Harnessing Physics-Informed Neural Networks for Performance Monitoring in SWRO Desalination
2025
Saloua Helali | Shadiah Albalawi | Nizar Bel Hadj Ali
Seawater Reverse Osmosis (SWRO) desalination is a critical technology for addressing global water scarcity, yet its performance can be hindered by complex process dynamics and operational inefficiencies. This study investigates the revolutionary potential of Physics-Informed Neural Networks (PINNs) for modeling SWRO desalination processes. PINNs are subsets of machine learning algorithms that incorporate physical information to help provide physically meaningful neural network models. The proposed approach is here demonstrated using operating data collected over several months in a Seawater RO plant. PINN-based models are presented to estimate the effects of operating conditions on the permeate TDS and pressure drop. The focus is on the feed water temperature variations and progressive membrane deterioration caused by fouling. Predictive models generated using PINNs showed high performances with a determination coefficient of 0.96 for the permeate TDS model and 0.97 for the pressure drop model. Results show that the use of PINNs significantly enhances the ability to predict membrane fouling and produced water quality, thereby supporting informed decision-making for RO process control.
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