Machine Learning-powered Performance Monitoring of Proton Exchange Membrane Water Electrolyzers for Enhancing Green Hydrogen Production as a Sustainable Fuel for Aviation Industry

Aviation is a major contributor to transportation carbon emissions but aims to reduce its carbon footprint. Sustainable and environmentally friendly green hydrogen fuel is essential for decarbonization of this industry. Using the extremely low temperature of liquid hydrogen in aviation sector unlocks the opportunity for cryoelectric aircraft concept, which exploits the advantageous properties of superconductors onboard. A significant barrier for green hydrogen adoption relates to its high cost and the immediate need for large-scale production, which Proton Exchange Membrane Water Electrolyzers (PEMWE) can address through optimal dynamic performance, high lifetimes, good efficiencies, and importantly, scalability. In PEMWE the cell is a crucial component that facilitates the electrolysis process and consists of a polymer membrane and electrodes. To control the required production rate of hydrogen, the output power of cell should be monitored which usually is done by measuring the cell’s potential and current density. In this paper, five different machine learning (ML) models based on different algorithms have been developed to predict this parameter. Findings of the work highlight that the model based on Cascade-Forward Neural Network (CFNN) is investigated to accurately predict the cell potential of PEMWE under different anodic material and working conditions with an accuracy of 99.998 % and 0.001884 in terms of R2 and root mean square error, respectively. It can predict the cell potential with a relative error of less than 0.65 % and an absolute error of below 0.01 V. The Standard deviation of 0.000061 for 50 iterations of stability analysis indicated that this model has less sensitivity to the random selection of training data. By accurately estimating different cell’s output with one model, and considering its ultra-fast response, CFNN model has the potential to be used for both monitoring and the designing purposes of green hydrogen production.