A new understanding on thermal efficiency of organic Rankine cycle: Cycle separation based on working fluids properties

Yongzhen Wang, Jun Zhao, Guibing Chen, Shuai Deng, Qingsong An*, Chao Luo, Junaid Alvi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)

Abstract

The pivotal of organic Rankine cycle (ORC) promotion and optimization is revealing the thermodynamic relationship between cycle configuration, condition and its working fluid properties. Different from the traditional numerical calculation method (TNCM) of ORC, a new thermodynamic cycle separating method (TCSM) is introduced in this paper. Then, efficiency of ORC is conducted expediently by TCSM where Triangle cycle (ηTC), Carnot cycle (ηCC) and Brayton cycle (ηBC) efficiencies are regarded as variables, that is, ηSORC=f(ηTCCCBC). When comparing with TNCM, TCSM not only has the acceptable precision for all the investigated 21 working fluids, but also the influence of critical temperature, molecular complexity of the working fluid and superheat degree as well as the reduced operating conditions of ORC can be revealed qualitatively and quantitatively. Finally, three conclusions are revealed: (1) Relationship between ORC limited efficiency (the reduced evaporating temperature of 0.9) and critical temperature of working fluid is revealed; (2) When superheat degree increases, ORC efficiency of dry fluid decreases and wet fluid increases linearly, while the variation of isotropic working fluid remains constant; (3) If the reduced temperatures of two different working fluids are equal, the corresponding efficiencies are equal too. The proposed thermodynamic cycle separating method provides an approach for working fluids selection and performance prediction of ORC.

Original languageEnglish
Pages (from-to)169-175
Number of pages7
JournalEnergy Conversion and Management
Volume157
DOIs
Publication statusPublished - 1 Feb 2018
Externally publishedYes

Keywords

  • Cycle separating
  • Molecular complexity
  • Organic Rankine cycle
  • Performance prediction
  • Superheat degree
  • Working fluids selection

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