Accurate determination of alcohol-based diesels using optimal chemical factors

Abstract

Sustainable environmental policies and energy crises have led to a trend of blending different alcohols into diesel to partly replace the decreasing fossil fuels. To improve the rapidity and accuracy of determining alcohols exist in methanol and ethanol diesel, optimal chemical factors (OCF) feature selection schemes were presented based on different near infrared (NIR) characteristic absorption bands generated by different chemical structure information utilizing support vector machine (SVM). Through comparative analysis with SVM based on entire spectra, Monte Carlo uninformative variable elimination (MC-UVE) spectra and competitive adaptive reweighted sampling (CARS) spectra, the proposed OCF-SVM not only achieved 100 % accuracy, precision, recall and F1-score in classification, but also exhibited the best performance in prediction analysis with the smallest sum of squares due to error (SSE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and the highest R-square. The overall outcomes indicate that the OCF method based on molecular chemical structures can select more pertinent and interpretable spectral features, thereby making the classification and prediction of alcohol-based diesels more exact and credible. Further, the developed OCF strategy together with SVM could supplement existing methods for analysis of alcohol-based diesels and is expected to be recommended for detecting the chemical composition of other fuels.