AI-assisted optimization of membrane desalination under dynamic feed conditions

Thesis Abstract

This study addresses the challenge of maintaining high-quality desalinated water and energy efficiency in membrane processes under dynamic feed conditions, where feed salinity, temperature, and flow rate fluctuate due to seasonal and operational factors. The aim is to develop an AI-assisted optimization framework that integrates real-time monitoring, predictive modeling, and control strategies to adapt membrane operation for robust permeate quality and reduced energy consumption. Specific objectives include (1) characterizing dynamic feed profiles in seawater and brackish water desalination streams, (2) developing a data-driven predictive model of membrane performance (flux, salts passage, fouling indicators) under transient conditions, (3) formulating a coupled optimization problem that balances permeate quality, energy use, and recovery rate, (4) implementing a real-time control strategy using deep reinforcement learning (DRL) and Bayesian optimization to adjust operating setpoints, and (5) validating the framework on pilot-scale experiments and simulation studies across multiple feed scenarios. The methodology combines a mixed-methods research design anchored in systems engineering and AI theory. The population comprises laboratory-scale membrane modules (a 4-inch spiral-wound RO module and a staged nanofiltration unit) subjected to controlled dynamic feed experiments and operational data from a municipal desalination plant over a 12-month period. A sample of 60 dynamic-feeding sessions is planned, with each session capturing 6 hours of continuous operation at 1 Hz sampling. Data collection instruments include inline sensors for salinity, conductivity, total dissolved solids, feed temperature, flow rate, pressure, and permeate quality; archived plant SCADA data for historical dynamic events; and periodic fouling indicators measured via transmembrane pressure and permeate conductivity. Analytical techniques comprise multivariate time-series analysis, regression modeling (LASSO and non-linear random forests) to predict performance metrics, and process mining to uncover causal relationships in dynamic conditions. The AI optimization framework integrates a deep reinforcement learning agent (proximal policy optimization) trained on simulated environments calibrated with empirical data, and a Bayesian optimization layer for hyperparameter tuning and exploration-exploitation management. The objective function prioritizes compliance with predefined permeate-quality targets (EC<0.5 dS/m, TDS within specification) while minimizing specific energy consumption and maximizing recovery, subject to physical and material constraints. Model validation employs cross-validation on held-out dynamic sessions and sensitivity analyses to feed perturbations. Ethical considerations focus on safety, data integrity, and compliance with industrial confidentiality where plant data are used. Expected findings include (i) a validated predictive model capable of accurately forecasting short-term membrane performance (±5% error in flux, ±0.1 dS/m in permeate conductivity) under dynamic feed perturbations, (ii) a DRL-based control policy that stabilizes permeate quality and maintains energy use within 8–12% of baseline but with enhanced resilience to feed swings, (iii) quantification of energy savings and recovery improvements attributable to AI-driven adjustments, and (iv) a robust set of operating envelopes delineating when conventional control suffices versus when AI-assisted control yields clear benefits. The study anticipates observing that dynamic feed scenarios with rapid salinity fluctuations require more frequent setpoint recalibration and benefit most from model-p-based predictive control integrated with reinforcement learning. The contribution to knowledge lies in delivering a novel AI-enabled, real-time optimization framework for membrane desalination under dynamic feed conditions, bridging process engineering with advanced AI techniques. It extends the theory of process control by operationalizing a hybrid DRL-Bayesian optimization scheme within membrane systems and provides practical guidelines for implementing AI-driven adaptability in industrial desalination to improve reliability, energy efficiency, and water quality. The main conclusion is that AI-assisted optimization significantly enhances process robustness to feed variability without compromising product quality, thereby enabling more flexible plant operation. Recommendations include scaling the framework to pilot and full-scale plants, investigating transfer learning to adapt policies across feed sources, incorporating reliable fouling prognosis modules, and evaluating long-term economic benefits through life-cycle assessment and techno-economic modeling.