Soyuztsvetmetavtomatika JSC, Moscow, Russia:

A. V. Kuzyakov, Senior Researcher, e-mail: 31@sсma.ru

Kola Mining and Metallurgical Company, Monchegorsk, Russia:

V. D. Zhidovetskiy, Principal Specialist, Automation Department, Candidate of Technical Sciences

Saint Petersburg Mining University, Saint Petersburg, Russia:

A. A. Kulchitskiy, Head of the Department of Process and Plant Automation, Associate Professor, Doctor of Technical Sciences, e-mail: Kulchitskiy_AA@pers.spmi.ru

Saint Petersburg State Institute of Technology (Technical University), Saint Petersburg, Russia:

L. A. Rusinov, Head of the Department of Chemical Process Automation, Professor, Doctor of Technical Sciences, e-mail: lrusinov@yandex.ru

This paper considers some examples of problem solving related to optimum automatic process control in nickel production implemented with the help of control unit VAZM-2U developed by Soyuztsvetmetavtomatika. It is noted that direct measurement of key parameters is not always possible. That’s why indirect parameters were used for object status evaluation, which have high-frequency interference and a time lag. Thus, special adaptive search algorithms were used for solving control problems. Such algorithms analyze process parameter trends. The paper considers the case study of an automatic control system that controls the water/converter matte ratio at the feed to the mill. The authors demonstrate how one can use the –45 μm output trend analysis to achieve the maximum possible output. This enables to achieve the best converter matte flotation performance. Another example would be the problem of automatic control over the size of nickel concentrate during fluidized bed roasting. Since there are no devices that could measure the size of material directly in the furnace, the paper demonstrates how using indirect data about the relationship between the particle size and the air pressure fluctuations in the air boxes underneath the furnace bottom, expressed as a regression equation of the relationship between the root-mean-square deviation of pressure fluctuations and the equivalent particle diameter, one can have a continuous analysis of the material size in the furnace. This enables to develop an automatic control system to control the coarsening of the fluidized bed particles.

1. Litvinenko V. S., Petrov E. I., Vasilevskaya D. V., Yakovenko A. V. et al. Analyzing the role of the state in the mineral resources management. Journal of Mining Institute. 2023. Vol. 259. pp. 95–111. DOI: 10.31897/PMI.2022.100
2. Cabascango V. E. Q., Bazhin V. Y., Martynov S. A., Pardo F. R. O. Automatic control system for thermal state of reverberatory furnaces in production of nickel alloys. Metallurgist. 2022. Vol. 66. pp. 104–116. DOI: 10.1007/S11015-022-01304-3.
3. Martynov S. A., Masko O. N., Fedorov S. N. Innovative ore-thermal furnace control systems. Tsvetnye Metally. 2022. No. 4. pp. 87–94. DOI: 10.17580/tsm.2022.04.11
4. Nguen Kh. Kh., Bazhin V. Yu. Optimization of copper electrorefining control system with the help of a digital twin while dendritic sediment is being formed. Metallurg. 2023. No. 1. pp. 49–56. DOI: 10.52351/00260827_2023_01_49
5. Chadeev V. M., Aristova N. I. Control of industrial automation. Tenth International Conference Management of Large-Scale System Development (MLSD). 2017. DOI: 10.1109/MLSD.2017.8109604
6. Buturuga A., Stoichescu D., Constantinescu R. Universal system for automation of small tasks. International Symposium on Fundamentals of Electrical Engineering (ISFEE). Conference Paper. 2016. DOI: 10.1109/ISFEE.2016.7803157
7. Fang Yu, Weijin Zhuang, Mingyang Sun. Research and application of operating monitoring and evaluation for dispatching automation and control system. IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Conference Paper. 2016. DOI: 10.1109/IMCEC.2016.7867495
8. Hodouin D. Methods for automatic control, observation, and optimization in mineral processing plants. Journal of Process Control. 2011. Vol. 21, Iss. 2. DOI: 10.1016/j.jprocont.2010.10.016
9. Patrick D. R., Fardo S. W. Industrial process control systems. in industrial process control systems. New York, 2021. 476 p. DOI: 10.1201/9781003151531
10. Saracin C. G., Tunsoiu R. A. Industrial process monitoring and control system. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science. 2022. Vol. 84, Iss. 1.
11. Boikov A., Payor V. The Present issues of control automation for levitation metal melting. Symmetry. 2022. Vol. 14. DOI: 10.3390/SYM14101968
12. Pshenin V., Liagova A., Razin A., Skorobogatov A. et al. Robot crawler for surveying pipelines and metal structures of complex spatial configuration. Infrastructures. 2022. Vol. 7. DOI: 10.3390/INFRASTRUCTURES7060075
13. Shestakov A. K., Sadykov R. M., Petrov P. A. Multifunctional crust breaker for automatic alumina feeding system of aluminum reduction cell. E3S Web of Conferences. 2021. Vol. 266. DOI: 10.1051/e3sconf/ 202126609002
14. Feng L., Yang F., Zhang W., Tian H. Model predictive control of duplex inlet and outlet ball mill system based on parameter adaptive particle swarm optimization. Mathematical Problems in Engineering. 2019. DOI: 10.1155/2019/6812754
15. Vasilieva N. V., Boykov A. V., Erokhina O. O., Trifonov A. Yu. Automatic digitalization of pie charts. Journal of Mining Institute. 2021. Vol. 247. pp. 82–87. DOI: 10.31897/PMI.2021.1.9
16. Shestakov A. K., Petrov P. A., Nikolaev M. Y. Automatic system for detecting visible emissions in a potroom of aluminum plant based on technical vision and a neural network. Metallurgist. 2023. Vol. 66. pp. 1308–1319. DOI: 10.1007/s11015-023-01445-z
17. Beloglazov I. I., Sabinin D. S., Nikolaev M. Yu. Modelling of the disintegration process in ball mills with the help of discrete element method. Gornyy infor matsionno-analiticheskiy byulleten. 2022. No. 6–2. pp. 268–282. DOI: 10.25018/0236_1493_2022_62_0_268
18. Fedorova E., Pupysheva E., Morgunov V. Modelling of red-mud particlesolid distribution in the feeder cup of a thickener using the combined CFDDPM approach. Symmetry. 2022. Vol. 14. DOI: 10.3390/SYM14112314
19. Rueda-Escobedo J. G., Fridman E., Schiffer J. Data-driven control for linear discrete-time delay systems. IEEE Transactions on Automatic Control. 2021, 14 July. pp. 3321–3336.
20. Baldivieso P. R., Paul Anthony Trodden. Model predictive control of linear systems with preview information: feasibility, stability, and inherent robustness. IEEE Transactions on Automatic Control. 2018, 09 December. pp. 3831–3838.
21. Juneja P. K., Sunori S. K., Sharma A., Sharma A. et al. A review on control system applications in industrial processes. IOP Conference Series: Materials Science and Engineering. 2021. Vol. 1022. DOI: 10.1088/1757-899X/1022/1/012010
22. Vasilieva N. V., Fedorova E. R. Process control quality analysis. Tsvetnye Metally. 2020. No. 10. pp. 70–76. DOI: 10.17580/tsm.2020.10.10
23. Kalashnikova T., Koshkalda I., Trehub O. Mathematical methods of data processing in formation and evaluation of sectoral structure in agricultural enterprises. Global Journal of Environmental Science and Management. 2019. Vol. 5. DOI: 10.22034/gjesm.2019.SI.10
24. Kuzyakov A. V., Zhidovetskiy V. D. Application of control units VAZM-2U in ore grinding. Tsvetnye Metally. 2021. No. 3. pp. 27–31. DOI: 10.17580/tsm.2021.03.02
25. Aryskin A., Grigorev A., Khelemendik R., Petrakov M. et al. System for monitoring control in industrial technological processes. Annals of DAAAM and Proceedings of the International DAAAM Symposium. 2020. Vol. 31, Iss. 1. pp. 644–649. DOI : 10.2507/31st.daaam.proceedings.089
26. Asbjornsson G., Tavares L. M., Mainza A., Yahyaei M. Different perspectives of dynamics in comminution processes. Minerals Engineering. 2022. Vol. 176. pp. 1–9. DOI: 10.1016/j.mineng.2021.107326
27. le Roux J. D., Steinboeck A., Kugi A., Craig I. K. Steady-state and dynamic simulation of a grinding mill using grind curves. Minerals Engineering. 2020. Vol. 152. pp. 1–21. DOI: 10.1016/j.mineng.2020.106208
28. Sokolov I. V., Shapirovskiy M. R., Kuzyakov A. V. Experience of technological processes optimal control (milling complexes) automated systems creation. Tsvetnye Metally. 2015. No. 9. pp. 53–57. DOI: 10.17580/tsm.2015.09.08
29. Salikhov Z. G., Arunyants G. G., Rutkovskiy A. L. Optimum control systems for complex processing lines. Moscow : Teploenergetik, 2004. 496 p.
30. Zhidovetskiy V. D., Kuzyakov A. V. Automatic control system for optimum control over converter matte grinding process: development and implementation. Tsvetnye Metally. 2020. No. 4. pp. 13–18. DOI: 10.17580/tsm.2020.04.01
31. Joon-Young Choi, Krstic M., Ariyur K. B., Lee J. S. Extremum seeking control for discrete-time systems. IEEE Transactions on Automatic Control. 2022, February. pp. 318–323.
32. Methods of classical and modern automatic control theories. Textbook in 5 volumes; 2nd revised edition. Vol. 2: Statistical dynamics and identification of automatic control systems. Ed. by K. A. Pupkov, N. D. Egupov. Moscow : Izdatelstvo MGTU im. N. E. Baumana, 2004. 640 p.
33. Methods of classical and modern automatic control theories. Textbook in 5 volumes; 2^nd revised edition. Vol. 5: Methods of modern automatic control theory. Ed. by K. A. Pupkov, N. D. Egupov. Moscow : Izdatelstvo MGTU im. N. E. Baumana, 2004. 784 p.
34. Bo Pang, Zhong-Ping Jiang. Adaptive optimal control of linear periodic systems: an off-policy value iteration approach. IEEE Transactions on Automatic Control. 2020, 16 April. pp. 888–894.
35. Bingyun Liang, Shiqi Zheng, Choon Ki Ahn, Feng Liu. Adaptive fuzzy control for fractional-order interconnected systems with unknown control directions. IEEE Transactions on Automatic Control Transactions on Fuzzy Systems. 2020, 16 October. pp. 75–87.
36. Astafiev A. F., Alekseev Yu. V. Fluidized bed processing of nickel middlings. 2^nd revised edition. Moscow : Metallurgiya, 1991. 253 p.
37. Draper N. R., Smith H. Applied regression analysis. Translated from English and ed. by M. Vlasenko et al. 3^rd edition. Moscow : Dialektika, 2007. 911 p.