Rapid Assessment and Optimization of Electrodes for Solid Oxide Fuel Cells and Electrolyzers using Long-Term Performance Modeling and Machine Learning
Computer vision techniques are combined with NETL’s machine learning based model trained on results from NETL's long-term SOFC/SOEC performance prediction tool, in order to produce a rapid assessment tool for evaluation and improvement of SOC electrodes from more easily collected microstructural data.