Supercomputing and machine learning-aided optimal design of high permeability seawater reverse osmosis membrane systems

Abstract

Concentration polarization (CP) should limit the energy and cost reducing benefits of high permeability seawater reverse osmosis (SWRO) membranes operating at a water flux higher than normal one. Herein, we propose a multiscale optimization framework coupling membrane permeability, feed spacer design (sub-millimeter scale) and system design (meter scale) via computational fluid dynamics and system level modeling using advanced supercomputing in conjunction with machine learning. Simulation results suggest energy consumption could be reduced by 27.5% (to 1.66 kWh m−3) predominantly through the use of high permeability SWRO membranes (12.2%) and a two-stage design (14.5%). Without optimization, CP approaches 1.52 at the system inlet, whereas the optimized CP is limited to 1.20. This work elucidates the optimized permeability, module design, operating scheme and benefits of high permeability SWRO membranes in seawater desalination.