(335l) Prediction of Temperature-Dependent Properties by Correlations Based on Similarity of Molecular Structures

Pure-compound property data are at present available only for a small fraction of the compounds, pertaining to such diverse areas as chemistry and chemical engineering, environmental engineering and environmental impact assessment, hazard and operability analysis. Therefore, methods for reliable prediction of property data are needed. In particular, prediction of temperature-dependent properties (like vapor pressure, vapor and liquid density, viscosity or specific heat) poses a special challenge because of the limited amount of experimental data available at widely varying temperature ranges. Current methods used to predict temperature-dependent properties can be classified into "group contribution" methods, methods based on the "corresponding-states principle" (for an extensive review of these methods see Poling et al., 2001), and "asymptotic behavior" correlations (see, for example, Marano and Holder, 1997). Reliable methods for predicting temperature-dependent properties have not yet been established. Even for vapor pressure, probably the most extensively investigated temperature-dependent property, prediction errors often reach several tens of percent (Poling et al., 2001). Furthermore, to predict properties of a target compound, many of these methods require experimental data about the target compound (critical properties, for example), and as such cannot be used for prediction of properties for compounds not yet synthesized. In recent years, there has been increasing interest in using, for prediction of constant properties, molecular descriptors integrated into Quantitative Structure Property Relationship (QSPR). However, very few attempts have been made to model, for prediction purposes, temperature-dependent properties (excluding vapor pressure; relate to Dearden et al., 2003). In this work, we apply several new techniques that we have developed recently (Shacham et al., 2004, Shore, 2005, Benson-Karhi et al., 2007, Cholakov et al., 2007) for prediction of temperature-dependent properties. To predict a particular property, a structure-structure correlation is first identified, using only molecular descriptors of the predictive compounds. The latter are compounds "similar" to the target compound, and for which experimental data for the target property are available. The QS2PR method of Shacham et al. (2004) is used to identify such similar compounds and to derive a linear structure-structure correlation. Typically, 2-4 predictive compounds are included in the QS2PR model. If the target compound and the potential predictive compounds belong to the same homologous series, use of the short-cut QS2PR method of Cholakov et al. (2007) can be considered. Two different methods have been developed to derive, for a given target compound, a model for the temperature-dependency of the target property. The first method is based on the availability of such models for the predictive compounds (e.g., Antoine equation parameters for vapor pressure calculations). In this case, the property value for the target compound can be calculated (point by point) at any specified temperature. To this aim, the calculated property values for the predictive compounds (at a specified temperature) are introduced into the structure-structure correlation to obtain the corresponding property value for the target compound. For some properties (vapor pressure, for example), it will be advantageous, when using this method, to set the property value and then calculate the matching temperature as this yields more accurate predictions In cases where models for the target property are not available, empirical (regression) models for the predictive compounds must first be derived. One possibility to derive such models is via the new Response Modeling Methodology (RMM; refer to Shore, 2005, for its introduction, and to Benson-Karhi et al., 2007, for a demonstrative application to modeling water properties). The main advantage of RMM-based models is that they typically deliver representation of the property's temperature-dependent variation, with adequate precision and high level of stability, using only two or three parameters. Thus, the same model can be used over a wide spectrum of properties, and for data available at various temperature ranges. The parameters of the RMM model, derived for the predictive compounds, can then be inserted into the structure-structure correlation to obtain RMM parameter values for the target compound. The proposed methods were applied to various properties of several hydrocarbons, and to some oxygen containing organic compounds. The properties tested included liquid density, vapor pressure, heat of vaporization, solid, liquid and ideal gas heat capacity, second virial coefficient, liquid and vapor viscosity, liquid and vapor thermal conductivity and surface tension. Highly accurate predictions were generally obtained. Detailed results of the evaluation of the proposed method will be presented in the conference.

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