Abstract
The types of crude oil for producing asphalt have a decisive influence on various performance measures (including aging resistance and durability) of asphalt. To discriminate and predict the crude oil source of different asphalt samples, a discrimination model was established using 12 greatly different infrared (IR) characteristic absorption peaks (CAPs) as predictive variables. The model was established based on diverse fingerprint recognition technologies (such as principal component analysis (PCA) and multivariate logistic regression analysis) by using attenuated total reflectance‐Fourier transform infrared spectroscopy (ATR‐FTIR). In this way, the crude oil source of different asphalt samples can be effectively discriminated. At first, by using PCA, the 12 CAPs in the IR spectra of asphalt samples were subjected to dimension reduction processing to control the variables of key factors. Moreover, the scores of various principal components in asphalt samples were calculated. Afterwards, the scores of principal components were analysed through modelling based on multivariate logistic regression analysis to discriminate and predict the crude oil source of different asphalt samples. The result showed that the logistic regression model shows a favourable goodness of fit, with the prediction accuracy reaching 93.9% for the crude oil source of asphalt samples. The method exhibits some outstanding advantages (including ease of operation and high accuracy), which is important when controlling the source and quality and improving the performance of asphalt.
Highlights
Asphalt pavements are widely used: as a black binding material produced from oil, asphalt is widely used as the binder in asphaltic mixtures [1,2,3]
Most of the petroleum asphalt is produced by distillation currently, and the molecules in the asphalt retain their original state in the crude oil. erefore, most of the composition and structure of asphalt are inherited from crude oil; that is to say, the structural performance of asphalt mainly depends on the source of crude oil
The principal component analysis (PCA) was used to reduce the dimension of the characteristic absorption peaks (CAPs) variables of the infrared spectrum, so that the variables with strong correlation were integrated into the same principal components. e principal components were independent of each other; the multiple collinear relationship between variables was eliminated. en, by using these principal components as independent variables, the discriminant model of crude oil source of asphalt was obtained by logistic regression analysis
Summary
Asphalt pavements are widely used: as a black binding material produced from oil, asphalt is widely used as the binder in asphaltic mixtures [1,2,3]. By applying multivariate statistical methods (including principal component analysis (PCA) and regression analysis), the chemical composition variables of oil fingerprints are summarised, classified, and discriminated [19, 20]. On this basis, qualitative and quantitative relationships between data are obtained to distinguish the crude oil source of asphalt, effectively controlling their qualities. Erefore, by utilising attenuated total reflectanceFourier transform infrared spectroscopy (ATR-FTIR), the characteristic functional groups of asphalt from different crude oil source were discriminated and quantitatively analysed. Nondestructive, stable method of discriminating the crude oil source of asphalt samples was explored, which provides a scientific basis for realising reasonable selection, supervision quality, and guaranteed origins of asphalt
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