Prediction of flammability characteristics of pure hydrocarbons from molecular structures

Abstract

A quantitative structure-property relationship study is performed to develop mathematical models for predicting the flammability characteristics of pure hydrocarbons. The molecular structures of the compounds are numerically represented by various kinds of molecular descriptors. Genetic algorithm based multiple linear regression is used to select most statistically effective descriptors on the flash point, the autoignition temperature, and the lower and upper flammability limits of hydrocarbons, respectively. The resulted models are four multilinear equations. These models are very simple and can predict the flash point, the autoignition temperature, and the lower and upper flammability limits for the test set with average absolute errors of 5.41 K, 28.00 K, 0.044 vol %, and 0.503 vol %, respectively. The models are further compared with other published method and are shown to be more superior. The proposed method can be used to predict the flammability characteristics of hydrocarbons from the knowledge of only the molecular structures. V

Predicting the net heat of combustion of organic compounds from molecular structures based on ant colony optimization

“…Quantitative structureeproperty relationship (QSPR) study which based on the law above has been widely applied in prediction of various simple and complex physicochemical properties, such as boiling point, melting point, flash point, vapor pressure, critical properties, water solubility, auto-ignition temperatures, octanol/ water coefficients, and so on, which have been extensively reviewed elsewhere (Katritzky & Fara, 2005;Katritzky, Lobanov, & Karelson, 1995;Katritzky, Maran, Lobanov, & Karelson, 2000;Pan, Jiang, Ding, Wang, & Jiang, 2010;Taskinen & Yliruusi, 2003). QSPR is a mathematical method that relates the properties of interest to the molecular structures of compounds which are represented by a variety of molecular descriptors, such as spatial, electronic, topological, thermodynamic, information-content, conformational, quantum mechanical, and shape descriptors.…”

Multi‐expression programming based model for prediction of formation enthalpies of nitro‐energetic materials

“…The development of a QSPR model involves the following series of steps (Gharagheizi et al ., ; Godavarthy et al ., ; Katritzky et al ., ; Pan et al ., ; Mehrpooya & Gharagheizi, ; Bagheri et al ., , , ; Fazeli et al ., ): (a) data set preparation, (b) descriptor calculation, (c) model developing, (d) model validation, and (e) model interpretation.…”

“…Another problem is the lack of measured data available to extend these methods to less‐common energetic materials such as molecules containing fused aromatic rings or polyfunctional substitutions. Last but not the least, the availability of different group contribution based methods is of questionable accuracy and precludes their applicability especially for the nitro‐energetic materials (Gharagheizi et al ., ; Godavarthy et al ., ; Katritzky et al ., ; Mehrpooya & Gharagheizi, ; Pan et al ., ; Bagheri et al ., ; ; ; Fazeli et al ., ). The above‐mentioned limitations demonstrate a need for a different modelling framework, such as that encompassed by QSPR. This technique is based on molecular structure, which not only allows for a fundamental modelling approach, but also allows for a more transparent interpretation of the structural relationship to a physical phenomenon.…”

“…One of the most important aspects in QSPR modelling is the selection of adequate descriptors containing the information stored in the molecular structure. Thus, one can conclude that, as the theoretical descriptors are solely derived from the molecular structure, the QSPR should, in principle, be applicable to any chemical structure (Gharagheizi et al ., ; Godavarthy et al ., ; Katritzky et al ., ; Mehrpooya & Gharagheizi, ; Pan et al ., ; Bagheri et al ., , , ; Fazeli et al ., ).…”

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

“…A QSPR study for predicting the flammability characteristics of pure hydrocarbons, expressed by flash point, autoignition temperature, and lower and upper flammability limits has been performed [26]. Multiple linear regression (MLR) combined with a genetic algorithm was used to correlate the flammability properties with calculated hydrocarbon structural descriptors.…”