Abstract
A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.
Highlights
The auto-ignition temperature (AIT) is defined as the lowest temperature at which the substance spontaneously ignites in ambient air, without an external ignition source, such as a spark or flame
The experimental AIT values are the main source of the AIT data used in production
For the mixtures, the measurement is more difficult, since the AITs of the mixtures are closely related to their compositions and ratios, which are rather difficult to test one-by-one
Summary
The auto-ignition temperature (AIT) is defined as the lowest temperature at which the substance spontaneously ignites in ambient air, without an external ignition source, such as a spark or flame. AIT is one of the most important parameters applied to classify the chemicals based on their degree of flammability [1]. The experimental AIT values are the main source of the AIT data used in production. The measurement of AITs is expensive and time-consuming. For the mixtures, the measurement is more difficult, since the AITs of the mixtures are closely related to their compositions and ratios, which are rather difficult to test one-by-one. It is of great significance to develop theoretical models for predicting the AITs of mixtures
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