Fragmentation Analysis of Blasted Rock using WipFrag Image Analysis Software

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Authors

  • Department of Mining Engineering, University of Engineering and Technology, Peshawar ,PK ORCID logo http://orcid.org/0000-0003-1425-4741
  • National Center of Artificial Intelligence NCAI, University of Engineering and Technology, Peshawar ,PK

DOI:

https://doi.org/10.18311/jmmf/2022/28875

Keywords:

Blasting, Fragmentation Analysis, WipFrag Software

Abstract

Blasting is an essential and the very first activity of hard rock mining. It is considered the cheapest source of energy for loosening/extracting hard rock. Improper planning and design can make basting a costly operation. Furthermore, as downstream processes are affected by properties of muckpile a blast should be designed properly to yield the desired muckpile and fragmentation. Among fragmentation and muckpile, fragmentation is the most important, and is the main parameter used to evaluate the efficiency of a blast. This paper analyzes the use of WipFrag software to evaluate the fragment size distribution of blasting with current blasting parameters of Cherat Cement quarry.

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Published

2022-07-22

How to Cite

Amin, I., & Salman, S. (2022). Fragmentation Analysis of Blasted Rock using WipFrag Image Analysis Software. Journal of Mines, Metals and Fuels, 70(5), 263–267. https://doi.org/10.18311/jmmf/2022/28875

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References

Bhatawdekar, R.M., Edy, M.T., & Danial, J.A. (2019). Building information model for drilling and blasting for tropically weathered rock. Journal of Mines, Metals and Fuels, 67(11): 494–500.

Bhatawdekar, R.M., Armaghani, D.J., & Azizi, A. Review of Empirical and Intelligent Techniques for Evaluating Rock Fragmentation Induced by Blasting. Environmental Issues of Blasting. 2021, pp. 21–39. https://doi.org/10.1007/978-981- 16-8237-7_2 DOI: https://doi.org/10.1007/978-981-16-8237-7_2

Bhatawdekar, R.M., Mohamad, E.T., Singh, T.N., Pathak, P., & Armaghani, D.J. (2021). Rock mass classification for the assessment of blastability in tropically weathered limestones. Proceedings of the International Conference on Innovations for Sustainable and Responsible Mining, pp. 13–44. https://doi.org/10.1007/978-3-030-60839-2_2 DOI: https://doi.org/10.1007/978-3-030-60839-2_2

Olofsson, S.O. (1990). Applied explosives technology for construction and mining, 2nd ed. APPLEX, P.O. Box 71, S-640 43 ARLA, Sweden, 1990.

Tiile, RN. Artificial neural network approach to predict blast—induced ground vibration, airblast and rock fragmentation. Missouri University of Science and Technology, 2016.

Kazmi, A.H. & Jan, M.Q. Geology and tectonics of Pakistan. Karachi, Pakistan: Graphic Publishers, 1997.

Armaghani, D.J. (1997). Artificial intelligence techniques for prediction of blasting performance. Conference: M Sand Seminar on sustainabilityAt: Hyderbad, India, 81310.

Shi, X.Z., Zhou, J., Wu, B.B., Huang, D., & Wei, W. (2012). Support vector machines approach to mean particle size of rock fragmentation due to bench blasting prediction, Transactions of Nonferrous Metals Society of China. 22(2): 432–441. https://doi.org/10.1016/S1003-6326(11)61195-3 DOI: https://doi.org/10.1016/S1003-6326(11)61195-3

Cunningham, C.V.B. (1983). The Kuz–Ram model for prediction of fragmentation from blasting. Proceedings of 1st International Symposium on Rock Fragmentation by Blasting, pp. 439–454.

Kuznetsov, V.M. (1973). The mean diameter of the fragments formed by blasting rock. Soviet Mining Science, 9(2): 144–148. https://doi.org/10.1007/BF02506177 DOI: https://doi.org/10.1007/BF02506177

Rosin, P. (2021). The laws governing the fineness of powdered coal. Journal of the Institute of Fuel, 7: 29–36.

Nefis, M., & Talhi, K. A model study to measure fragmentation by blasting. Journal of Mining Science, 23: 91–104.

Shehu, S.A., Yusuf, K.O., & Hashim, M.H.M. Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating. Geomechanics and Geoengineering, 6025.

Siddiqui, F., Shah, S., & Behan, M. (2009). Measurement of size distribution of blasted rock using digital image processing ???????? ???????? ?????? ??????? ??????? ???? ?????? ??????? ????? , Journal of King Saud University – Science, 20(2): 81–93. https://doi.org/10.4197/Eng.20-2.4 DOI: https://doi.org/10.4197/Eng.20-2.4

de Souza, J.C., da Silva, A.C.S., & Rocha, S.S. (2018), Analysis of blasting rocks prediction and rock fragmentation results using split-desktop software, Tecnologia em Metalurgia, Materiais e Mineração, 15(1): 22–30. https:// doi.org/10.4322/2176-1523.1234 DOI: https://doi.org/10.4322/2176-1523.1234

Lawal, A.I. (2021). A new modification to the Kuz-Ram model using the fragment size predicted by image analysis, International Journal of Rock Mechanics and Mining Sciences, 138: 104595. https://doi.org/10.1016/j.ijrmms.2020.104595 DOI: https://doi.org/10.1016/j.ijrmms.2020.104595

Babaeian, M., Ataei, M., Sereshki, F., Sotoudeh, F., & Mohammadi, S. (2019). A new framework for evaluation of rock fragmentation in open pit mines, Journal of Rock Mechanics and Geotechnical Engineering, 11(2): 325–336. DOI: https://doi.org/10.1016/j.jrmge.2018.11.006

Palangio, T.C. (1985). A new tool for blast evaluation. 11th Annual Symposium on Explosives and Blasting Research, Nashville, TN, pp. 269–285.

Palangio, T.C., John, A.F., & Norbert, H.M. (1995). WipFrag — A breakthrough in fragmentation measurement, 6th High-Tech Seminar on State of the Art Blasting Technology, Instrumentation and Explosives, pp. 943–971.

Maerz, N.H., Palangio, T.C., & John, A.F. (1996). WipFrag image based granulometry system, Proceedings of the FRAGBLAST 5 Workshop on Measurement of Blast Fragmentation, pp. 91–99. https://doi. org/10.1201/9780203747919-15 DOI: https://doi.org/10.1201/9780203747919-15