Research on Infrared Spectral Quantitative Analysis of Hydrocarbon Gases Based on Adaptive Boosting Classifier and PLS

Abstract

The traditional gas logging using hydrogen flame gas chromatography is gradually difficult to meet the requirements of the current complex oil and gas reservoir exploration because of its long detection cycle, many additional equipment, complex use, maintenance process and other defects. Infrared spectroscopy has obvious advantages in detection speed and convenience. But when it comes to homologues with similar molecular structure, it is difficult to quantitatively analyze the component concentration because of the serious overlap of characteristic absorption peaks. To solve this problem, this paper uses the self-developed experimental platform to carry out a large number of infrared spectral experimental studies on the single substance and six component mixtures. Based on a large number of experimental data, a method combining the AdaBoost classifier with optimization window partial least squares (PLS) modeling is proposed. Through the PLS local modeling for the spectra of different components and different concentration ranges, the boosting algorithm is used to classify the original spectral data, and then the component concentration is calculated. This method solves the problems of difficult identification and inaccurate quantitative analysis of alkane mixed gas components in traditional methods, and effectively improves the detection accuracy of infrared spectrum quantitative analysis of mixed components, which provides the core theoretical support for infrared spectroscopy to replace hydrogen flame gas chromatography which is widely used in gas logging.