PB PJSC MMC Norilsk Nickel, Norilsk, Russia

M. V. Glibovets, Chief Engineer – Director of the Production Support Directorate, e-mail: GlibovetsMV@nornik.ru

R. M. Botsiev, Chief Engineer, Talnakh Concentrator, e-mail: BotsievRM@nornik.ru
A. A. Miller, Leading Engineer-Technologist of the Engineering Support Laboratory of the Talnakh Concentrator, Center for Engineering Support of Production, e-mail: MillerALA@nornik.ru

Nornikel Sputnik Ltd., Norilsk, Russia.
I. F. Zaporozhtsev, Chief Manager for Internal Development and Attraction of External Contractors of the Mineral Resources Complex, e-mail: ZaporozhtsevIF@nornik.ru

Digitalization of industrial sites at the current stage of development includes not only equipping them with sensors and a single control interface via SCADA systems, but also unification of processes (according to the methodology of Data Governance — data quality management). The desire to increase the volumes and quality of finished products, reduce costs inevitably leads to experimental modes of technological processes that were not included at the design stage. The accumulated statistics of changes in physicochemical properties in such conditions is an invaluable source for comparing control algorithms. Generalization of the results by means of machine learning allows to identify and programmatically implement the most effective of them, taking into account multi-purpose, multi-criteria optimization problems — to obtain automatic control services, operators` digital twins. The methodological and practical experience of creating a production optimization system that ensures an increase in metal extraction and product quality at the Talnakh Concentrator of the Polar branch of PJSC MMC Norilsk Nickel is considered. An approach to formalizing management in a plant environment is presented: classification of observed physical and chemical parameters for identifying conditions (Condition-Based Maintenance), a hybrid modeling format combining the results of simplified physical and mathematical models and machine learning, control based on forecast data (Model Predictive Control), division of control scales into dispatcher and operator levels, development and updating of services in a subject-oriented paradigm (Domain-Driven Design) and taking into account the flow of requests from technologists who carry out the actual enrichment process using automatic control services. The result of this work by the end of 2024 is an increase in the through extraction of nickel (by 0.36% (rel.)) and copper (by 0.16% (rel.)) into collective concentrate, and stabilization of flotation processes in conditions of ore variability. A technical effect was also achieved due to a more optimal distribution of metals in profile concentrates: an increase in Ni extraction in nickel concentrates by 0.5% (rel.) and a decrease in Ni extraction in copper concentrate by 0.5% (rel.) with a fixed through extraction (an effect due to the absence of losses at the Copper Plant).

1. Abrarov A. D., Datsiev M. S., Chikildin D. E., Fedotov D. N. Optimization of bulk flotation process at Talnakh Concentrator based on machine learning algorithms. Tsvetnye Metally. 2022. No. 2. pp. 87–93.
2. Klokov A., Abrarov A., Danilov P. Flotation froth monitoring using unsupervised multiple object tracking methods. Journal of Mineral and Material Science. 2023. Vol. 4, Iss. 1. pp. 1–4.
3. Wang Xu et al. An unsupervised method for extracting semantic features of flotation froth images. Minerals Engineering. 2022. Vol. 176. pp. 24–32.
4. Chen Y., Xu D., Wan K. A froth velocity measurement method based on improved U-Net++ semantic segmentation in flotation process. International Journal of Minerals, Metallurgy and Materials. 2024. Vol. 31. pp. 1816–1827.
5. Prasad A. Introduction to data governance for machine learning systems: fundamental principles, critical practices, and future trends. California: Apress Media, 2024. 984 p.
6. Rudolph M., Kurz S., Rakitsch B. Hybrid modeling design patterns. Journal of Mathematics in Industry. 2024. Vol. 4. 3.
7. Osipova N. V. Review of projects and solutions for concentrators`digital twins. Avtomatizatsiya v promyshlennosti. 2023. No. 7. pp. 37–42.
8. Melekhina K. A., Ananyev P. P., Plotnikova A. V., Shestak S. A. Mathematical model of the process of homogenizing the bulk materials properties during their flow. Modelirovanie, optimizatsiya i informatsionnye tekhnologii. 2020. Vol. 8. 14 p.
9. Eurocement and GlowByte have introduced a digital assistant for cement production. Available at: https://glowbyteconsulting.com/yevrotsement_i_glowbyte_vnedrili_tsifrovogo_pomoshchnica (accessed: 20.05.2025).
10. Bu X. et al. Kinetics of flotation. Order of process, rate constant distribution and ultimate recovery. Physicochemical Problems of Mineral Processing. 2017. Vol. 53, Iss. 1. pp. 342–365.
11. Oosthuizen D., Williams B., Van der Spuy D. Barriers to integrated flotation control solutions: lessons from an industrial implementation. Proceedings of International Federation of Automatic Control Conference. 2023. Vol 56. pp. 2335–2340.
12. Marek P. Fundamentals of flotation. ChemTexts. 2022. Vol. 8. 19.
13. Mavros P., Matis K. A. Innovations in flotation technology. Vol. 208. Dordrecht: Springer, 1991. 533 p.
14. Wang P., Yvon M., Parkes S., Galvin K. Improving flotation hydrodynamics to maximize nickel recovery from tailings. Minerals Engineering. 2024. Vol. 216. pp. 214–230.
15. Klemyatov A. A., Magaev M. A., Shorikov A. P., Bityugin I. V. Forecasting performance and analysis of operations of the ore processing plant of the Kola Mining and Metallurgical Company using simple dependencies. Tsvetnye Metally. 2024. No. 7. pp. 8–14.
16. Ayo-Imoru R. M., Cilliers A. C. A survey of the state of conditionbased maintenance (CBM) in the nuclear power industry. Annals of Nuclear Energy. 2018. Vol. 112. pp. 177–188.
17. Dawson P., Koorts R. Flotation control incorporating fuzzy logic and image analysis. The International Federation of Automatic Control: Proceedings of the 19th World Congress. 2014. Vol. 47, Iss. 3. pp. 352–357.
18. Jovanović et al. Comparison of fuzzy and neural network computing techniques for performance prediction of an industrial copper flotation circuit. Minerals. 2022. Vol. 12, No. 12. 1493.
19. Krupnov L. V., Midyukov D. O., Malakhov P. V. Ways to cover the raw material demand in the copper-nickel sector. Obogashchenie Rud. 2022. No. 2. pp. 37–41.
20. Krupnov L. V., Midyukov D. O., Datsiev M. S., Iliin V. B. Change in mineral resource supplies in production of heavy nonferrous metals in terms of copper and nickel. Gornyi Zhurnal. 2024. No. 3. pp. 10–16.
21. Khononov V. Learning Domain-Driven Design. USA: O’Reilly Media, 2022. 340 p.