LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Optimizing Thermodynamic Models: The Relevance of Molar Fraction Uncertainties

Photo from wikipedia

Parameters contained in thermodynamic models are usually optimized over vapor–liquid equilibrium data of binary systems, applying methods that enable consideration of the uncertainty of experimental data, such as the Maximum… Click to show full abstract

Parameters contained in thermodynamic models are usually optimized over vapor–liquid equilibrium data of binary systems, applying methods that enable consideration of the uncertainty of experimental data, such as the Maximum Likelihood Method (MLM). However, authors of these data generally indicate only the maximum or average uncertainty associated with an overall set of data. Specific uncertainties, associated with each measurement, are rarely mentioned. As a consequence, the optimization of models by, for example, MLM is usually performed by associating constant overall uncertainty values to data points, instead of their specific values. This paper aims to show that results obtained from the application of MLM are strongly affected by the use of constant rather than specific uncertainties. In particular, the optimization of models over highly nonideal high-pressure vapor–liquid equilibrium data is strongly affected by the uncertainty of molar fractions, rather than by the uncertainty of pressure and tem...

Keywords: uncertainty; relevance molar; thermodynamic models; optimizing thermodynamic; molar fraction; models relevance

Journal Title: Journal of Chemical & Engineering Data
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.