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.

翻译：电导率在电弧炉（EAF）中具有基础重要性，这一现象与过程炉渣的相互作用会导致能量损失和优化程度降低。由于数学建模有助于理解现象行为，本研究通过人工神经网络预测电弧炉炉渣的电导率。最优人工神经网络在隐藏层包含100个神经元，采用6个预测变量和1个被预测变量（即电导率）。计算了平均绝对误差和绝对误差的标准差，并进行了敏感性分析以关联各预测变量对被预测变量的影响。