A coupled and interactive influence of operational parameters for optimizing power output of cleaner energy production systems under uncertain conditions

The mechanisms in proton-exchange membrane fuel cells (PEMFCs) cannot be explicitly represented by a mathematical function because the PEMFC system is multi-dimensional and complex and represents uncertainty in operation variables, which cannot be modeled by experiments or by trial-and-error approach. Therefore, this work proposes to study the coupled and interactive influence of stack current (SC), stack temperature (ST), oxygen excess ratio (OER), hydrogen excess ratio (HER), and inlet air humidity (IAH) for optimizing the power output of PEMFC. The data obtained from the experiments have been inserted into architecture of automated neural-network search, which automates the selection of error function, activation function, uncertainties in inputs and number of hidden neurons in formulation of a robust and accurate model for power density as a function of five operational variables. Among the operational variables, the correlation coefficient between the SC and the output power is the highest, followed by OER, and the ST. However, for HER and IAH, the power output follows negative nonlinear relation. The optimization converged at 130(th) iteration results in maximum power output of 3410 W for an optimum value of SC (51A), ST (59 degrees C), OER (3:2), HER (1:10), and IAH (0.8).