Reverse Electrodialysis (RED) harvests electrical energy from a salinity gradient. The maximum obtainable net power density (NPD) depends on many physical and geometric variables. Some have a monotonic (beneficial or detrimental) influence on NPD, and can be regarded as “scenario” variables chosen by criteria other than NPD maximization. Others, namely the thicknesses HCONC, HDIL and the velocities UCONC, UDIL in the concentrate and diluate channels, have contrasting effects, so that the NPD maximum is obtained for some intermediate values of these parameters. A 1-D model of a RED stack was coupled here with an optimization algorithm to determine the conditions of maximum NPD in the space of the variables HCONC, HDIL,UCONC, UDIL for different combinations of the “scenario” variables. The model accounts for entrance effects, property variation, concentration polarization, axial concentration changes, osmotic, electro-osmotic and diffusive fluxes, and can deal with complex channel geometries using Ohmic resistances, friction factors and mass transfer coefficients computed by 3-D simulations.

Michele Ciofalo, M.L.c. (2018). Maximum Net Power Density Conditions in Reverse Electrodialysis Stacks. In 13th SDEWES conference Palermo 2018 book of abstracts.

Maximum Net Power Density Conditions in Reverse Electrodialysis Stacks

Michele Ciofalo;Mariagiorgia La cerva;Massimiliano Di Liberto;Luigi Gurreri;Andrea Cipollina;Giorgio Micale
2018-01-01

Abstract

Reverse Electrodialysis (RED) harvests electrical energy from a salinity gradient. The maximum obtainable net power density (NPD) depends on many physical and geometric variables. Some have a monotonic (beneficial or detrimental) influence on NPD, and can be regarded as “scenario” variables chosen by criteria other than NPD maximization. Others, namely the thicknesses HCONC, HDIL and the velocities UCONC, UDIL in the concentrate and diluate channels, have contrasting effects, so that the NPD maximum is obtained for some intermediate values of these parameters. A 1-D model of a RED stack was coupled here with an optimization algorithm to determine the conditions of maximum NPD in the space of the variables HCONC, HDIL,UCONC, UDIL for different combinations of the “scenario” variables. The model accounts for entrance effects, property variation, concentration polarization, axial concentration changes, osmotic, electro-osmotic and diffusive fluxes, and can deal with complex channel geometries using Ohmic resistances, friction factors and mass transfer coefficients computed by 3-D simulations.
Reverse Electrodialysis, Net power density, Salinity Gradient, Concentration Polarization, Optimization, Gradient Ascent
Michele Ciofalo, M.L.c. (2018). Maximum Net Power Density Conditions in Reverse Electrodialysis Stacks. In 13th SDEWES conference Palermo 2018 book of abstracts.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/302240
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