Physics-Informed Dynamic Mode Decomposition with Control for Predictive Maintenance in Chlor-Alkali Electrolysis

Chlor-alkali electrolysis is a cornerstone of chemical manufacturing, enabling global production of chlorine and related chemicals while consuming over 10% of industrial electricity. However, impurity-driven degradationcaused by calcium and magnesium hydroxidesremains a critical challenge, escalating energy costs and shortening system lifespans due to the lack of real-time impurity monitoring and adaptive predictive maintenance strategies. Here, we propose a unified framework integrating physics-informed Dynamic Mode Decomposition with Control (piDMDc). Our method reduces resistance prediction errors by 61% compared to baseline and quantifies degradation trends through deviation models validated against historical resistance data. Applied to a 3 MW industrial electrolyzer in Italy, the framework demonstrates stable, interpretable degradation tracking by enforcing physical constraints on system dynamics. While direct RUL validation awaits maintenance records, the approach establishes a methodology to reconcile sparse laboratory measurements with operational data for impurity-driven failure forecasting. This work advances predictive maintenance in electrochemical systems by balancing data-driven insights with domain-specific constraints, offering a pathway to optimize maintenance costs.