Mining of Mineral Deposits

ISSN 2415-3443 (Online)

ISSN 2415-3435 (Print)

Flag Counter

Identification of mineralogical ore varieties using ultrasonic measurement results

Volodymyr Morkun1, Natalia Morkun1, Gerhard Fischerauer1, Vitalii Tron2, Alona Haponenko2, Yevhen Bobrov1

1Bayreuth University, Bayreuth, Germany

2Kryvyi Rih National University, Kryvyi Rih, Ukraine


Min. miner. depos. 2024, 18(3):1-8


https://doi.org/10.33271/mining18.03.001

Full text (PDF)


      ABSTRACT

      Purpose. To improve the measurement and information base of ultrasonic measurements rock characteristics to assess their mineralogical varieties. It is proposed to use a combination of measurement results of the acoustic quality factor of the test sample in relation to longitudinal and transverse ultrasonic waves, as well as the characteristic coefficient based on the dispersion and the average amplitude value of the received signal, for fuzzy identification of mineralogical and technological varieties of iron ore.

      Methods. As elastic waves propagate through the rock mass, they undergo attenuation due to absorption and dissipation of ultrasonic signal energy. The degree of attenuation, as well as the wave propagation velocity, is dependent on the physical-mechanical and chemical-mineralogical properties of the medium through which they travel. In this paper, we analyze a rock characterized by a complex structure comprising ore inclusions and surrounding matrix, each of which differs in its physical-mechanical and chemical-mineralogical properties. In particular, in iron ore samples, the distribution of mineral grains and aggregates exhibits significant heterogeneity in terms of both amount and size.

      Findings. An iterative method of fuzzy identification of mineralogical-technological iron ore varieties, based on the analysis of their properties in vector space of features, allows, by minimizing the sums of weighted distances between the analyzed and reference values of ultrasonic measurement results, to attribute them with a certain degree of belonging to the main technological types of ores mined at the deposit, and define them as magnetite quartzite with a confidence probability of 0.93.

      Originality. As an information base for identification of mineralogical iron ore varieties, the results of measuring the velocity and attenuation of longitudinal and transverse ultrasonic waves of appropriate frequency are used, on the basis of which the acoustic quality factor of the rock sample is calculated, as well as the characteristic parameter S, which is determined by the dispersion and average values of the received ultrasonic signal intensity, which has traveled a certain distance in the studied environment.

      Practical implications. The results of tests and practical approbation of the method for identifying mineralogical iron ore varieties based on the data of ultrasonic well logging testify to its high efficiency, which allows recommending the developed scientific-technical solutions for wide industrial application at mining enterprises.

      Keywords: iron ore, identification, ultrasound, fuzzy clustering


      REFERENCES

  1. Onur, A.H., Bakraç, S., & Karakuş, D. (2012). Ultrasonic waves in mining application. Ultrasonic Waves, 189-210. https://doi.org/10.5772/29565
  2. Morkun, V., Semerikov, S., Hryshchenko, S., & Slovak, K. (2017). Environmental geo-information technologies as a tool of pre-service mining engineer’s training for sustainable development of mining industry. Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, 303-310. https://doi.org/10.31812/0564/730
  3. Bakhorji, A.M. (2010). Laboratory measurements of static and dynamic elastic properties in carbonate. Alberta, Canada: University of Alberta.
  4. Vasconcelos, G., Lourenco, P.B., Alves, C.A.S., & Pamplona, J. (2008). Ultrasonic evaluation of the physical and mechanical properties of granites. Ultrasonics, 48(5), 453-466. https://doi.org/10.1016/j.ultras.2008.03.008
  5. Morkun, V., Fischerauer, G., Morkun, N., Tron, V., & Haponenko, A. (2022). Determining rock varieties on the basis of fuzzy clustering of ultrasonic measurement results. CEUR Workshop Proceedings, 3156, 274-283.
  6. Kahraman, S. (2007) The correlations between the saturated and dry p-wave velocity of rocks. Ultrasonics, 46(4), 341-348. https://doi.org/10.1016/j.ultras.2007.05.003
  7. Morkun, V., Morkun, N., & Pikilnyak, A. (2014). Iron ore flotation process control and optimization using high-energy ultrasound. Metallurgical and Mining Industry, 6(2), 36-42.
  8. Zeng, X., Xiao, Y., Ji, X., & Wang, G. (2021). Mineral identification based on deep learning that combines image and Mohs hardness. Minerals, 11(5), 506. https://doi.org/10.3390/min11050506
  9. Sun, T., Li, H., Wu, K., Chen, F., & Hu, Z. (2020). Data-driven predictive modeling of mineral prospectivity using machine learning and deep learning methods: A case study from southern Jiangxi province, China. Minerals, 10(2), 102. https://doi.org/10.3390/min10020102
  10. Morkun, V., Morkun, N., & Tron, V. (2015). Distributed closed-loop control formation for technological line of iron ore raw materials beneficiation. Metallurgical and Mining Industry, 7(7), 16-19.
  11. Aligholi, S., Lashkaripour, G.R., Khajavi, R., & Razmara, M. (2017). Automatic mineral identification using color tracking. Pattern Recognition, 65, 164-174. https://doi.org/10.1016/j.patcog.2016.12.012
  12. Izadi, H., Sadri, J., & Bayati, M. (2017). An intelligent system for mineral identification in thin sections based on a cascade approach. Computers & Geosciences, 99, 37-49. https://doi.org/10.1016/j.cageo.2016.10.010
  13. Karimpouli, S., Tahmasebi, P., & Saenger, E.H. (2020). Coal cleat/fracture segmentation using convolutional neural networks. Natural Resources Research, 29, 1675-1685. https://doi.org/10.1007/s11053-019-09536-y
  14. Juliani, C., & Ellefmo, S.L. (2019). Prospectivity mapping of mineral deposits in northern Norway using radial basis function neural networks. Minerals, 9, 131. https://doi.org/10.3390/min9020131
  15. Voznesensky, A.S., Kutkin, Ya.O., & Krasilov, M.N. (2015). Interrelation of the acoustic Q-factor and strength in limestone. Journal of Mining Science, 51(1), 23-30. https://doi.org/10.1134/s1062739115010044
  16. Varma, M., Maji, V., & Boominathan, A. (2017). A study on ultrasonic wave propagation across fractures in jointed rocks. Proceedings of 51st US Rock Mechanics / Geomechanics Symposium, 17-483.
  17. Li, M., & Zhao, Y. (2014). Geophysical exploration technology. Amsterdam, Netherland: Elsevier, 449 p. https://doi.org/10.1016/B978-0-12-410436-5.00001-0
  18. Pardo, D., Matuszyk, P., Puzyrev, V., Torres-Verdín, C., Nam, M.J., & Calo, V. (2021). Modeling of resistivity and acoustic borehole logging measurements using finite element methods. Amsterdam, Netherland: Elsevier, 312 p.
  19. Golik, V., Komashchenko, V., Morkun, V., & Zaalishvili, V. (2015). Enhancement of lost ore production efficiency by usage of canopies. Metallurgical and Mining Industry, 7(4), 325-329.
  20. Wellmann, F., & Caumon, G. (2018). 3-D Structural geological models: Concepts, methods, and uncertainties. Advances in Geophysics, 59, 1-121. https://doi.org/10.1016/bs.agph.2018.09.001
  21. Fjer, E., Holt, R., Horsrud, P., Raaen, A., & Risnes, R. (2021). Mechanics of hydraulic fracturing. Developments in Petroleum Science, 72, 555-600. https://doi.org/10.1016/B978-0-12-822195-2.00020-6
  22. Golik, V., Komashchenko, V., Morkun, V., & Irina G. (2015). Impro-ving the effectiveness of explosive breaking on the bade of new methods of borehole charges initiation in quarries. Metallurgical and Mining Industry, 7(7), 383-387.
  23. Fjer, E., Holt, R.M., Horsrud, P., Raaen, A.M., & Risnes, R. (1992). Acoustic wave propagation in rocks. Developments in Petroleum Science, 33, 135-160. https://doi.org/10.1016/S0376-7361(09)70191-8
  24. Wang, H., Toksoz, M.N., & Fehler, M.C. (2020). Borehole acoustic logging – theory and methods. Amsterdam, Netherland: Springer Cham, 317 p. https://doi.org/10.1007/978-3-030-51423-5
  25. Lazarenko, E.K. (1977). Mineralogy of the Kryvyi Rih basin. Kyiv, Ukraine: Naukova Dumka, 543 p.
  26. Spichak, V. (2015). Electromagnetic sounding of the earth’s interior. Amsterdam, Netherland: Elsevier, 441 p. https://doi.org/10.1016/C2014-0-01934-X
  27. Morkun, V., & Morkun, N. (2018). Estimation of the crushed ore particles density in the pulp flow based on the dynamic effects of high-energy ultrasound. Archives of Acoustics, 43(1), 61-67. https://doi.org/10.24425/118080
  28. Morkun, V., Morkun, N., & Pikilniak, A. (2019). The propagation of ultrasonic waves in gas-containing suspensions. Cambridge, United Kingdom: Cambridge Scholars Publishing, 130 p.
  29. Voznesensky, A.S., Kutkin, Ya.O., & Krasilov, M.N. (2015). Relationship of acoustic q-factor with strength properties of limestones. Physical and Technical Problems of Mineral Development, 1(1).
  30. Morkun, V., Morkun, N., & Tron, V. (2015). Distributed control of ore beneficiation interrelated processes under parametric uncertainty. Metallurgical and Mining Industry, 7(8), 18-21.
  31. Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32-57. https://doi.org/10.1080/01969727308546046
  32. Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms. New York, United States: Springer, 272 p. https://doi.org/10.1007/978-1-4757-0450-1
  33. Bataineh, K., Naji, M., & Saqer, M. (2011). A comparison study between various fuzzy clustering algorithms. Journal of Mechanical and Industrial Engineering, 5, 335-343.
  34. Bensaid, A., Hall, L.O., Bezdek, J.C., Clarke, L.P., Silbiger, M.L., Arrington, J.A., & Murtagh, R.F. (1996). Validity-guided (Re) clustering with applications to image segmentation. IEEE Transactions on Fuzzy Systems, 4, 112-123. https://doi.org/10.1109/91.493905
  35. Xie, X.L., & Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8), 841-847. https://doi.org/10.1109/34.85677
  36. Лицензия Creative Commons