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Research on compression process and compressors in supercritical carbon dioxide power cycle systems: A review

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  • Liu, Yunxia
  • Zhao, Yuanyang
  • Yang, Qichao
  • Liu, Guangbin
  • Li, Liansheng

Abstract

The utilization of supercritical carbon dioxide (sCO2) in the Brayton cycle presents several advantages, such as compact equipment, high efficiency, and rapid response time. The sCO2 power cycle can be applied in coal-fired power, solar thermal power, and nuclear power systems. Over the past 10 years, many scholars have researched sCO2 power systems. The compression (pressurization) process is a core thermodynamic process in sCO2 power cycles, achieved through the use of compressors. Therefore, compressors are important components in sCO2 power systems. This paper provides a summary of recent studies on compression processes and compressors for sCO2 power systems. The impact of near-critical-point characteristics of the compression process on equipment and sCO2 power cycle systems is discussed. The investigations of the performance parameters, design considerations, design methods, and performance prediction methods of sCO2 compressors are reviewed. The typical research results on CO2 condensation at the compressor inlet, the effects of operating conditions on compressor performance, as well as optimization of design parameters, are also summarized. Additionally, it provides a summary of sCO2 compressor prototypes developed by research institutes worldwide and experimental studies. Finally, the current issues with sCO2 compressors are addressed, and the main future research directions are proposed. This paper will contribute to the development of compressors and promote the acceleration of the commercialization of sCO2 power systems.

Suggested Citation

  • Liu, Yunxia & Zhao, Yuanyang & Yang, Qichao & Liu, Guangbin & Li, Liansheng, 2024. "Research on compression process and compressors in supercritical carbon dioxide power cycle systems: A review," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224010612
    DOI: 10.1016/j.energy.2024.131288
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    1. Li, Haozhe & Song, Meiqi & Liu, Xiaojing, 2024. "Interpretable machine learning-based prediction and analysis of supercritical fluid heat transfer characteristics at different boundary conditions," Energy, Elsevier, vol. 308(C).
    2. Feng, Jiaqi & Zhang, Enbo & Bai, Bofeng, 2024. "Dynamic modeling and response characteristics of closed CO2 cycle with Li/SF6 fuel boiler to external disturbances," Energy, Elsevier, vol. 312(C).

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