Optimization of net power density in Reverse Electrodialysis.

Reverse Electrodialysis (RED) extracts electrical energy from the salinity difference between two solutions using selective ion exchange membranes. In RED, conditions yielding a large net power density (NPD) are generally desired, due to the still large cost of the membranes. NPD depends on a large number of physical and geometric parameters. Some of these, for example the inlet concentrations of concentrate and diluate, can be regarded as "scenario" variables, imposed by external constraints (e.g., availability) or chosen by different criteria than NPD maximization. Others, namely the thicknesses H CONC , H DIL and the velocities U CONC , U DIL in the concentrate and diluate channels, can be regarded as free design parameters and can be chosen so as to maximize NPD. In the present study, a simplified model of a RED stack was coupled with an optimization algorithm in order to determine the conditions of maximum NPD in the space of the variables H CONC , H DIL , U CONC , U DIL for different sets of "scenario" variables. The study shows that an optimal choice of the free design parameters for any given scenario, as opposed to the adoption of standard fixed values for the same parameters, may provide significant improvements in NPD. • A simplified model of Reverse Electrodialysis is used to predict Net Power Density. • Quantities affecting NPD are classified into optimization and scenario variables. • The model is coupled with a gradient-ascent optimization algorithm. • Conditions of maximum NPD in the space of optimization variables are determined. • Optimum working conditions change significantly with the scenario variables. [ABSTRACT FROM AUTHOR]