A numerical analysis as a good tool for a prediction of final sulphur steel ladle content

  • Z. Slović
  • Lj. Nedeljković
  • K. Raić
  • S. Dević
Keywords: numerical analysis, sulphur prediction, VD degassing, Al-Si killed steels, sulphide capacity

Abstract

This work presents the industrial results of sulfur level prediction at the end of vacuum degassing (VD) of low carbon Al-Si killed steels. The effect of plant conditions, such as slag chemistry, temperature, oxygen levels of the molten steel, and slag weight on desulphurization was investigated based on the measured results and thermodynamic calculations. The variables which influence steel desulfurization such as the sulfur capacity, the initial sulfur content, and the amount of ladle slag at the end of the VD process are also defined. The desulfurization procedure was numerically analyzed using the results of 31 heats under real plant conditions in which the measured final sulfur content had been reduced to less than of 10 ppm. A method for prediction of the slag amount based on the material balance of sulfur and aluminum is also presented. The values of the sulfur capacity were determined according to the well-known KTH and optical basicity based models. The obtained results of the regression equation show a predictive final sulfur level ability of R=0.911. This was proved as satisfactory.

References

N.S.Cyril, A.Fatemi, Int. J. Fatigue 31, (3), (2009), p.526–537;

W. Lv, Z. Xie, Z. Mao, P. Yuan, M.Jia, Neural Computing and Applications, October 2014, Volume 25, Issue 5, p. 1125-1136;

H.Gay, J.Lehmann, VII International Conference on Molten Slags Fluxes and Salts, The South African Institute of Mining and Metallurgy, 2004, p.619-624

B.Ozturk, E.T.Turkdogan, Metal science, 18, 1984, p. 299-305;

B.Ozturk, E.T.Turkdogan, Metal science, 18, 1984, p. 306-309;

B.T Tsao, H.G. Katayama, Trans. ISIJ, 26, (1986), p.717-723;

H. Lachmund, Y. Xie, K.Harste, Steel Research, 72, (2001), p.452-459;

M.Andersson, G.P. Jönsson, M.M. Nzotta, ISIJ International, 39 (1999), p.1140-1149;

H-wei Nian, Z-Zhong Mao, 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), 2010, p.196-199;

Y. Wang, Y. Zhang, Control and Decision Conference (CCDC), 2011 Chinese, 2011, p.1684-1687, ISBN 978-1-4244-8737-0

D.J. Sosinsky, I.D. Sommerville, Metallurgical Transactions B, Volume 17B, (1986), p.331-337;

R.W. Young, J.A. Duffy, G.J.Hassall, Z.Xu, Ironmaking and Steelmaking, Vol 19, No 3, (1992),p.201-219;

T.B.Tsao, H.G.Katayama, Trans. ISIJ, Vol.26, (1986), p.717-723;

Y.Taniguchi, N.Sano, S.Seetharaman, ISIJ International, Vol. 49 (2009), No 2, p. 156–163;

M. M. Nzotta, D. Sichen, S. Seetharaman, ISIJ Int., 38( 1998), p. 1170-1179;

H.Ohta, H.Suito, Metallurgical and Materials Transactions B, Volume 29B, (1998), p.119-129;

M.T.Andersson, G.P.Jönsson, M.Hallberg, Ironmaking and Steelmaking Vol.27, No4, (2000), p. 286-293;

C.Wagner, Metallurgical Transactions B, Volume 6B, (1975), p.405-409;

Z. Slović, Doctoral dissertation, TMF, 2013, Beograd, Serbia, UDК number: 669.01/.09

Z.Slović, LJ.Nedeljković, K.Raić, Z.Odanović, Kovove Mater. 50, (2012), p. 185–192, doi: 10.4149/km 2012 3 185;

Z.Slović, LJ.Nedeljković, K.Raić, T.Volkov-Husović, Materiali in tehnologije/Materials and Technology, Vol.46, N°6, 2012, p.683-688;ISSN 1580-2949;

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