Donbass State Technical University, Alchevsk, Russia

N. A. Denisova, Cand. Eng., Associate Prof., Head of the Dept. of Metallurgical Complex Machines, e-mail: natdeny@yandex.ru
A. L. Sotnikov, Dr. Eng., Prof., Leading Researcher, e-mail: 0713019870@mail.ru
L. E. Podlipenskaya, Cand. Eng., Associate Prof., Leading Researcher, Advanced ResearchDirectorate, e-mail: lida.podlipensky@gmail.com
T. R. Kozlov, Assistant, e-mail: romovaldovich@mail.ru

This paper examines the hazardous emergency situation of “metal overflow through the mold” that occurs during the operation of a continuous slab casting machine. Based on an analysis of production incidents and expert assessments, 14 key process parameters influencing the overflow risk are identified. A two-level expert system is proposed for predicting and diagnosing emergency situations using Mamdani fuzzy logic. At the first level, the system evaluates four intermediate prerequisites for an emergency (metal level instability, non-closing of the stopper at operating speed and during startup, and slab slippage). At the second level, it generates a generalized indicator of the occurrence of the emergency, assessed on a five-level scale. The system maintains active fuzzy rules, ensuring the explainability of its conclusions. This allows the operator not only to receive a warning but also to diagnose specific causes of the increased hazard level for immediate correction.
This work was supported by the federal budget under the topic “Expert system for ensuring the reliability of metallurgical equipment taking into account the operator’s psychophysiological state in real time” (topic code: FRRU-2023-0005 in the Unified State Information System for Research and Development).

1. Eronko S. P. et al. Innovative metallurgical equipment. Steelmaking: textbook. Moscow; Vologda : Infra-Inzheneriya, 2023. 276 p.
2. Smirnov A. N., Kubersky S. V., Shtepan E. V. Continuous casting of steel: textbook. Donetsk : DonNTU, 2011 482 p.
3. Tsuprun A. Yu., Fedosov A. V., Pashchuk D. V. Development of tools for searching and analyzing technological parameters of CCM molds. Metallurgicheskie protsessy i oborudovanie. 2013. No. 4 (34). pp. 31–38.
4. Sotnikov A. L. Tasks and methods of control and diagnostics of CCM technological equipment. Metallurgicheskie protsessy i oborudovanie. 2014. No. 3. pp. 33–44.
5. Risk assessment for industrial accident prevention: a review of risk assessment methods, selected case studies, and an available software tool. Geneva : United Nations, 2023. 76 p.
6. Sotnikov A. L., Denisova N. A., Knyazkov O. V., Belelyubsky B. F., Balakhnina E. E. Methodological approach to determining the influence of the human factor on the reliability of a production system. Metallurg. 2024. No. 12. pp. 116–122. DOI: 10.52351/00260827_2024_12_116
7. Solovieva O. I., Kozhevnikov A. V. Mathematical model for predicting the safety level of steelpouring equipment. Vestnik ChGU. Nauchny zhurnal. 2012. No. 3 (41). Vol. 2. pp. 25–31.
8. Kozhevnikov A. V., Solovieva O. I. Development of a methodology for predicting the state of steel-pouring equipment based on fuzzy control. Metallurgicheskie protsessy i oborudovanie. 2014. No. 2 (36). pp. 43–51.
9. Ansari M. O., Ghose J., Chattopadhyaya S., Ghosh D. et al. An intelligent logic-based mold breakout prediction system algorithm for the continuous casting process of steel: A novel study. Micromachines. 2022. Vol. 13. 2148. DOI: 10.3390/mi13122148
10. Rosati R., Romeo L., Cecchini G. et al. From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0. J. Intell. Manuf. 2023. Vol. 34. pp. 107–121. DOI: 10.1007/s10845-022-01960-x
11. Poleshchenko D. A., Kovrizhnykh O. A. Development of an intelligent decision support system based on precedents for operational planning of a steelmaking shop. Ekonomika. Informatika. 2024. Vol. 51. No. 2. pp. 453–465. DOI: 10.52575/2687-0932-2024-51-2-453-465
12. Narkevich M. Yu. Development of a methodology for creating a quality management system for a metallurgical enterprise operating hazardous production facilities, based on an applied digital platform: Dissertation ... of Doctor of Engineering Sciences. Magnitogorsk, 2023. 332 p.
13. Vishnevsky D. A., Denisova N. A., Kozlov T. R. et al. Development of an expert system for diagnosing emergency situations in a CCM based on fuzzy logic. Vestnik Cherepovetskogo gosudarstvennogo universiteta. 2025. No. 1 (124). pp. 7–23. DOI: 10.23859/1994-0637-2025-1-124-1
14. Vishnevsky D. A., Denisova N. A., Kozlov T. R. et al. Automatic control system for non-closing of the CCM tundish stopper of a continuous casting machine based on fuzzy logic. Chernye Metally. 2025. No. 3. pp. 61–69.
15. Mamdani E. H., Assilian S. An experiment in linguistic synthesis with fuzzy logic controller. International Journal Man-Machine Studies. 1975. Vol. 7, Iss. 1. pp. 1–13.
16. Rutkovskaya D., Pilinsky M., Rutkovsky L. Neural networks, genetic algorithms and fuzzy systems. Moscow : Goryachaya liniya Telekom, 2004. 452 p.
17. Zadeh L. A. The concept of a linguistic variable and its application to approximate reasoning. Information Sciences. 1975. Vol. 8, No. 3. pp. 199–249. DOI: 10.1016/0020-0255(75)90036-5
18. Leonenkov A. V. Fuzzy modeling in MATLAB and FuzzyTECH environments. Saint-Petersburg : BKhV-Peterburg, 2005. 736 p.
19. Akimov A. A., Valitov D. R., Kubryak A. I. Data preprocessing for machine learning. Nauchnoe obozrenie. Tekhnicheskie nauki. 2022. No. 2. pp. 26–31. DOI: 10.17513/srts.1391
20. Rojek I., Prokopowicz P., Kotlarz P., Mikołajewski D. Extended fuzzy-based models of production data analysis within ai-based Industry 4.0 paradigm. Applied Sciences. 2023. Vol. 13, Iss. 11. 6396. DOI: 10.3390/app13116396