New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters

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@Article{HEIDARI:2023:chemolab,

• author = "Zeinab Heidari and Mohammad Amin Sobati",
• title = "New structure-based models for the prediction of flash point and autoignition temperatures of alkyl esters",
• journal = "Chemometrics and Intelligent Laboratory Systems",
• volume = "240",
• pages = "104877",
• year = "2023",
• ISSN = "0169-7439",
• DOI = "doi:10.1016/j.chemolab.2023.104877",
• URL = "https://www.sciencedirect.com/science/article/pii/S0169743923001272",
• keywords = "genetic algorithms, genetic programming, Alkyl esters, Enhanced replacement method (ERM), QSPR, Genetic programming (GP), Support vector regression (SVR), Random forest regression (RFR)",
• abstract = "In this study, new models based on quantitative structure-property relationship (QSPR) have been proposed for the prediction of autoignition temperature (AIT) and flash point (FP) of different alkyl esters. In this regard, data sets containing 126 and 179 alkyl esters from 10 categories were used for AIT and FP, respectively. The enhanced replacement method (ERM) was applied for choosing the appropriate molecular descriptors, and linear models were developed based on the selected descriptors. Nonlinear models were also developed for AIT and FP using genetic programming (GP), support vector regression (SVR), and random forest regression (RFR). Then, the predictive performance of each model was evaluated through internal and external validation techniques considering several statistical parameters such as coefficient of determination (R2), root mean square error (RMSE), and percent of average absolute relative deviation (AARD). The outcome of the validation techniques confirmed the satisfactory agreement between the predicted and experimental data. The AARDpercent, R2, and RMSE for the best corresponding models (GP-based nonlinear models) was reported as 4.54percent, 0.74, and 39.01 for AIT and 2.51percent, 0.98, and 12.32 for FP, respectively. The superiority of the new models over the previous models was also proved by comparing the prediction capability of different models",

}

Genetic Programming entries for Zeinab Heidari Mohammad Amin Sobati

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