Modeling the phase behavior of substances using thermodynamic equations of state (EOS) is a vital area of research, as it is a quick and inexpensive way of predicting the phase properties at desired conditions. Prior optimization techniques used for parameterization of pure components are methods that converge at a local minimum. The minimum found and ultimately used to predict pure component and mixture properties can have a very large impact on the quality of the resulting predictions. This work attempts to characterize and maximize the predictive power of complex thermodynamic equations of state (here, SAFT-VR EOS) by mitigating spurious conclusions that are based on results from local minimization schemes and, thus, allow for more definitive conclusions on the properties of systems in the absence of experimental data. To this end, this work employs the global terrain methodology [1] – an advanced global optimization technique and applies it to the field of thermodynamic modeling.

The terrain methodology was used to perform the parameterization to obtain multiple pure component parameter sets for a series of alkanes (C1-C10) the hydrofluorocarbons. The effects of using multiple sets of parameters to calculate thermodynamic response functions such as isothermal compressibility, heat capacity, volume expansivity etc are investigated in this work. Additionally, this work analyzes the effects of multiple parameter-sets and binary interaction parameters on the prediction of mixture properties. Through this work, parameter rules will be developed that will allow for the optimal prediction of pure component properties.

1. Lucia, A.; Feng, Y. (2002), Global terrain methods, Computers and Chemical Engineering, 26, 529-546.

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