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On the CAMD method based on PC-SAFT for working fluid design of a high-temperature organic Rankine cycle

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  • Wang, Enhua
  • Mao, Jingwen
  • Zhang, Bo
  • Wang, Yongzhen

Abstract

Working fluid is of great importance on the performance of organic Rankine cycle (ORC). Novel working fluids can be designed based on the computer-aided molecular design (CAMD) method. In this study, a working fluid design approach using CAMD for a high-temperature ORC is investigated. First, the molecular structures of 129 working fluids are obtained. Then, the thermodynamic performances are determined using the group-contribution PC-SAFT and the results of the top 10 working fluids are analysed. Finally, the feasibility of three correlations for a quick selection of the working fluid is estimated and the relation between the PC-SAFT parameters and the ORC performance is discussed. The results indicate that a high prediction accuracy on the net power of ORC is achieved by PC-SAFT Equation of State with a deviation of less than 5%. However, certain deviations exist for the PC-SAFT parameters when the group contribution method is used. Meanwhile, the PC-SAFT parameters of the top 20 working fluids distribute in a large space and the conventional two-stage optimization algorithm may ignore some suboptimal working fluids. With regard to the high-temperature ORC for exhaust heat recovery, the designed top 5 working fluids are toluene, ethynyl cyclohexane, benzene, propenyl cyclopentane, vinyl cyclohexane. It is better to select the suitable working fluid from a candidate set containing the top 10–20 fluids such that the negative impact of the uncertainties originated from the group-contribution PC-SAFT method is avoided.

Suggested Citation

  • Wang, Enhua & Mao, Jingwen & Zhang, Bo & Wang, Yongzhen, 2023. "On the CAMD method based on PC-SAFT for working fluid design of a high-temperature organic Rankine cycle," Energy, Elsevier, vol. 263(PD).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pd:s0360544222028213
    DOI: 10.1016/j.energy.2022.125935
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    2. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Xing, Chengda & Yan, Yinlian & Yang, Anren & Wang, Yan, 2023. "Information theory-based dynamic feature capture and global multi-objective optimization approach for organic Rankine cycle (ORC) considering road environment," Applied Energy, Elsevier, vol. 348(C).

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