QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index
Author(s) -
Long Jiao,
Huanhuan Liu,
Le Qu,
Zhiwei Xue,
Yuan Wang,
Yanzhao Wang,
Bin Lei,
Yunlei Zang,
Rui Xu,
Zhen Zhang,
Hua Li,
Omar Abdulaziz Ahmed Alyemeni
Publication year - 2020
Publication title -
acs omega
Language(s) - English
Resource type - Journals
ISSN - 2470-1343
DOI - 10.1021/acsomega.9b03139
Subject(s) - quantitative structure–activity relationship , octane , partial least squares regression , linear regression , cross validation , toluene , chemistry , stepwise regression , regression analysis , octane rating , mathematics , statistics , combustion , organic chemistry , stereochemistry
The quantitative structure-property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n -heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.
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