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Interpretable Machine Learning—Tools to Interpret the Predictions of a Machine Learning Model Predicting the Electrical Energy Consumption of an Electric Arc Furnace

Machine learning (ML) is a promising modeling framework that has previously been used in the context of optimizing steel processes. However, many of the more advanced ML models, capable of providing more accurate predictions to complex problems, are often impossible to interpret. This makes the domain experts in the steel industry, to a large extent, hesitant to adopt these models. The valuable increase in model accuracy is diminished by the lack of model interpretability. Herein, Shapley additive explanations (SHAP) is applied to an advanced ML model, predicting the electrical energy (EE) consumption of an electric arc furnace (EAF). The insights from SHAP reveal the contributions from each input variable on the EE for every single heat in the prediction domain. These contributions are then evaluated based on process metallurgical experience.