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Working fluid selection for organic Rankine cycles – Impact of uncertainty of fluid properties

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  • Frutiger, Jérôme
  • Andreasen, Jesper
  • Liu, Wei
  • Spliethoff, Hartmut
  • Haglind, Fredrik
  • Abildskov, Jens
  • Sin, Gürkan

Abstract

This study presents a generic methodology to select working fluids for ORC (Organic Rankine Cycles) taking into account property uncertainties of the working fluids. A Monte Carlo procedure is described as a tool to propagate the influence of the input uncertainty of the fluid parameters on the ORC model output, and provides the 95%-confidence interval of the net power output with respect to the fluid property uncertainties. The methodology has been applied to a molecular design problem for an ORC using a low-temperature heat source and consisted of the following four parts: 1) formulation of process models and constraints 2) selection of property models, i.e. Peng–Robinson equation of state 3) screening of 1965 possible working fluid candidates including identification of optimal process parameters based on Monte Carlo sampling 4) propagating uncertainty of fluid parameters to the ORC net power output. The net power outputs of all the feasible working fluids were ranked including their uncertainties. The method could propagate and quantify the input property uncertainty of the fluid property parameters to the ORC model, giving an additional dimension to the fluid selection process. In the given analysis 15 fluids had an improved performance compared to the base case working fluid.

Suggested Citation

  • Frutiger, Jérôme & Andreasen, Jesper & Liu, Wei & Spliethoff, Hartmut & Haglind, Fredrik & Abildskov, Jens & Sin, Gürkan, 2016. "Working fluid selection for organic Rankine cycles – Impact of uncertainty of fluid properties," Energy, Elsevier, vol. 109(C), pages 987-997.
    • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:987-997
    DOI: 10.1016/j.energy.2016.05.010
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    References listed on IDEAS

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    2. Goran Durakovic & Geir Skaugen, 2019. "Analysis of Thermodynamic Models for Simulation and Optimisation of Organic Rankine Cycles," Energies, MDPI, vol. 12(17), pages 1-12, August.
    3. Yi, Zhitong & Luo, Xianglong & Chen, Jianyong & Chen, Ying, 2017. "Mathematical modelling and optimization of a liquid separation condenser-based organic Rankine cycle used in waste heat utilization," Energy, Elsevier, vol. 139(C), pages 916-934.
    4. Varma, G.V. Pradeep & Srinivas, T., 2017. "Power generation from low temperature heat recovery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 402-414.
    5. Mohan, Sooraj & Dinesha, P. & Campana, Pietro Elia, 2022. "ANN-PSO aided selection of hydrocarbons as working fluid for low-temperature organic Rankine cycle and thermodynamic evaluation of optimal working fluid," Energy, Elsevier, vol. 259(C).
    6. Magdalena Santos-Rodriguez, M. & Flores-Tlacuahuac, Antonio & Zavala, Victor M., 2017. "A stochastic optimization approach for the design of organic fluid mixtures for low-temperature heat recovery," Applied Energy, Elsevier, vol. 198(C), pages 145-159.
    7. 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.
    8. Boukelia, T.E. & Arslan, O. & Djimli, S. & Kabar, Y., 2023. "ORC fluids selection for a bottoming binary geothermal power plant integrated with a CSP plant," Energy, Elsevier, vol. 265(C).
    9. Zhang, Bo & Wang, Enhua & Meng, Fanxiao & Zhang, Fujun & Zhao, Changlu, 2020. "Prediction accuracy of thermodynamic properties using PC-SAFT for high-temperature organic Rankine cycle with siloxanes," Energy, Elsevier, vol. 204(C).

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