Group-contribution+ (GC+) based estimation of properties of pure components: Improved property estimation and uncertainty analysis

The aim of this work is to present revised and improved model parameters for group-contribution(+) (GC(+)) models (combined group-contribution (GC) method and atom connectivity index (CI) method) employed for the estimation of pure component properties, together with covariance matrices to quantify uncertainties in the estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis of GC models and CI models using maximum-likelihood estimation theory is developed. For parameter estimation, large data-sets of experimentally measured property values of pure components of various classes (hydrocarbons, oxygenated components, nitrogenated components, poly-functional components, etc.) taken from the CAPEC database are used. In total 18 pure component properties are analyzed, namely normal boiling point, critical temperature, critical pressure, critical volume, normal melting point, standard Gibbs energy of formation, standard enthalpy of formation, normal enthalpy of fusion, enthalpy of vaporization at 298 K, enthalpy of vaporization at the normal boiling point, entropy of vaporization at the normal boiling point, flash point, auto ignition temperature, Hansen solubility parameters, Hildebrand solubility parameter, octanol/water partition coefficient, acentric factor, and liquid molar volume at 298 K. Important issues related to property modeling such as reliability and predictive capability of the property prediction models, and thermodynamic consistency of the predicted properties (such as, relation of normal boiling point versus critical temperature) are also analyzed and discussed. The developed methodology is simple, yet sound and effective and provides not only the estimated pure component property values but also the uncertainties (e.g. prediction errors in terms of 95% confidence intervals) in the estimated property values. This feature allows one to evaluate the effects of these uncertainties on product-process design, simulation and optimization calculations, contributing to better-informed and more reliable engineering solutions. (C) 2012 Elsevier B.V. All rights reserved.