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Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R2 score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Abstract: Ni-doped perovskite anodes have been investigated for oxidation of hydrogen in IT-SOFC. Perovskite materials are characterized by mixed electronic–ionic conductivity that combined to the high intrinsic catalytic activity of Ni allows to achieve high reaction rates. Suitable power densities of about for the direct oxidation of hydrogen were achieved at 800°C. Temperature programmed reduction analysis carried out in hydrogen reveals that the catalyst at 800°C has totally lost the perovskite structure showing formation of substructures such as NiOOH and . [Copyright &y& Elsevier]