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Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing
ACS Sensors ( IF 8.2 ) Pub Date : 2024-08-16 , DOI: 10.1021/acssensors.4c01514 Hao Xiong 1, 2 , Ligang Shao 2 , Yuan Cao 2 , Guishi Wang 2 , Ruifeng Wang 2 , Jiaoxu Mei 2 , Kun Liu 2 , Xiaoming Gao 1, 2
ACS Sensors ( IF 8.2 ) Pub Date : 2024-08-16 , DOI: 10.1021/acssensors.4c01514 Hao Xiong 1, 2 , Ligang Shao 2 , Yuan Cao 2 , Guishi Wang 2 , Ruifeng Wang 2 , Jiaoxu Mei 2 , Kun Liu 2 , Xiaoming Gao 1, 2
Affiliation
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Owing to the overlapping and cross-interference of absorption lines in multicomponent gases, the simultaneous measurement of such gases via laser absorption spectroscopy frequently necessitates the use of supplementary pressure sensors to distinguish the spectral lines. Alternatively, it requires multiple lasers combined with time-division multiplexing to independently scan the absorption peaks of each gas, thereby preventing interference from other gases. This inevitably escalates both the cost of the system and the complexity of the gas pathway. In response to these challenges, a mid-infrared sensor employing a neural network-based decoupling algorithm for aliasing spectral is developed, enabling the simultaneous detection of methane(CH4), water vapor(H2O), and ethane(C2H6). The sensor system underwent evaluation in a controlled laboratory environment. Allan deviation analysis revealed that the minimum detection limits for CH4,H2O, and C2H6 were 6.04, 118.44, and 1 ppb, respectively, with an averaging time of 3 s. The performance of the proposed sensor demonstrates that the aliasing spectral decoupling algorithm based on neural network combined with wavelength-modulated spectroscopy technology has the advantages of high sensitivity, low cost and low complexity, showing its potential for simultaneous detection of multicomponent trace gases in various fields.
中文翻译：
基于神经网络的混叠光谱解耦算法，用于精确中红外多组分气体传感
由于多组分气体中吸收线的重叠和交叉干扰，通过激光吸收光谱同时测量此类气体通常需要使用辅助压力传感器来区分光谱线。或者，它需要多个激光器结合时分复用来独立扫描每种气体的吸收峰，从而防止其他气体的干扰。这不可避免地增加了系统的成本和气体路径的复杂性。针对这些挑战，开发了一种采用基于神经网络的混叠光谱解耦算法的中红外传感器，能够同时检测甲烷（CH 4 ）、水蒸气（H 2 O）和乙烷（C 2 H） 6 ).传感器系统在受控实验室环境中进行了评估。 Allan偏差分析显示CH 4 、H 2 O和C 2 H 6的最低检测限分别为6.04、118.44和1 ppb，平均时间为3 s。该传感器的性能表明，基于神经网络结合波长调制光谱技术的混叠光谱解耦算法具有灵敏度高、成本低、复杂度低的优点，显示出其在多个领域同时检测多组分痕量气体的潜力。
更新日期：2024-08-16
中文翻译：
基于神经网络的混叠光谱解耦算法，用于精确中红外多组分气体传感
由于多组分气体中吸收线的重叠和交叉干扰，通过激光吸收光谱同时测量此类气体通常需要使用辅助压力传感器来区分光谱线。或者，它需要多个激光器结合时分复用来独立扫描每种气体的吸收峰，从而防止其他气体的干扰。这不可避免地增加了系统的成本和气体路径的复杂性。针对这些挑战，开发了一种采用基于神经网络的混叠光谱解耦算法的中红外传感器，能够同时检测甲烷（CH 4 ）、水蒸气（H 2 O）和乙烷（C 2 H） 6 ).传感器系统在受控实验室环境中进行了评估。 Allan偏差分析显示CH 4 、H 2 O和C 2 H 6的最低检测限分别为6.04、118.44和1 ppb，平均时间为3 s。该传感器的性能表明，基于神经网络结合波长调制光谱技术的混叠光谱解耦算法具有灵敏度高、成本低、复杂度低的优点，显示出其在多个领域同时检测多组分痕量气体的潜力。
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