Identification of Liquid State of Scrap in Electric Arc Furnace by the Use of Computational Intelligence Methods
A constant aspiration to optimize electric arc steelmaking process causes an increase of the use of advanced analytical methods for the process support. Optimization of the production processes lead to real benefits, which are, for example, lower costs of production. More often computational intelligence methods are used for this purpose. In this paper authors present three methods used for identification of liquid state of scrap in electric arc furnace using analysis of signals of the current of furnace electrodes.
Keywordsindustrial application process modeling electric arc furnace signal processing noise estimation classification
Unable to display preview. Download preview PDF.
- 2.Wieczorek, T., Pilarczyk, M.: Classification of steel scrap in the EAF process using image analysis methods. Archives of Metallurgy and Materials 53(2), 613–618 (2008)Google Scholar
- 3.Millman, M.S., Nyssen, P., Mathy, C., Tolazzi, D., Londero, L., Candusso, C., Baumert, J.C., Brimmeyer, M., Gualtieri, D., Rigoni, D.: Direct observation of the melting process in an EAF with a closed slag door. Archives of Metallurgy and Materials 53(2), 463–468 (2008)Google Scholar
- 4.Kendall, M., Thys, M., Horrex, A., Verhoeven, J.P.: A window into the electric arc furnace, a continuous temperature sensor measuring the complete furnace cycle. Archives of Metallurgy and Materials 53(2), 451–454 (2008)Google Scholar
- 6.Wieczorek, T., Mączka, K.: Modeling of the AC-EAF process using computational intelligence methods. Electrotechnical Review 11, 184–188 (2008)Google Scholar
- 7.Blachnik, M., Mązka, K., Wieczorek, T.: A model for temperature prediction of melted steel in the electric arc furnace (EAF). LNCS, vol. 4839, pp. 371–378. Springer, Heidelberg (2010)Google Scholar
- 9.Liitiäinen, E., Corona, F., Lendasse, A.: Nearest Neighbor Distributions and Noise Variance Estimation. In: ESANN, Belgium (2007)Google Scholar
- 11.Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufman, San Francisco (1993)Google Scholar