Quality Assessment Algorithm for Vapor−Liquid Equilibrium Data

Abstract

A quality assessment algorithm for vapor-liquid equilibrium (VLE) data has been developed. The proposed algorithm combines four widely used tests of VLE consistency based on the requirements of the Gibbs-Duhem equation, with a check of consistency between the VLE binary data and the pure compound vapor pressures. A VLE data-quality criterion is proposed based on the developed algorithm, and it has been implemented in a software application in support of dynamic data evaluation. VLE predictions (NRTL and UNIFAC) were deployed to detect possible anomalies in the data sets. The proposed algorithm can be applied to VLE data sets with at least three state variables reported (pressure, temperature, plus liquid and/ or vapor composition) and is applicable to all nonreacting chemical systems at subcritical conditions. Application of the developed algorithms to identification of erroneous published VLE data sets is demonstrated.

Isobaric Vapor–Liquid Equilibrium Data for Binary System of Glycerol Ethyl Acetal and Acetonitrile at 60.0 kPa and 97.8 kPa

“…As can be seen in Figure 4 a very good agreement was found, presenting average absolute deviations of δy = 0.004 and δT = 0.08 K for the vapor phase composition and temperature, respectively. Second, following the strategy suggested by Kang et al, 22 thermodynamic consistency tests were implemented: Herington test (test 1), Van Ness test (test 2), infinite dilution test (test 3), and pure component consistency test. The point test was not used because it is not applicable to isobaric data sets.…”

“…The point test was not used because it is not applicable to isobaric data sets. Following their approach, 22 the overall quality factor (Q) for a VLE data set of acetone/methanol system was about 0.99, where Q VLE = [F pure (F test1 + F test2 + F test3 )]/0.75, showing that the data are of very good quality. Finally, it is important to mention that, when using eq 4 to acetone/methanol system, the fugacity coefficients were obtained in accordance to the Tsonopoulos correlation, 23 calculating the second virial coefficient of pure substances using information available in the DIPPR database.…”

Phase equilibrium data and modeling of ethylic biodiesel, with application to a non-edible vegetable oil

“…More adequately, the quality of the experimental data was analyzed by applying a quality assessment algorithm for VLE data proposed by Kang et al [21,22]. Four consistency tests were combined: the Herington test, the Van Ness test, the infinite dilution test, and the pure component test [21][22][23][24][25][26].…”

“…The maximum value is 1.0 for the pure component test and 0.25 for the remaining three tests. Then, for each VLE data set, an overall quality factor (Q VLE ) can be calculated as follows [12,21]:…”

ThermoData Engine (TDE): Software Implementation of the Dynamic Data Evaluation Concept. 8. Properties of Material Streams and Solvent Design

“…19,20 It is also a core component in implementation of the concept of Global Information Systems in Science with application to the field of thermodynamics. 21 Other recent additions include a vapor−liquid equilibrium (VLE) data modeling test that complements consistency tests implemented previously for low-pressure/subcritical VLE data, 15,22 as well as addition of the NIST-KT-UNIFAC prediction method 23 for VLE data, which was developed based on data quality factors (i.e., data set weighting factors) described previously 22 and automatic decomposition of molecular structures into KT-UNIFAC groups and subgroups. 24 The present article describes extension of TDE (version 7, released in 2012) 25 for evaluation of properties of material streams (defined as a mixture with any number of components and specified overall composition).…”