A group contribution model for the prediction of the freezing point of organic compounds

• A reliable group contribution (GC) method is proposed for the prediction of the freezing point temperature of pure compounds.
• A very large dataset of 16,941 pure mostly organic components are used to develop and validate the model.
• The model results show an average absolute relative deviation of 10.76% with respect to the literature data.

The freezing point is a fundamental thermo-physical property which is important in describing the transition between the liquid and solid phases. As this property is required for describing phase behavior and the design of separation unit operations, an efficient, applicable and reliable method which can predict it is of great importance, especially for compounds where there are no experimental data available. In this article, an efficient and reliable group contribution (GC) model is developed for the determination of the freezing point of organic compounds. The sequential search mathematical approach is used in this study to select an optimal collection of functional groups (112 functional groups) and subsequently to develop the model. A large dataset of freezing point data for about 17,000 pure mostly organic compounds was used to develop and validate the model. A comparison between the model results and the database shows a squared correlation coefficient of 0.735 (R2). Moreover, the proposed group contribution model is able to predict the freezing point of organic compounds to within an average absolute relative deviation of 10.76%, which is of adequate accuracy for many practical applications. Furthermore, the leverage approach (Williams plot) is used to determine the applicability domain of the model and to detect probable erroneous data points.

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