Articles

Justification of the criterion for optimal control of the self-grinding process of ores in drum mills

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №4 2024

Last Updated on 28 August 2024

Published on 30 November -0001

Hits: 2515

Authors:

I.V.Novytskyi, orcid.org/0000-0002-8780-6589, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Y.O.Shevchenko*, orcid.org/0000-0002-3895-3937, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2024, (4): 061 - 066

https://doi.org/10.33271/nvngu/2024-4/061

Abstract:

Purpose. Justification of the criterion for automatic optimization of the ore grinding process in self-grinding drum mills by compiling and researching mathematical models of material movement inside the drum of a rotating mill. 

Methodology. Methods of mathematical modeling of the internal mechanics of drum mills in combination with experimental studies of the spectral density of the variable component of the active power consumed by the drive motor of the mills are used. 

Findings. As a result of the simulation, it was established that the loose material placed on the inner surface of the rotating drum undergoes periodic oscillations under certain conditions. The parameters of these oscillations depend on the radius of the drum, the amount of material and the coefficient of friction. It is theoretically justified that in the case of load fluctuations, the work of friction forces and, therefore, the process of material destruction intensifies. The theoretically obtained conclusions are confirmed by the results of experimental studies of the active power signal spectrum of the drive motor of the mills in the characteristic low-frequency range. It is found that the most intense fluctuations occur at a certain degree of filling of the drum of the mill in the working range of fillings and their intensity correlates with the productivity of the mill according to the newly created finished class. 

Originality. The mechanism of occurrence of fluctuations in the ore load of drum mills is revealed and the connection of this phenomenon with indicators of the technological efficiency of the grinding process is substantiated.  

Practical value. It is advisable to use the intensity of fluctuations of the ore load as a criterion for automatic control of mill loading, since this parameter characterizes the technological efficiency of the mill and can be measured quickly. Using the intensity of ore loading fluctuations as a control criterion allows implementing a search system for extreme control of mill loading. 

Keywords: performance, drum mill, oscillations, ore load, self-crushing, optimization, mathematical model

References.

1. Sokur, M. I., Biletskyi, V. S., Vidmid, I. O., & Robota, E. M. (2020). Ore preparation (crushing, grinding, classification): monograph. ISBN 978-617-639-272-9.

2. Maruta, O. N., & Butnyk, A. M. (2003). Making rational economic decisions in gaming, risky and uncertain situations. Kharkiv: PH “Ingek”, 167-168. ISBN 966-8327-93-4.

3. Novytskyi, I. V., & Us, S. A. (2017). Modern theory of healing: textbook for universities. National Mining University. ISBN 978-966-350-661-6.

4. Sokur, V., Biletskyy, L., Sokur, D., & Bozyk, I. (2016). Investigation of the process of crushing solid materials in the centrifugal disintegrators. Eastern-European Journal of Enterprise Technologies. 3/7(81), 34-40.

5. Novytskyi, I., Sliesariev, V., & Shevchenko, Y. (2022). Self-adjusting filling control system for self-grinding drum mills. Collection of research papers of the National Mining University, 71, 203-210.
https://doi.org/10.33271/crpnmu/71.203.

6. Pageau, J., Pouliot, M., Bouchard, J., & Poulin, É. (2023). A misconception in regulatory control of secondary grinding circuits. IFAC-PapersOnLine, 56(2), 2689-2694. https://doi.org/10.1016/j.ifacol.2023.10.1362.

7. Zuñiga, J. M., & Mantari, J. L. (2017). A computational methodology to calculate the required power in disc crushers. Original Research Article. Journal of Computational Design and Engineering, 4(1), 14-20. https://doi.org/10.1016/j.jcde.2016.09.003.

8. Akande, S., Adebayo, B., & Akande, J. M. (2013). Comparative Analysis of Grindability of Iron ore and Granite. Journal of Mining World Express, 2(3), 55-62.

9. Silva, M., & Casali, A. (2015). Modelling SAG milling power and specific energy consumption including the feed percentage of intermediate size particles. Minerals Engineering, 70, 156-161. https://doi.org/10.1016/j.mineng.2014.09.013.

10. Jankovic, A., Dundar, H., Mehta, R., & Jankovic, A. (2010). Relationships between comminution energy and product size for a magnetite ore. The Journal of the Southern African Institute of Mining and Metallurgy, 110, 141-146. Retrieved from https://www.scielo.org.za/pdf/jsaimm/v110n3/07.pdf.

11. Morrell, S. (2009). Predicting the overall specific energy requirement of crushing, high pressure grinding roll and tumbling mill circuits. Minerals Engineering, 22(6), 544-549. https://doi.org/10.1016/j.mineng.2009.01.005.

12. Ting, D., Shiliang, Y., & Shuai, W. (2024). Super-quadric DEM study of cylindrical particle behaviors in a rotating drum. Powder Technology, 437. https://doi.org/10.1016/j.powtec.2024.119511.

13. Shevchenko, Y. O., & Novytskyi, I. V. (2012). Adaptive control system for the coarse crushing process. Mining electromechanics and automation, 88, 10-105.

14. Novytskyi, I. V., & Shevchenko, Y. O. (2014). Adaptive loading control system for autogenous drum mills. Collection of research papers of the National Mining University, 44, 103-109.

15. 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.

16. Monov, V., Sokolov, B., & Stoenchev, S. (2012). Grinding in Ball Mills: Modeling and Process Control. The Journal of Institute of Information and Communication Technologies of Bulgarian Academy of Sciences, 12(2). https://doi.org/10.2478/cait-2012-0012.

17. Tavares, L. M. (2017). A Review of Advanced Ball Mill Modelling. KONA Powder and Particle Journal, 34, 106-124. http://doi.org/10.14356/kona.2017015.

18. Dubé, O., Alizadeh, E., Chaouki, J., & Bertrand, F. (2013). Dynamics of non-spherical particles in a rotating drum. Chemical Engineering Science, 101, 486-502. https://doi.org/10.1016/j.ces.2013.07.011.

19. Cunkui, Huang, & Masami, Nakagawa (2023). Effects of rotation axis on mixing behavior of dissimilar particles in rotating drums. Powder Technology, 428. https://doi.org/10.1016/j.powtec.2023.118868.

20. Vu, D. Ch., Amarsid, L., Delenne, J.-Y., Richefeu, V., & Radjai, F. (2024). Rheology and scaling behavior of polyhedral particle flows in rotating drums. Powder Technology, 434. https://doi.org/10.1016/j.powtec.2023.119338.

21. Kumar, S., Khatoon, S., Parashar, Sh., Dubey, P., Yogi, J., & Anand, A. (2023). Effect of aspect ratio of ellipsoidal particles on segregation of a binary mixture in a rotating drum. Powder Technology, 427. https://doi.org/10.1016/j.powtec.2023.118682.

22. Tomaru, T., Miyamoto, K., Amemoto, H., & Akaboshi, K. (2010). The Characteristics and Self-Stabilizing Control of the Grinding Mill Process. IFAC Proceedings Volumes, 20(8), 85-90. https://doi.org/10.1016/S1474-6670(17)59075-8.

23. Mariuta, A. N. (2001). Theory of modeling vibrations of working bodies of mechanisms and its applications. Dnepropetrovsk: National Mining University. ISBN 5-86400-001-9.

• < Prev
• Next >