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

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

doi:10.1016/j.energy.2016.05.010

Cited by 58 publications

(44 citation statements)

References 45 publications

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“…However, developers only rarely provide the covariance matrix for EoS studies. Recently, Frutiger et al [17] presented a Monte Carlo-based methodology to propagate and quantify the impact of property parameter uncertainty on a process model output of an ORC system. Further, this methodology was used to assess and compare the uncertainty propagation for two different types of EoS: Cubic (SRK) and PC-SAFT [18].…”

Section: Uncertainty Quantification For Eosmentioning

confidence: 99%

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Uncertainty assessment of equations of state with application to an organic Rankine cycle

et al. 2017

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Abstract:Evaluations of equations of state (EoS) with application to process systems should include uncertainty analysis. A generic method is presented for determining such uncertainties from both the mathematical form and the data for obtaining EoS parameter values. The method is implemented for the Soave-Redlich-Kwong (SRK), the Peng-Robinson (PR) cubic EoS, and the perturbed-chain statistical associating fluid theory (PC-SAFT) EoS, as applied to an organic Rankine cycle (ORC) power system to recover heat from the exhaust gas of a marine diesel engine with cyclopentane as the working fluid. Uncertainties of the EoS input parameters, including their corresponding correlation structure, are quantified from the data using a bootstrap method. A Monte Carlo procedure propagates parameter input uncertainties onto the process output. Regressions have been made of the three cubic EoS parameters from both critical point matching and vapor pressure and density data, as used for the three PC SAFT parameters. ORC power uncertainties of 2-5 % are found for all models from the larger data sets. Mean power values for the cubic EoS are similar for both parameter regressions. The mean power from the PC-SAFT EoS is less than for the cubic EoS, with no overlap of the uncertainty distributions.

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Section: Uncertainty Quantification For Eosmentioning

confidence: 99%

“…The Monte Carlo method is based on the work of Frutiger et al [17][18] and is as follows: 1. Specification of fluid property and parameter input uncertainties: The quantified uncertainties of the fluid parameters serve as input uncertainties to be propagated through the ORC model.…”

Section: Monte Carlo Procedures For Parameter Uncertainty Propagation mentioning

confidence: 99%

Uncertainty assessment of equations of state with application to an organic Rankine cycle

et al. 2017

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“…The future needs to find fluids that respect regulations and perform sufficiently well to replace existing refrigeration's that will be out-phased. However, given the high sensitivity between the fluid properties and the cycle process operating conditions and performance, this is a challenging tasks with existing heuristic approaches [38]. Recent literature point at the need for use of CAMD techniques for finding novel refrigerants [25].…”

Section: Figure 4: Vapor-compression Cyclementioning

confidence: 99%

Systematic Optimization-Based Integrated Chemical Product–Process Design Framework

Cignitti

Mansouri

Woodley

et al. 2018

Ind. Eng. Chem. Res.

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“…Frutiger et al [1] studied how the simulation result of a power cycle can significantly change as a result of the uncertainty of a few critical parameters such as critical temperature, critical pressure and the acentric factor. Applying a Monte Carlo procedure to 40 different working fluids, they showed how ignoring the uncertainties in the parameters can lead to highly erroneous simulation results, as the 95% confidence interval may be large in cases where important parameters are very uncertain.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Thermodynamic Models for Simulation and Optimisation of Organic Rankine Cycles

Durakovic

Skaugen

2019

Energies

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Equations of state (EOSs) form the base of every thermodynamic model used in the design of industrial processes, but little work has been done to evaluate these in the context of such models. This work evaluates 13 EOSs for their accuracy, computational time and robustness when used in an in-house optimisation program that finds the maximum power output of an organic Rankine cycle. The EOSs represent popular choices in the industry, such as the simple cubic EOSs, and more complex EOSs such as the ones based on corresponding state principles (CSP). These results were compared with results from using the Groupe Européen de Recherches Gazières (GERG) EOS, whose error is within experimental uncertainty. It appears that the corresponding state EOSs find a solution to the optimisation problem notably faster than GERG without significant loss of accuracy. A corresponding state method which used the Peng–Robinson EOS to calculate the shape factors and a highly accurate EOS for propane as the reference EOS, was shown to have a total deviation of just 0.6% as compared to GERG while also being 10 times as fast. The CSP implementation was also more robust, being able to converge successfully more often.

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

Cited by 58 publications

References 45 publications

Uncertainty assessment of equations of state with application to an organic Rankine cycle

Uncertainty assessment of equations of state with application to an organic Rankine cycle

Systematic Optimization-Based Integrated Chemical Product–Process Design Framework

Analysis of Thermodynamic Models for Simulation and Optimisation of Organic Rankine Cycles

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