(649i) Association Thermodynamic Models in Aspen Process Simulators

Accurate thermophysical property prediction is essential for the design, optimization, and scale-up of chemical processes, particularly those involving complex associating systems such as water, alcohols, glycols, and organic acids. These systems exhibit strong hydrogen bonding and non-ideal behavior, making conventional equation-of-state and activity coefficient models inadequate for reliable phase equilibrium and property estimation. To address these challenges, this work presents a methodology within Aspen process simulators to accurately model associating systems using the Cubic-Plus-Association (CPA) and Association Non-Random Two-Liquid (aNRTL) models. The CPA model combines the Soave-Redlich-Kwong (SRK) cubic equation of state with an association term derived from SAFT theory to account for hydrogen bonding. Similarly, the aNRTL model enhances the classical NRTL framework by incorporating Wertheim's perturbation theory, blending physical interaction parameters with molecule-specific association strengths. The use of CPA and aNRTL significantly improves the prediction of phase behavior in associating systems, capturing both vapor–liquid and liquid–liquid equilibria with a unique set of parameters. This study further demonstrates the modeling of several associating and non-associating systems to highlight the importance of accurate thermodynamic modeling and its impact on process simulation.