MANAGEMENT OF DISTRIBUTED SYSTEMS TECHNOLOGICAL COMPLEX

The article deals with the problem arising in systems with a set of consecutive separately controllable technological circuits and units. These circuits at the input and output have technological links with neighboring ones, but at the control level their integration into production provides only for the transfer of information parameters to the operator of the entire technological complex. The operator often does not have time to process the entire flow of incoming information and make corrective decisions. This leads to problems of mutual influence of such circuits and units on each other, which reduces the efficiency of management, quality of finished products and can lead to unscheduled downtime and emergencies. As a solution, it is proposed to assign some production control functions to the main ACS, allocating a special process controller and subnetwork for controlling local control and regulation systems. Then, with the help of special subsystems ("agents") it is possible to introduce corrective actions into the technological setpoints and parameters of each circuit so as to minimize the deviations of the setpoints of the finished products of the whole complex. In the case of modern production, the role of such "agents" is played by digital advisors, but they provide only possible solutions and leave the choice to the human operator. For well-established advisors built on the basis of physico-chemical, balance and technological, statistical, neural network, expert or combined natural-mathematical and physico-chemical models. As an example, the scheme of the complex of technical means of ACS of the main building of the enrichment plant "Mine No. 12" is given. As a difficulty of realization of such a solution the closedness of local control systems, especially foreign ones, is noted and as a solution the application of complex reverse-engineering methods is proposed.

1. Esenbaev T.D. Automation of the design of local control systems in the automated control system of the NPP. In: Collection of materials of the V All-Russian Scientific and Practical Conference with international participation "Achievements of university science: from theory to practice", Kumertau, April 21, 2022. Meleuz: State Unitary Enterprise "Meleuz city Printing House", 2022, pp. 48–52. (In Russ.).

2. Zagidulin I.R., Salamatin A.S., Makarov G.V., Korshunov S.Yu. Features of integration of local control systems in the automated control system of the processing plant. In: Automation Systems (in Education, Science and Production): AS'2021: Pro-ceedings of the XIII All-Russian Scientific and Practical Conference (with international participation), Novokuznetsk, 02-03 December 2021. Novokuznetsk: ITs SibGIU, 2021, pp. 155–159. (In Russ.).

3. Tsiryapkina A.V., Myshlyaev L.P., Ivushkin K.A., Gracheva V. V. Control systems for objects with positive internal feedback. Steel in Translation. 2015, vol. 45, pp. 943–948. https://doi.org/10.3103/S0967091215120141

4. Albagachiev A.Yu., Krasko A.S., Radaikin D.A. The need to use automated systems in the 21st century. Prerequisites for the emergence of digital production. Kuznechno-shtampovochnoe proizvodstvo. Obrabotka materialov davleniem. 2023, no. 2, pp. 62–74. (In Russ.).

5. Ghobakhloo Morteza. Industry 4.0, Digitization, and Opportunities for Sustainability. Journal of Cleaner Production. 2020. vol. 252, pp. 119869. https://doi.org/10.1016/j.jclepro. 2019.119869

6. Görner S., Luse A., Maheshwari N., Malladi R., Mori L., Samek R. The potential of ad-vanced process controls in energy and materials. McKinsey & Company. 2020. URL: https://www.mckinsey.com/industries/metals-and-mining/our-insights/the-potential-of-advanced-process-controls-in-energy-and-materials#/. (Дата обращения: 28.08.2023).

7. Helgers H., Schmidt A, Lohmann L.J., Vetter F.L., Juckers A,. Jensch C., Mouellef M., Zobel-Roos S., Strube J. Towards Autonomous Operation by Advanced Process Control-Process Analytical Technology for Continuous Biologics Antibody Manufacturing. Processes. 2021, vol. 9, no. 1, pp. 172. https://doi.org/ 10.3390/pr9010172

8. Klebanov D.A., Makeev M.A. Digital advisors for the coal industry. Implementation methodology. Ugol'. 2022, no. 8 (1157), pp. 112–115. (In Russ.). https://doi.org/10.18796/0041-5790-2022-8-112-115

9. Spirin N.A., Lavrov V.V., Rybolovlev V.Yu., Shnaider D.A., Krasnobaev  A.V., Gurin I.A. Digital transformation of pyrometallurgical technologies: State, scientifc problems and prospects of de-velopment. Izvestiya. Ferrous Metallurgy. 2021, vol. 64, no. 8, pp. 588–598. (In Russ.). https://doi.org/10.17073/0368-0797-2021-8-588-598

10. Chambers R., Beaney P. The potential of placing a digital assistant in patients' homes. British Journal of General Practice. 2019, vol. 70, no. 690, pp. 8, 9. https://doi.org/10.3399/ bjgp20X707273

11. Evtushenko V.F., Ivushkin A.A., Venger K.G. On the full-scale model approach and the theory of similarity in relation to control systems. In: Automation systems in education, science and production. AS'2019: Proceedings of the XII All-Russian Scientific and Practical Conference (with international participation), Novokuznetsk, November 28-30, 2019. Novokuznetsk: ITs SibGIU, 2019, pp. 21–24. (In Russ.).

12. Emel'yanov S.V., Korovin S.K., Myshlyaev L.P. Theory and practice of forecasting in control systems. Kemerovo; Moscow: Izdatel'skoe ob"edinenie «Rossiiskie universitety»: Kuzbassvuzizdat ASTSh, 2008, 487 p. (In Russ.).

13. Krasovskii A.A., Platov A.S. Stability, Control and Differential Games: Proceedings of the International Conference. Lecture Notes in Control and Information Sciences – Proceedings, Yekaterinburg, 16-20 September 2019. Swit-zerland: Springer Nature, 2020, pp. 27–37. https://doi.org/10.1007/978-3-030-42831-0_3.

14. Amrit R., Canney W., Carrette P., Linn R., Martinez A., Singh A., Skrovanek T., Valiquette J., Williamson J., Zhou J., Cott B.J. Platform for Advanced Control and Estimation (PACE): Shell's and Yokogawa's Next Generation Advanced Process Control Technology. IFAC-PapersOnLine. 2015, vol. 48, no. 8, pp. 1–5. https://doi.org/10.1016/j.ifacol.2015.08.148

15. Vyatkin A.Yu., Smirnov D.V., Kochetov I.A. Multi-agent systems as an opportunity to implement deci-sion support systems. In: Electronic means and con-trol systems. Materials of the reports of the Interna-tional Scientific and Practical Conference. 2015, no. 1-2, pp. 234–238. (In Russ.).

16. Rotach V.Ya. Calculation of the dynamics of industrial automatic control systems. Moscow: Energiya, 1973, 439 p. (In Russ.).

17. Myshlyaev L.P., Ivushkin A.A., Evtushenko V.F., Burkov V.N., Makarov G.V., Burkova E.V. Application of physical models in the tasks of testing and configuring control sys-tems (for example, a coal-water fuel combustion plant). In: Science-intensive technologies for the development and use of mineral resources. 2015, no. 2, pp. 211–218. (In Russ.).

18. Hewing L., Wabersich K.P., Menner M., Zeilinger M.N. Learning-based model predictive control: To-ward safe learning in control. Annual Review of Con-trol, Robotics, and Autonomous Systems. 2020, vol. 3, pp. 269–296. http://dx.doi.org/10.1146/annurev-control-090419-075625

19. Berberich J., Köhler J., Müller M.A., Allgöwer F. Data-driven model predictive control with stability and robustness guarantees. IEEE Transactions on Automatic Control. 2020, vol. 66, no. 4, pp. 1702–1717. https://doi.org/ 10.1109/TAC.2020.3000182

20. Kuhn M., Johnson K. Feature engineering and se-lection: A practical approach for predictive models. Chapman and Hall/CRC. 2019, 310 p. http://dx.doi.org/10.1080/00031305.2020.1790217

21. Goryainov S., Krasil'nikov A., Raiffershaid A., Klanke Sh. Predictive assistant of quality control of the MNLZ. In: Foundry and Metallurgy, 2017. Belarus: Proceedings of the 25th International Scientific and Technical Conference, Minsk, October 18-19, Minsk : BNTU, 2017, pp. 169–179. (In Russ.).

22. Tagil'tsev-Galeta K.V., Kol'churina I.Yu., Korshunov S.Yu., Makarov G.V. The concept of system reverse engineering in the context of complex substitution of production technologies. In: Modern Technologies and Reverse Engineering: Collection of articles of All-Russian scientific and practical conferences ("Modern technologies: Problems and prospects", April 17-20, 2023. "Promising technologies of reverse engineering and rapid pro-totyping", May 10-12, 2023) / Ministry of Science and Higher Education of the Russian Federation, Sevastopol State University, Polytechnic Institute, Sevastopol: Sevastopol State University, 2023, pp. 184–187. (In Russ.).