A mathematical method for predicting heat of reaction of organic peroxides. (November 2015) - Zhou, L.L.; Jiang, J.C.; Pan, Y.; Wang, Z.R.
Abstract: The heat of reaction of organic peroxide is widely used to estimate the risk of fire and explosion in process industries. In this study, the quantitative relationship between the heat of reaction and the molecular structures of organic peroxides was established based on quantitative structure property relationship (QSPR). The genetic algorithm combined with partial least squares (PLS) was employed to select optimal subset of descriptors which had significant contribution to the overall heat of reaction. The best resulted model was consisted of seven variables which were Ss, Me, IVDM, HDcpx, ATS4m, MATS1e and MOR14m. Ss and Me are constitutional descriptors which are related to the electrical states of the atom, IVDM and HDcpx are topological descriptors which are related to the shape of the molecule, ATS4m and MATS1e are 2D autocorrelations descriptors which can reflect the topology structure of molecules and MOR14m is a 3D-MoRSE descriptor which can represent the spatial structure of the molecular. The correlation coefficient was 0.995 which mean the model had high fitting capacity. Model validation was also performed to check the stability and predictive capability of the presented model. The results showed that the presented model was a valid and predictive model. This study can provide a new way for predicting the heat of reaction of the organic peroxides. Highlights: The quantitative structure property relationship was used to predict the heat of reaction of organic peroxides. DRAGON software was used to calculate the molecular descriptors of the organic peroxides. The genetic algorithm was employed to select optimal subset of descriptors which had significant contribution to the overall heat of reaction. The model is a seven-variable equation which can be used easily to predict the heat of reaction of organic peroxides. The model was proved to be a valid and predictive model through the correlation coefficient, the model validation and the applicability domain.