Overall model of the dynamic behaviour of the steel strip in an annealing heating furnace on a hot-dip galvanizing line

  1. F. J. Martínez-de-Pisón 1
  2. A. Pernía 1
  3. E. Jiménez-Macías 1
  4. R. Fernández 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Journal:
Revista de metalurgia

ISSN: 0034-8570

Year of publication: 2010

Volume: 46

Issue: 5

Pages: 405-420

Type: Article

DOI: 10.3989/REVMETALM.0948 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de metalurgia

Abstract

Predicting the temperature of the steel strip in the annealing process in a hot-dip galvanizing line (HDGL) is important to ensure the physical properties of the processed material. The development of an accurate model that is capable of predicting the temperature the strip will reach according to the furnace’s variations in temperature and speed, its dimensions and the steel’s chemical properties, is a requirement that is being increasingly called for by industrial plants of this nature. This paper presents a comparative study made between several types of algorithms of Data Mining and Artificial Intelligence for the design of an efficient and overall prediction model that will allow determining the strip’s variation in temperature according to the physico-chemical specifications of the coils to be processed, and fluctuations in temperature and speed that are recorded within the annealing process. The ultimate goal is to find a model that is effectively applicable to coils of new types of steel or sizes that are being processed for the first time. This model renders it possible to fine-tune the control model in order to standardise the treatment in areas of the strip in which there is a transition between coils of different sizes or types of steel.

Bibliographic References

  • [1] R.Mehta and S.S. Sahay, J.Mat. Eng. Perform 18 (2009) 8-15. doi:10.1007/s11665-008-9250-4
  • [2] S.S. Sahay and K. Krishnan, Ironmaking Steelmaking 34 (2007) 89-94. doi:10.1179/174328106X118170
  • [3] W. Wenfei, Y. Fan, Z. Xinxin and Z. Yi, J. Therm. Sci. 11 (2002) 2-134.
  • [4] X. Zhang, F. Yu, W. Wu and Y. Zuo, Int. J. Thermophys. 24 (2003) 1395-1405. doi:10.1023/A:1026115521232
  • [5] M.M. Prieto, F.J. Fernández and J.L. Rendueles, Ironmaking Steelmaking 32 (2005) 165-170. doi:10.1179/174328105X15896
  • [6] Y. Lu and S.W. Markward, IEEE Trans. on Neural Networks 6 (1997) 1328-1337.
  • [7] J. Tenner, D.A. Linkens, P.F. Morris and T.J. Bailey, Ironmaking Steelmaking 28 (2001) 15-22. doi:10.1179/030192301677803
  • [8] A.A. Gorni, The modelling of hot rolling processes using neural networks, http://www.gorni.eng.br/e/neural_1998.html, 1998.
  • [9] M. Schlang, B. Lang, T. Poppe, T. Runkler and K. Weinzierl, Control Eng. Pract 9 (2001) 975-986. doi:10.1016/S0967-0661(01)00086-7
  • [10] A. González-Marcos, J.B. Ordieres-Meré, A.V. Pernía-Espinoza and V. Torre-Suárez, Rev. Metal. Madrid 44 (2008) 29-38.
  • [11] F. J. Martínez-de-Pisón, J. Ordieres, A. Pernía, F. Alba and V. Torre, Rev. Metal. Madrid 43 (2007) 325-336.
  • [12] J. B. Ordieres-Meré, A. González-Marcos, J.A. González and V. Rubio, Ironmaking Steelmaking 31 (2004) 43–50. doi:10.1179/030192304225012060
  • [13] A. V. Pernía-Espinoza, M. Castejón-Limas, A. González-Marcos and V. Lobato-Rubio, Ironmaking Steelmaking 32 (2005) 1-9.
  • [14] C. Schiefer, H.P. Jörgl, F.X. Rubenzucker and H. Aberl, Proc. 14th IFAC World Congress, 1999, pp. 61-66.
  • [15] Y.I. Kim, K. CheolMoon, B. Sam Kang, C. Han and K. Soo Chang, Control Eng. Practice 6 (1998) 1009-1014. doi:10.1016/S0967-0661(98)00098-7
  • [16] F.J. Martínez-de-Pisón, F. Alba, M. Castejón and J.A. González, Ironmaking Steelmaking 33 (2006) 344-352. doi:10.1179/174328106X101565
  • [17] G. Bloch, F. Sirou, V. Eustache and P. Fatrez, IEEE T. Neural Networks 8 (1997) 910-918. doi:10.1109/72.595889 PMid:18255694
  • [18] J.R. Quinlan, Proc. Australian Joint Conf. on Art. Int. World Scientific, 1992, pp. 343-348.
  • [19] S. Haykin, Neural networks, a comprehensive foundation (2nd ed.), Prentice Hall, New Jersey, EE.UU, 1999.
  • [20] A.V. Pernía-Espinoza, J.B. Ordieres-Meré, F.J.Martínez-de-Pisón and A. González-Marcos, Neural Networks 18 (2005) 191-204. doi:10.1016/j.neunet.2004.11.007 PMid:15795116
  • [21] G.N.Wilkinson and C.E. Rogers, Appl. Statistics 22 (1973) 392-399. doi:10.2307/2346786
  • [22] S. Portnoy and R. Koenke, Statistical Sci. 12 (1997) 279-300. doi:10.1214/ss/1030037960
  • [23] D. Aha and D. Kibler,Mach. Learning 6 (1991) 37-66.
  • [24] I.H.Witten and E. Frank, DataMining: Practical machine learning tools and techniques, 2nd Edition, MorganKaufmann, San Francisco, EE.UU., 2005.
  • [25] M. Castejón-Limas, J.B. Ordieres Meré, E.P. Vergara, F.J. Martínez-de-Pisón, A.V. Pernía and F. Alba, The AMORE package: A MORE flexible neural network package, CRAN Repository, http://wiki.r-project.org/rwiki/doku.php?id=packages:cran:amore, 2009.
  • [26] R Development Core Team, R: A language and environment for statistical computing, http://www.R-project.org, R Foundation for Statistical Computing, Vienna, Austria, 2008, URL.
Data source: Dialnet