Insight into polycyclic aromatic hydrocarbons in unconventional oil via

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Insight into Polycyclic Aromatic Hydrocarbons in Unconventional Oil via Concentration-Resolved Fluorescence Spectroscopy Coupled with Data Mining Techniques Lujun Zhang,† Xiaodong Huang,† Chunyan Wang,*,† and Chun Yang*,‡

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Department of Physics and Optoelectronic Engineering, Weifang University, Weifang, Shandong 261061, People’s Republic of China ‡ Emergencies Science and Technology Section (ESTS), Science and Technology Branch, Environment and Climate Change Canada, 335 River Road, Ottawa, Ontario K1A 0H3, Canada 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.

1. INTRODUCTION Unconventional oil, with tremendous potentiality several times that of conventional crude oil resources, has dominated growth in production and transportation over the past several years and radically created significant economic, energy-security, and environmental concerns all over the world.1,2 Unconventional oils, such as heavy oils, oil sands, bitumen, and oil shale, inevitably generate much more hazardous tailings and waste during their extraction, production, and also spillage compared to conventional oils. Moreover, 48% of the world’s marine oil pollution is related to fuel oil.3 After the refining procession of blending, distillation, and cracking of crude oil, the nature of fuel oil has changed significantly and many biomarkers are often lost. When it comes to the fuel oil derived from unconventional crude oil, the identification of marine fuel oil spills is more complicated and the related studies are rarely reported.4 Given the current analytical condition, the chemical composition of the unconditional oil is still not wellunderstood and requires comprehensive and in-depth investigation. Gas chromatography−flame ionization detector (GC−FID) and gas chromatography−mass spectrometry (GC−MS) are widely used tools for routine oil analysis, such as oil exploring and refining, forensic oil spill identification, and their environmental fate and behavior.5−9 However, the previous techniques mainly focusing on hydrocarbon components with good separation property now encounter bottlenecks because of their limited peak capability and the low-resolution ratio of the heavy components of unconventional oil. The baseline of gas chromatography is © XXXX American Chemical Society

often elevated to form a hump of unresolved complex mixtures (UCMs), which makes many biomarkers concealed and unable to be detected.7 These new problems make researchers begin to put more attention on the detection of polycyclic aromatic hydrocarbons (PAHs), which are important constituent parts of crude oil and petroleum-refined products.10−12 In comparison with saturated hydrocarbons, the special ring structure of PAHs brings it some unique capabilities, such as high resistance to weathering, thermal evolution, and biodegradation, providing more information about thermal maturity, oil−source correlation, etc.13−15 To better understand the characteristics of aromatic hydrocarbons in unconventional oil, some new technologies, such as two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC−TOF MS)16−18 and Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS),19 have been developed and applied to the detection of PAHs. These technologies, however, are quite time- and money-consuming, bearing high training burdens and, thus, not suitable for large-scale experiments. Fluorescence spectroscopy, with advantages of fast, economic, high-sensitivity, anti-interference, and simple operation, is regarded as one of the important technologies for PAH analysis20−22 and has played an important role in early petroleum geochemical research. However, with the rapid Received: May 3, 2019 Revised: June 18, 2019 Published: July 8, 2019 A

DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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aromatic hydrocarbon components can be reflected more comprehensively, accurately, and reliably. 2.2. Spectral Data Processing Method. Dual-tree complex wavelet transform (DTCWT) is selected as a feature extraction method for CRFS spectra. In comparison to typical wavelet transform, it has the advantages of shift invariance, low redundancy, and low computational complexity.32 The 2D DTCWT can generate six subbands in each level, which can effectively extract the feature information in six directions, ±15°, ±45°, and ±75°.33 Thus, the feature extraction based on 2D DTCWT can better extract the spectral detailed information on the whole CRFS spectrum, which is regarded as a 2D image in this paper. PCA is used to reduce the dimensions of feature spectra extracted by DTCWT. PCA can effectively identify the most important elements and structures in the data, removing noise and redundancy and using a few less principal components to reflect all aspects of information on the research target.34,35 On the basis of the principal component data of feature spectra from CRFS, the generalized regression neural network (GRNN) is finally used as a classifier for the classification and recognition of crude oil samples. The GRNN has fast speed, strong nonlinear mapping ability, high fault tolerance, and robustness and is suitable for solving nonlinear problems.36 The network eventually converges to an optimal regression surface with more sample accumulation, and the prediction accuracy is quite good, even with small training sets.37

development of mass spectrometry technology in the 1950s, it has been gradually ignored by the main study for decades. Fluorescence spectroscopy provides a general feature rather than detailed information, and the overlap of the fluorescence spectrum brings some difficulties to obtain individual PAH information, restraining the effectiveness of fluorescence analysis for multi-PAH substances. In recent years, the continuous development and popularization of the powerful data analysis technology and effective applicable software provide both methodological and technical opportunities for solving the overlap problem of the fluorescence spectrum. Studies on the detection of PAHs by fluorescence spectroscopy show a rapid increase in both quantity and quality.23−27 On the basis of the idea that time-resolved fluorescence spectroscopy28,29 reflects the temporal variation of fluorescence spectroscopy, in this paper, a concentration-resolved fluorescence spectroscopy (CRFS) technique is proposed to extend the concentration from a fixed value in the traditional linear concentration range to the nonlinear concentration range, which can reflect PAH information more comprehensively and increase the accuracy and reliability of identification. The distillation and biodegradation simulation experiments were carried out to verify the anti-geological evolution properties and explore the potential application of CRFS in characterization and identification analyses of unconventional oil. A data processing strategy combining dual-tree complex wavelet, principal component analysis (PCA), and generalized regression neural network was applied to enhance the identification ability of CRFS for similar oil samples. A thorough and comprehensive study of this method is bound to show its unique and practical value in the field of online detection of petrochemical refining, geochemical investigation of PAHs, traceability of environmental pollution, and monitoring of spilled oil pollutants.

3. EXPERIMENTAL SECTION The CRFS experiments that contrast with GC−FID were performed at the Spill Monitoring Laboratory of Environment and Climate Change Canada. GC−FID-related experiments were analyzed using the currently universal method of ESTS Laboratory of Environment and Climate Change Canada.7 The classification and identification experiments of similar crude oils, 210 day weathered, were carried out in the Optical Optoelectronic Laboratory of Ocean University of China. 3.1. Sample Sources and Reagents. In the CRFS experiments that contrast with GC−FID, heavy oil samples IFO180, Diesel2002, Lube 10w-30, Federal Oil, Mississippi Canyon Block 807 (MC), and ANS came from the Environment and Climate Change Canada Laboratory and Nanhai (NH), Lvda A-12 (LD), Bozhong (BZ), and Suizhong 36-1 (SZ) were provided by Shengli Oilfield Logging Company of China. The extractant for the fluorescence spectrum is nhexane (chromatographic grade) purchased from Sigma-Aldrich. To highlight the detailed information on CRFS spectra, in the concentration range from 10−4 to 40 mg/mL, the samples were prepared in a series of crude oil extracts with a more fine concentration, according to the dilution ratio of the diluted concentration/pre-dilution concentration of 0.8. Samples for GC−FID were prepared as follow: 80 mg/mL of crude oil stock solution was prepared by dissolving 800 mg of crude oil sample in 10 mL of n-hexane. Then, 200 μL of solution was added to a 10 mm micro chromatographic column packed with 3 g of activated silica gel (1.0 cm of anhydrous sodium sulfate was placed at the top), spiked with 100 μL of substitution standards (including acenaphthene-d10, phenanthrene-d10, benzoanthracene-d12, and perylene-d12, with a mass concentration of 10 μg/mL). The saturated hydrocarbon fraction (F1) was eluted with 15 mL of n-hexane, and the aromatic fraction (F2) was eluted with the mixture solution of n-hexane and dichloromethane (DCM) (1:1, v/v, 15 mL). The F2 eluent was carefully condensed to about 1.0 mL under a stream of nitrogen and then spiked with 100 μL of PAHs (triphenyl-d14, 10 μg/mL) as internal standards. The 210 day weathering samples for classification and identification are LD, BZ, NH, and SZ, mentioned above. Each oil sample was subjected to a regime of weathering by placing a 2−5 mm thick oil slick over seawater in a beaker outside of the window of the laboratory for 210 days in Qingdao, China. The weathered samples were collected once every 7 or 8 days and prepared in 5 mg/mL stock

2. PRINCIPLES AND METHODS 2.1. Principle of CRFS. The excitation−emission matrix (EEM) fluorescence spectrum presents the three-dimensional (3D) information on emission spectra at serial excitation wavelengths, which have a dramatic concentration-dependent “red-shift cascade” behavior of the maximum peaks.30 The synchronous fluorescence spectrum (SFS) is obtained by scanning both monochromators simultaneously at a constant wavelength difference, which can represent the twodimensional (2D) information on the 45° section cut through the EEM spectra to demonstrate the main characteristic of the EEM by a single scan.31 For the purpose of showing the fluorescence spectral information covering from lower ring to higher ring PAHs, the concentration is expanded to the nonlinear range as a new dimension to form the CRFS spectra. The idea of expanding the concentration from a fixed value in the traditional linear range to the nonlinear range as a new information dimension is based on the fact that the fluorescence response concentration range of PAHs with different ring numbers in crude oil varies greatly and there is no uniform linear concentration range (the dilute concentration range of the Beer−Lambert law) for all PAHs. Experiments and theories show that the high concentration reflects the information on high-ring PAHs, while the low concentration reflects the information on low-ring PAHs; therefore, the traditional linear concentration value (low concentration) can only reflect the information on low-ring PAHs, which has insufficient information for conventional oil containing a lot of low-ring PAHs, not to mention the unconventional oil, such as heavy oil containing PAHs with different ring numbers from low to high. Therefore, by increasing the information on the concentration dimension, the information on B

DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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Figure 1. Comparisons between CRFS spectra (left) of different heavy oil samples IFO180, Diesel2002, Lube 10w-30, Federal Oil, Lvda A-12, and Suizhong 36-1) and GC−FID chromatograms (right) of aromatic fraction F2. C

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Figure 2. CRFS spectra and GC−FID chromatograms of MC under different weathering degrees (crude oil and weathering oil). solution without further purification and dehydration, and eventually, each oil species produced 27 weathered samples. 3.2. Instruments and Measuring Methods. In the CRFS experiments, the Agilent Cary Eclipse fluorescence spectrometer was used to collect fluorescence spectra. The slit widths of excitation and emission wavelengths were 5.0 and 1.5 nm, respectively. The initial excitation wavelength was set at 220 nm, and the initial emission wavelength was set at 260 nm; that is, the synchronous fluorescence spectra were acquired with the optimum wavelength interval Δλ = 40 nm.30 The detection fluorescence spectral range was from 260 to 700 nm. The synchronous spectra were collected respectively for each concentration series of heavy oil extracts to obtain the CRFS matrix. For GC−FID experiments, the PAH separation was carried out on Agilent 6890 GC equipped with a flame ionization detector (FID) and Agilent 7683 autosampler. Samples were injected in splitless mode with 1.0 mL/min, using helium as the carrier gas. The GC oven was programmed at 50 °C and held for 2 min, heated to 300 °C at 6 °C/min, and held at 300 °C for 15 min. 3.3. Data Processing Experiment. DTCWT, PCA, and GRNN algorithms used for classification and identification are carried out using MATLAB 2015b. For DTCWT, this paper uses two-scale DTCWT decomposition with 2 × 6 directional sub-bands as the feature representation of the spectral image. For PCA, the feature data in all sub-bands of DTCWT for each oil sample are arranged in a row vector and then the row vectors of all oil samples are put together to form an input matrix of PCA. For GRNN, it is found that the best classification accuracy can be obtained by setting the smoothing factor as 0.1. The first three principal components of PCA (accumulated contribution above 98%) are input into the first layer of GRNN, while the output layer outputs the classification types, which are set as 1, 2, 3, and 4 for LD, BZ, NH, and SZ, respectively.

experiments show strong stability and interference immunity of CRFS. On the basis of these characteristics of CRFS, the experiment of classification and identification of 210 day weathering oil samples shows that CRFS combined with computer data processing methods can achieve quite good accuracy of automatic identification for unconventional oil. Details are as follows. 4.1. CRFS Experiment That Contrasts with GC−FID. Figure 1 shows the CRFS spectra (left) and GC−FID chromatograms (right) of aromatic fraction F2 of different heavy oil samples (Diesel2002, IFO180, Lube 10w-30, and Federal Oil). To better reflect the variation characteristics of the main peaks in the wavelength range, the 3D CRFS spectra are displayed in 2D intensity−wavelength maps in this paper, with multiple curves representing different concentrations. The GC−FID chromatograms are normalized according to the peak values of the internal standards. It is obvious that the CRFS spectra of the diesel oil (Diesel2002), fuel oil (IFO180), synthetic lubricating oil (Lube 10w-30), and crude oil (Federal Oil) samples shown here are significantly different, which is consistent with the corresponding GC−FID curves of aromatic fraction F2 shown on the right in Figure 1. The peak value of CRFS spectra of each sample has an obvious blue shift with the dilution of the concentration. For example, the diesel sample gradually appears with different peaks at 438.3, 409.9, 376.5, 358.3, 344.6, 333.4, 285−320, and 270.6 nm in the whole concentration range of 40−0.02 mg/ mL. If the concentration is a fixed single value, the spectrum at a low concentration will inevitably lose the information on a long wavelength, while the spectrum at a high concentration will lose the information on a short wavelength. The spectral peaks of Lube oil are relatively concentrated, and the blue shift process with concentration dilution is mainly from 350 to 250 nm. The spectrum of IFO has the highest spectral peak at 450 nm, indicating that IFO samples contain heavier aromatic components. It is noticed especially that the

4. RESULTS AND DISCUSSION The CRFS experiments that contrast with GC−FID indicate that the CRFS spectra have good distinguishing ability for different crude oil samples and, thus, can be used for classification and identification of unconventional oil. Furthermore, the evaporative weathering and distillation D

DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels GC−FID chromatogram of IFO has a hump in the retention time of 10−12 min, corresponding to the special fluorescence peaks from 400 to 470 nm in its CRFS spectrum. For different heavy oil samples, the spectral profiles of CRFS in the whole concentration range are also very distinct. Federal Oil, as a crude oil sample, has the same characteristics as the crude oil samples from Shengli Oilfield (Lvda A-12 and Suizhong 36-1), all of which have peaks at 382, 365, 340, 290, and 275 nm. However, the spectra of these samples are still quite distinct, and the ratios between the peaks of their CRFS are also very different. It is more noteworthy that the spectra of crude oil at a specific single concentration are often comparatively similar. Considering that the dilution of the concentration often inevitably exists with 10% error, then using EEM and SFS at a single concentration as the identification basis will inevitably result in a low accuracy of identification. However, if it is CRFS that is used as the identification basis, although different crude oil samples tend to have the highest peak at the same wavelength, the ratios of the highest peaks with different concentrations are still quite different. Figure 1 also shows the CRFS spectra and the GC−FID chromatograms of aromatic fraction F2 of Lvda A-12 and Suizhong 36-1, which are from similar oil sources. In comparison to the first four results of the different oil types (Diesel2002, IFO180, Lube 10w-30, and Federal Oil) in Figure 1, these two samples obviously have similar PAH compositions; thus, their CRFS spectra and also GC−FID chromatograms both show similar profiles and characteristics, which brings difficulties to the identification. However, with data mining techniques, it can be relatively simple for CRFS to be classified and identified (see section 4.4 for details), while it is too time- and money-consuming when GC−FID meets the requirement of data mining. 4.2. Effects of Evaporative Weathering on CRFS Spectra. When heavy oil releases into the environment, the weathering processes, such as diffusion, volatilization, oil− water emulsification, dissolution, photochemical oxidation, and microbial degradation, are inevitable. Evaporation is usually the most important and dominant weathering process in the short term after an oil spill.38 In this paper, the CRFS spectra analyses were performed for various crude oils and petrochemical products with different degrees of evaporative weathering. Environment and Climate Change Canada has systematically studied the chemical composition changes of the corresponding samples using GC−FID and GC−MS. The experimental results can be referred to the relevant literature.5,7,39 Figure 2 shows the CRFS spectra and GC−FID chromatograms of the aromatic fraction of MC fresh and MC weathering at 30.7% mass loss. Obviously, evaporative weathering has little effect on the fluorescence spectra. To quantitatively explain the stability of CRFS spectra, Table 1 also shows the spectral difference degree between fresh samples and samples with different weathering for both GC−FID and CRFS. The differences among the spectra are represented by the overall structure differences, specifically the 3D volume difference ratio for CRFS and 2D area difference ratio for GC−FID. It is obvious that, although the weathering mass loss of the weathered samples of MC is as high as 30.7%, the difference of CRFS is very small, which is only 2.86%, achieving a coincidence degree of 97.14%, while the difference of GC−FID is 21.35%, showing the strong stability of CRFS under an evaporative weathering situation.

Table 1. Information of MC under Different Weathering Degrees concentration (mg/mL) mass loss (%) GC−FID difference (%) CRFS difference (%)

MC fresh

MC at 12.0%

MC at 19.1%

MC at 30.7%

80.73

80.44

81.23

79.88

12.0 5.84

19.1 22.67

30.7 21.35

0.24

0.48

2.86

The main cause of the mass loss of evaporation is the decrease of low-molecular-weight n-alkanes and also benzene, toluene, ethylbenzene, and xylene (BTEX) and C3-benzene, which has no effect on aromatic hydrocarbons with two or more rings. The loss of low-ring aromatic hydrocarbons has almost no contribution to CRFS because PAHs with different rings have a different fluorescence concentration response range and high-ring PAHs have a strong quenching effect on low-ring PAHs. This is consistent with the evidence from GC− FID, as shown in Figure 2. Previous experiments of 1:1 blending gasoline and crude oil also showed that gasoline had no effect on the CRFS spectra of the blended oil.39 4.3. Effects of Distillation on CRFS Spectra. Contrast experiments between CRFS and GC−FID were carried out for different temperature distillation fractions of ANS crude oil. Table 2 shows the temperature information on the distillation Table 2. Information of ANS Distillation Fractions and Spectral Difference Comparison between CRFS and GC− FID label code ANS ANS ANS ANS ANS

fresh fraction fraction fraction fraction

1 2 3 4

wavelength range (nm)

distillation temperature (°C)

CRFS difference (%)

GC−FID difference (%)

260−553 230−300 275−350 275−450 260−550

IBP−173 173−287 287−481 481+

45.52 38.25 9.49 0.03

77.77 56.18 36.89 21.76

fractions, the main wavelength range, and the spectral differences of the corresponding spectra. Figure 3 shows the CRFS spectra (left) and GC−FID chromatograms (right) of fresh crude oil and four distillation fractions of ANS. The comparative analysis shows that the main components of distillation fraction 1 are low-ring aromatic hydrocarbons, whose spectrum is very similar to that of gasoline, in which the fluorescence peaks are near 250 and 280 nm. Distillation fraction 2 contains two- and three-ring PAH components, similar to diesel in CRFS in the range of 275−350 nm. Distillation fraction 3 shows spectrum peaks at 275, 290, 330, 340, 380, and 440 nm in the range of 275−450 nm, which has close spectral characteristics to fuel-relevant samples. In comparison of the CRFS spectra of ANS fresh and ANS fraction 4, it is obvious that the spectrum of ANS fraction 4, containing residues after multiple distilling processes, has high coincidence with that of ANS fresh in the range of 300−600 nm, while the GC−FID chromatograms of fraction 4 and fresh differ greatly. It is also supported by the quantitative results in Table 2 that the difference between the CRFS spectra of fraction 4 and fresh is only 0.03% (3D volume difference ratio in the range of 300−600 nm), while 21.67% for GC−FID (2D area difference ratio). E

DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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Figure 3. CRFS spectra (left) and GC−FID chromatograms (right) of different distillation fractions of ANS. F

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Figure 4. Original spectra of four oil samples (LD, BZ, NH, and SZ) and their feature spectra (contour results) on three sub-bands (directions 15°, −45°, and −75°) of DTCWT.

This result suggests that, even after a high-temperature distillation of 481 °C, the ratio of high-ring aromatic hydrocarbons, corresponding to CRFS in the range of 300− 600 nm, can still be maintained as stable. These aromatic hydrocarbon components, which are not easy to be distilled, correspond to UCM, which are also not easy to be separated by chromatography. Therefore, from the view of the practical application of oil identification, the stability of the CRFS spectra in the distillation situation indicates that it can be used as a feasible method for unconventional oil identification in an extensive weathering environment and thermal geological evolution. Moreover, the simplicity, convenience, and strong

adaptation of CRFS further strengthen its competitiveness for unconventional oil identification. The difference between the CRFS spectra of ANS fresh and ANS fraction 4 in the range of 250−300 nm can be explained by the loss of the low-ring aromatic hydrocarbon components during the distillation process, also proven by GC−FID chromatograms. 4.4. Classification and Identification of 210 Day Weathering Samples. The CRFS spectra of 27 × 4 oil samples from four oil species LD, BZ, NH, and SZ were collected and processed by DTCWT, PCA, and GRNN for automatic classification and recognition. G

DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels DTCWT, which is widely used in the image processing field, is barely applied to the fluorescence spectra analysis. On the basis of the state-of-the-art image processing method, DTCWT is carried out for feature extraction of CRFS, which can be regarded as an image. The 2D DTCWT can generate six subbands, effectively extracting the feature information on the CRFS spectrum in six directions, which is more conducive to classification and recognition. At the same time, DTCWT can allow for slight geometric distortion of image features and reduce the impact of image noise, which is helpful for the robust representation of CRFS under certain external disturbances. Figure 4 shows the original spectra and feature spectra (contour display) of four oil samples on three subbands of DTCWT. The first column gives the original spectra without extracting features, and the second to fourth columns show the feature spectra filtered in three sub-band directions, 15°, −45°, and −75°. It can be seen that the difference of the original contour maps is insignificant, but the difference of the feature maps is obviously enhanced in comparison to that of the original contour maps. It indicates that DTCWT has great advantages in mining detailed and comprehensive information for spectral images. Considering that the amount of information data in all subbands becomes very large and the dimension of feature space is too high after the original spectral image decomposed by DTCWT, which will lead to a large amount of computation and reduce the classification performance of the classifier, therefore, PCA is used to reduce the dimension of all feature coefficients of all sub-bands. Although PCA processing may cut off the spectral links between neighboring concentrations, DTCWT has kept the correlation of the 2D spectral image of different wavelengths between neighboring concentrations. The first three principal components for each sample, whose accumulated contribution can reach up to 98%, are chosen as the input data basis for classification and recognition. Figure 5

to take the Nth parallel samples of all oil samples as test samples, while the rest are the training samples, and the test samples will be alternated in turn until all of the samples are circulated once. For all of the samples, with the output classification types 1, 2, 3, and 4 for LD, BZ, NH, and SZ exactly, the accuracy of classification achieves 100%, which demonstrates that CRFS of 210 day weathering samples from similar oil sources still has great stability and enough discrimination after data processing.

5. CONCLUSION CRFS can not only reflect more detailed information on PAHs but also be less affected by distillation, weathering, and seawater adulterating. The analyzing work of CRFS is relatively simple and less time-consuming. Thus, it has large potential for the actual fast identification of unconventional oil. In this paper, CRFS spectra are regarded as image information and DTCWT and PCA are used to capture the local structure characteristics and obtain the most important information to realize the effective feature extraction of similar crude oil sample sets with external disturbances of weathering. Finally, GRNN is applied to the automatic identification and classification. Results show that this data processing method can effectively solve the problem of information extraction and classification for the CRFS spectra of similar crude oil and further improve the accuracy of identification. CRFS combined with computer data mining technology provides a good idea for establishing a fast, real-time, and economic fingerprint identification technology of unconventional oil. Further verification of the accuracy, effectiveness, reproducibility, and universal adaptability of CRFS is needed to provide and widen its practical application scopes related to unconventional oil. Therefore, more experiments under extensive realistic weathering situations with increasing sample size of different classes of unconventional oil, including realistic geological oil samples and refining products, and more detailed correlation analyses and geological interpretation of PAH information among CRFS, GC−FID, and GC−MS will be carried on in the future.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Lujun Zhang: 0000-0001-5432-0059 Chunyan Wang: 0000-0002-8523-7108

Figure 5. First three principal component distribution of different oil samples (LD, BZ, NH, and SZ) with different weathering degrees.

Notes

The authors declare no competing financial interest.

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shows the first three principal component distribution of different oil samples (LD, BZ, NH, and SZ) with different weathering degrees. It can be obviously seen that all oil samples are distinctly divided into four groups, while samples of the same species are well-clustered together in the principal component space, showing quite good effectiveness of this DTCWT and PCA combined method. The principal components are then inputted into the GRNN pattern recognition model for classification and identification, which is a powerful classifier of an artificial neural network, as mentioned in section 2.2. The method of leave-one-out crossvalidation is used to verify the performance of this method. To make all samples with equal probability, the specific method is

ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China under Grants 61805178 and 61701349 and the Shandong Provincial Natural Science Foundation under Grants ZR2019MD011, ZR2016HL42, ZR2018PF016, ZR2017MF042, and ZR2017QF012.

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DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX

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DOI: 10.1021/acs.energyfuels.9b01377 Energy Fuels XXXX, XXX, XXX−XXX