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Electrical conductivity is of fundamental importance in electric arc furnaces(EAF) and the interaction of this phenomenon with the process slag results inenergy losses and low optimization. As mathematical modeling helps inunderstanding the behavior of phenomena and it was used to predict theelectrical conductivity of EAF slags through artificial neural networks. Thebest artificial neural network had 100 neurons in the hidden layer, with 6predictor variables and the predicted variable, electrical conductivity. Meanabsolute error and standard deviation of absolute error were calculated, andsensitivity analysis was performed to correlate the effect of each predictorvariable with the predicted variable.

Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates