Abstract
In recent years there has been a great interest in the design and selection of working fluids for low-temperature Organic Rankine Cycles (ORC), to efficiently produce electrical power from waste heat from chemical engineering applications, as well as from renewable energy sources such as biomass combustion, geothermal and solar heat sources. The working fluid is essential to the performance of the cycle. In order to evaluate and test promising fluid candidates, an appropriate Equation of State (EoS) [1] is necessary.
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 [2] to propagate and quantify the impact of input property uncertainty and fluid property parameter uncertainty on the ORC model output. It is applied using different EoS: Cubic EoS such as Soave-Redlich-Kwong (SRK), Peng-Robinson (PR) and Perturbed Chain Statistical Association Fluid Theory (PC-SAFT). 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 of parameters, the sensitivity of the property parameter w.r.t to the EoS and the overall cycle, all influence the model output uncertainty.
The procedure is highlighted for an ORC for with a low-temperature heat source from exhaust gas from a marine diesel engine.[1] Saleh B, Koglbauer G, Wendland M, Fischer J. Working fluids for lowtemperature organic Rankine cycles. Energy 2007;32:1210–21.[2] Frutiger J, Andreasen JG, Liu W, Spliethoff H, Haglind F, Abildskov J, Sin G. Working fluid selection for organic Rankine cycles - impact of uncertainty of fluid properties. Energy (accepted s.t. revision).
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 [2] to propagate and quantify the impact of input property uncertainty and fluid property parameter uncertainty on the ORC model output. It is applied using different EoS: Cubic EoS such as Soave-Redlich-Kwong (SRK), Peng-Robinson (PR) and Perturbed Chain Statistical Association Fluid Theory (PC-SAFT). 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 of parameters, the sensitivity of the property parameter w.r.t to the EoS and the overall cycle, all influence the model output uncertainty.
The procedure is highlighted for an ORC for with a low-temperature heat source from exhaust gas from a marine diesel engine.[1] Saleh B, Koglbauer G, Wendland M, Fischer J. Working fluids for lowtemperature organic Rankine cycles. Energy 2007;32:1210–21.[2] Frutiger J, Andreasen JG, Liu W, Spliethoff H, Haglind F, Abildskov J, Sin G. Working fluid selection for organic Rankine cycles - impact of uncertainty of fluid properties. Energy (accepted s.t. revision).
| Original language | English |
|---|---|
| Publication date | 2016 |
| Number of pages | 1 |
| Publication status | Published - 2016 |
| Event | 2016 AIChE Annual Meeting - Hotel Nikko San Francisco, San Francisco, CA, United States Duration: 13 Nov 2016 → 19 Nov 2016 http://www.aiche.org/conferences/aiche-annual-meeting/2016 |
Conference
| Conference | 2016 AIChE Annual Meeting |
|---|---|
| Location | Hotel Nikko San Francisco |
| Country/Territory | United States |
| City | San Francisco, CA |
| Period | 13/11/2016 → 19/11/2016 |
| Internet address |
