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

Utilization of membrane humidifiers is one of the methods commonly used to humidify reactant gases in polymer electrolyte membrane fuel cells. 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 and operating conditions, 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 models were verified by experimental data. The results showed that the feedforward models, especially FFGA, were suitable for this type of membrane humidifiers.