IMPROVING THE INTENSITY OF THE STEELMAKING TECHNOLOGICAL PROCESS BASED ON FUZZY LOGIC

Main Article Content

Rakhmonov, I.
Korjobova, M.

Abstract

This article analyzes the application of a fuzzy logic system for controlling complex and uncertain technological processes occurring in a 30-ton Electric Arc Furnace (EAF-30). Research findings show that 80% of the melting process (42 minutes) is carried out using an electric arc. In traditional control systems, rapid fluctuations in arc current exceeding or falling below 17 kA have been identified as a cause of process interruptions. Therefore, this study proposes the fuzzification of arc current, voltage, and arc length using Gaussian membership functions. Based on critical thresholds—arc current in the range of 17–38 kA and voltage from -6% to +8%—linguistic variables were developed. Using the Mamdani algorithm, optimal electrode control during the 17–20-minute melting phase ensures improved energy efficiency, reduced electrode consumption, and increased product output. The research results demonstrate that integrating Fuzzy control into traditional systems is an effective approach for intensifying the steelmaking technological process.

Downloads

Download data is not yet available.

Article Details

Section

Mining, Metallurgy, and Manufacturing Industry

How to Cite

Rakhmonov, I., & Korjobova, M. (2025). IMPROVING THE INTENSITY OF THE STEELMAKING TECHNOLOGICAL PROCESS BASED ON FUZZY LOGIC. Digital Technologies in Industry, 3(3), 16-21. https://doi.org/10.70769/3030-3214.SRT.3.3.2025.7

References

1. Paranchuk Y., Shabatura Y., Kuznyetsov O. The electrodes positioning control system for the electric arc furnace based on fuzzy logic. In: Proceedings of the IEEE International Conference on Modern Electrical and Energy Systems, Kremenchuk, Ukraine, 21–24 September 2021, pp. 1–5. DOI: https://doi.org/10.1109/MEES52427.2021.9598585

2. Moghadasian M., Al-Nasser E. Modelling and control of electrode system for an electric arc furnace. In: 2nd International Conference on Research in Science, Engineering and Technology (ICRSET-2014), Dubai, UAE, March 21–22, 2014. Available at: http://dx.doi.org/10.15242/IIE.E0314558. DOI: https://doi.org/10.15242/IIE.E0314558

3. Rakhmonov I.U., Ushakov V.Ya., Najimova A.M., Obidov K.K., Suleymanov S.R. Mathematical modeling for minimizing electricity consumption in industrial enterprises with continuous production. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2024, vol. 335, no. 4, pp. 43–51. (In Russ.) DOI: 10.18799/24131830/2024/4/4423 DOI: https://doi.org/10.18799/24131830/2024/4/4423

4. Wang L.-X., Mendel J.M. Back-propagation fuzzy system as nonlinear dynamic system identifiers. In: Proceedings of the IEEE International Conference on Fuzzy Systems, San Diego, CA, USA, 8–12 March 1992, pp. 1409–1418 DOI: https://doi.org/10.1109/FUZZY.1992.258711

5. Rakhmonov I.U., Ushakov V.Ya., Niyozov N.N., Kurbonov N.N. Forecasting electricity consumption by LSTM neural network. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2023, vol. 334, no. 12, pp. 125–133. (In Russ.) DOI: 10.18799/24131830/2023/12/4407 Maslov D.V. Study of mechanical processes during electrode impact with scrap. In: Science. Technologies. Innovations: Proceedings of the All-Russian Scientific Conference of Young Scientists, in 10 parts, part 6. Novosibirsk: NSTU Publishing House, 2013, pp. 18–22. (In Russ.). DOI: https://doi.org/10.18799/24131830/2023/12/4407

6. Napoles-Baez Y., Gonzalez-Yero G., Martínez R., Valeriano Y., Nuñez-Alvarez J.R., Llosas-Albuerne Y. Modeling and control of the hydraulic actuator in a ladle furnace. Heliyon, 2022, vol. 8, e11857. DOI: https://doi.org/10.1016/j.heliyon.2022.e11857 DOI: https://doi.org/10.1016/j.heliyon.2022.e11857

7. Nikolaev A.A., Tulupov P.G., Astashova G.V. The comparative analysis of electrode control systems of electric arc furnaces and ladle furnaces. In: 2nd International Conference on Industrial Engineering, Applications, and Manufacturing. ICIEAM-2016, 2016, pp. 1–7. DOI: https://doi.org/10.1109/ICIEAM.2016.7910888

8. Liu Y.-J., Chang G.W., Hong R.-C. Curve-fitting-based method for modeling voltage-current characteristic of an AC electric arc furnace. Electric Power Systems Research, 2010, vol. 80, no. 7, pp. 807–814. DOI: https://doi.org/10.1016/j.epsr.2009.10.015

9. Ghiormez L., Prostean O. Electric arc current control for an electric arc furnace based on fuzzy logic. In: Proceedings of the IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, Timisoara, Romania, 21–23 May 2015, pp. 359–364. DOI: https://doi.org/10.1109/SACI.2015.7208229

10. Maslov D.V. Determination of key parameters affecting the integrity of electrode columns in arc furnaces. Elektrotekhnika, 2013, no. 8, pp. 43–47. (In Russ.) DOI: https://doi.org/10.3103/S1068371213080099

Similar Articles

You may also start an advanced similarity search for this article.