi SI, Kwon S, Aladejare AE, Edo TM. Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm. Acta Geophys 2021; 69(4):1313-24.

[8] Ding XH, Hasanipanah M, Ulrikh DV. Hybrid metaheuristic optimization algorithms with least-squares support vector machine and boosted regression tree models for prediction of air-blast due to mine blasting. Nat Resour Res 2024; 33(3):1349-63.

[9] Taiwo BO, Yewuhalashet F, Adamolekun LB, Bidemi OO, Famobuwa OV, Victoria AO. Development of artificial neural network based mathematical models for predicting small scale quarry powder factor for efficient fragmentation coupled with uniformity index model. Artif Intell Rev 2023; 56(12):14535-56.

[10] Dimitraki L, Christaras B, Marinos V, Vlahavas I, Arampelos N. Predicting the average size of blasted rocks in aggregate quarries using artificial neural networks. Bull Eng Geol Environ 2019; 78(4):2717-29.

[11] Salmi EF, Sellers EJ. A review of the methods to incorporate the geological and geotechnical characteristics of rock masses in blastability assessments for selective blast design. Eng Geol 2021; 281:105970.

[12] Olamide Taiwo B, Gebretsadik A, Abbas HH, Khishe M, Fissha Y, Kahraman E, Rabbani A, Akinlabi AA. Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics. Heliyon 2024; 10(12):e33099.

[13] de Miguel-García E, Gómez-González JF. A new methodology to estimate the powder factor of explosives considering the different lithologies of volcanic lands: A case study from the island of Tenerife. Spain Tunn Undergr Space Technol 2019; 91:103023.

[14] Mohamed F, Hafsaoui A, Talhi K, Menacer K. Study of the powder factor in surface bench blasting. Procedia Earth Planet Sci 2015; 15:892-9.

[15] Jong YH, Lee CI. Influence of geological conditions on the powder factor for tunnel blasting. Int J Rock Mech Min Sci 2004; 41(3):461.

[16] Kahriman A, Özkan ŞG, Sül ÖL, Demirci A. Estimation of the powder factor in bench blasting from the Bond work index. Min Technol 2001; 110(2):114-8.

[17] Agyei G, Nkrumah MO. A review on the prediction and assessment of powder factor in blast fragmentation. Nig J Tech 2021; 40(2):275-83.

[18] Rezaei M, Monjezi M, Moghaddam SG, Farzaneh F. Burden prediction in blasting operation using rock geomechanical properties. Arab J Geosci 2012; 5(5):1031-7.

[19] Wang XY, Zhang XT, Zhang JS, Zhou HM, Zhang P, Li D. Study on the influence of the joint angle between blast holes on explosion crack propagation and stress variation. Processes 2023; 11(9):2805.

[20] Moomivand H, Vandyousefi H. Development of a new empirical fragmentation model using rock mass properties, blasthole parameters, and powder factor. Arab J Geosci 2020; 13(22):1173.

[21] Ramesh Murlidhar B, Bejarbaneh BY, Armaghani DJ, Mohammed AS, Tonnizam ME. Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Nat Resour Res 2021; 30(2):1865-87.

[22] Ding XH, Liu X, Ao ZC, Qin H, Li XS, Huang KX, Xiao SS, Wu M, Zhang DH, Zhu C. Research on blasting mechanism and blasting effect of aqueous media in open pit coal mines. Sci Rep 2023; 13(1):19140.

[23] Nguyen H, Drebenstedt C, Bui XN, Bui DT. Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res 2020; 29(2):691-709.

[24] Chakraborty AK, Jethwa JL, Paithankar AG. Effects of joint orientation and rock mass quality on tunnel blasting. Eng Geol 1994; 37(3-4):247-62.

[25] Li JD, Yang TH, Liu FY, Zhao Y, Liu HL, Deng WX, Gao Y, Li HB. Modeling spatial variability of mechanical parameters of layered rock masses and its application in slope optimization at the open-pit mine. Int J Rock Mech Min Sci 2024; 181:105859.

[26] Liu FY, Yang TH, Zhou JR, Deng WX, Yu QL, Zhang PH, Cheng GW. Spatial variability and time decay of rock mass mechanical parameters: A landslide study in the Dagushan open-pit mine. Rock Mech Rock Eng 2020; 53(7):3031-53.

[27] Liu FY, Yang TH, Deng WX, Zhou JR, Li JD. Geostatistics-block-based characterization of heterogeneous rock mass and its application on ultimate pit limit optimization: A case study. Bull Eng Geol Environ 2021; 80(2):1683-700.

[28] Yu QL, Li Y, Li WT, Wu JW, Wang YH, Jin RJ. Prediction model of explosive unit consumption in open-pit mine based on zoning of blasting compliance method. Met Mine 2024; 1(13):1-13. in Chinese.

[29] Z.T. Bieniawski, Rock mass classification in rock engineering. Proc. Symposium on Exploration for Rock Engineering, (ed. Z. T. Bieniawski), Rotterdam: A.A. Balkema, 1976:97-106.

[30] Bieniawski ZT. Engineering rock mass classifications. New York: John Wiley & Sons; 1989. p. 251.

[31] Priest SD, Hudson JA. Discontinuity spacings in rock. Int J Rock Mech Min Sci Geomech Abstr 1976; 13(5):135-48.

[32] Li JD, Gao Y, Yang TH, Zhang PH, Deng WX, Liu FY. Effect of water on the rock strength and creep behavior of green mudstone. Geomech Geophys Geo Energy Geo Resour 2023; 9(1):101.

[33] Jiang SH, Papaioannou I, Straub D. Optimization of site-exploration programs for slope-reliability assessment. ASCE-ASME J Risk Uncertainty Eng Syst, Part A: Civ Eng 2020:6.

[34] Fernández A, Sanchidrián JA, Segarra P, Gómez S, Li EM, Navarro R. Rock mass structural recognition from drill monitoring technology in underground mining using discontinuity index and machine learning techniques. Int J Min Sci Technol 2023; 33(5):555-71.

[35] Both C, Dimitrakopoulos R. Integrating geometallurgical ball mill throughput predictions into short-term stochastic production scheduling in mining complexes. Int J Min Sci Technol 2023; 33(2):185-99.