Volume 30 Issue 3
Nov.  2010
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PAN Yong, JIANG Jun-Cheng, WANG Rui. Prediction of the lower flammability limits of hydrocarbons based on the quantitative structure-property relationship studies[J]. Explosion And Shock Waves, 2010, 30(3): 288-294. doi: 10.11883/1001-1455(2010)03-0288-07
Citation: PAN Yong, JIANG Jun-Cheng, WANG Rui. Prediction of the lower flammability limits of hydrocarbons based on the quantitative structure-property relationship studies[J]. Explosion And Shock Waves, 2010, 30(3): 288-294. doi: 10.11883/1001-1455(2010)03-0288-07

Prediction of the lower flammability limits of hydrocarbons based on the quantitative structure-property relationship studies

doi: 10.11883/1001-1455(2010)03-0288-07
  • Publish Date: 2010-05-25
  • The quantitative relationships between the lower flammability limits (LFL) and the molecular structures of hydrocarbon compounds were investigated based on the quantitative structure-property relationship (QSPR) studies. Various structure parameters were calculated to describe the structure characteristics of the molecules based on their structures. A set of structure parameters which have significant contribution to the LFL were chosen as the molecular descriptors by employing the variable selection method of genetic algorithm (GA). Both the multiple linear regression (MLR) and support vector machine (SVM) were employed to model the possible quantitative relationship existed between these selected descriptors and LFL,respectively,and the corresponding prediction models for the LFL of hydrocarbons based on the molecular structures were constructed. The models were tested by internal and external validations. The results showed that,for both models,the predicted LFL values agreed well with the experimental ones,and the predicted errors were within the range of the experimental error of LFL measurements. The mean absolute error and the root mean square error for the test set of SVM model were 0.036% and 0.046%,respectively,which were better than those of the MLR model and previous models. This paper provides a new method for predicting LFL of hydrocarbons for engineering.
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