Genetic algorithm based optimization design and coupling performance analysis of multi-stage series hydrogen turbo-expanders

Bingyao Niu, Liang Chen, Kunyu Deng, Haodong Wang, Jie Qu, Shanfeng Li, Ze Zhang, Shuangtao Chen and Yu Hou

Energy, 2025, vol. 335, issue C

Abstract: The energy consumption and stability of hydrogen liquefaction system directly depend on the performance of turbo-expanders. A performance prediction model for hydrogen turbo-expanders based on the loss model is proposed. The accuracy of the model is validated against the experimental data of the second-stage hydrogen turbo-expander in a 5 tpd hydrogen liquefaction system, with an error within 10%. The coupling performance of the three-stage hydrogen turbo-expanders is evaluated through the prediction model. The maximum efficiency of the third-stage turbo-expander is only 81.83% and the efficiency peak appears later, resulting in poor inter-stage coupling performance. In this context, this paper proposes an optimization design method for multi-stage series hydrogen turbo-expanders, which combines genetic algorithm and mean-line design method to optimize the design of the turbine string composed of the second and third stage turbo-expanders. The peak efficiencies of the optimized second and third stage turbo-expanders are 84.77% and 86.31%, increasing by 0.21% and 4.48%, respectively, with a corresponding mass flow rate difference of only 4 g·s−1 (1.1% of designed mass flow rate) between their peak efficiency points. The coupling performance of the optimized two-stage expanders is improved under off-design conditions.

Keywords: Hydrogen turbo-expander; Prediction model; Coupling performance; Optimization design; Genetic algorithm (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225037703

DOI: 10.1016/j.energy.2025.138128

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