Enhanced Temporal Convolutional Network Based Approach for Degrada-tion Prediction of Reverse Osmosis Systems

Reverse Osmosis (RO) degradation underscores the importance of predictive capabilities to develop optimal maintenance strategies that minimize losses. In this study, we develop a Temporal Convolutional Network (TCN) model to predict the RO system states using the primary indicator for RO analysis: the fluctuations in differential pressure across the RO vessel. Specifically, data from a real desalination plant for the period 2015 to 2020 are used. The dataset encompasses 14 RO train operations, including routine operations, significant maintenance events, temporary shutdowns, and element replacements. The proposed approach uses temporal convolutional operations to capture the dynamic pressure behavior at both ends of the membrane, enabling faster, more accurate anomaly detection. A key challenge in applying deep learning to this domain is the heavy reliance on real-world operational data. The approach involves a strong data preprocessing strategy that reveals subtle relationships between operating time and pressure dynamics. Accurate prediction of membrane degradation also ena-bles preventive and recovery actions, which reduce maintenance expenses. The proposed method is evaluated against con-ventional models, including LSTM, CNN-LSTM, and GRU, using data from the real desalination plant. Experimental results demonstrate that the proposed model achieves the lowest predic¬tion error and shows strong potential for deployment in practical desalination operations.