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A proton exchange membrane (PEM) electrolyzer is fed with water and powered by electric power to electrochemically produce hydrogen at low operating temperatures and emits oxygen as a by-product. Due to the complex nature of the performance of PEM electrolyzers, the application of an artificial neural network (ANN) is capable of predicting its dynamic characteristics. A handful of studies have examined and explored ANN in the prediction of the transient characteristics of PEM electrolyzers. This research explores the estimation of the transient behavior of a PEM electrolyzer stack under various operational conditions. Input variables in this study include stack current, oxygen pressure, hydrogen pressure, and stack temperature. ANN models using three differing learning algorithms and time delay structures estimated the hydrogen mass flow rate, which had transient behavior from 0 to 1 kg/h, and forecasted better with a higher count (>5) of hidden layer neurons. A coefficient of determination of 0.84 and a mean squared error of less than 0.005 were recorded. The best-fitting model to predict the dynamic behavior of the hydrogen mass flow rate was an ANN model using the Levenberg–Marquardt algorithm with 40 neurons that had a coefficient of determination of 0.90 and a mean squared error of 0.00337. In conclusion, optimally fit models of hydrogen flow from PEM electrolyzers utilizing artificial neural networks were developed. Such models are useful in establishing an agile flow control system for the electrolyzer system to help decrease power consumption and increase efficiency in hydrogen generation. [ABSTRACT FROM AUTHOR]

A proton exchange membrane fuel cell (PEMFC) is a more environmentally friendly alternative to deliver electric power in various applications, including in the transportation industry. As PEMFC performance characteristics are inherently nonlinear and involved, the prediction of the performance in a given application for different operating conditions is important in order to optimize the efficiency of the system. Thus, modelling using artificial neural networks (ANNs) to predict its performance can significantly improve the capabilities of handling the multi-variable nonlinear performance of the PEMFC. However, further investigation is needed to develop a dynamic model using ANNs to predict the transient behavior of a PEMFC. This paper predicts the dynamic electrical and thermal performance of a PEMFC stack under various operating conditions. The input variables of the PEMFC stack for the analysis consist of the cathode inlet temperature, anode inlet pressure, anode and cathode inlet flow rates, and stack current. The performances of the ANN models using three different learning algorithms are determined based on the stack voltage and temperature, which have been shown to be consistently predicted by most of these models. Almost all models with varying hidden neurons have coefficients of determination of 0.9 or higher and mean squared errors of less than 5. Thus, the results show promise for dynamic modelling approaches using ANNs for the development of optimal operation of a PEMFC in various system applications. [ABSTRACT FROM AUTHOR]