Prediction of the Effect of Polymer Membrane Composition in a Dry Air Humidification Process via Neural Network Modeling

Abstract

Utilization of membrane humidifiers is one of the methods commonly used to humidify reactant gases in polymer electrolyte membrane fuel cells (PEMFC). In this study, polymeric porous membranes with different compositions were prepared to be used in a membrane humidifier module and were employed in a humidification test. Three different neural network models were developed to investigate several parameters, such as casting solution composition, membrane thickness, operating pressure, and flow rate of input dry air which have an impact on relative humidity of the exhausted air after humidification process. The three mentioned models included Feed- Forward Back- Propagation (FBP), Radial Basis Function (RBF), and Feed- Forward Genetic Algorithm (FFGA). The developed models were verified by experimental data. The results showed that the feed- forward neural network models, especially FFGA, were suitable for prediction of the effect of membrane composition and operating conditions on the performance of this type of membrane humidifiers