A new empirical model for prediction of jumbo drills' penetration rate in underground mines based on the rock mass characteristics

Jumbo drills are widely used in underground mining for tunnel drivages. The drilling rate is significantly influenced by intact rock characteristics and the structural parameters of the rock mass. A non-linear multiple regression model (NLMR) was employed to assess the rate of penetration of jumbo drills. For this purpose, field data, including 737 boreholes, were utilized. This paper developed an empirical model that incorporated the Rock Mass Drillability Index (RDi) to predict the rate of penetration (ROP) of jumbo drills in underground mines. Performance indexes such as R2, RMSE, MAE, MAPE, and VAF were evaluated to gauge the prediction accuracy of the model. The developed model exhibited values of 0.88, 0.22, 0.17, 7.3, and 88.84 for R2, RMSE, MAE, MAPE, and VAF, respectively, when applied to the training data. By considering both intact rock mechanical properties and structural properties of rock masses, the model demonstrated accurate predictions of jumbo drill penetration rates.

Sasan Heydari, Seyed Hadi Hoseinie, Raheb Bagherpour A new empirical model for prediction of jumbo drills' penetration rate in underground mines based on the rock mass characteristics. MIAB. Mining Inf. Anal. Bull. 2024;(5):17-35. DOI: 10.25018/0236_1493_2024_ 5_0_17.

S. Heydari¹, PhD Candidate, e-mail: sasanheydari@mi.iut.ac.ir, ORCID ID: 0000-0001-9140-3486,
S.H. Hoseeinie¹, Associate Professor, e-mail: hadi.hoseinie@iut.ac.ir, ORCID ID: 0000-0002-9767-3644,
R. Bagherpour¹, Professor, e-mail: bagherpour@iut.ac.ir, ORCID ID: 0000-0002-7235-9966,
¹ Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran.

S.H. Hoseeinie, e-mail: hadi.hoseinie@iut.ac.ir.

1. Thuro K. Drillability prediction: geological influences in hard rock drill and blast tunneling. International Journal of Earth Sciences. 1997, vol. 86, pp. 426—438. DOI: 10.1007/s005310050151.

2. Hoseinie S. H., Aghababaei H., Pourrahimian Y. Development of a new classification system for assessing of rock mass drillability index (RDi). International Journal of Rock Mechanics and Mining Sciences. 2008, vol. 45, no. 1, pp. 1—10. DOI: 10.1016/j.ijrmms.2007.04.001.

3. Su O. Evaluation of various mining equipment used for roadway development in coal mines. Archives of Mining Sciences. 2019, vol. 64, no. 4, pp. 797—812. DOI: 10.24425/ams.2019.131067.

4. Akbay A., Altındag R. An investigation of different drill bits and drilling angles in blast hole drilling. Rock Engineering and Rock Mechanics. 2014, pp. 1033—1038. DOI: 10.1201/b16955-178.

5. Darbor M., Faramarzi L., Sharifzadeh M. Performance assessment of rotary drilling using nonlinear multiple regression analysis and multilayer perceptron neural network. Bulletin of Engineering Geology and the Environment. 2017, vol. 78, pp. 1501—1513. DOI: 10.1007/s10064-017-1192-3.

6. Najjarpour M., Jalilfar H., Norouzi-Apourvari S. Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms. A review. Journal of Petroleum Science and Engineering. 2022, vol. 208, pp. 1—18. DOI: 10.1016/j.petrol.2021.109575.

7. Soares C., Gray K. Real-time predictive capabilities of analytical and machine learning rate of penetration (ROP) models. Journal of Petroleum Science and Engineering. 2019, vol. 172, pp. 934— 959 . DOI: 10.1016/j.petrol.2018.08.083.

8. Awuah-Offei K. Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations. Journal of Cleaner Production. 2016, vol. 117, pp. 89—97. DOI: 10.1016/j.jclepro.2016.01.035.

9. Elmgerbi A. M., Ettinger C. P., Tekum P. M., Thonhauser G., Nascimento A. Application of machine learning techniques for real time rate of penetration optimization. Proceedings of the SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, United Arab Emirates, 2021, pp. 20—27. DOI: 10.2118/202184-MS.

10. Zhao S., Wang M., Yi W., Yang D., Tong J. Intelligent classification of surrounding rock of tunnel based on 10 machine learning algorithms. Applied Science. 2022, vol. 12, no. 5, pp. 1—20. DOI: 10.3390/app12052656.

11. Bauer A., Calder P. N. Open pit drilling — factors influencing drilling rates. Proceedings of the 4th Canadian Symposium on Rock Mechanics, Ottawa, 1967, pp. 1—33.

12. Bauer A., Crosby W. A. Drilling application. Section 6.1.2 Surface Mining, 2nd Edition. Society for Mining, Metallurgy and Exploration, Inc. Port City Press, Baltimore, 1990, pp. 524—539.

13. Rabia H. Specific energy as a criterion for drill performance prediction. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. 1982, vol. 19, pp. 39—42. DOI: 10.1016/0148-9062(82)90709-4.

14. Howarth D. F. Experimental study on the relationship between rock texture and mechanical performance. Transactions of the Institution of Mining and Metallurgy. 1986, vol. 95, pp. 41—44.

15. Workman L., Szumanski M. Which blasthole rig? World Mining Equipment, 1997.

16. Tutmez B., Kahraman S., Gunaydin O. Multifactorial fuzzy approach to the sawability classification of building stones. Construction and Building Materials. 2007, vol. 21, no. 8, pp. 1672—1679. DOI: 10.1016/j.conbuildmat.2006.05.023.

17. Ersoy A., Waller M. D. Prediction of drill-bit performance using multivariable linear regression analysis. Transactions of the Institution of Mining and Metallurgy. 1995, vol. 104, pp. 101—114.

18. Kahraman S., Balci C., Yazici S., Bilgin N. Prediction of the penetration rate of rotary blast hole drills using a new drillability index. International Journal of Rock Mechanics and Mining Sciences. 2000, vol. 37, no. 5, pp. 729—743. DOI: 10.1016/S1365-1609(00)00007-1.

19. Kahraman S., Bilgin N., Feridunoglu C. Dominant rock properties affecting the penetration rate of percussive drills. International Journal of Rock Mechanics and Mining Sciences. 2003, vol. 40, pp. 711—723. DOI: 10.1016/S1365-1609(03)00063-7.

20. Kahraman S., Ipek M., Guleryuz U., Gunaydin N. Performance prediction of a jumbo drill in Pozanti—Ankara Motorway Tunnel (Turkey). Tunnelling and Underground Space Technology. 2006, vol. 21, pp. 265—265. DOI: 10.1016/j.tust.2005.12.126.

21. Kahraman S. The prediction of penetration rate for percussive drills from indirect tests using artificial neural networks. Journal of the South African Inst of Mining and Metallurgy. 2016, vol. 116, pp. 793—800. DOI: 10.17159/2411-9717/2016/v116n8a12.

22. Hoseinie S. H., Ataei M., Osanloo M. A new classification system for evaluating rock penetrability. International Journal of Rock Mechanics and Mining Sciences. 2009, vol. 46, pp. 1329—1340. DOI: 10.1016/j.ijrmms.2009.07.002.

23. Ataei M., Kakaei R., Ghavidel M., Saeidi O. Drilling rate prediction of an open pit mine using the rock mass drillability index. International Journal of Rock Mechanics and Mining Sciences. 2015, vol. 73, pp. 130—138. DOI: 10.1016/j.ijrmms.2014.08.006.

24. Munoz H., Taheri A., Chanda E. K. Rock drilling performance evaluation by an energy dissipation based rock brittleness index. Rock Mechanics and Rock Engineering. 2016, vol. 49, pp. 3343— 3355. DOI: 10.1007/s00603-016-0986-0.

25. Su O. Performance evaluation of button bits in coal measure rocks by using multiple regression analyses. Rock Mechanics and Rock Engineering. 2016, vol. 49, pp. 541—55. DOI: 10.1007/s00603015-0749-3.

26. Capik M., Yilmaz A. O., Yasar S. Relationships between the drilling rate index and physicomechanical rock properties. Bulletin of Engineering Geology and the Environment. 2017, vol. 76, pp. 253—261. DOI: 10.1007/s10064-016-0991-2.

27. Shi X., Gong X., Zhang J., Wang J., Zhang H. An efficient approach for real-time prediction of rate of penetration in offshore drilling. Mathematical Problems in Engineering. 2016, vol. 3, pp. 375— 380 DOI: 10.1155/2016/3575380.

28. Hung N. V., Gerbaud L., Souchal R., Urbanczyk C., Fouchard C. Penetration Rate prediction for percussive drilling with rotary in very hard rock. Journal of Science and Technology. 2016, vol. 54, no. 1, pp. 132—149. DOI: 10.15625/0866-708X/54/1/5956.

29. Navarro J., Segarra P., Sanchidrian J. A., Castedo R., Lopez L. M. Assessment of drilling deviations in underground operations. Tunnelling and Underground Space Technology. 2019, vol. 83, pp. 254—264. DOI: 10.1016/j.tust.2018.10.003.

30. Wang M., Zhao S., Tong, J., Tong J., Wang Z., Yao M., Li J., Yi W. Intelligent classification model of surrounding rock of tunnel using drilling and blasting method. Underground Space. 2021, vol. 6, no. 5, pp. 539—550. DOI: 10.1016/j.undsp.2020.10.001.

31. Broch E., Franklin J. A. The point-load strength test. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts. 1972, vol. 9, pp. 669—676. DOI: 10.1016/01489062(72)90030-7.

32. Jimeno C. L., Jimeno E. L., Carcedo F. J. A. Drilling and blasting of rocks. Rotterdam, Balkema, 1995, 389 p.