In the present study

a novel approach based on an evolutionary wavelet neural network (EWNN) is proposed to estimate the slag quality in an electric arc furnace (EAF) employing power quality indices. In the EWNN

an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets. For this purpose

all of the electrical parameters for six melting processes are measured with a power quality analyzer

attached to the secondary component of an EAF transformer at a Saba steel complex

to estimate the foaming slag quality. Experimental results on various combinations of measured electrical parameters

applying the designed EWNN estimator

demonstrate that utilizing five leading indicators leads to the highest precision. The obtained 99% accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.