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Performance Evaluation of LiBr-H 2 O and LiCl-H 2 O Working Pairs in Compression-Assisted Double-Effect Absorption Refrigeration Systems for Utilization of Low-Temperature Heat Sources

Author

Listed:
  • Tong Lei

    (School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Zuoqin Qian

    (School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

  • Jie Ren

    (School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China)

Abstract

To improve the performance of conventional double-effect absorption refrigeration systems (DEARS), new series parallel (SP) and reverse parallel (RP) configurations using LiCl-H 2 O and LiBr-H 2 O as working fluids, combined with two vapor compressors (VC), are proposed and thermodynamically evaluated. The effects of the distribution ratio (D) and compression ratio (CR) on the system performance are discussed. The results reveal that both configurations can extend the operation ranges of DEARS effectively at a higher distribution ratio, and the performance for low-grade heat source utilization is improved substantially by the use of VC. The compressor positioned between the evaporator and absorber is superior to that between the high-pressure generator and low-pressure generator because of the better performance improvement and larger operating ranges. In all the examined cases, LiCl-H 2 O systems perform better than LiBr-H 2 O systems in terms of the coefficient of performance (COP) and exergetic efficiency. At the higher CR of approximately 2, the compression-assisted DEARS can be driven by heat sources below 100 °C with high levels of COPs above 1.16 for the LiBr-H 2 O working pair and 1.29 for the LiCl-H 2 O working pair. The system can operate at the optimum condition by adjusting the CR values according to the characteristics of the heat sources.

Suggested Citation

  • Tong Lei & Zuoqin Qian & Jie Ren, 2023. "Performance Evaluation of LiBr-H 2 O and LiCl-H 2 O Working Pairs in Compression-Assisted Double-Effect Absorption Refrigeration Systems for Utilization of Low-Temperature Heat Sources," Energies, MDPI, vol. 16(16), pages 1-19, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:6036-:d:1219481
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    References listed on IDEAS

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    1. Chi, Fang'ai & Xu, Liming & Peng, Changhai, 2020. "Integration of completely passive cooling and heating systems with daylighting function into courtyard building towards energy saving," Applied Energy, Elsevier, vol. 266(C).
    2. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    3. Ebrahimi, Khosrow & Jones, Gerard F. & Fleischer, Amy S., 2015. "Thermo-economic analysis of steady state waste heat recovery in data centers using absorption refrigeration," Applied Energy, Elsevier, vol. 139(C), pages 384-397.
    4. Mahmoudi, S.M.S. & Akbari Kordlar, M., 2018. "A new flexible geothermal based cogeneration system producing power and refrigeration," Renewable Energy, Elsevier, vol. 123(C), pages 499-512.
    5. Chi, Fang'ai & Liu, Yang & Yan, Jianxiong, 2021. "Integration of Radiative-based air temperature regulating system into residential building for energy saving," Applied Energy, Elsevier, vol. 301(C).
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