Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures

Abstract

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Explainable Predictive Modeling for Limited Spectral Data

“…Additionally, fewer topics are dedicated to extracting or explaining (see Interpretability column in Table 1), spectroscopic features. To this end, they mainly applied model-based feature selection techniques to identify and remove noisy features in order to improve prediction accuracy and computational efficiency [64,61,62,65,25,66,57,26,60,67]. In another body of work, popular feature selection methods such as PCA [68], and PLS [69] were employed to discover the correlation between decomposed fuel spectra and fuel sample clustering results [54], help isolate certain chemical groups responsible for the deviation in predicted values [49,25], and correlate certain spectra regions of pharmaceutical tablets to the concentration of antiviral drug [62].…”

Identification and Quantification of Hydrocarbon Functional Groups in Gasoline Using 1H-NMR Spectroscopy for Property Prediction

“…Experimental measurements of RON and MON are very expensive, involve many manhours, require large volumes of reference fuels, and necessitate skilled operators and sophisticated instrumentation, thereby warranting the need for predictive models. There are many works [6,[31][32][33] present in literature that deal with the development of models to predict the ON of pure components and blends using group contribution and structureproperty relationships. Abdul Jameel et al [19] developed a neural network model that predicts the RON and MON of gasoline-ethanol fuels by using seven functional groups that make up most of the fuel.…”

“…The CN calculated from the ASTM D6980 standard is called the DCN, which is functionally similar to the CN. As is the case with RON and MON measurements, experimental measurement of CN/DCN is expensive, and there are multiple studies [33,38,39] reported in the literature that have developed models for CN/DCN prediction. Recently, an artificial neural network-based technique [40] was developed for predicting of the DCN of fuels containing oxygenated classes like alcohols and ethers by using the functional groups as input parameters.…”

Comparison of Deep Eutectic Solvents and Organic Solvent Effects on the Separation of Ternary Azeotropes by the Experimental Study and Molecular Simulation

“…The study of solvent extraction mechanism is of great significance for solvent screening and molecular design. At present, there are many methods for solvent screening, such as empirical method, group contribution method, , and computer design method, , but they cannot explain the extraction mechanism of solvents from a micro perspective view. Therefore, it is of great significance to explore the micro mechanism of solvents in extractive distillation.…”