Process Monitoring–Driven Predictive Thermal Modeling in Aluminum Electrolysis Cells Under High-Penetration Wind and Solar Power

Continuous thermal monitoring of aluminum electrolysis cells—which operate at ~950 °C under strong magnetic fields in a corrosive fluoride melt—remains an unsolved process monitoring challenge. This paper presents the first fiber-optic Raman distributed temperature sensing (DTS) deployment for high-temperature cathode steel bar monitoring in a 380 kA industrial cell supplied by a grid with over 30% wind and solar penetration. A custom fiber ring packaging scheme achieved >1 m thermal contact length pressed against the underside of the cathode bar within the confined space between the bar and the cell bottom steel structure. Features encoding supply-side renewable power periodicity, thermal inertia, and local fluctuation intensity were engineered to anchor the learning task in the process physics of the electrolysis cell. CatBoost, LightGBM, and Random Forest were combined in a stacking ensemble with a linear regression meta-learner, attaining RMSE = 0.7451 °C and R2 = 0.9944 over two months of continuous industrial operation across seven cathode bars. Frequency-domain residual decomposition revealed why LightGBM—the aggregate-weakest base learner—received the dominant meta-learner weight (+2.15) while CatBoost—the aggregate-strongest—received a negative weight (−1.85): LightGBM uniquely minimized high-frequency error (1.06 vs. 1.29 °C for CatBoost), precisely the band where thermal anomaly precursors manifest under short-cycle renewable power fluctuations. Model rankings were spatially robust across all seven bars despite substantial thermal heterogeneity. The framework demonstrates that process-informed feature engineering and frequency-resolved stacking ensemble learning deliver predictive accuracy suitable for early-warning deployment in high-temperature industrial environments under increasing renewable power penetration.