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Optimization of Reverse Osmosis Networks with Spiral-Wound Modules
43
Citations
27
References
2012
Year
Salt ConcentrationEngineeringElectrodialysisWater Resources EngineeringMarine EngineeringBiomedical EngineeringWater TreatmentMembrane ProcessMicrofluidicsMembrane TechnologyReverse Osmosis NetworksWater DistributionBrine DisposalWater TechnologySpiral-wound ModulesOcean EngineeringEnvironmental EngineeringWater PurificationReverse Osmosis
An optimization of reverse osmosis (RO) networks for seawater desalination with spiral-wound modules (SWM) was presented in this work. The membrane transport model, which was based on the mass and momentum transport equations, took into consideration the longitudinal variation of the velocity, the pressure, and the salt concentration in the membrane modules. The pressure exchanger (PX) was included in the RO superstructure, and salinity increase caused by volumetric mixing in the PX was considered. The results obtained from the presented model were compared with the actual plant operational data from literature and found to be in good agreement with relative errors of 0.81%∼2.15% and 0.01%∼0.09%, in terms of water recovery and salt rejection, respectively. The optimum design problem was formulated as a mixed integer nonlinear programming (MINLP) problem. The variation of feed salinity was studied using the RO networks model. For the feed concentration higher than 32 kg/m3, one-stage RO system is favored. When the feed concentration is below 28 kg/m3, two-stage RO system is the better choice. The unit product cost increases with the decreases of permeate concentration requirement. For the looser permeate concentration requirement (0.30 kg/m3), one-pass configuration can meet the required quality of desalted water. When the lower permeate quality requirement of concentration is from 0.050–0.20 kg/m3, a two-pass system is more suitable. The influence of system recovery rate on the plant performance was discussed. Finally, sensitivity analysis showed that the total annualized cost is highly sensitive to the feed flow rate, the operating pressure, and electricity cost, while the energy consumption is highly sensitive to the operating pressure, the feed salinity, and the feed temperature.
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