A comprehensive flowsheet model of a multi-cell SOEC system is developed using AVEVA Process Simulation. The model incorporates electrochemical kinetics, heat and mass transfer phenomena, and key balance-of-plant components, such as heat exchangers and gas separators. Validation against experimental data for a five-cell stack demonstrates high accuracy, with less than 5% deviations in Current-Voltage characteristics and hydrogen production predictions [2,3]. The study systematically evaluates operating conditions (temperature, current) to optimize voltage efficiency and hydrogen output while ensuring long-term electrochemical stability.
Operability analysis [4,5,6] is then employed to define feasible operating regions by systematically assessing input constraints, including temperature, steam flow rate, and current density, while accounting for critical limitations such as thermal stress and degradation boundaries. Following this analysis, a techno-economic optimization is performed to minimize capital expenditures and hydrogen production costs ($/kg), and maximize energy efficiency. The proposed optimization strategy demonstrates a significant reduction in hydrogen production costs compared to baseline designs, showing the potential for cost-effective and scalable SOEC implementation. Therefore, this work is expected to enhance understanding of stack-level modeling of SOEC systems for hydrogen production, thus facilitating overall process design and improving efficiency and costs.
References:
[1] Kamlungsua, K., Su, P.-.-C. and Chan, S.H. (2020), Hydrogen Generation Using Solid Oxide Electrolysis Cells. Fuel Cells, 20: 644-649. https://doi.org/10.1002/fuce.202070602
[2] Lang, M.; Bohn, C.; Couturier, K.; Sun, X.; McPhail, S. J.; Malkow, T.; Pilenga, A.; Fu, Q.; Liu, Q. Electrochemical Quality Assurance of Solid Oxide Electrolyser (SOEC) Stacks. J. Electrochem. Soc. 2019 , 166 (15), F1180.
[3] Tang, Eric, et al. "Solid Oxide Based Electrolysis and Stack Technology with Ultra-High Electrolysis Current Density (>3A/cm<sup>2</sup>) and Efficiency." Mar. 2018. https://doi.org/10.2172/1513461
[4] Gazzaneo, V.; Lima, F. V. Multilayer Operability Framework for Process Design, Intensification, and Modularization of Nonlinear Energy Systems. Ind. Eng. Chem. Res. 2019, 58 (15), 6069–6079. https://doi.org/10.1021/acs.iecr.8b05482
[5] Alves, V., Dinh, S., Kitchin, J. R., Gazzaneo, V., Carrasco, J. C., & Lima, F. V. (2024). Opyrability: A Python package for process operability analysis. Journal of Open Source Software, 9(94), 5966. https://doi.org/10.21105/joss.05966
[6] Gazzaneo, V., Carrasco, J. C., Vinson, D. R., & Lima, F. V. (2020). Process Operability Algorithms: Past, Present, and Future Developments. Industrial & Engineering Chemistry Research, 59(6), 2457–2470. https://doi:10.1021/acs.iecr.9b05181