Development and efficiency analysis of slag criteria during steel casting

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Currently, one of the key tasks in industry is to ensure high production efficiency, also in the steel industry. One of the unsolved problems in this area in the process of continuous casting of steel is the determination of the moment when slag begins to enter the intermediate ladle when pouring metal from the ladle. A comparative analysis of methods of early slag detection shows that currently there is no highly effective slag cut-off system. In this paper, in order to solve the problem of early slag detection, the vibration method was used due to the high informativeness of the vibration acceleration signal. Two methods of analyzing the vibration acceleration signal of the protective tube manipulator were tested for timely slag cutoff and preventing its entering into the intermediate ladle. Analysis of the results of testing showed that the best efficiency, equal to one hundred percent, was provided by the approach based on the analysis of the power spectrum of the vibration acceleration signal together with the data on the weight of the melting. Slag cutoff criteria based on discrete wavelet analysis worked in 67 percent of cases, which demonstrates their performance and gives grounds for more thorough research of this method in order to increase its efficiency.

About the authors

Dmitry Aleksandrovich Poleshchenko

Stary Oskol technological institute n.a. A.A. Ugarov (branch) NUST «MISIS»

Email: po-dima@yandex.ru
Stary Oskol

Artem Viktorovich Korenev

Stary Oskol technological institute n.a. A.A. Ugarov (branch) NUST «MISIS»

Email: korenev01@mail.ru
Stary Oskol

References

  1. ЕРЕМЕНКО Ю.И., ПОЛЕЩЕНКО Д.А. О разработке и промышленной апробации системы отсечки шлака при разливке стали // Известия высших учебных заведе-ний. Черная металлургия. – 2019. – Т. 62, №5. – С. 353–359.
  2. КРИВОНОСОВ В.А., МИТИН А.С. Повышение точно-сти оценки уровней металла в стальковше и промковше МНЛЗ с использованием нелинейного наблюдателя со-стояния // Вестник Воронежского государственного тех-нического университета. – 2010. – Т. 6, №4. – С. 41–45.
  3. ПОЛЕЩЕНКО Д.А., КОРЕНЕВ А.В. Разработка метода раннего распознавания шлака сталеразливочного ковша машины непрерывного литья заготовок // Управление большими системами. – 2024. – Вып. 107. – С. 121–141.
  4. СЕМЕНОВ М.В., КРАСИЛЬНИКОВ С.С., ШВИДЧЕН-КО Д.В. и др. Вибродетектирование шлака при сливе стали из стальковша в промежуточный ковш // Автома-тизированные технологии и производства. – 2015. – №2. – С. 40–42.
  5. ALSHARABI K., BIN SALAMAH Y., AB-DURRAQEEB A.M. et al. EEG signal processing for Alz-heimer’s disorders using discrete wavelet transform and ma-chine learning approaches // IEEE Access. – 2022. – Vol. 10. – P. 89781–89797.
  6. BELKACEMI B., SAAD S., GHEMARI Z. et al. Detection of induction motor improper bearing lubrication by discrete wavelet transforms (DWT) decomposition // Instrum. Mes. Metrol. – 2020. – Vol. 19, No. 5. – P. 347–354.
  7. CHAKRABORTY A., GHOSE J., CHAKRABORTY S. et al. Vision-based detection system of slag flow from ladle to tun-dish with the help of the detection of undulation of slag layer of the tundish using an image analysis technique // Ironmak-ing & Steelmaking. – 2022. – Vol. 49, No. 1. – P. 10–15.
  8. CHEN D., XIAO H., JI Q. Vibration style ladle slag detec-tion method based on discrete wavelet decomposition // The 26th Chinese Control and Decision Conference (CCDC-2014). – IEEE, 2014. – P. 3019–3022.
  9. GUO T., ZHANG T., LIM E. et al. A review of wavelet anal-ysis and its applications: Challenges and opportunities // IEEE Access. – 2022. – Vol. 10. – P. 58869–58903.
  10. GÜVENÇ M.A., KAPUSUZ H., MISTIKOĞLU S. Experi-mental study on accelerometer-based ladle slag detection in continuous casting process // The Int. Journal of Advanced Manufacturing Technology. – 2020. – Vol. 106. – P. 2983–2993.
  11. KAPUSUZ H., GÜVENÇ M.A., MISTIKOĞLU S. A review study on ladle slag detection technologies in continuous casting process // Int. Advanced Researches and Engineering Journal. – 2019. – Vol. 3, No. 3. – P. 144–149.
  12. LOUHENKILPI S. Continuous casting of steel // Treatise on Process Metallurgy. – Elsevier, 2014. – P. 373–434.
  13. NISHAT T.R., KIM J.M. Bearing fault classification of in-duction motors using discrete wavelet transform and ensem-ble machine learning algorithms // Applied Sciences. – 2020. – Vol. 10, No. 15. – 5251.
  14. POUYANI M.F., VALI M., GHASEMI M.A. Lung sound signal denoising using discrete wavelet transform and artifi-cial neural network // Biomedical Signal Processing and Control. – 2022. – Vol. 72. – 103329.
  15. TAN D.P., JI S.M., LI PEIYU et al. Development of vibration style ladle slag detection methods and the key technologies // Science China. Technological Sciences. 2010. – Vol. 53, No. 9. – P. 2378–2387.
  16. TAN D.P., LI P.Y., JI Y.X. et al. SA-ANN-based slag carry-over detection method and the embedded WME platform // IEEE Trans. on Industrial Electronics. – 2012. – Vol. 60, No. 10. – P. 4702–4713.
  17. WAHAB M.F., O'HAVER T.C. Wavelet transforms in sepa-ration science for denoising and peak overlap detection // Journal of Separation Science. – 2020. – Vol. 43, No. 9–10. – P. 1998–2010.
  18. WANG M.H., LU S.D., LIAO R.M. Fault diagnosis for pow-er cables based on convolutional neural network with chaot-ic system and discrete wavelet transform // IEEE Trans. on Power Delivery. – 2021. – Vol. 37, No. 1. – P. 582–590.
  19. WANG Y., LI Z., WANG C. et al. Implementation of discrete wavelet transform // 12th IEEE Int. Conf. on Solid-State and Integrated Circuit Technology (ICSICT). – IEEE, 2014. – P. 1–3.
  20. YOUNGWORTH R.N., GALLAGHER B.B., STAMPER B.L. An overview of power spectral density (PSD) calculations // Optical Manufacturing and Testing VI. – 2005. – Vol. 5869. – P. 206–216.
  21. ZHANG Z., BIN L., JIANG Y. Slag detection system based on infrared temperature measurement // Optik. – 2014. – Vol. 125, No. 3. – P. 1412–1416.

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