A comparison between semi-empirical and molecular-based equations of state for describing the thermodynamic of supercritical micronization processes

Abstract

Recently, supercritical fluids have been widely studied as media for fine particle formation and the thermodynamic description of multi-phase and multi-component systems are of fundamental importance for exploiting the use of supercritical fluid processes at industrial level. This paper compares the capability of three equations of state, based on different physical models, for describing phase equilibrium of systems containing compressed gases and supercritical fluids. In particular, a semi-empirical Peng–Robinson, a lattice based Sanchez–Lacombe and an off-lattice theory, such as perturbed-hard-sphere-chain theory, were used. Calculations were performed for vapor–liquid equilibria for organic solvent–supercritical anti-solvent systems, solid–fluid and solid–liquid–fluid equilibria for solute–supercritical fluid systems, and solid–liquid–fluid equilibria for solute–solvent–anti-solvent ternary systems. For binary organic solvent–supercritical fluid systems, all equations of state fairly reproduce both equilibrium and volumetric properties. For the solid-supercritical fluid systems, the binary interaction parameters were obtained by fitting solubility experimental data; these values fairly reproduce melting point depression at high-pressure only when cubic equation of state and off-lattice model were used. Phase behaviors of ternary systems were calculated using binary interaction parameters fitted on binary systems. Solubility of high molecular weight compounds in solvent–anti-solvent systems are fairly reproduced only by the perturbed hard sphere equation of state. The comparison between the three models was performed in terms of statistical errors and methods for parameter determination. The results here reported may give useful information in order to select the more appropriate and suitable model for reasonably representing the phase behaviors involved in micronization processes.