Based on our original development of a new laser absorption spectroscopy chamber (LASC) system, we further report ammonia emission concentration measurement using the LASC system based on deep belief networks (DBNs), aiming to present an effective approach for the retrieval of the gas concentration to increase the measurement accuracy of the LASC system and expand its application to monitoring ammonia emission in farmland. Surrounding the LASC system, an experimental system was constructed, and a DBN algorithm was introduced for gas concentration retrieval. The absorption spectroscopy obtained by the experimental system was first pretreated by an empirical wavelet transform algorithm and principal component analysis method, which greatly improved the signal-to-noise ratio of the signal and reduced the dimensionality of the processed signal to meet the need of the training of DBN model. The results showed that the measured gas concentrations were close to true values with small errors, and the mean relative error obtained by the DBN algorithm (0.37%) was much smaller than those obtained by the back-propagation neural network algorithm (0.97%) and absorbance peak method (2.37%) in a wide range of NH3 standard concentrations. Field experiments verified the effectiveness and reliability of the LASC system when it was applied to ammonia emission measurement in farmland with the concentration retrieval based on the DBN algorithm, which is of importance for its applications in air pollution detection.
KEYWORDS: Signal intensity, Signal detection, Laser spectroscopy, Signal to noise ratio, Signal processing, Modulation, Gas lasers, Modulation frequency
In-situ laser absorption spectroscopy is commonly used for environmental monitoring and industrial process control, but fluctuations in field environmental conditions can affect measurement accuracy and stability. In the wavelength modulated spectroscopy (WMS) technique, the first harmonic or DC signal is often used for light intensity normalization, which to a certain extent weakens the influence of light intensity fluctuations caused by vibration, turbulence, etc. in the measurement optical path. However, the simultaneous extraction of different harmonic signals from the same absorbed spectrum using a lock-in amplifier requires at least two channels, and the inconsistency between the channels increases the complexity and uncertainty of the system. Therefore, a harmonic extraction method based on the short-time Fourier transform (STFT) is proposed, in which the discrete Fourier transform (DFT) is performed on the signal segment by segment by shifting the window function, and the DC component and the harmonic signals of each order can be extracted simultaneously according to the multiples of the modulation frequency. The effectiveness of this method is verified in the experimental system of CO2 in-situ measurement of laser absorption spectroscopy, and the results show that the relative errors of the harmonic signals extracted by the method in this paper are always kept within 0.6%, and the average time saved is about 34.62%.
Vertical Radial Plume Mapping (VRPM) technique is often used in the measurement of gas emission flux in open space. It is necessary to use optical remote sensing equipment (ORS) to scan multiple measurement points to reconstruct the gas concentration field, but the fluctuation of field environmental conditions and the mechanical error of the system will lead to the optical path deviation. Although the optical path calibration can be completed by researching and positioning the central position of the measurement point according to the signal strength, the search range needs to be preset, which can not balance the time cost and positioning accuracy, reducing the time resolution of the concentration data, and resulting in flux calculation error. To solve this problem, this paper proposes a Q-learning multi-optical path localization method based on detection signal quality. This method uses the change of signal strength when the optical path moves as a reward to learn the environment, affects the selection of the next calibration direction, and makes the optical path preferentially choose the direction with enhanced signal strength. The effectiveness of this method is verified on the 25 * 25 map established of simulating the optical path offset. The results show that this method can get the optimal path to the center point, the minimum number of steps is 14, the running time is less than 2 seconds, and the success rate can reach 100% after many episodes of learning, which proves the effectiveness of Q-learning method in multi-optical path scanning.
During the detection process of atmospheric laser absorption spectroscopy in open space, the useful signal may be submerged in the noise due to the inherent noise of the instrument and external environmental noise. In order to effectively improve the signal-to-noise ratio, a new method of cascaded stepwise singular value decomposition has been proposed. By constructing different matrices, the low-frequency noise and high-frequency noise are removed in stages. A laser absorption spectroscopy atmospheric detection system in open space has been established to experimentally verify. The results show that, compared with the traditional method, the signal-to-noise ratio of the spectral signal processed by the method of cascaded stepwise singular value decomposition is increased from 0.211 to 1.029, the standard deviation is also reduced by 1.927 times. It is proved that the proposed method can effectively remove noise and obtain high-quality laser absorption spectrum signal to improve gas detection accuracy, which has practical value.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.