Escalating global freshwater scarcity demands more energy-efficient and sustainable brackish water reverse osmosis (BWRO) desalination. This study demonstrates how integrating high-fidelity Artificial Neural Network (ANN) surrogates with a robust Non-dominated Sorting Genetic Algorithm II (NSGA-II) can deliver reliable multi-objective optimization for pilot-scale BWRO systems. Unlike conventional polynomial response surface models (RSM), which rely on static assumptions and often oversimplify dynamic membrane processes (and exhibit prediction errors of 15–25 %), the proposed framework directly learns the complex, nonlinear relationships among feed salinity, flow rate, pressure, temperature, and membrane type.

Validated against pilot-scale data with R2 > 0.99 and absolute average relative errors below 5 %, the ANN models accurately predict energy consumption (EC) and recovery (Re) under realistic operational conditions. Coupled with NSGA-II, the framework systematically generates Pareto-optimal operating regions that balance low EC (0.6 kWh/m³) with high Re (up to 80 %) while respecting fouling and scaling constraints. This multi-objective approach provides a flexible operating envelope, such as 3–4.5 LPM feed flow and 90–125 psi with higher-permeability membranes, surpassing the limitations of single-point optima. The optimized recovery represents a 3- to 5-fold increase over the typical factory baseline (∼15 %), translating to energy savings of >50 % and CO₂ emission reductions of 0.1–0.2 kg/m³. Sensitivity analysis confirms feed flow rate and pressure as dominant drivers of EC (31.3 % and 28.6 % relative factor) and membrane type and flow rate as primary influencers of Re (32.2 % and 30.2 %).

This optimum region approach surpasses the limitations of traditional single-point design optimization by providing flexible operating envelopes that accommodate seasonal feed variability, equipment aging, and membrane fouling. All models and the optimization framework are shared via an open-source repository to ensure full reproducibility and facilitate industrial adoption.

Overall, this AI-driven multi-objective optimization framework bridges the gap between theoretical performance and field-ready operation, laying the foundation for more adaptive, cost-effective, and climate-smart brackish water desalination. The modular approach is directly adaptable to multi-stage and hybrid systems, offering a scalable and resilient solution to urgent global water scarcity challenges. ... Read more

Reverse osmosis (RO) systems offer a viable solution for treating brackish water (BW), a common but underutilized water resource. However, the energy-intensive nature of brackish water reverse osmosis (BWRO) systems poses affordability challenges to water supply, necessitating a focus on minimizing their energy consumption to support SDG6's goal of providing safe and affordable drinking water for all. This study addresses the critical need to minimize the specific energy consumption (SEC) of a typical BWRO system, defined as the energy consumed per unit of water recovered, mathematically and experimentally. Empirical models were developed proving there is a global minimum SEC while adjusting the operating conditions. Furthermore, we identified the key operating factors influencing SEC and their priority levels, along with their interactive effects. Notably, no prior study has discussed the significance and interaction of these operating factors (e.g., feed water salinity, temperature, pressure, flowrate and membrane permeability) on SEC of a BWRO system. Employing a full factorial experimental design with mixed levels of operating parameters, the study developed regression models that elucidate the mechanistic interaction between these parameters and system performance. Moreover, the models were validated experimentally, with a new dataset demonstrating their accuracy and reliability. ANOVA statistical analysis identified feed salinity, pressure, flow rate, feed flow rate×pressure, salinity×pressure, and temperature as influential operating parameters in reducing SEC, in descending order of importance. Operating within the determined optimum range resulted in a 36 % decrease in SEC and a more than fourfold increase in water recovery. The study's systematic approach and findings can be extrapolated to optimize the performance of other desalination technologies and diverse feed water types, contributing significantly to global water sustainability efforts. ... Read more