Models Development for Prediction of Blast Efficiency and Total Charge in a Typical Quarry
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Abstract
The prediction of blast efficiency is usually achieved by using models; this in turn, gives better and more efficient rock fragmentation. However, the accuracy of the prediction often times relies on the model development validation. In this study, models were developed and compared upon validation for predicting the blast efficiency and total charge required for efficient fragmentation using artificial neural network (ANN). Rock samples were gathered from the study are, and the uniaxial compressive strength (UCS) test was carried out on all the samples based on international standard. The average UCS obtained from the rock samples at the Eminent quarry (EQ) is 153.61 MPa. The dimension of in-situ rock mass considered in the study area is 60 m x 40 m, and the in-situ block sizes obtained vary from 2.02 m2 to 3.20 m2. The average percentage value of F50 obtained from the Split-Desktop image analyses is approximately 72.44 cm. The various results obtained from the UCS, in-situ block size distribution, image analysis of the blasted rocks and the total charge were used to develop the models for the prediction of blast efficiency. The key issue of concern about these models is that they are mostly site specific and the fact that if they perform well in a location does not guarantee the other. Hence, the validation and suitability of these models on the mine site. The blast efficiency prediction using ANN is compared with measured efficiency and the value of coefficient of determination, R2 obtained is 0.9733. The value of the coefficient of determination, R2 obtained from ANN by comparing the prediction of the total charge and the measured total charge is 0.9773. The findings showed that, the proposed ANN based mathematical models are suitable and thus, give better prediction to blasting efficiency and the possible total charge.
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References
MacKenzie, A. S. (1966). The Cost of Explosives, Do You Evaluate it Properly? International Mining Congress Journal, 52(5), 32-41.
MacKenzie, A. S. (1967). Optimum Blasting. 28th Annual Mining Symposium and the 40th Annual Meeting of the Minnesota Section, AIME, 122-138.
Monjezi, M., Hasanipanah, M. & Khandelwal, M. (2013). Evaluation and Prediction of Blast-Induced Ground Vibration at Shur River Dam, Iran, by Artificial Neural Network. Neural Comput. Appl., 22(1), 1637-1643. DOI: https://doi.org/10.1007/s00521-012-0856-y
Abzalov, M. Z., Drobov, S. R., Gorbatenko, O., Vershkov, A. F., Bertoli, O., Renard, D. & Beucher, H. (2014). Resource Estimation of In-situ Leach Uranium Projects. Applied Earth Science, 123(2), 71-85. DOI: https://doi.org/10.1179/1743275814Y.0000000055
Shehu, S. A., Yusuf, K. O. & Hashim, M. H. M. (2020). 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, Taylor and Francis Group, 1-10. DOI: https://doi.org/10.1080/17486025.2020.1720830
Lawal, A. I., Kwon, S. & Kim, G. Y. (2021). Prediction of the Blast-Induced Ground Vibration in Tunnel Blasting Using ANN, Metaheuristic ANNs and Gene Expression Programming. ActaGeophysica 10.1007/s11600-020-00532-y. DOI: https://doi.org/10.1007/s11600-020-00532-y
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. DOI: https://doi.org/10.4314/gm.v21i1.5
Idowu, K. A., Olaleye, B. M. & Saliu, M. A. (2022). Prediction of Blast Efficiency of a Typical Granite Quarry. in 7th UMAT Biennial International Mining and Mineral Conference, Tarkwa, Ghana 1-8.
Kanchibotla, S. S. (2001). Optimum Blasting, Is it Minimum Cost per Broken Rock or Maximum Value per Broken Rock? International Society of Explosive Engineers, Hunter Valley, NSW, 35-40. DOI: https://doi.org/10.1076/frag.7.1.35.14059
Valery, W. & Jankovic, A. (2002). The Future of Comminution JKMRC. University of Queensland, Brisbane, Australia, 34th IOC on Mining and Metallurgy, Bor Lake, Yugoslavia, 131-147.
Ouchterlony, F. (2003). Influence of Blasting on the Size Distribution and Properties of Muckpile Fragments, A State-of-the-Art Review. In. MinFo project P2000-10: Energioptimering vid nedbrytning/Energy optimization in comminution, Lulea University of Technology, 127-132.
Workman, L. & Eloranta, J. (2003). The Effects of Blasting on Crushing and Grinding Efficiency and Energy Consumption. International Proceedings of 29th Conference on Explosives and Blasting Technique, Nashville, TN, 23-57.
Valery, Jnr. W., La Rosa, D. & Jankovic, A. (2004). Mining and Milling Process Integration and Optimisation. Presented at the SME Conference, Denver, 23-25.
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. DOI: https://doi.org/10.33271/mining15.04.034
International Society for Rock Mechanics (1999). Committee on Standardization of Laboratory and Field Tests. Suggested methods: Rock Characterization, Testing, and Monitoring. E. T. Brown, ed., Pergamon Press, London, Great Britain, 21.
Bobo, T. (2010). What’s New with the Digital Image Analysis Software Split-Desktop. Split Engineering, LLC, Tucson, 13-42.
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. DOI: https://doi.org/10.1007/s10706-019-00995-x
Deere, D. U. & Miller, R. P. (1966). Engineering Classification and Index Properties for Intact Rock. Air Force Weapon Laboratory, New Mexico, Tech. Rept., AFNL-TR, 65-116. DOI: https://doi.org/10.21236/AD0646610