Mining of Mineral Deposits

ISSN 2415-3443 (Online)

ISSN 2415-3435 (Print)

Flag Counter

Prediction of efficient fragmentation in a typical crystalline limestone quarry

Kayode A. Idowu1, Godsave N. Dakan1, Danjuma Suleiman1, Zakari Adamu1, Musa G. Sayyadi1, Hosea A. Kutman1

1University of Jos, Jos, Nigeria


Min. miner. depos. 2025, 19(2):75-82


https://doi.org/10.33271/mining19.02.075

Full text (PDF)


      ABSTRACT

      Purpose. Rock fragmentation is the first result of blasting, and is directly related to the costs of mining. It is therefore imperative to predict the best possible way to achieve more economic and efficient fragmentation by blasting.

      Methods. The study was carried out on two pits of Dangote crystalline limestone quarry at Obajana, Kogi State, Nigeria. The average uniaxial compressive strength (UCS) obtained from the rock samples from both pits of the quarry (i.e. OP1 and OP2) was determined in accordance to the international standard. The in-situ block sizes of the rock mass distribution were determined using AutoCAD, while the average percentage values of F50 was obtained from the Split-Desktop analyses. The total charge of explosive was obtained at each location. All these variables were used to develop a model for prediction of effective fragmentation.

      Findings. With the aid of artificial neural network (ANN), the proposed model was found to be suitable for prediction of blast efficiency. Interestingly, the model uses pre-blasting parameter of in-situ block size which can be determined using AutoCAD and post blasting parameter of fragmentation size distribution that can be determined using Split-Desktop.

      Originality. The findings compared the predicted value obtained with the measured efficiency, and the value of coefficient of determination, R2 obtained is 0.9733, which makes it suitable.

      Practical implications. The outcomes of the investigation have significant implications for the practical application. The model was used at the Freedom quarry, and it predicts good fragmentation during blasting. However, it does not consider the timing effect on the mining operation.

      Keywords: crystalline limestone deposit, rock fragmentation, Split-Desktop software, AutoCAD, ANN


      REFERENCES

  1. Saliu, M.A., & Idowu, K.A. (2014). Investigating the effect of fracture on rock fragmentation efficiency – A case study of Kopec Granite Quarries, South Western, Nigeria. Journal of Earth Sciences and Geotechnical Engineering, 4(4), 53-69.
  2. Idowu, K.A., Olaleye, B.M., & Saliu, M.A. (2021). Application of split-desktop image analysis and Kuz-Ram empirical model for evaluation of blast fragmentation efficiency in a typical granite quarry. Ghana Mining Journal, 21(1), 45-52. https://doi.org/10.4314/gm.v21i1.5
  3. Idowu, K.A., Olaleye, B.M., & Saliu, M.A. (2021). Analysis of blasted rocks fragmentation using digital image processing (Case study: Limestone quarry of Obajana Cement Company). Mining of Mineral Deposits, 15(4), 34-42.https://doi.org/10.33271/mining15.04.034
  4. International Society for Rock Mechanics & Committee on standardization of laboratory and field tests. (1999). Suggested methods: Rock characterization, testing, and monitoring. London, United States: Pergamon Press, 21 p.
  5. Engelder, T. (1987). Joints and shear fractures in rocks. New York, United States: Academic Press, 69 p. https://doi.org/10.1016/B978-0-12-066266-1.50007-7
  6. Wines, D.R., & Lilly, P.A. (2002). Measurement and analysis of rock mass discontinuity spacing and frequency in part of the fimiston open pit operation in Kalgoorlie, Western Australia, a case study. International Journal of Rock Mechanics and Mining Sciences, 39, 589-602. https://doi.org/10.1016/S1365-1609(02)00003-5
  7. Idowu, K.A., & Saliu, M.A. (2014). Determination of fracture index and blast efficiency for effective blast operation – A case study of Kopec Granite Quarry South-Western, Nigeria. Proceedings of the International Conference on Science, Technology, Education, Arts, Management and Social Sciences, 1033-1038.
  8. Singh, S.P., Narendrula, R., & Duffy, D. (2005). Influence of blasted muck on the performance of loading equipment. Proceedings of the 3rd EFEE World Conference on Explosives and Blasting, 347-353.
  9. Siddiqui, F., Shah, S., & Behan, M. (2009). Measurement of size distribution of blasted rock using digital image processing. Journal of King Abdulaziz University Engineering Sciences, 20(2), 81-93. https://doi.org/10.4197/Eng.20-2.4
  10. Akande, J.M., & Lawal, A.I. (2013). Optimization of blasting parameters using regression models in Ratcon and NSCE granite quarries, Ibadan, Oyo State, Nigeria. Geomaterials, 3(1), 28-37. https://doi.org/10.4236/gm.2013.31004
  11. Voulgarakis, A.G., Michalakopoulos, T.N., & Panagiotou, G.N. (2016). The minimum response time in rock blasting: A dimensional analysis of full-scale experimental data. Journal of Mining Technology, 125(4), 242-248. https://doi.org/10.1080/14749009.2016.1175163
  12. Kou, S.Q., & Rustan, A. (2015). Fragmentation prediction in rock blasting: A review. Journal of Rock Mechanics and Geotechnical Engineering, 7(4), 431-444.
  13. Idowu, K.A., & Adamu, Z. (2024). Models development for prediction of blast efficiency and total charge in a typical quarry. ABUAD Journal of Engineering Research and Development, 7(2), 69-77. https://doi.org/10.53982/ajerd.2024.0702.07-j
  14. Singh, P.K., Singh, R., & Singh, T.N. (2017). Effect of joint orientation on fragmentation in limestone blasting. Journal of Rock Mechanics and Geotechnical Engineering, 9(3), 531-538.
  15. Shahrin, M.I., Abdullah, R.A., Jeon, S., Jeon, B., & Sa’ari, R. (2019). Numerical simulation of rock fragmentation by blasting using discrete element method and particle blast method. IOP Conference Series: Materials Science and Engineering, 527(1), 012032. https://doi.org/10.1088/1757-899X/527/1/012032
  16. Abzalov, M. (2016). Applied mining geology. Bern, Switzerland: Springer International Publishing, 448 p. https://doi.org/10.1007/978-3-319-39264-6
  17. Khandelwal, M., & Singh, T.N. (2009). Prediction of blast-induced ground vibrations in limestone mines. Journal of Rock Mechanics and Geotechnical Engineering, 1(2), 151-162.
  18. Monjezi, M., Ghafurikalajahi, M., & Bahrami, A. (2011). Prediction of blast-induced ground vibrations in limestone quarries. Journal of Mining Science, 47(3), 341-353.
  19. Kuznetsov, V.M. (1973). The mean diameter of fragments formed by blasting. Journal of Mining Science, 9(3), 144-148. https://doi.org/10.1007/BF02506177
  20. Cunningham, C.V.B. (1987). Fragmentation estimation and the Kuz-Ram model. Journal of Mining Science, 23(3), 241-254.
  21. Aladejare, A.E., Idowu, K.A., & Ozoji, T.M. (2024). Reliability of Monte Carlo simulation approach for estimating uniaxial compressive strength of intact rock. Earth Science Informatics, 17, 2043-2053. https://doi.org/10.1007/s12145-024-01262-1
  22. Miao, Y., Zhang, Y., Wu, D., Li, K., Yan, X., & Lin, J. (2021). Rock fragmentation size distribution prediction and blasting parameter optimization based on the muck-pile model. Mining, Metallurgy & Exploration, 38, 1071-1080. https://doi.org/10.1007/s42461-021-00384-0
  23. Miao, S., Konicek, P., Pan, P. Z., & Mitri, H. (2022). Numerical modelling of destress blasting – A state-of-the-art review. Journal of Sustainable Mining, 21(4), 278-297. https://doi.org/10.46873/2300-3960.1366
  24. Eloranta, J. (2014). Non-ideal blasting for ideal grinding – Part two. International Journal of Explosives Engineering, 31, 26-31.
  25. Lawal, A.I., Olajuyi, S.I., Kwon, S., Aladejare, A.E., & Edo, T.M. (2021). Prediction of blast-induced ground vibration using GPR and blast-design parameters optimization based on novel grey-wolf optimization algorithm. Acta Geophysica, 69, 1313-1324. https://doi.org/10.1007/s11600-021-00607-4
  26. International Society for Rock Mechanics. (1989). Suggested methods: Rock characterization testing and monitoring. Oxford, United States: Pergamon Press, 221 p.
  27. American Society for Testing and Materials. (2014). Standard test methods for compressive strength and elastic moduli of intact rock core specimens under varying states of stress and temperatures. West Conshohocken, United States: ASTM International, D7012-14.
  28. Zhou, D. (2017). Theory and technology of rock excavation for civil engineering. Singapore, Singapore: Metallurgical Industry Press and Springer Science Plus Business Media, 699 p.
  29. George, O. (2006). Just enough AutoCAD. London, United Kingdom: SUBEX Publisher San Francisco, 379 p.
  30. Bobo, T. (2010). What’s new with the digital image analysis software Split-Desktop. Tucson, United States: LLC Split Engineering.
  31. Deere, D.U., & Miller, R.P. (1966). Engineering classification and index properties for intact rock. Illinois, United states: University of Illinois Urbana-Champaign, Department of Civil Engineering. https://doi.org/10.21236/AD0646610
  32. Hassan, N.F., Jimoh, O.A., Shehu, S.A., & Hareyan, Z. (2019). The effect of mineralogical composition on strength and drillability of granitic rocks in Hulu Langat, Selangor Malaysia. Geotechnics and Geological Engineering, 37, 5499-5505. https://doi.org/10.1007/s10706-019-00995-x

    33. Лицензия Creative Commons

Copyright © 2025 Dnipro University of Technology

Underground Mining Department

All Rights Reserved.

Journal homepage Authors