(180ab) Prediction of Saturated Vapor Pressure with Simple Structure Property- Property Relationships
AIChE Annual Meeting
Monday, November 9, 2009 - 6:00pm to 8:00pm
Pure component vapor pressure data are essential for process and product design, in assessing the environmental impact of a chemical compound and in modeling some types of toxicity (Dearden, 2003). At present, vapor pressure data are available only for a small fraction of the compounds of interest to the chemical industry. Even if the data are available they may not cover the full temperature range of interest. In product design vapor pressure values may be required for substances that have not been synthesized yet. Thus prediction of saturated vapor pressure data is often essential.
Current methods used to predict temperature-dependent properties can be classified into "group contribution" methods, methods based on the "corresponding-states principle" (for reviews of these methods see, for example, Poling et al, 2001 and Godavarty et al.,2006), and "asymptotic behavior" correlations (see, for example, Marano and Holder,1997). These methods rely on several other property values, such as normal boiling temperature: Tb, critical temperature: Tc critical pressure, Pc and acentric factor: w. However, such data for properties may not be available for a target compound for which the vapor pressure has to be predicted. Moreover, experimental values for Pc and calculated values for w are usually of low precision causing deterioration of the accuracy of the predicted vapor pressure values. Furthermore, these methods contain adjustable parameters that were fitted to a training set, which may not represent well enough the target compound. A detailed discussion of these issues can be found, for example in Vetere (2006).
In recent years, there has been increasing interest in using molecular descriptors integrated into Quantitative Structure Property Relationships (QSPR) for prediction of vapor pressure. However, the great majority of the currently available QSPR models are limited to prediction at a single temperature of 298.15 K. The exceptions are the methods of Godavarty et al., 2006 which combine the use of the corresponding state principle with a QSPR (thus using certain properties and adjustable parameters) and Yaffe and Cohen, 2001 who's QSPR is neural-network which may not be readily applicable for potential users.
Shacham et al., 2009 suggested a new QSPR-based method for predicting temperature-dependent variation of vapor pressure of chemical compounds. The method is based on the identification of potential predictive compounds, which are structurally similar to the target compound (a similarity group) and for which data for a vapor pressure related property (e.g., normal boiling temperature or acentric factor) are available. For this step, the algorithm of the Targeted QSPR method of Brauner et al. (2006) is employed. In the following step, stepwise regression is used to identify a molecular descriptor (or a combination of several descriptors- a "pseudo descriptor"), which is collinear with this property for the members of the similarity group. This descriptor is used to develop a simple structure-structure relation (short-cut QS2PR). The latter uses two predictive compounds (selected from the training set), which are the closest to the target (in terms of the descriptor value). This relation is then applied for predicting target-compound vapor pressure (or saturation temperatures) in the temperature (or pressure) range where valid vapor pressure data exist for two selected predictive compounds.
The method can be considered as refinement of the well-established ?Two Reference Fluids? method (Poling et al., 2001), in use for over 30 years now. Two different techniques are considered. In the first one the property y is the saturation temperature Ts at pressure P. The descriptor to be used in the QS2PR is selected in this case based on collinearity with the normal boiling temperature (Ts at atmospheric pressure). Sets of Tsi versus Pi are generated using the Antoine, Riedel or Wagner equations. Substituting these values into the QS2PR yields the corresponding Ts values for the target compound. In the second technique the property y is the saturation pressure Ps at temperature T. Following the "Two fluid Model" method for prediction of vapor pressure, the descriptor to be used in the QS2PR is selected, in this case based on collinearity with the acentric factor.
The method proposed has several advantages over the ?Two Reference Fluids? methods, namely:
? Only structural information (no measured property values) are needed for the target compound;
? Predictive compounds similar to the target are selected in a systematic manner;
? The temperature - vapor pressure relationships of the predictive compounds are used only in their valid range of applicability;
? No the need to use generalized correlations with adjustable coefficient;
? It is possible to predict either saturation temperature, or vapor pressure, giving more flexibility regarding the range and uncertainty of the predictions.
The method was applied for prediction of vapor pressure in wide temperature ranges for various homologous series (n-Alkane, 1-alkene, 1-Alcanol, n-Alcanoic Acid and Alkyl-Benzene series) and various compounds which do not belong to homologous series. It was demonstrated that the method enables the prediction of vapor pressure within experimental uncertainty, depending on the level of similarity between the predictive compounds and the target compound. In cases of longer range interpolation or extrapolation, some degradation of the prediction is noticed; however, even in those cases the proposed method is superior to the traditional prediction techniques.
In the presentation the new vapor pressure prediction methods will be described in more detail and their use for vapor pressure prediction both for compounds belonging to homologous series and compounds that do not belong to such series will be demonstrated.
1. Brauner, N.; Stateva, R. P.; Cholakov, G. St.; Shacham, M. A. Structurally ?Targeted? QSPR Method for Property Prediction. Ind. Eng. Chem. Res. 45, 8430?8437 (2006)
2. Dearden, J. C. ?Quantitative Structure?Property Relationships for Prediction of Boiling Point, Vapor Pressure, and Melting Point?, Environmental Toxicology and Chemistry, 22( 8), 1696?1709 (2003).
3. Godavarthy, S.S.; Robinson, R.L.; Gasem, K.A.M. "SVRC-QSPR model for predicting saturated vapor pressures of pure fluids", Fluid Phase Equilibria 246, 39-51, 2006.
4. Marano, J.J., Holder, G.D., "General Equations for Correlating the Thermo-physical Properties of n-Paraffins, n-Olefins and other Homologous Series. 2. Asymptotic Behavior Correlations for PVT Properties", Ind. Eng. Chem. Res., 36, 1887-1894 (1997).
5. Poling, B.E., Prausnitz, J. M., O'Connel, J. P., Properties of Gases and Liquids, 5th Ed., McGraw-Hill, New York (2001).
6. Shacham, M., Cholakov, G. St., Stateva R. P., Brauner, N., ?Quantitative Structure ? Structure ? Relationships for Prediction of Phase Equilibrium Related Properties?, JCED., Submitted for publication, 2009
7. Vetere, A., "Again the Riedel Equation", Fluid Phase Equilibria 240, 155-160(2006)
8. Yaffe D and Cohen Y, "Neural network based temperature-dependent quantitative structure property relations (QSPRs) for predicting vapor pressure of hydrocarbons", Journal of Chemical Information and Computer Sciences 41, 463-477(2001)
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