Development and applications of a hetero-based statistical associating fluid theory
DEVELOPMENT AND APPLICATIONS OF A HETERO-BASED STATISTICAL ASSOCIATING FLUID THEORY <P> YUN PENG <P> Dissertation under the direction of Professor Clare McCabe <P> Understanding the thermodynamic properties of chemical processes is essential to equipment design and process operation. For complex systems experimental data are frequently not available and so tools, such as equations of state (EOS), are very useful for calculating the thermodynamic properties and phase behavior of fluid systems. Molecular-based equations of state in particular, such as the statistical associating fluid theory (SAFT), provide a framework in which the effects of molecular shape, size and interactions on the thermodynamic properties and phase behavior can be taken into account. In order to explicitly describe molecular heterogeneity (e.g., different functional groups and side chains or branches within a molecule) we have developed the hetero-SAFT-VR EOS, in which molecules are modeled as chains composed of segments of different size and/or energy of interaction. In contrast to other SAFT equations of state, which use a homonuclear model, we can now capture the nature of molecular structure in an exact rather than in an ad hoc way through the use of effective size and/or energy parameters. Based on the hetero-SAFT-VR EOS, the group contribution SAFT-VR (GC-SAFT-VR) approach has also been developed. Parameters for functional groups have been determined from regression to experimental vapor pressure and saturated liquid density data for several classes of small molecules. The fitted parameters for these functional groups have been used transferably to predict the phase behavior of pure fluids that were not included in the fitting process and their mixtures. Additionally, we have extended the GC-SAFT-VR equation to model vapor-liquid and liquid-liquid equilibria in polymer systems.