Thermodynamic, sensitivity analyses and optimization of a dual-stage diaphragm compressor system: A model-based and experimental study

Chongzhou Sun, Zhilong He, Xiaoqian Chen, Dantong Li, Kai Ma, Mengyao Chen, Tao Wang and Xiaolin Wang

Energy, 2025, vol. 330, issue C

Abstract: The large-scale utilization of hydrogen energy is currently hindered by challenges in low-cost production, storage, and transportation. This study focuses on investigating and optimizing hydrogen pressurization systems for tube-trailer transportation applications, specifically examining a dual-stage diaphragm compressor system. First, an experimental platform was established to evaluate the system's performance under varying rotational speed and pressure conditions. Then, an integrated numerical model combining physics-based submodels with artificial neural networks was constructed and validated by experiments. Finally, thermodynamic and sensitivity analyses, as well as optimization strategies, were further discussed. Under the design condition of case #D-3 (420 rpm, 1.6 MPa suction pressure, and 20 MPa discharge pressure), experiments indicated the volumetric efficiencies of the first-stage and second-stage diaphragm compressors were measured at 59.2 % and 56.5 %, respectively, the gas mass flow rate reached 460.6 kg/h, the motor input power reached 68.6 kW, and the system isentropic efficiency was 52.0 %. Exergy analysis showed that the gas exergy increment, frictional loss, total diaphragm compressors' exergy loss, and total heat exchangers' exergy loss were 26.07 kW, 11.87 kW, 6.74 kW, and 8.18 kW, respectively. The gas exergy increment decreased by 41.6 % as the rotational speed was reduced to 200 rpm, while the total input power increased by 27.6 %. By reducing the oil temperature to 300 K and the water temperature to 280 K, the gas mass flow rate and exergy efficiency could increase by 4.2 % and 5.7 %, respectively. This study proposed a viable simulation approach for dual-stage diaphragm compressor systems and offered valuable insights for advancing industrial scalability and reducing costs.

Keywords: Diaphragm compressor; Experimental study; Performance optimization; Sensitivity analysis; Artificial neural network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:330:y:2025:i:c:s0360544225024909

DOI: 10.1016/j.energy.2025.136848

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