From fixed points to optimum regions
AI–NSGA-II framework for high-recovery, low-energy brackish water RO
Leili Abkar (Dalhousie University)
Shima Kamyab (University of Victoria)
Amirreza Aghili Mehrizi (Concordia University)
Pezhman Abbasi (University of British Columbia)
Mark van Loosdrecht (TU Delft - BT/Environmental Biotechnology)
Abbas Ghassemi (University of California)
Madjid Mohseni (Concordia University)
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Abstract
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.
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