Research On Fuel Composition Detection Based On Spectral Analysis

The composition and quality of engine fuel directly influence its combustion efficiency, engine life and emissions which could cause the environmental pollution. The existing standard testing methods for engine fuel are generally slow, complex and high-cost. Spectral analysis technology is fast, nondestructive, low cost and safe, which makes it better than many existing standard methods. This thesis applies near infrared (NIR) and Raman spectroscopy in quantitative analysis of engine fuel composition and quality indices by combining with sample classification, calibration modeling and outlier detection techniques. Moreover, a fast NIR analyzer for methanol-gasoline is developed. Detailed research contents include:1. A detection method for methanol-gasoline composition is proposed based on NIR spectroscopy with support vector machine (SVM) classification model. Firstly, the NIR spectra of the methanol gasoline is measured and pre-processed by using derivative and standard normal variate. Secondly, an SVM classification model to distinguish methanol gasoline and regular gasoline without methanol is built. Thirdly, the feature wavelength of methanol content is found by correlation analysis between the spectra and methanol content. Finally, a least square SVM (LSSVM) quantitative model for the methanol content is established. Experimental results show that using NIR spectroscopy to detect the methanol content in methanol-gasoline can achieve high predictive accuracy. The corresponding multiple correlation coefficient of prediction (R2) is up to 0.9926; the root mean square error of prediction (RMSEP) is 0.58% (volume).2. The LSSVM algorithm with PLS feature extraction (PLS-LSSVM) is applied to the NIR spectroscopy detection of diesel quality. Based on the pre-processed diesel NIR spectrum, PLS-LSSVM quantitative models of diesel quality properties, such as cetane number, sulfur content, density and 50% recycling temperature, are respectively established. Compared with common-used PLS, LSSVM and other models, the results show that the PLS-LSSVM model integrates the benefits of PLS and SVM algorithms, which can reduce the influence of nonlinear degree between the properties and spectra of samples.3. By using a dispersive Raman spectrometer, PLS quantitative analysis models of gasoline aromatics content, olefin content and oxygen content are established combining with outlier detection. By removing the outlier samples, the prediction model accuracy is effectively improved. The RMSEP of aromatics, olefin and oxygen content are 0.30,0.52 and 0.083 respectively, and the corresponding R2 are 0.997, 0.927 and 0.984 respectively. Experimental results show the effectiveness of Raman spectroscopy to analyze the hydrocarbon group of gasoline, and the analysis accuracy is significantly higher than that of NIR spectroscopy.4. A portable rapid NIR analyzer for methanol gasoline is developed because of the strong absorption of methanol in NIR spectra. To meet the need of rapid detection of the methanol content, a portable fast analyzer based on dispersive NIR spectrometer is independently developed. It has been applied in several production enterprises of methanol gasoline. Practical application results show that this instrument can correctly classify methanol gasoline and regular gasoline, and it can precisely detect the methanol content. It is easy to operate and maintain this instrument. All these advantages make it to satisfy the needs of the methanol-gasoline production enterprises and other users in China.