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Multi-objective optimization analysis of a supercritical CO2 cycle integrated with an absorption refrigeration and organic Rankine cycle system

Author

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  • Sun, Yuying
  • Han, Yixuan
  • Huo, Zhaoyi
  • Guo, Tingyu

Abstract

This paper proposes an innovative multi-stage waste heat recovery system that efficiently recovers high-, medium-, and low-grade waste heat by combining a supercritical carbon dioxide (sCO2) Brayton cycle with the supercritical organic Rankine cycle (sORC) and an absorption refrigeration cycle (ARC) or a subcritical organic Rankine cycle (ORC). To solve the problem of insufficient thermal energy utilization, this study devises two optimization schemes: System Ⅰ (sCO2-sORC-ARC) and System Ⅱ (sCO2-sORC-ORC). Key performance indicators such as thermal efficiency, exergy efficiency, net output power, and unit energy cost were evaluated using energy, exergy, and economic analysis and a multi-objective optimization strategy. A sensitivity analysis was used to determine the influence of the split ratio, main compressor pressure ratio, condenser temperature, and other parameters on system performance. The multi-objective combination was optimized using the NSGA-II algorithm. The results show that system Ⅰ demonstrates optimal performance with exergy efficiency (48.39 %), net income (43M$), and unit energy cost (4.05$/GJ). Additionally, under the given conditions, compared to the basic system, the net output power of system Ⅱ is increased by 1.13 MW, and the exergy loss is decreased by 0.75 MW. This study innovatively verifies the collaborative optimization potential of the multi-stage system waste heat recovery system, and provides new ideas and theoretical support for industrial waste heat utilization and achieving the goal of 'double carbon'.

Suggested Citation

  • Sun, Yuying & Han, Yixuan & Huo, Zhaoyi & Guo, Tingyu, 2025. "Multi-objective optimization analysis of a supercritical CO2 cycle integrated with an absorption refrigeration and organic Rankine cycle system," Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:energy:v:333:y:2025:i:c:s0360544225029159
    DOI: 10.1016/j.energy.2025.137273
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    Cited by:

    1. Zhou, Kang & Cao, Yue & Si, Fengqi, 2025. "Multiobjective optimization of ammonia-based hydrogen-storage systems using thermodynamic and neural-network models," Energy, Elsevier, vol. 340(C).

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