Group Contribution Model for the Prediction of Refractive Indices of Organic Compounds

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• 作者：Farhad Gharagheizi ; Poorandokht Ilani-Kashkouli ; Arash Kamari ; Amir H. Mohammadi ; Deresh Ramjugernath
• 刊名：Journal of Chemical & Engineering Data
• 出版年：2014
• 出版时间：June 12, 2014
• 年：2014
• 卷：59
• 期：6
• 页码：1930-1943
• 全文大小：677K
• 年卷期：v.59,no.6(June 12, 2014)
• ISSN：1520-5134

文摘

The determination of a wide range of optical parameters for the evaluation of thermodynamic properties and process variables is required in chemical thermodynamics and process engineering. Thus, in this study, the prediction of the refractive indices of pure chemical compounds as a potential thermophysical property is pursued by a reliable model. An accurate group contribution (GC) method is presented for the estimation of the refractive indices of pure compounds. The model was developed by use of a very large data set of 11918 pure components, most of which are organic compounds. Approximately 80 % of the data set (9536 data points) was used to develop the model, and the remaining 20 % (2382 data points) was implemented to evaluate the predictive capability of the proposed model. The method uses a total of 80 substructures or structural functional groups to estimate the refractive index. The model has an average absolute relative deviation with respect to the literature data of 0.83 %, with a squared correlation coefficient of 0.888. The model therefore performs very satisfactorily with regard to the prediction of refractive indices of pure compounds.