Improving a method for determining the spatial parameters in the mathematical model of a distributed automated information-measuring system for real-time control over the quality of iron ore raw materials

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.343936

Keywords:

Compton effect, albedo, gamma quantum, irradiation, rock, operational control

Abstract

This study investigates processes of irradiating iron ore raw materials by a source of low-energy gamma quanta and registering radiation scattered as a result of the Compton effect, as well as parameters of the irradiation geometry. This work addressed the task of improving the accuracy of existing nuclear-physical methods of control over the quality of iron ore raw materials, which make it possible to promptly determine the material composition of the crushed rock mass.

The results essentially indicate that when using a centrally shifted irradiation geometry, the sensitivity of registration of the integral backscatter flux increases compared to the side and central irradiation geometries. This is attributed to improved visibility conditions of the detector, and the formation of a scattering angle close to the optimal.

The constructed mathematical model has made it possible to identify the main geometric parameters for the system of operational control over the quality of iron ore raw materials. A formula has been derived that connects the basic parameters in the system of operational control over iron content in ore with the use of centrally shifted irradiation geometry.

The studies demonstrated changes in the sensitivity of the registration of the integral backscatter flux when changing the vertical location of the gamma-ray source. The results revealed a maximum sensitivity with a value of 6.08·10-7 at a minimum distance of the radiation source from the single crystal and a distance of 110 mm from the irradiated surface. The value of the correlation coefficient between the model and experimental data is 0.981.

The findings could be practically applied to improve the accuracy of methods for operational control over the content of a usable component in iron-containing ores under industrial conditions at ferrous metallurgy enterprises

Author Biographies

Albert Azaryan, Kryvyi Rih National University

Doctor of Technical Sciences, Professor

Problem-Oriented Scientific Research Laboratory “Operational Control and Quality Management of Mineral Raw Materials”

Dmitriy Shvets, Kryvyi Rih National University

PhD, Associate Professor

Department of Modeling and Software

Andrіі Hrytsenko, Kryvyi Rih National University

PhD, Associate Professor

Department of Modeling and Software

Annait Trachuk, Kryvyi Rih National University

PhD, Associate Professor

Department of Modeling and Software

Oleksii Cherkasov, Kryvyi Rih National University

Senior Researcher

Research Part

Oleksandr Shvydky, Kryvyi Rih National University

Researcher

Problem-Oriented Scientific Research Laboratory “Operational Control and Quality Management of Mineral Raw Materials”

References

  1. Hryhoriev, Y., Lutsenko, S., Joukov, S. (2023). Dominujące uwarunkowania adaptacji kompleksu górniczego w warunkach środowiska dynamicznego. Inżynieria Mineralna, 1 (1). https://doi.org/10.29227/im-2023-01-02
  2. Azaryan, A., Pikilnyak, A., Shvets, D. (2015). Complex automation system of iron ore preparation for beneficiation. Metallurgical and mining industry, 8, 64–66. Available at: https://www.metaljournal.com.ua/assets/Journal/english-edition/MMI_2015_8/011Azaryan.pdf
  3. Azaryan, A., Gritsenko, A., Trachuk, A., Shvets, D. (2018). Development of the method to operatively control quality of iron ore raw materials at open and underground extraction. Eastern-European Journal of Enterprise Technologies, 5 (5 (95)), 13–19. https://doi.org/10.15587/1729-4061.2018.144003
  4. Boisvert, L., Bazin, C., Caron, J., Lavoie, F. (2022). Development and Testing of a Method to Estimate the Mineral Composition of Ore from Chemical Assays with a View toward Geometallurgy: Application to an Iron Ore Concentrator. Geomaterials, 12 (04), 70–92. https://doi.org/10.4236/gm.2022.124006
  5. Satmagan 135. A fast, accurate and reliable instrument for measuring the magnetite content in samples. Available at: https://www.rapiscansystems.com/en/products/satmagan-135
  6. Morkun, V., Morkun, N., Fischerauer, G., Tron, V., Haponenko, A., Bobrov, Y. (2024). Identification of mineralogical ore varieties using ultrasonic measurement results. Mining of Mineral Deposits, 18 (3), 1–8. https://doi.org/10.33271/mining18.03.001
  7. Hryhoriev, Y., Lutsenko, S., Shvets, Y., Kuttybayev, A., Mukhamedyarova, N. (2024). Predictive calculation of blasting quality as a tool for estimation of production cost and investment attractiveness of a mineral deposit development. IOP Conference Series: Earth and Environmental Science, 1415 (1), 012027. https://doi.org/10.1088/1755-1315/1415/1/012027
  8. Porkuian, O., Morkun, V., Morkun, N., Tron, V., Haponenko, I., Davidkovich, A. (2020). Influence of the Magnetic Field on Love Waves Propagation in the Solid Medium. 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), 761–766. https://doi.org/10.1109/elnano50318.2020.9088802
  9. Krapyvnyi, N. S., Azaryan, A. A., Shvydkyi, O. V., Shvets, D. V., Hrytsenko, A. M. (2024). Development of an automated system for preparing mineral raw material samples for discrete analysis. CEUR Workshop Proceedings, 3917, 237–244. Available at: https://cssesw.easyscience.education/cssesw2024/CSSESW2024/paper41.pdf
  10. Zuo, Y.-H., Zhu, J.-H., Shang, P. (2021). Monte Carlo simulation of reflection effects of multi-element materials on gamma rays. Nuclear Science and Techniques, 32 (1). https://doi.org/10.1007/s41365-020-00837-z
  11. Kiran, K. U., Ravindraswami, K., Eshwarappa, K. M., Somashekarappa, H. M. (2016). Albedo factors of 123, 320, 511, 662 and 1115 keV gamma photons in carbon, aluminium, iron and copper. The European Physical Journal Plus, 131 (4). https://doi.org/10.1140/epjp/i2016-16087-5
  12. Turşucu, A. (2023). Seryum ve Bazı Seçilmiş Seryum Bileşiklerinde Gama Radyasyonu Yansıtma Parametreleri. Karadeniz Fen Bilimleri Dergisi, 13 (4), 1242–1250. https://doi.org/10.31466/kfbd.1180268
  13. Qin, R., Li, C., Qin, Z., Zhang, Z., Cai, J. (2025). A Compton scattering background subtraction method of gamma energy spectrum based on Gaussian function convolution. Radiation Physics and Chemistry, 226, 112202. https://doi.org/10.1016/j.radphyschem.2024.112202
  14. Abdelnour, M. R., Liu, J., Hossny, K., Wajid, A. M., Li, W., Liu, Z. (2025). Prompt gamma neutron activation analysis: A review of applications, design, analytics, challenges, and prospects. Radiation Physics and Chemistry, 234, 112693. https://doi.org/10.1016/j.radphyschem.2025.112693
  15. Huang, H., Cai, P., Jia, W., Zhang, Y. (2023). Identification of Pb–Zn ore under the condition of low count rate detection of slim hole based on PGNAA technology. Nuclear Engineering and Technology, 55 (5), 1708–1717. https://doi.org/10.1016/j.net.2023.01.005
  16. Jie, C., Reng-Bo, W., Yan, Z., Wen-bao, J., Chong-gui, Z., Rui, C. et al. (2024). MCNP simulation and experimental study in situ low-grade copper analysis based on PGNAA. Applied Radiation and Isotopes, 206, 111224. https://doi.org/10.1016/j.apradiso.2024.111224
  17. PTC Mathcad Prime. Available at: https://www.mathcad.com/
  18. Microsoft Excel. Available at: https://www.microsoft.com/uk-ua/microsoft-365/excel
  19. Makek, M., Bosnar, D., Pavelić, L. (2019). Scintillator Pixel Detectors for Measurement of Compton Scattering. Condensed Matter, 4 (1), 24. https://doi.org/10.3390/condmat4010024
  20. Kaur, T., Sharma, J., Singh, T. (2020). Experimental measurement of effective atomic numbers and albedo factors for some alloys using the backscattering technique. Applied Radiation and Isotopes, 158, 109065. https://doi.org/10.1016/j.apradiso.2020.109065
  21. Azaryan, A. (2015). Research of influence single crystal thickness NaJ (TL) on the intensity of the integrated flux of scattered gamma radiation. Metallurgical and Mining Industry, 2, 43–46.
  22. Chaddock, R. E. (1925). Principles and methods of statistics. Boston: Houghton Mifflin Company, 471.
  23. Azaryan, A., Gritsenko, A., Trachuk, A., Serebrenikov, V., Shvets, D. (2019). Using the intensity of absorbed gamma radiation to control the content of iron in ore. Eastern-European Journal of Enterprise Technologies, 3 (5 (99)), 29–35. https://doi.org/10.15587/1729-4061.2019.170341
  24. Azarian, A. A., Azarian, V. V., Trachuk, A. A. (2021). Operatyvnyi kontrol ta upravlinnia yakistiu pry rozrobtsi zalizorudnykh rodovyshch. Praha: OKTAN PRINT, 144. https://doi.org/10.46489/oktuj-11
Improving a method for determining the spatial parameters in the mathematical model of a distributed automated information-measuring system for real-time control over the quality of iron ore raw materials

Downloads

Published

2025-12-23

How to Cite

Azaryan, A., Shvets, D., Hrytsenko, A., Trachuk, A., Cherkasov, O., & Shvydky, O. (2025). Improving a method for determining the spatial parameters in the mathematical model of a distributed automated information-measuring system for real-time control over the quality of iron ore raw materials. Eastern-European Journal of Enterprise Technologies, 6(5 (138), 43–53. https://doi.org/10.15587/1729-4061.2025.343936

Issue

Section

Applied physics