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Study on a coalbed methane liquefaction system based on thermoacoustic refrigeration method

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Listed:
  • Wang, Chenghong
  • Shen, Qie
  • Zhang, Jie
  • Qiao, Xin
  • Yu, Hongyuan
  • Shen, Keyi
  • Sun, Daming

Abstract

Recovery of coalbed methane (CBM) is significant for the safe production of coal mines, environmental protection, and clean energy supply. A novel CBM liquefaction process composed of a looped three-stage thermoacoustic engine and three pulse tube coolers (PTCs) is proposed. Each PTC is connected to one thermoacoustic engine unit by bypass. Potential liquefaction processes using thermoacoustically driven PTCs are proposed and studied numerically. The operating parameters of the system are globally optimized by performing genetic algorithm procedures, using specific power consumption (SPC) as the objective function. Additionally, exergy analyses are conducted on the main components of the system to reveal the potential directions for improvement. It is found that a three-stage thermoacoustically driven PTCs is an appropriate configuration considering the overall efficiency and geometric compactness of the system. The optimized SPC of the system is 0.58 kWh·Nm−3, which is comparable to that of the traditional liquefiers. The overall exergy efficiency of the system is 36.25%. Total exergy loss of the three PTCs is 4.978 kW, accounting for 80% of that of the system. Having the advantages of being driven by heat instead of electricity, safe operation, compact structure, and high efficiency, the proposed liquefaction process is promising for small-scale gas reserves, especially for CBM.

Suggested Citation

  • Wang, Chenghong & Shen, Qie & Zhang, Jie & Qiao, Xin & Yu, Hongyuan & Shen, Keyi & Sun, Daming, 2023. "Study on a coalbed methane liquefaction system based on thermoacoustic refrigeration method," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024331
    DOI: 10.1016/j.energy.2022.125547
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    References listed on IDEAS

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    Cited by:

    1. Min, Chao & Wen, Guoquan & Gou, Liangjie & Li, Xiaogang & Yang, Zhaozhong, 2023. "Interpretability and causal discovery of the machine learning models to predict the production of CBM wells after hydraulic fracturing," Energy, Elsevier, vol. 285(C).

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