Modeling Thermodynamic Properties of Mixtures of CO2 + O2 in the Allam Cycle by Equations of State

Staubach, Jens; Schwarz, Gerhard; Möbius, Stephan; Hasse, Hans; Stephan, Simon

doi:10.1007/s10765-023-03297-w

Cited by 2 publications

(1 citation statement)

References 65 publications

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“…Here, X can be, for example, the compressibility, the heat capacity, and the speed of sound. Homogeneous-state property data is particularly important for the development of equation of state models. − …”

Section: Introductionmentioning

confidence: 99%

Using Autonomous Outlier Detection Methods for Thermophysical Property Data

Schnorr,

Kaldi,

Staubach

et al. 2024

J. Chem. Eng. Data

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The reliability and accuracy of thermophysical property data are of central importance for the development of models that describe these properties. In this work, we compare different autonomous algorithms for identifying the outliers in an existing database. Therefore, the comprehensive database on thermophysical property data for the Lennard-Jones fluid [J. Chem. Inf. Model. 2019, 59, 4248−4265] is used. We focus on homogeneous state property data at given temperature and density for the pressure p, thermal expansion coefficient α, isothermal compressibility β, thermal pressure coefficient γ, internal energy u, isochoric heat capacity c v , isobaric heat capacity c p , Gruneisen coefficient Γ, Joule−Thomson coefficient μ JT , speed of sound w, chemical potential μ, (reduced) Helmholtz energy a = a/T, and its derivatives a nm . A comprehensive comparison of 19 outlier detection methods is carried out, which provides insights into the applicability of generic outlier detection algorithms for thermophysical property data. Different classes of outlier detection algorithms are included in the study, namely, machine learning, distance-based, density-based, statistical, ensemble, and model-informed. Two approaches are used for the method evaluation: in approach (a), the original database (comprising real outliers) is used. In approach (b), synthetic outliers are introduced. The results and findings from both approaches are consistent. Machine learning methods yield in some cases better performance compared to that of the distance-based, density-based, ensemble, and statistical methods. The best performance is obtained from the model-informed method (called MoDOD). The results also provide insights into the nature of the outliers in the Lennard-Jones database.

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Section: Introductionmentioning

confidence: 99%

Using Autonomous Outlier Detection Methods for Thermophysical Property Data

Schnorr,

Kaldi,

Staubach

et al. 2024

J. Chem. Eng. Data

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Measurements and Equation of State Modeling of the Density of Five 1-Alcohols (C6–C10) at Pressures of up to 120 MPa

Schmitt,

Hasse,

Stephan

2024

J. Chem. Eng. Data

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The density of five 1-alcohols (1-hexanol, 1heptanol, 1-octanol, 1-nonanol, and 1-decanol) was measured using a vibrating-tube densimeter at pressures of up to 120 MPa for temperatures between 298.15 and 423.15 K. The relative expanded uncertainty of the experimental data is below 0.2%. The new results significantly extend the available literature data. The density of the five alcohols was modeled using four molecular-based equations of state (EOSs), namely PC-SAFT, SAFT-VR Mie, soft SAFT, and CPA. New component-specific EOS models were parametrized for all considered EOSs and substances using vapor−liquid equilibrium data from the literature and the density data from this work. The resulting EOS models describe the density with deviations of about 0.2−1.5%. For the thermal second-order derivative properties, the relative deviations were 10−50% for the isothermal compressibility and 5−35% for the thermal expansion coefficient. Also, the extrapolation behavior of the EOS models was assessed. Overall, the best results were obtained from the SAFT-VR Mie EOS.

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Modeling Thermodynamic Properties of Mixtures of CO2 + O2 in the Allam Cycle by Equations of State

Abstract: Different equations of state (EOS) were applied for describing thermodynamic properties of the system $$\text {CO}_{2} + \text {O}_{2},$$ CO 2 + O 2 , which is important for the Allam cycle: cubic EOS (Soave–Redlich–Kwong, Peng–Robinson), molecular… Show more

Cited by 2 publications

References 65 publications

Using Autonomous Outlier Detection Methods for Thermophysical Property Data

Using Autonomous Outlier Detection Methods for Thermophysical Property Data

Measurements and Equation of State Modeling of the Density of Five 1-Alcohols (C6–C10) at Pressures of up to 120 MPa

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