Energy-efficient reverse osmosis desalination through GA-PSO motor control and hydraulic energy recovery

Improving the energy performance of seawater reverse osmosis (SWRO) requires coordinated consideration of the electric drive, hydraulic subsystem, membrane transport, and energy recovery. This study develops a numerical framework integrating a field-oriented-controlled permanent-magnet synchronous motor (PMSM), a high-pressure pump, hybrid genetic algorithm-particle swarm optimization (GA-PSO), and a solution-diffusion reverse osmosis model applied to nine commercial thin-film composite membranes. GA-PSO optimizes six proportional-integral controller gains using a multi-criterion objective comprising transient stator copper-loss energy, mechanical-speed tracking error, and speed overshoot; SWRO specific energy consumption (SEC) is not included in this optimization objective. Relative to an untuned midpoint-gain baseline, the optimized controller reduced transient stator copper-loss energy from 303.334 to 42.602 J (85.96%) and improved speed-transient performance under the simulated conditions. This reduction is specific to short-duration motor copper losses and does not establish a corresponding reduction in plant-level SEC. Eight independent optimization runs yielded a 2.71% coefficient of variation in the final normalized objective value. Comparisons with original equipment manufacturer design-tool calculations showed mean absolute percentage differences of 64.4% in net driving pressure, 30.0% in permeate flow, and 46.9% in SEC, highlighting substantial model discrepancies rather than experimental validation. At the reference ERD efficiency of 55%, the modeled membrane-specific SEC decreased by 24.9-30.5% across the nine commercial membranes, while increasing ERD efficiency to 85% yielded predicted SEC reductions of 38.4-47.1%. These improvements are attributable to hydraulic energy recovery rather than GA-PSO controller tuning. The findings distinguish drive-level transient improvements from membrane- and energy-recovery-related effects and identify the need for validated, dynamically coupled system-level energy assessment.