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Inhomogeneous current and temperature distributions are harmful to thedurability of the solid oxide electrolysis cell (SOEC). Segmented SOECexperiments reveal that a high steam utilization, which is favorable for systemefficiency, leads to local steam starvation and enhanced the inhomogeneity. Itis necessary to consider inhomogeneity and efficiency jointly in optimizationstudies. Three-dimensional (3D) multiphysics models validated with experimentscan simulate the inhomogeneity in a reliable manner, but they are unsuitablefor optimization due to the high computational cost. This study proposes amethod that combines segmented SOEC experiments, multiphysics simulation, andartificial intelligence to optimize the inhomogeneity and efficiency of SOECjointly. A 3D cell model is first built and verified by segmented SOECexperiments. Then, fast neural network surrogate models are built from thesimulation data and integrated into a multi-objective optimization problem. Itssolutions form a Pareto front reflecting the conflicting relationships amongdifferent objectives. It is found that the down-stream current is 60%-65% ofthe up-stream current when the steam utilization is 0.7. To increase the steamutilization to 0.8, the down-stream current will further drop to 50%-60% of theup-stream current. The Pareto fronts enable system operators to achieve abalance between efficiency and inhomogeneity.

Optimizing the Homogeneity and Efficiency of an SOEC Based on Multiphysics Simulation and Data-driven Surrogate Model