| Some properties of petroleum products in refinery such as gasoline octane number,diesel cetane number and viscosity of lube base oil are necessary to be on-line monitored. Laboratory analysis methods are usually not suitable for online monitoring because of their high cost and long time delay. Near-infrared (NIR) spectroscopy is a non-destructive real-time analytical method, and it is preferable to online monitoring for the properties of petroleum products. Therefore, this thesis researched the application techniques of NIR. The main contributions of this thesis are as follows: 1. Introduce the concepts and the principles of NIR quantitative analysis methods, multivariate calibration modeling and model evaluating index. Then review the applications and developments of NIR in petroleum products analysis. 2. Two chemometrics methods that back propagation artificial neural network (BP-ANN) and partial least squares (PLS) have been introduced. And one quantified correction applications of BP-ANN has been exploited. The software applies to petroleum products analysis in NIR and has better result for non-linear problem. 3. Based on 85 gasoline samples which motor octane number (MON) and research octane number (RON) were evaluated by ASTM-CFR engine in advance, two separate near infrared spectroscopy calibration models for gasoline octane number have been established by using BP-ANN and PLS. The mean standard deviations of the forecasted values were all less than 0.5 unit. For the relation between the gasoline octane number and the near-infrared absorbency is non-linear, experimental results show that the BP-ANN model gains higher prediction accuracy. Compared with former study, BP-ANN gains better prediction result. 4. Based on the results of high resolution capillary Gas Chromatography (GC) for a set of gasoline samples, the PLS calibration method is used to set up the calibration model of near infrared spectroscopy for determining major hydrocarbon classes content in gasoline. GC has tested the validity of the calibration models by comparison with results determined by the standard methods, which show that the accuracy of NIR method conforms with that required. Experimental results show that standard error of prediction (SEP) accord with demand of repeatability, the result of paired t test less than significance level 0.05 distributing value. Compared with GC, the NIR method has advantages of high speed, simplicity, better repeatability and lower analysis cost, etc. The NIR is suggested as a rapid method for the intermediate control analysis in gasoline production. 5. Applied Fourier transform-near infrared spectrometer, many kinds of lube base oils are determined in the range of near-infrared long wave. The PLS and BP-ANN calibration method are used to set up three calibration models of NIR spectra-viscosity index, 40℃viscosity and 100℃viscosity which are suitable for the lube base oils. Study result has shown that near infrared spectroscopy has capability to obtain the spectrum information that has the relationship with the viscosity of lube base oil. As a sort of non-linear method, back propagation artificial neural network (BP-ANN) is preferable to evaluate the relationship between the spectrum information and the viscosity of lube base oil.
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