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甲烷重整过程监测用气体传感器阵列的开发。

Development of Gas Sensor Array for Methane Reforming Process Monitoring.

机构信息

Department of Process Engineering and Chemical Technology, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G, Narutowicza Str., 80-233 Gdańsk, Poland.

Department of Analytical Chemistry, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G, Narutowicza Str., 80-233 Gdańsk, Poland.

出版信息

Sensors (Basel). 2021 Jul 22;21(15):4983. doi: 10.3390/s21154983.

Abstract

The article presents a new method of monitoring and assessing the course of the dry methane reforming process with the use of a gas sensor array. Nine commercially available TGS chemical gas sensors were used to construct the array (seven metal oxide sensors and two electrochemical ones). Principal Component Regression (PCR) was used as a calibration method. The developed PCR models were used to determine the quantitative parameters of the methane reforming process: Inlet Molar Ratio (IMR) in the range 0.6-1.5, Outlet Molar Ratio (OMR) in the range 0.6-1.0, and Methane Conversion Level (MCL) in the range 80-95%. The tests were performed on model gas mixtures. The mean error in determining the IMR is 0.096 for the range of molar ratios 0.6-1.5. However, in the case of the process range (0.9-1.1), this error is 0.065, which is about 6.5% of the measured value. For the OMR, an average error of 0.008 was obtained (which gives about 0.8% of the measured value), while for the MCL, the average error was 0.8%. Obtained results are very promising. They show that the use of an array of non-selective chemical sensors together with an appropriately selected mathematical model can be used in the monitoring of commonly used industrial processes.

摘要

本文提出了一种使用气体传感器阵列监测和评估干甲烷重整过程的新方法。该阵列由九个市售的 TGS 化学气体传感器组成(七个金属氧化物传感器和两个电化学传感器)。主成分回归(PCR)被用作校准方法。开发的 PCR 模型用于确定甲烷重整过程的定量参数:入口摩尔比(IMR)在 0.6-1.5 范围内,出口摩尔比(OMR)在 0.6-1.0 范围内,以及甲烷转化率(MCL)在 80-95%范围内。测试是在模型气体混合物上进行的。在摩尔比为 0.6-1.5 的范围内,确定 IMR 的平均误差为 0.096。然而,在过程范围内(0.9-1.1),该误差为 0.065,约为测量值的 6.5%。对于 OMR,获得了 0.008 的平均误差(约为测量值的 0.8%),而对于 MCL,平均误差为 0.8%。得到的结果非常有前景。它们表明,使用非选择性化学传感器阵列和适当选择的数学模型可以用于监测常用的工业过程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a4be/8346959/0d4524f5b63d/sensors-21-04983-g001.jpg

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