Abstract
The exploration, production, and transportation of unconventional oils have attracted increasing attention for their economic value and environmental pressure. However, the previous analytical techniques of conventional oils encounter bottlenecks because of the separation difficulties of the unresolved complex mixtures. It is of great value to develop new methods to pursue a more detailed investigation of the chemical compositions of unconventional oil. Concentration-resolved fluorescence spectroscopy (CRFS) was developed to characterize the multi-dimensional fluorescence features of polycyclic aromatic hydrocarbons in unconventional oil samples. Laboratory simulation experiments of thermal evolution and biodegradation were designed to verify the effectiveness of CRFS compared to gas chromatography–flame ionization detector and gas chromatography–mass spectrometry. Dual-tree complex wavelet analysis and principal component analysis were used to remove redundant information and extract more detailed and effective information on CRFS spectra, and then a generalized regression neural network was used to classify and identify crude oil samples of different heavy oil species. With 100% accuracy, this computer data processing combined CRFS method is proven to be fast, accurate, and economical and is expected to be an effective method to solve the present problem of unconventional oil analysis.
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