基于分离重叠谱峰的在线过程质谱气体定量分析方法

Gas Quantitative Analysis Method of Online Process Mass Spectrometry Based on Separation of Overlapping Spectral Peaks

  • 摘要: 在线定量分析工业生产过程的反应气体或尾气,获知各组分气体含量,有助于及时调整生产工艺、提高产品质量,对相关工业的转型升级有着重要的意义。但受工业生产成本的影响,价格高昂的高分辨率质谱仪在工业在线定量分析中的应用相对局限,而采用低分辨率质谱仪检测出的带有重叠谱峰的质谱图可能会对反应气体或尾气的准确定量造成困难。虽然现有的在线定量分析方法较为成熟,但在定量准确度、方法适用性以及设备复杂性方面仍存在优化空间。为实现更准确且高效的在线定量分析,本研究提出了采用在线过程质谱仪对反应气体或尾气进行检测并分离重叠谱峰的定量分析方法。首先,根据工业生产场景精确预估待测反应气体或尾气的各组分气体及其含量,获得预测矩阵并配制校正标气。其次,通过查找各组分气体的标准质谱图,获得校正标气的所有重叠谱峰,根据各组分气体的主峰与重叠谱峰之间的关系构造最简单的比值矩阵,并配制与相关组分气体含量相同的比值标气修正比值矩阵。然后,利用质谱仪测量校正标气获得校正矩阵,并与预测矩阵和比值矩阵构建校正数学模型求得相对灵敏度矩阵。最后,根据计算出的相对灵敏度矩阵、修正后的比值矩阵与测算待测反应气体或尾气后获得的检测矩阵一同构建定量数学模型,实时求得各组分气体的含量矩阵,实现对工业生产过程的实时监测与调控。采用该方法分析具有代表性的酵母菌发酵罐尾气时,表现出准确度高且适用性强的特点,经过多次在线定量分析实验后,其最大定量误差可控制在0 4%以内,最大定量相对标准偏差可限制在2%以内。

     

    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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