Abstract
Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance.
Highlights
The critical properties, such as the critical temperature (Tc ) and pressure (Pc ), of organic compounds are widely used in the chemical industry to understand the thermodynamic behavior of pure compounds or their mixtures, in particular when this is predicted through an equation of state.The acentric factor (ω) is used in phase equilibrium calculations
The above discussion has pointed out that six out of ten descriptors employed to develop the Quantitative Structure-Property Relationship (QSPR) models for the acentric factor can be related to descriptors employed for the critical temperature and the critical pressure
The results show that the radial basis-function-neural-network (RBFNN) models provide much better correlations of the data and have a higher prediction capability to point out the non-linear nature of the relationship between these physical properties and the molecular structure
Summary
The critical properties, such as the critical temperature (Tc ) and pressure (Pc ), of organic compounds are widely used in the chemical industry to understand the thermodynamic behavior of pure compounds or their mixtures, in particular when this is predicted through an equation of state. The acentric factor (ω) is used in phase equilibrium calculations. Evaluating the acentric factor through its definition is often impossible for many compounds because the critical properties and/or the vapor pressure are experimentally unknown. Molecules 2018, 23, 1379 it is clear that estimation methods of both the acentric factor and the critical properties are necessary. High accuracy in estimation is required because phase equilibrium calculations are rather sensitive to these values [1]. “Group contribution methods” (GC methods) are the most commonly used estimation approaches
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