Recognition of the operational states in electric arc furnaces

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.