Quantitative structure—property relationships for auto‐ignition temperatures of organic compounds

Abstract

Quantitative structureproperty relationship techniques were applied to develop a predictive method for autoignition temperatures of a wide range of organic molecules, ioeludmg hydrocarbons, alcohols, phenols, ethers, aldehydes, ketones, acids, amines, esters and halogenated compounds. Multivariate linear regression models in terms of easily available molecular descriptors or intrinsic molecular properties such as critical pressure, parachor, atomic charges, etc were p r o m . Principal component analysis on the set of descriptors employed uncovered the significant contributions of the polarity-related factors to auto-ignition temperatures. For the majority of the 250 compounds over an auto-ignition temperature range of 170-6WC, good agreement between observed and calculated autoignition temperatures was confirmed. The method could be useful for assessing the flammability of new compounds.

Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine

“…As can be seen from Table 2, for the SVM model, the resulting average absolute error (AAE) values of both training and external prediction sets are within the experimental error of AIT determination, which is around ±30 • C [4,44]. Meanwhile, it is noteworthy that the root mean square error (RMSE) values are not only low but also as similar as possible for the training and test sets, which suggests that the proposed model has both predictive ability (low values) and generalization performance (similar values) [37].…”

“…Such as the works of Suzuki [4] and Tetteh et al [5], both of them used the same six parameters, which contained unconventional physicochemical parameters such as critical pressure and parachor, to predict the AIT of organic compounds by means of multiple linear regression (MLR) and artificial neural network (ANN) technologies, respectively. Although these methods are able to predict AIT with moderate success, they also suffer from some important disadvantages.…”

Correlations of limiting oxygen index with structural polyphosphoester features by QSPR approaches

“…A QSPR study on autoignition temperatures of 250 organic molecules, including hydrocarbons, alcohols, phenols, ethers, aldehydes, ketones, acids, amines, esters, and halogenated compounds has been reported [25]. Multivariate linear regression models in terms of easily available molecular descriptors or intrinsic molecular properties such as critical pressure, parachor, atomic charges, etc.…”

Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices

“…Many previous studies have shown that the AIT of a compound is very dependent on its structure, and several methods for estimating pure components AIT from their molecular structure alone have been reported in the literature [1][2][3][4][5]. Mitchell and Jurs [3] developed mathematical models which related the structures of a heterogeneous group of organic compounds to their AIT values.…”