Gas Quantitative Analysis Method of Online Process Mass Spectrometry Based on Separation of Overlapping Spectral Peaks
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Abstract
Online quantitative analysis of the reaction gas or tail gas of industrial production process can help to adjust the production process and improve product quality in time, which is of great significance to the transformation and upgrading of the relevant industries. However, due to the influence of industrial production cost, the application of expensive high-resolution mass spectrometer in industrial online quantitative analysis is relatively limited, while low-resolution mass spectrometer may detect the mass spectra with overlapping peaks, causing difficulties in the accurate quantification of reaction or tail gases. Although the current online quantitative analysis methods are relatively mature, there are still some spaces for optimization in terms of quantitative accuracy, method applicability and equipment complexity. For more accurate and more efficient online quantitative analysis, an online process mass spectrometer was proposed to detect the reaction gas or tail gas and separate the overlapping spectral peaks. At first, the component gases and their corresponding contents of the reaction gas or tail gas under measurement were accurately predicted according to industrial production scenarios, obtaining a prediction matrix and formulating a calibration standard gas. Secondly, all the overlapping spectral peaks of the calibration standard gas were obtained through finding the standard mass spectra of each component gas, and the simplest ratio matrix was constructed according to its relationship with the main peaks of each component gas, which was corrected by formulating the ratio standard gases with the same content as the relevant component gases. Then, the calibration matrix was obtained through detecting and calculating the calibration standard gas with the mass spectrometer, together with the prediction matrix and the ratio matrix, the calibration mathematical model was constructed to obtain the relative sensitivity matrix. Finally, a quantitative mathematical model was constructed with the calculated relative sensitivity matrix, the corrected ratio matrix and the detection matrix obtained after detecting and calculating the reaction gas or tail gas under measurement, and the content matrix of each component gas was obtained to monitor and control the industrial production process in real time. The online quantitative analysis of representative yeast fermenter tail gas showed that the method has high accuracy and strong applicability. After several rounds of online quantitative analysis experiments, the maximum quantitative error could be controlled within 0.4%, and the maximum quantitative relative standard deviation could be limited within 2%.
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