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Simultaneous working fluids design and cycle optimization for Organic Rankine cycle using group contribution model

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  • Su, Wen
  • Zhao, Li
  • Deng, Shuai

Abstract

The performance of Organic Rankine Cycle (ORC) is significantly influenced by the used working fluid and the operating condition. Consequently, this paper presents a systematic model for the efficient design of working fluids and the optimization of cycle parameters at the molecular scale, so that optimal working fluids can be identified by simultaneously considering cycle parameters, environmental and safety properties. In the proposed model, working fluids are generated via the combination of groups. The required properties, which consist of thermodynamic, environmental and safety properties, are estimated by the published group contribution methods. Based on these estimated properties, cycle optimizations are performed to obtain the optimal performance of working fluids using an ORC model. Thereafter, optimal working fluids are identified, according to the cycle parameters, environmental and safety properties. Furthermore, working fluids design and cycle optimization for an example are conducted to demonstrate the proposed model. The optimal candidates, namely R254eb, R254cb, are found for the considered example through proposed methodology. The novel working fluids, which are firstly reported in ORC applications, are worth being studied in-depth through time-consuming and expensive experiments.

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  • Su, Wen & Zhao, Li & Deng, Shuai, 2017. "Simultaneous working fluids design and cycle optimization for Organic Rankine cycle using group contribution model," Applied Energy, Elsevier, vol. 202(C), pages 618-627.
    • Handle: RePEc:eee:appene:v:202:y:2017:i:c:p:618-627
    DOI: 10.1016/j.apenergy.2017.03.133
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    3. White, M.T. & Oyewunmi, O.A. & Chatzopoulou, M.A. & Pantaleo, A.M. & Haslam, A.J. & Markides, C.N., 2018. "Computer-aided working-fluid design, thermodynamic optimisation and thermoeconomic assessment of ORC systems for waste-heat recovery," Energy, Elsevier, vol. 161(C), pages 1181-1198.
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    5. Su, Wen & Hwang, Yunho & shao, Yawei & Deng, Shuai & Zhao, Li & Nie, Xianhua & Zhang, Yue, 2019. "Error analysis of ORC performance calculation based on the Helmholtz equation with different binary interaction parameters of mixture," Energy, Elsevier, vol. 166(C), pages 414-425.
    6. Lukawski, Maciej Z. & DiPippo, Ronald & Tester, Jefferson W., 2018. "Molecular property methods for assessing efficiency of organic Rankine cycles," Energy, Elsevier, vol. 142(C), pages 108-120.
    7. Yan Gao & Qianxi Song & Wen Su & Xinxing Lin & Zhi Sun & Zhisheng Huang & Yaping Gao, 2023. "Experimentally Identifying the Influences of Key Parameters for an Organic Rankine Cycle Using R123," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    8. Yachao Pan & Fubin Yang & Hongguang Zhang & Yinlian Yan & Anren Yang & Jia Liang & Mingzhe Yu, 2022. "Performance Prediction and Working Fluid Active Design of Organic Rankine Cycle Based on Molecular Structure," Energies, MDPI, vol. 15(21), pages 1-22, November.
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    10. Hoang, Anh Tuan, 2018. "Waste heat recovery from diesel engines based on Organic Rankine Cycle," Applied Energy, Elsevier, vol. 231(C), pages 138-166.
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    13. van Kleef, Luuk M.T. & Oyewunmi, Oyeniyi A. & Markides, Christos N., 2019. "Multi-objective thermo-economic optimization of organic Rankine cycle (ORC) power systems in waste-heat recovery applications using computer-aided molecular design techniques," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
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