(262k) Equation of State Selection for Organic Rankine Cycle Modeling Under Uncertainty
For ORC applications, an EoS is commonly selected based on goodness-of-fits to data, width of range of availability of fluid data and complexity of formulation, which is closely related to numerical expenses. We have explored an additional criterion for the selection of a particular EoS, namely the influence on the input uncertainty of the fluid parameters on the ORC model output.
We have recently presented a methodology  to propagate and quantify the impact of input property uncertainty and fluid property parameter uncertainty on the ORC modelÂ output. It is applied using different EoS: Cubic EoS such as Soave-Redlich-KwongÂ (SRK), Peng-Robinson (PR) and Perturbed Chain Statistical Association Fluid TheoryÂ (PC-SAFT).Â The different EoS are assessed based on the uncertainty propagated in the model output.
The study demonstrates that the range of property parameter uncertainty, the number ofÂ parameters, the sensitivity of the property parameter w.r.t to the EoS and the overallÂ cycle, all influence the model output uncertainty.
The procedure is highlighted for an ORC for with a low-temperature heat source fromÂ exhaust gas from a marine diesel engine.
 Saleh B, Koglbauer G, Wendland M, Fischer J. Working fluids for lowtemperatureÂ organic Rankine cycles. Energy 2007;32:1210â??21.
 Frutiger J, Andreasen JG, Liu W, Spliethoff H, Haglind F, Abildskov J, Sin G.Â Working fluid selection for organic Rankine cycles - impact of uncertainty of fluid properties. Energy (accepted s.t. revision).