电子测量与仪器学报

陈剑虹,孙超越,林志强,杨佳,任军怡.基于 TDLAS 技术的 CO2 浓度检测方法研究[J].电子测量与仪器学报,2022,36(6):229-235

基于 TDLAS 技术的 CO2 浓度检测方法研究

Research on CO2concentration detection method based on TDLAS technology

DOI：

英文关键词:TDLAS gas detection discrete wavelet transform spectrum analysis

基金项目:陕西省重点研发计划（2020ZDLGY10 04)项目资助

作者	单位
陈剑虹	1.西安理工大学机械与精密仪器工程学院
孙超越	1.西安理工大学机械与精密仪器工程学院
林志强	1.西安理工大学机械与精密仪器工程学院
杨佳	1.西安理工大学机械与精密仪器工程学院
任军怡	1.西安理工大学机械与精密仪器工程学院

Author	Institution
Chen Jianhong	1.Faculty of Mechanical and Precision Instrument Engineering, Xi′an University of Technology
Sun Chaoyue	1.Faculty of Mechanical and Precision Instrument Engineering, Xi′an University of Technology
Lin Zhiqiang	1.Faculty of Mechanical and Precision Instrument Engineering, Xi′an University of Technology
Yang Jia	1.Faculty of Mechanical and Precision Instrument Engineering, Xi′an University of Technology
Ren Junyi	1.Faculty of Mechanical and Precision Instrument Engineering, Xi′an University of Technology

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中文摘要:

全球变暖日益严重,二氧化碳作为温室气体的主要成分,需要精准把控。可调谐半导体激光吸收光谱技术因其具有高灵敏度、高分辨率的特点,被广泛应用于气体检测等领域。为了进一步提高 TDLAS 系统的测量精度,在小波去噪的基础上,对去噪后的 TDLAS 二次谐波信号进行了频域分析处理,利用离散小波变换提取与 CO2 浓度变化相关的频域特征信号,建立回归模型反演气体浓度。时域回归模型校正集与预测集的相关系数分别为 0. 998 5 和 0. 997 3,RMSE 值分别为 0. 045 9% 和 0. 017 9%,预测集的最大相对误差为 4. 62%;频域回归模型校正集与预测集的相关系数分别为 0. 999 3 和 0. 999 7,RMSE 值分别为 0. 032 0%和 0. 006 9%,预测集的最大相对误差为 1. 54%。实验结果表明 TDLAS 系统的预测能力和测量精度均有效提高, 验证了该方法的可行性。

英文摘要:

Global warming is becoming more and more serious, and carbon dioxide, as the main component of greenhouse gases, needs to be precisely controlled. Tunable semiconductor laser absorption spectroscopy is widely used in gas detection and other fields due to its high sensitivity and high resolution. In order to further improve the measurement accuracy of the TDLAS system, the denoised TDLAS second harmonic signal was analyzed in the frequency domain on the basis of wavelet denoising, and the frequency domain characteristic signal related to the change of CO2 concentration was extracted by discrete wavelet transform. And establish a regression model to invert the gas concentration. The correlation coefficients of the time domain regression model calibration set and prediction set are 0. 998 5 and 0. 997 3, the root mean square error (RMSE) values were 0. 045 9% and 0. 017 9%, respectively, and the maximum relative error of the prediction set is 4. 62%. The correlation coefficients of the frequency domain regression model calibration set and prediction set were 0. 999 3 and 0. 999 7, the RMSE values were 0. 032 0% and 0. 006 9%, respectively, and the maximum relative error of the prediction set was 1. 54%. The experiment results show that the prediction ability and measurement accuracy of the TDLAS system were effectively improved, which verifies the feasibility of the method.

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