Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
586524 | Journal of Loss Prevention in the Process Industries | 2009 | 6 Pages |
Abstract
An accurate quantitative structure-property relationship (QSPR) model, based on the atom-type electrotopological state (E-state) indices and artificial neural network (ANN) technique, for prediction of standard net heat of combustion (ÎHco) was developed. An extended set of 49 atom-type electrotopological state (E-state) indices that combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors for a diverse set of 1496 organic compounds. Both multilinear regression (MLR) and artificial neural network (ANN) were employed in the modeling. The ANN model with the final optimum network architecture of [49-35-1] gave a significant better performance than the MLR model. The squared correlation coefficient R2 for the ANN model was R2Â =Â 0.991 for the training set of 1196 compounds. For the test set of 300 compounds, the corresponding statistics was R2Â =Â 0.992. The results of this study showed that it would be successful to predict ÎHco by using the easily calculated atom-type E-state indices, which can provide one more way for predicting the ÎHco of organic compounds for engineering based on only their molecular structures.
Keywords
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Health and Safety
Authors
H.Y. Cao, J.C. Jiang, Y. Pan, R. Wang, Y. Cui,