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Steel making
Название Development and research of improved automatic control systems for electrotechnological modes of high-power ladle-furnace units
DOI 10.17580/chm.2023.12.06
Автор A. A. Nikolaev, P. G. Tulupov, R. R. Dema, S. S. Ryzhevol
Информация об авторе

Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia:

A. A. Nikolaev, Cand. Eng., Associate Prof., Head of the Dept. of Automated Electric Drive and Mechatronics, e-mail: aa.nikolaev@magtu.ru
P. G. Tulupov, Cand. Eng., Associate Prof., Dept. of Automated Electric Drive and Mechatronics, e-mail: tulupov.pg@mail.ru
R. R. Dema, Dr. Eng., Prof., Dept. of Machines and Technologies of Metal Forming and Mechanical Engineering, e-mail: demarr@magtu.ru
S. S. Ryzhevol, Postgraduate Student, Dept. of Automated Electric Drive and Mechatronics, e-mail: snaffls18@gmail.com


A new method for selecting optimal asymmetrical arcing modes in ladle furnace (LF) units under various argon blowing modes, used when setting up advanced automatic control systems for the electrotechnological modes of LF, is described. The main configuration options for one- and two-position LF based on the location of purge plugs and emergency lances are considered. For each option, criteria for the optimal electrical modes of the LF have been formed, which allow a consistent search for the optimal values of the impedance settings of the secondary electrical circuit. A new algorithm and automatic control system for the LF with the ability to adapt to various slag modes and argon blowing modes are considered. This algorithm differs from the known ones in that it provides the possibility of dynamically adapting the lengths of electric arcs in phases subject to the strongest influence of the liquid metal bath level, by moving to an operating curve with a predetermined set of optimal impedance settings. For the new method for selecting optimal asymmetrical modes and control algorithm, the main results of their practical implementation at leading metallurgical enterprises are presented.
The work was carried out with financial support from the Ministry of Science and Higher Education of the Russian Federation (project No. FZRU-2023-0008).

Ключевые слова Ladle-furnace unit, electrical mode control system, electric arc, asymmetrical arcing modes, bottom argon purging, emergency lance, optimization of electrical modes
Библиографический список

1. Nikolaev A. A., Lukyanov S. I., Tulupov P. G. Improved electrical control of ladle-furnace units by means of arc-current harmonics. Steel in Translation. 2019. Vol. 49, Iss. 4. pp. 265–270.
2. Nikolaev A. A., Tulupov P. G., Ryzhevol S. S., Lozhkin I. A. Development of a methodology for selecting optimum asymmetric arc combustion modes in ladle-furnace installations under different argon purging regimes. 2022 International Ural Conference on Electrical Power Engineering (UralCon). Magnitogorsk, Russian Federation. 2022. pp. 353–358.
3. Nikolaev A. A., Bulanov M. V., Tulupov P. G. Improving the two-position ladle-furnace efficiency using advanced electrical mode control algorithms. 2021 International Ural Conference on Electrical Power Engineering (UralCon). Magnitogorsk, Russian Federation. 2021. pp. 592–597.
4. Nikolaev A. A., Tulupov P. G. Technique for modeling random disturbances of the electric arcs lengths for tuning a nonlinear P-controller of impedance. Chernye Metally. 2021. No. 11. pp. 74–80.
5. Mironov Yu. M. Electric arc in electrotechnological installations : monograph. Cheboksary : Izdatelstvo Chuvashskogo universiteta, 2013. 290 p.
6. Shpiganovich A. N., Zakharov K. D. Features of power supply systems for steelmaking and ferroalloy production. Lipetsk : LGTU, 2004. 213 p.
7. Makarov A. N. Laws of heat exchange of an electric arc and torch in metallurgical furnaces and power plants. Tver : Izdatelstvo Tverskogo gosudarstvennogo tekhnicheskogo universiteta, 2012. 164 p.
8. Krüger K. Modellbildung und regelung der elektrischen energieumsetzung von lichtbogenöfen (modeling and control of the electrical energy conversion in arc furnaces). Dr.-Ing. Dissertation, Fachbereich Maschinenbau, Universität der Bundeswehr Hamburg, Fortschritt-Berichte VDI. Reihe 6, Nr. 382. Düsseldorf : VDI-Verlag, 1998. 234 p.
9. Bowman B., Krüger K. Arc furnace physics. Düsseldorf : Verlag Stahleisen GmbH, 2009. 245 p.
10. Köhle S. Ersatzschaltbilder und modelle für die elektrischen größen von drehstromlichtbogenöfen (Equivalent circuit diagrams and models for the electrical parameters of AC arc furnaces). Habilitationsschrift, Fachbereich Elektrotechnik, Bergische Universität – Gesamthochshule Wuppertal. Düsseldorf : Verlag Stahleisen, 1990. pp. 234–239.
11. Köhle S. Lichtbogenreaktanzen von Drehstrom-Lichtbogenöfen (Arc reactances of AC arc furnace). Elektrowärme International. 1993. Vol. 51. No. B4. pp. 175–185.
12. Nikolaev A. A., Kornilov G. P., Tulupov P. G., Povelitsa E. V. Analysis of various options for constructing automatic control systems for the movement of electrodes of arc steel-smelting furnaces and ladle-furnace units. Vestnik Magnitogorskogo gosudarstvennogo tekhnicheskogo universiteta imeni G. I. Nosova. 2015. No. 2 (50). pp. 90–100.
13. Xiang F., Zhi Z., Jiang G. Digital twins technolgy and its data fusion in iron and steel product life cycle. 2018 IEEE 15th International Conference on Networking. Sensing and Control (ICNSC). Zhuhai, China. 2018. pp. 1–5.
14. Dietz M., Grabowski D., Klimas M., Starkloff H. J. Estimation and analysis of the electric arc furnace model coefficients. IEEE Transactions on Power Delivery. 2022. Vol. 37. No. 6. pp. 4956–4967.
15. Klimas M., Grabowski D. Application of the deterministic chaos in AC electric arc furnace modeling. 2022 IEEE International Conference on Environment and Electrical Engineering and 2022 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). Prague, Czech Republic. 2022. pp. 1–6.
16. Xu R., Ma S., Zhang M. Modeling of electric arc furnace for power quality analysis. 2022 IEEE 3rd China International Youth Conference on Electrical Engineering (CIYCEE). Wuhan, China. 2022. pp. 1–5.
17. Lee C., Kim H., Lee E. J., Baek S. T. et al. Measurement-based electric arc furnace model using ellipse formula. IEEE Access. 2021. Vol. 9. pp. 155609–155621.
18. Klimas M., Grabowski D. Application of shallow neural networks in electric arc furnace modeling. IEEE Transactions on Industry Applications. 2022. Vol. 58. No. 5. pp. 6814–6823.
19. Svenchansky A. D., Zherdev I. T., Kruchinin A. M. et al. Electric industrial furnaces: Arc furnaces and special heating installations : Textbook for universities. Moscow : Energoizdat, 1981. 296 p.
20. Andreev S. M., Parsunkin B. N., Golovko N. A. et al. Development of the concept of an exterminal fuzzy system for automatic optimization of control of the energy mode of steel smelting in EAF. Vestnik Magnitogorskogo gosudarstvennogo tekhnicheskogo universiteta imeni G. I. Nosova. 2011. No. 3. pp. 88–91.

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