Artificially intelligent framework for multi-output performance prediction in diverse solid oxide electrolyzer cells for green hydrogen production plants

Abstract Hydrogen sourced from renewable resources has been increasingly regarded as a promising alternative chemical energy carrier to fossil fuels, with support being given to efforts for climate change mitigation and the achievement of net-zero emission targets. Solid oxide electrolyzer cells (SOECs) operating in higher temperatures have shown greater system-level efficiency in comparison to other types of electrolyzers, making them a promising option for mass production of hydrogen. However, diverse SOEC architectures—such as varying anode and cathode materials—and wide operating ranges make it difficult to develop a single, multi-output, multi-parameter, broadly applicable performance model. In this paper, artificial intelligence (AI) techniques have been applied to SOEC data to develop an advanced model based on a cascade-forward neural network (CFNN) that holistically captures these design and operating variables. The resulting CFNN model reliably predicts ohmic resistance, current density, and hydrogen production rate with coefficients of determination ( R 2 ) of 0.9981, 0.9752, and 0.9878, respectively, in comparison with experimentally sourced testing data and across a wide range of SOEC configurations and conditions. Essentially, this paper delivers a highly accurate and computationally efficient digital surrogate model based on an AI technique, enabling smarter design, operation, and scaling of SOECs, and contributing to broader decarbonization goals through cost-effective and efficient sustainable hydrogen production.