4.7 Article

Predicting the performance of spiral-wound membranes in pressure-retarded osmosis processes

Journal

RENEWABLE ENERGY
Volume 189, Issue -, Pages 66-77

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.renene.2022.02.125

Keywords

Pressure-retarded osmosis; Spiral-wound; Power density; PRO membranes; Q-electrolattice

Funding

  1. Qatar National Research Fund under its National Priorities Research Program [NPRP10-1231-160069]
  2. ConocoPhillips Global Water Sustainability Center (GWSC)

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A process simulator has been developed to model and predict the performance of spiral-wound membrane modules in pressure retarded osmosis processes. The simulator allows for the calculation of total permeation and power density by taking into account the driving force profile consistent with flow patterns specific to spiral-wound membranes.
A process simulator has been developed to model and predict the performance of spiral-wound membrane modules in pressure retarded osmosis processes. This has involved automation of generalized protocols for the numerical integration of the solvent and solute flux equations (in conjunction with a suitable electrolyte equation of state) along the surface area of a spiral-wound membrane leaf. Performance equations are solved for discrete area elements and the spiral-wound character of the module as a whole is realized through the programmed sequence in which discrete elements are evaluated. This arrangement allows for mirroring the parabolic flow pattern of the feed stream in the spiral-wound membrane leaf. The total permeation (and, by extension, power density) is thus calculated in a manner that accounts for the driving force profile consistent with flow patterns specific to spiral-wound membranes. This effective treatment of each discrete element as a flat-sheet membrane enables the transferability of membrane parameters characterized in standard, coupon-scale experiments to the simulation of spiral-wound modules. This transferability is illustrated through comparisons of model predictions with published pilot-scale PRO data.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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