Publication

Abstract :

Electric Arc Furnaces (EAFs) are vital to sustainable steelmaking by enabling scrap metal recycling and reducing raw material demand. However, their high energy usage typically 400–470 kWh per ton poses economic and environmental challenges. This study proposes a machine learning framework to predict energy consumption through model-level optimization in EAFs using real data from a 120 MVA DSP-120 furnace. The dataset includes variables like transformer tap stages, oxidation rates, oxygen/gas injection rates, and post-process chemical compositions, all mapped to unique Heat IDs. After preprocessing and standardization, five regression models Random Forest, CatBoost, XGBoost, Bi-LSTM, and ANN were developed and evaluated. Random Forest hyperparameters were optimized using a Bayesian search strategy to improve model robustness and forecasting accuracy. To improve robustness, the top three models were combined through ensemble averaging. The final model achieved an MAE of 0.2533 MW, RMSE of 0.5858 MW, R2 of 0.9918, SMAPE of 48.51%, and 99% of predictions within ±5 MW, clearly demonstrating the industrial reliability of the proposed BO-RF approach. SHAP explainability identified key drivers of energy use, supporting interpretability and operator trust. This optimized ensemble approach offers both high predictive accuracy and a industry ready solution for intelligent energy control in modern steel production.