Automatic Fitting of Binary Interaction Parameters for Multi-fluid Helmholtz-Energy-Explicit Mixture Models

In the highest-accuracy mixture models
available today, these being
the multi-fluid Helmholtz-energy-explicit formulations, 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, among 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 more
than 830 of the binary pairs, the median absolute relative error in
bubble-point 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.