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ArticleName Prediction of ore flow quality in a potash mine with regard to the ore face-to-shaft haulage logistics
DOI 10.17580/gzh.2022.04.07
ArticleAuthor Gets A. K., Onika S. G., Kologrivko A. A., Shodiev А. N.

Belarusian National Technical University, Minsk, Belarus:

A. K. Gets, Associate Professor, Candidate of Engineering Sciences,
S. G. Onika, Head of Department, Professor, Doctor of Engineering Sciences
A. A. Kologrivko, Dean, Associate Professor, Candidate of Engineering Sciences


Karshi Engineering–Economics Institute, Karshi, Uzbekistan:
А. N. Shodiev, Head of Department, Associate Professor, Doctor of Engineering Sciences


The industrial know-how, analytical estimation and special experimentation proves that reduction in quality variation of the ore feed for processing can essentially raise productivity and improve final product quality at cheapening of the production at the same time. In pota sh production, the ore quality stabilization by the potassium content criterion enables a high economic profit owing to the increased recovery of the useful component, reduced consumption of chemical agents, saving of power and fuel, decreased losses of KCl with tailings, deloading of salt dumps and tailings ponds and, finally, the environmental destressing. Alongside with the technological blending of ore flow (reclaiming rooms, reservoirs, warehouses, etc.), there are potentially much cheaper managerial approaches. The quality control method includes the use of certain data acquisition, transfer and interpretation facilities arranged underground, relevant software support, as well as control units to operate the mine logistics. At the present time, the level of automation at Belaruskali meets all requirements imposed on the quality control of ore flow to outlet shaftAt the Mining Department of the Belarusian National Technical University, the service simulation procedure is developed for mine faces with various heading and cutting machines in specific geological conditions (mineral seam structure, mineral quality) and geotechnical conditions (seam thickness, cutting head width, face advance velocity, mining flowsheet, downtime duration, etc.).

keywords Potassium mine conveyor system, ore flow blending, simulation and mathematical modeling, real-time auditing, statistic processing and prediction

1. Gets A. K., Onika S. G. To the issue of ore quality management on a potash mine. Gornaya mekhanika i mashinostroenie. 2016. No. 1. pp. 27–30.
2. Gets A. K., Kruk Yu. S., Ostapuk M. I., Omsharuk A. S. Computer modelling of the process of dynamic quality management of potash ore at the mine. Gornaya mekhanika i mashinostroenie. 2017. No. 4. pp. 7–13
3. Gets A. K., Onika S. G., Vishnyak B. A., Dolgikh A. S. Concept of digital management in the potash mining industry. Gornyi Zhurnal. 2020. No. 11. pp. 37–40. DOI: 10.17580/gzh.2020.11.04
4. Gets A. K. Arrangement of operational mining control in potash mines of Belaruskali : Thesis of Dissertation … of Candidate of Engineering Sciences. Moscow, 1978. 18 p.
5. Shannon R. E. Sistems Simulation: the art and science. Englewood Cliffs : Prentice-Hall, 1975. 368 p.
6. Schriber T. J. Simulation Using GPSS. New York : John Wiley & Sons, 1974. 534 p.
7. Toluev Yu. I. Simulation modeling of logistic networks. Logistika i upravlenie tsepyami postavok. 2008. No. 2(25). pp. 53–63.
8. Tsvirkun A. D., Akinfiev V. K., Filippov V. A. Simulation modeling in synthesis of structure of complex systems (simulation-and-optimization approach). Moscow : Nauka, 1985. 176 p.
9. Vavilov A. A. (Ed.). Simulation modeling of production systems. Moscow : Mashinostroenie, 1983. 416 p.
10. Shemet S. F., Kologrivko A. A. Mitigation of geoecological impact of underground potash mining. Gornyi Zhurnal. 2015. No. 5. pp. 100–104. DOI: 10.17580/gzh.2015.05.21
11. Venttsel E. S. Analysis of operations : Objectives, principles, methodology. 2nd ed. Moscow : Nauka, 1988. 208 p.
12. Shmelev V. A. Simulation modeling in the context of managerial prediction. Moscow : Laboratoriya knigi, 2010. 55 p.
13. Khakulov V. A., Shapovalov V. A., Ignatov V. N., Ignatov M. V., Karpova Zh. V. et al. Improvement of mining design and control using modern hardware/software intelligence. Moscow : Gornaya kniga, 2020. 76 p.
14. Dmitriev V. G., Verzhanskiy A. P. Statistical methods in transportation in mining. Moscow : Gornaya kniga, 2021. 512 p.
15. Dmitrieva V. V., Sizin P. E. Conveyor belt velocity control as function of random load flow. Moscow : Gornaya kniga, 2020. 72 p.
16. Mustacoglu A. F., Fox G. C. A novel digital information service for federating distributed digital entities. Information Systems. 2016. Vol. 55. pp. 20–36.
17. Anderson N. G. Information as a physical quantity. Information Sciences. 2017. Vol. 415-416. pp. 397–413.
18. Huang L., Balamurali M., Silversides K. L. Machine learning classification of geochemical and geophysical data. Mining Goes Digital : Proceedings of the 39th International Symposium on Application of Computers and Operations Research in the Mineral Industry. London : CRC Press, 2019. pp. 101–105.
19. Feng S., Ding E. Designing top layer in Internet of Things for underground mines. Mining Goes Digital : Proceedings of the 39th International Symposium on Application of Computers and Operations Research in the Mineral Industry. London : CRC Press, 2019. pp. 695–702.

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