Towards Smart Aluminum Smelting: An AI-Driven Approach for Real Time Thermal Anomaly Detection Using Distributed Optical Fiber Sensors

Abnormal temperature rise in the cathode steel bars of electrolytic aluminum cells, a core smelting equipment, is a major cause of furnace leakage accidents. However, traditional thermocouple and infrared temperature measurement technologies are susceptible to electromagnetic interference under extreme operating conditions such as strong magnetic fields, high temperatures, and severe corrosion, making continuous and accurate monitoring difficult. To address this technical bottleneck, this study developed an intelligent monitoring system that integrates distributed fiber optic sensing and stacking ensemble learning. By deploying electromagnetic interference-resistant fiber optic sensors on the surface of the cathode steel bars, continuous temperature data acquisition was achieved. A multidimensional feature set was constructed, including time series lag, rolling statistics, and periodic features, and the prediction performance of models such as CatBoost, LightGBM, and random forest was systematically compared. Ultimately, a stacking ensemble strategy was employed to combine the strengths of each base model to achieve accurate cathode steel bar temperature prediction (RMSE < 2.1°C, R² >0.99 on the test set). This system can proactively identify abnormal temperature rises, reducing warning time by over 85% compared to manual inspections, providing a reliable technical path for intelligent safety monitoring in electrolytic aluminum production.