Development of Non-Linear Equations for Predicting Electrical Conductivity in Silicates
This work addresses energy losses and optimization issues in electric arc furnaces for metallurgical industries, but it is incremental as it applies existing neural network methods to a specific dataset.
The researchers tackled the problem of predicting electrical conductivity in electric arc furnace slags using artificial neural networks, achieving a model with 100 neurons in the hidden layer and analyzing sensitivity to predictor variables.
Electrical conductivity is of fundamental importance in electric arc furnaces (EAF) and the interaction of this phenomenon with the process slag results in energy losses and low optimization. As mathematical modeling helps in understanding the behavior of phenomena and it was used to predict the electrical conductivity of EAF slags through artificial neural networks. The best artificial neural network had 100 neurons in the hidden layer, with 6 predictor variables and the predicted variable, electrical conductivity. Mean absolute error and standard deviation of absolute error were calculated, and sensitivity analysis was performed to correlate the effect of each predictor variable with the predicted variable.