(262ap) Empirical Fundamental Equations of State Correlations Based on Hybrid Datasets

The process engineering industry is in an ever increasing demand for thermodynamic data that cannot be satisfied exclusively with experimental measurements due to cost and time inefficiency. By using fundamental equations of state (EOS), this problem can be circumvented, since EOS extend the range of available experimental data by offering interpolation and extrapolation capability. If the EOS is expressed in terms of a thermodynamic potential, it has the additional ability that every other time-independent thermodynamic property can be obtained as a combination of its derivatives with respect to its natural variables. Unfortunately, EOS exist only for a very limited subset of the pure compounds that are in technological use.

The construction of an EOS normally requires less data than the amount that would be needed to map the entire fluid region of technological relevance. However, in cases of extreme thermodynamic conditions or hazardous fluids, the experimental database is too small anyway. For mixtures, where the range of required data increases drastically with the number of components, the situation is much worse.

Molecular simulation has evolved to a point where it can contribute effectively to thermodynamic data retrieval and complement experimental databases. Its predictive capability is limited only by the molecular interaction model that represents the investigated substance. In contrast to experiments, molecular simulation can straightforwardly target any state point of interest while the associated financial cost and time requirement is only a fraction than that of a corresponding laboratory measurement.

A number of EOS for several substances were constructed using both experimental and simulation data, i.e. with a hybrid database, in the course of this work. Results for Hydrogen Chloride [1], Ethylene Oxide [2], Hexamethyldisiloxane [3] and Octamethylcyclotetrasiloxane [4] are presented.

[1] The Journal of Chemical Physics 139: 041102 (2013)

[2] Chemical Engineering Science 121: 87-99 (2014) and 134: 887-890 (2015).

[3] Fluid Phase Equilibria 418: 133-151 (2016).

[4] Journal of Chemical & Engineering Data, in press (2016).