Yubing Wang

The side ledge is essential for maintaining thermal stability and protecting the sidewall lining in an aluminum reduction cell. Its shape and temperature distribution strongly influence cell operation and energy efficiency. However, conventional computational fluid dynamics (CFD) methods, while accurate, are computationally intensive and unsuitable for fast evaluation under changing operating conditions. To overcome this limitation, this study develops a CFD-driven machine learning surrogate model for rapid prediction of cross-sectional temperature fields and side ledge morphology in an aluminum reduction cell under magnetohydrodynamic (MHD) conditions. High-fidelity training data are first generated from MHD-CFD simulations. Key physical variables, including heat generation, effective thermal transport properties, and velocity components, are extracted from multiple cross-sections prior to side ledge formation to form the input dataset. Five machine learning methods—Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, and Support Vector Regression—are then developed to predict the resulting temperature field and side ledge profile after solidification. The performance of each model is systematically compared in terms of accuracy and applicability. Among the tested models, Random Forest demonstrates the best overall performance. Its predicted temperature fields closely match CFD results, with a maximum temperature deviation below 0.23 °C, while the average side ledge thickness error is only 0.0153 m. Moreover, the proposed surrogate model reduces computation time from approximately 168 h to 3 min compared with CFD simulations, while maintaining high predictive accuracy. Overall, the proposed method enables fast and accurate evaluation of thermal states, supports operational optimization, and provides a foundation for digital twin development of aluminum reduction cells.