Article ID Journal Published Year Pages File Type
586524 Journal of Loss Prevention in the Process Industries 2009 6 Pages PDF
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 (ΔHco) 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 R2 = 0.991 for the training set of 1196 compounds. For the test set of 300 compounds, the corresponding statistics was R2 = 0.992. The results of this study showed that it would be successful to predict ΔHco by using the easily calculated atom-type E-state indices, which can provide one more way for predicting the ΔHco of organic compounds for engineering based on only their molecular structures.
Related Topics
Physical Sciences and Engineering Chemical Engineering Chemical Health and Safety
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