关键词: 光谱分析, 定量分析, 回归模型, PX装置

Abstract: The existing calibration algorithms for Raman spectral quantitative analysis, such as principal component regression, partial least squares and support vector machine need a large number of training samples and the regression model may have unsatisfactory generalization ability. Indirect hard modeling (IHM)is a recently introduced method for spectral quantitative analysis, which is used to analyze spectra with highly overlapping band and nonlinear variation. This method can be used to build a calibration model with high generalization ability only by a few training samples. However, all the spectra of pure-components in the mixture is required in IHM method. Obviously, this condition is difficult to achieve in practical applications. To overcome the limitation of IHM, a novel method based on Voigt function for spectral quantitative analysis, i.e., direct hard modeling (DHM), was presented. The novel method did not require the pure-component spectra. By DHM algorithm, the characteristic peaks of every pure component in the spectra of mixture could be fixed, and then a linear calibration model could be built based on the areas of unique peaks and the concentration of pure-components. The DHM algorithm was successfully demonstrated while testing on the quantitative analysis of xylene mixture in a PX unit. Experimental results showed that the DHM algorithm had the advantages of robustness and fewer training samples.

Key words: spectral analysis, quantitative analysis, regression model, PX unit