Modeling of spiral wound membrane desalination modules and plants – review and research priorities

• Systematic review presented of SWM-module modelling requirements and approaches
• Module geometric parameters play dominant role in SWM desalination performance
• The complicated SWM modeling problem characterized by spatio-temporal variability
• “Separation of scales” approach to global modelling demonstrated to be effective
• R&D priorities outlined regarding development of a global dynamic simulator

Spiral Wound Membrane (SWM) modules are the basic components of modern desalination and water treatment technology. To advance this technology, a comprehensive SWM-element model and related performance simulator are indispensable tools. A flexible and efficient simulator is needed to optimize SWM modules, and to be integrated into general-purpose software for designing and monitoring/controlling entire desalination plants. Desirable features of SWM-model are outlined first, considering practical constraints. Reviewing related work, it is recognized that the complicated physico-chemical phenomena (and interactions) occurring in SWM-modules extend over several length- and time-scales, thus rendering impossible direct solution of the complete problem. Therefore, a tractable modeling-structure is needed, whereby properly correlated results of detailed studies (at small scale) on flow and mass transfer in spacer-filled channels, and sub-models representing the membrane function, are integrated into an appropriate modeling framework for a broad spatial domain, i.e. for performance simulation of entire SWM modules. Available steady-state models are reviewed and investigations toward development of dynamic simulators are outlined. Typical results are discussed of detailed two-dimensional distributions of process parameters, throughout the SWM-modules in a pressure vessel, for steady-state operation. An overall assessment of simulating SWM-module performance and of design-parameter effects, considering industry requirements, leads to suggestions on R&D priorities.