An Interpretable Hybrid Deep Learning Model for Molten Iron Temperature Prediction at the Iron-Steel Interface Based on Bi-LSTM and Transformer

Hot metal temperature is a key factor affecting the quality and energy consumption of iron and steel smelting. Accurate prediction of the temperature drop in a hot metal ladle is very important for optimizing transport, improving efficiency, and reducing energy consumption. Most of the existing studies focus on the prediction of molten iron temperature in torpedo tanks, but there is a significant research gap in the prediction of molten iron ladle temperature drop, especially as the ladle is increasingly used to replace the torpedo tank in the transportation process, this research gap has not been fully addressed in the existing literature. This paper proposes an interpretable hybrid deep learning model combining Bi-LSTM and Transformer to solve the complexity of temperature drop prediction. By leveraging Catboost-RFECV, the most influential variables are selected, and the model captures both local features with Bi-LSTM and global dependencies with Transformer. Hyperparameters are optimized automatically using Optuna, enhancing model performance. Furthermore, SHAP analysis provides valuable insights into the key factors influencing temperature drops, enabling more accurate prediction of molten iron temperature. The experimental results demonstrate that the proposed model outperforms each individual model in the ensemble in terms of R 2 , RMSE, MAE, and other evaluation metrics. Additionally, SHAP analysis identifies the key factors contributing to the temperature drop.