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应用案例 | 基于深度神经网络的无需压力校准和轮廓拟合的气体传感光谱技术

更新日期:2023-08-30      点击次数:628


Recently, the research team from Associate Professor Zhou Sheng's from Anhui University published an academic papers Pressure calibration- and profile fitting-free spectroscopy technology based on deep neural network for gas sensing.



Methane (CH4), which is the main component of natural gas, is widely used as fuel in industrial production and daily life. In addition, CH4 is an important greenhouse gas whose concentration has a substantial influence on global climate. Therefore, the measurement of CH4 has significant importance for environmental monitoring, biomedicine, and energy research. The gas concentrations are commonly measured by various trace gas sensors, such as gas chromatographs, semiconductor gas sensors, and electrochemical devices. The semiconductor gas sensors have a sensitivity of ppm level under a suitable operating environment. The laser absorption spectroscopy, which has the advantages of high selectivity, high sensitivity, and fast and multi-component monitoring, is currently widely used in the detection of a variety of gases. Laser absorption spectroscopy technology can accurately measure the characteristic absorption lines of gas molecules and effectively reduce the interference of other gas spectral lines based on the tunable lasers. Moreover, it provides the possibility of real-time in-situ gas detection, which is crucial for understanding and monitoring a variety of phenomena from industrial processes to environmental change. A gas molecule can be effectively identified by its fingerprint absorption spectrum, including typical so-called “self-broadening" parameters and “air-broadening" parameters. The spectral line parameters are functions of pressure and temperature. The accuracy of concentration measurement depends on pressure stability and spectral fitting accuracy. For quantitative spectral analysis, the spectra are traditionally fitted by an accurate model, while the pressure and temperature must be calibrated on time, especially in the case of relatively large environmental fluctuations. Consequently, the complexity of system is increased to achieve the required accuracy.



Currently, the rapid development of artificial intelligence provides a new way to solve this problem. The artificial neural network has been used for gas identification and shows a good performance under the condition of sufficient data for training. The infrared spectra of five similar alcohols has been identified by a neural network based on the Hopfield self-associative memory algorithm . A back propagation neural network is used to recognize target gas from the mixtures of gases, which proved that the convolutional neural networks (CNN) model can improve identification accuracy effectively. In addition, recent studies indicate that deep neural networks can also be applied to vibrational spectral analysis. The convolutional neural and auto encoder networks are used to process onedimensional vibrational spectroscopic data. Compared with traditional gas detection technology, the gas sensors assisted with deep learning can achieve accurate sensitivity measurement and reduce the robustness of anomaly detection.

A deep neural network (DNN), which can learn features directly from the absorption spectra after training with sufficient samples, achieves the direct identification of gas concentration free of pressure calibration and profile fitting. This network provides a new way to retrieve gas concentrations without expensive and complicated pressure controllers. To demonstrate the performance of proposed DNN assisted algorithm, a DFB diode laser-based gas sensor system for CH4 detection is constructed. The predicted concentrations are in good agreement with the calibrated values. This study indicates that DNN-based laser absorption  spectroscopy has remarkable potential in atmospheric environmental monitoring, exhaled breath detection and etc..




用于获取甲烷(CH4)气体吸收光谱的实验装置如图1所示。一台近红外DFB激光二极管,最大峰值输出功率为20毫瓦,被用作光源。通过控制激光温度和电流,激光可以在6045 cm-1到6047 cm-1范围内进行调谐宁波海尔欣光电科技有限公司为此项目提供激光驱动器,型号为QC-1000所选CH4在6046.95 cm-1附近的吸收线在图2中基于从HITRAN数据库获取的光谱线参数进行了模拟。DFB激光二极管经过纤维准直器进行准直,然后由一块CaF2分束器进行对准,分束后的可见红光(632.8纳米)光束用作跟踪激光。随后,光束被送入一个7米有效光程的多程传输池,并且池内的压力由压力控制器、流量控制器和隔膜泵协同控制。一个典型频率为100赫兹的三角波被用作扫描信号,以驱动激光二极管。最后,激光通过一个InGaAs光电探测器进行检测,并被数据采集单元卡获取。信号随后传输到计算机,并由自制的LabVIEW程序进行分析。

Experimental setup

The experimental setup used to obtain CH4 gas absorption spectra is depicted in Fig. 1. A near-infrared DFB diode laser with a maximum peak output power of 20 mW is used as the optical source. The laser can be tuned from 6045 cm−1 to 6047 cm−1 by controlling the laser temperature and current via the controller (QC-1000, Healthy photon Co., Ltd.). The absorption line of selected CH4 near 6046.95 cm−1 is simulated based on spectral line parameters obtained from the HITRAN database in Fig. 2. The DFB diode laser is collimated by a fiber collimator and aligned by a CaF2 beam splitter with a beam of visible red light (632.8 nm) as the tracking laser. Subsequently, the beam is sent to a multi-pass cell with a 7 m effective optical length, and the pressure inside the cell is collaborative controlled by a pressure controller, a flow controller, and a diaphragm pump. A triangular wave with a typical frequency of 100 Hz is used as a scanning signal to drive the diode laser. Finally, the laser is detected through an InGaAs photodetector and acquired by a data acquisition unit card. The signal is subsequently transmitted to the computer and analyzed by the homemade LabVIEW program.



 QC-1000, Healthy photon Co., Ltd.



Fig. 2. Experimental device diagram. 

Fig. 1. Experimental device diagram.


Fig. 3. 

Fig. 2. The spectral line intensities of CH4 in the tuning range of 6046.93–6046.96 cm−1 and the cross-section of the selected line obtained from the HITRAN database.








Overall, a proof-of-concept gas sensor based on the DNN algorithm and laser absorption spectroscopy is developed, and a CH4 detection sensor system based on the DFB diode laser is designed in this paper. In addition, the performance of the DNN algorithm is evaluated by calculating RMSE and training times, and the parameters, which include DNN layers, neuron number, and epochs, are optimized to obtain optimal parameters. The modified system is proposed to analyze and predict the gas absorption spectrum data, demonstrating good accuracy and stability in the prediction of CH4 concentrations. The predicted values of methane with different concentrations are linearly fitted with the corresponding theoretical value, which proves it has great potential in practical field applications, especially for harsh environments.




Pressure calibration- and profile fitting-free spectroscopy technology based on deep neural network for gas sensing, Measurement 204 (2022) 11207