Modeling techniques applied to the study of gas diffusion electrodes and proton exchange membrane biochemical fuel cells

Mathematical modeling has been extensively applied to the study and development of fuel cells. In this laboratory, modeling studies of gas diffusion electrodes and proton exchange membrane biochemical fuel cells are being developed. Regarding the modeling of usual physical systems, the available knowledge makes it possible to develop mechanistic models. For biochemical fuel cells, on the other hand, semi-empirical and empirical models can be used. In this work, there are three objectives: characterize a phenomenological model for a Pt–air cathode and perform appropriate simulations; characterize a semi-empirical model to predict the performance of a Pt–H2/H2O2-peroxidase fuel cell; investigate the effectiveness of (empirical) neural networks to predict the performance of a Pt–H2/O2-peroxidase fuel cell. The mechanistic model of a Pt–air cathode developed here is based on proper material balances, on Fick's law of diffusion and on Tafel kinetics. It can provide details of the physical system (such as the limit of the one-phase regime). A semi-empirical model based on Michaelis–Menten kinetics, in turn, can predict the performance of a Pt–H2/H2O2-peroxidase biochemical fuel cell. Artificial neural networks were capable of fitting the potential/current relationship of a Pt–H2/O2-peroxidase biochemical fuel cell.