Recognition of the operational states in electric arc furnaces

Author

Raisz, D. ; Sakulin, M. ; Renner, Herwig ; Tehlivets, Y.

Author_Institution

Dept. of Power Syst., Univ. of Technol. & Econ., Budapest, Hungary

Volume

2

fYear

2000

fDate

6/22/1905 12:00:00 AM

Firstpage

475

Abstract

For the optimization of the operation of electric arc furnaces (EAFs) it is important that the actual operational state of the furnace can be quickly and exactly determined. This paper presents a new approach that allows tracking of the melting process. This method uses a neural network in order to classify the dynamic characteristics and is compared in this paper with other methods, like the smoothed standard deviation of arc voltages and the partial harmonic distortion approaches. Finally, an application example for the introduced procedure is shown

Keywords

arc furnaces; harmonic distortion; melting; neural nets; power engineering computing; power system harmonics; arc voltage; electric arc furnaces; melting process tracking; neural network; operation optimization; operational states recognition; partial harmonic distortion; smoothed standard deviation; Electrodes; Furnaces; Iron; Neural networks; Power generation economics; Power system economics; Power system protection; Productivity; Slag; Steel;

fLanguage

English

Publisher

ieee

Conference_Titel

Harmonics and Quality of Power, 2000. Proceedings. Ninth International Conference on

Print_ISBN

0-7803-6499-6

Type

conf

DOI

10.1109/ICHQP.2000.897725

Filename

897725