摘要
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.I 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.
