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A study on solid oxide electrolyzer stack and system performance based on alternative mapping models
Affiliation:1. School of Chemical & Environment Engineering, China University of Mining and Technology-Beijing, Beijing, 100083, China;2. National Institute of Clean-and-low-carbon Energy, Beijing, 102209, China;3. School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing, 100096, China
Abstract:In this study, a solid oxide electrolyzer cell (SOEC) stack model is developed based on an alternative mapping concept. The SOEC stack performance in a commercial hot box is systematically studied under different operating currents, flow rates, and flow directions. The results revealed that the SOEC stack operated in a hot box has thoroughly different temperature distributions, resulting in additional efficiency losses and an increase in thermal neutral voltage. The SOEC stack model computation results are summarized into stack performance diagrams and used in the system design. A 6-Nm3/h SOEC system with preheaters and recycling cathode materials is designed, and its performance is studied. The system efficiency is greatly influenced by the steam generator, and an external steam source can help increase the total efficiency of the system to more than 83%. Even the current increase may deteriorate the stack performance. It can increase the SOEC system efficiency by saving energy in the steam generator and preheaters. An increase in the flow rate around anode and cathode can improve the system capacity and efficiency. The system's maximum capacity is limited by the preheater heat balance and the stack output temperature. The feasible maximum system capacity is 33.4 kW electrolysis electric power input and 9.93 Nm3/h hydrogen production rate. At a constant system capacity, decreasing the air flow rate can minimize the heat losses in anode off-gas and achieve more than 87% nonsteam system efficiency.
Keywords:Solid oxide electrolyzer  Alternative mapping  System efficiency  Multi-physics  Multi-dimensional  BP neural network
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