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Automatic fitting of binary interaction parameters for multi-fluid Helmholtz-energy-explicit mixture models

Published

Author(s)

Ian H. Bell, Eric W. Lemmon

Abstract

In the highest-accuracy mixture models available today, those of the multi-fluid Helmholtz-energy-explicit formulation, there are a number of binary interaction parameters that must be obtained through correlation or estimation schemes. These binary interaction parameters are used to shape the thermodynamic surface and yield higher-fidelity predictions of various thermodynamic properties including vapor-liquid equilibria and homogeneous p-v-T data, amongst others. In this work, we have used a novel and entirely automatic evolutionary optimization algorithm written in the python programming language to fit the two most important interaction parameters for more than 1100 binary mixtures. This fitting algorithm can be run on multiple processors in parallel, resulting in a reasonable total running time for this large set of binary mixtures. For nearly 700 of the binary pairs, the median absolute relative error in pressure is less than 5%. The source code for the fitter is provided as supplemental data, as well as the entire set of binary interaction parameters obtained and comparisons with the best experimental vapor-liquid-equilibrium data that are available.
Citation
Journal of Chemical and Engineering Data
Volume
61
Issue
11

Keywords

VLE, mixtures, Helmholtz mixture models

Citation

Bell, I. and Lemmon, E. (2016), Automatic fitting of binary interaction parameters for multi-fluid Helmholtz-energy-explicit mixture models, Journal of Chemical and Engineering Data, [online], https://doi.org/10.1021/acs.jced.6b00257 (Accessed April 23, 2024)
Created September 26, 2016, Updated November 10, 2018