Abstract
The rock fragmentation is a generic term used to describe the size distribution of blasted material. Several controllable parameters as well as rock properties themselves influence fragmentation. Even though there is no method or equation that gives an exact prediction, during the past few years, numerous investigators have developed models and techniques to computerize simulation. An effective method to assess fragmentation presently is to acquire digital images of rock fragments and to process these images using digital image processing techniques. In the case of post-blast fragmentation, this is the only practical method to estimate fragmentation since screening is impractical on a large scale. The aim of this paper is to develop a model to predict rock fragmentation after blasting. For that purpose, GoldSize software was used to determine the size of fragmentation by capturing images of fragmented rock in muck piles. The resulting size distribution data was then compared with the results obtained from some prediction models such as Larsson, Kuz-Ram, and Kuznetsov. This study shows that the Kuz-Ram model has the most accurate results and is appropriate, but the confidence of the model decreases when changing the rock type. To increase the accuracy of the results, the model was modified by determination of a confidence index. The results of model verification show that the modified Kuz-Ram model can predict rock fragmentation with an accuracy of 80%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Singh, P.K., Roy, M.P., Paswan, R.K., Sarim, M.D., Kumar, S., Ranjan, J.R.: Rock fragmentation control in open cast blasting. J. Rock Mech. Rock Eng. 8, 225–237 (2016)
Osanloo, M., Hekmat. A.: Prediction of shovel productivity in the Gol-e-Gohar iron mine. J. Min. Sci. 41(2), 177–184 (2005)
Soofastaei, A., Aminossadati, S.M., Kizil, M.S., Knights, P.: A comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation. Int. J. Min. Sci. Technol. 26(6), 995–1001 (2016)
Ouchterlony, F.: The Swebrec© function: linking fragmentaton by blasting and crushing. Min. Technol. 114(1), 29–44 (2005)
Franklin, J.A., Kemeny, J.M., Girdner K.K.: Evolution of measuring systems: a review. In: Proceedings of the Fragblast-5 Workshop on Measurement of Blast Fragmentation, A.A. Balkema, Montreal, Quebec, Canada, pp. 47–52 (1996)
Higgins, M., BoBo, T., Girdner, K., Kemeny, J., Seppala, V.: Integrated software tools and methodology for optimization of blast fragmentation. In: Proceedings of the Twenty- Fifth Annual Conference on Explosives and Blasting Technique, Nashville, Tennessee, USA, 2, pp. 355–368 (1999)
Siddiqui, F., Shah, S., Behan, M.: Measurement of size distribution of blasted rock using digital image processing. J. King Abdulaziz Univ. Eng. Sci. 20(2), 81–93 (2009)
Kemeny J., Girdner, K., BoBo, T.: New advances in digital image analysis software to quantify the size distribution of fragmented rock. In MINNBLAST, pp. 27–43 (1999)
Cunningham, C.V.B.: The Kuz-Ram model for prediction of fragmentation from blasting. In: Proceedings of the First International Symposium on Rock Fragmentation by Blasting, Lulea, Sweden, pp. 439–454 (1983)
Kuznetsov, V.M.: The mean diameter of the fragments formed by blasting rock. Sov. Min. Sci. 9, 144–148 (1973)
Jimeno, L., Carcedo, F.: Drilling and Blasting of Rocks. A.A. Balkema, Rotterdam, Netherlands (1995)
Liu, Q.: Modification of the Kuz-Ram model for underground hard rock mine. In: Proceedings of 8th International Symposium of Rock Fragmentation by Blasting - FRAGBLAST Santiago, Chile, 8, pp. 185–192 (2006)
Hudaverdi, T., Kulatilake, P.H., Kuzu, C.: Prediction of blast fragmentation using multivariate analysis procedures. Int. J. Numer. Anal. Meth. Geomech. 35(12), 1318–1333 (2010)
Kulatilake, P.H., Hudaverdi, T., Wu, Q.: New Prediction models for mean particle size in rock blast fragmentation. Geotech. Geol. Eng. 30(3), 665–684 (2012)
Abbas Aghajani Bazzazi, E.I., Asadi, A.: Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech. Rock Eng. 47(2), 799–807 (2013)
Mario, A., Ficarazzo, F.: Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz-Ram model. Comput. Geosci. 32(3), 352–359 (2006)
Lilly, P.A.: An empirical method for assessing rock mass blastability. In: Large Open Pit Mining Conference, pp. 41–44. The Auslmm/lE Aust. Newman Combined Group (1986)
Cunningham, C.V.B.: Fragmentation estimations and the Kuz-Ram model—four years. In: Proceedings of the Second International Symposium on Rock Fragmentation by Blasting, Keystone, CO, pp. 475–487 (1987)
Chung, S.H., Katsabanis, P.D.: An integrated approach for estimation of fragmentation. In: Proceedings of 6th International Symposium of Rock Fragmentation by Blasting - FRAGBLAST Johannesburg, South Africa: South African Institute of Mining and metallurgy, 6, pp. 231–219 (2001)
Onederra, I., Riihioja, K.: An alternative approach to determine the uniformity index of Rosin-Rammler based fragmentation models. In: 8th International Symposium of Rock Fragmentation by Blasting-FRAGBLAST, Santiago 8, pp. 193–199 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Hekmat, A., Munoz, S., Gomez, R. (2019). Prediction of Rock Fragmentation Based on a Modified Kuz-Ram Model. In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds) Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection - MPES 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-99220-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-99220-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99219-8
Online ISBN: 978-3-319-99220-4
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)