[Discrimination of Crude Oil Samples Using Laser-Induced Time-Resolved Fluorescence Spectroscopy].

The Laser-induced fluorescence spectra combined with pattern recognition method has been widely applied in discrimination of different spilled oil, such as diesel, gasoline, and crude oil. However, traditional three-dimension fluorescence analysis method, which is not adapted to requirement of field detection, is limited to laboratory investigatio ns. The development of oil identification method for field detection is significant to quick response and operation of oil spill. In this paper, a new method based on laser-induced time-resolved fluorescence combined with support vector machine (SVM) model was introduced to discriminate crude oil samples. In this method, time-resolved spectra data was descended into two dimensions with selecting appropriate range in time and wavelength domains respectively to form a SVM data base. It is found that the classification accurate rate increased with an appropriate selection. With a selected range from 54 to 74 ns in time domain, the classification accurate rate has been increased from 83.3% (without selection) to 88.1%. With a selected wavelength range of 387.00~608.87 nm, the classification accurate rate of suspect oil was improved from 84% (without selection) to 100%. Since the detection delay of fluorescence lidar fluctuates due to wave and platform swing, the identification method with optimizing in both time and wavelength domains could offer a better flexibility for field applications. It is hoped that the developed method could provide some useful reference with data reduction for classification of suspect crude oil in the future development.

[Characterization of Time-Resolved Laser-Induced Fluorescence from Crude Oil Samples].

To evaluate the feasibility of laser induced time-resolved fluorescence technique for in-situ detection of underwater suspended oil spill, extensive investigations have been carried out with different densities of crude oil samples from six different wells of Shengli Oilfield in this work. It was found that the fluorescence emission durations of these crude oil samples were almost the same, the Gate Pulse Delay of DDG (Digital Delay Generator) in the ICCD started at 52ns and ended at 82ns with a width (FWHM) of 10 ns. It appears that the peak location and lifetime of fluorescence for different crude oil samples varied with their densities, and those with similar densities shared a similar lifespan with the closer peak locations of fluorescence. It is also observed that the peak of fluorescence remained the same location before reaching the maximum intensity, subsequently shift to longer wavelength as fluorescence attenuated from maximum intensity with a red shift among 17-30 nm varied with samples. This demonstrated that the decay rate of fluorescent components in the crude oils was different, and energy transfer between these components might exist. It is hoped that those obtained results and characteristics could be the useful information for identification of suspended spilled-oil underwater.

[Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

[Fingerprint discrimination technique of spill oil based on concentration auxiliary parameter fluorescence spectra].

In the present paper, concentration as an auxiliary parameter was introduced to the synchronous fluorescence to form concentration synchronous fluorescence matrix of the oil spill samples within the concentration range of 10(-1)-10 g x L(-1). Principal component analysis was used to classify the oil spill samples of 0# diesel, 93# gasoline and 5 crude oil simples from the Shengli oilfield. Experiments show that the introducing of concentration can reflect more composition information of the PAHs. This newly method has a better discrimination than the routine method of the synchronous fluorescence spectra obtained from spill oil samples in linear concentration range. It indicates that the spill oil samples of different type and source can be discriminated precisely, even from the same oilfield. The influence of the errors caused in the samples extraction procedure can be eliminated. All the results suggest that the technique may become a more convenient, rapid and accurate means in spill oil identification.