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.
For the technical requirements of automobile emission CO and CO2 detector’s data processor, the scheme is based on the detection principle of NDIR method and the implementation of the data processor software as well as hardware is discussed. High-speed, high-precision DSP is selected as the core of the detector’s data acquisition and processing, while four-channel thermoelectricity sensor TPS4339 as infrared detector, digital-analog data acquisition circuit of NDIR is designed and simulated. Then Fast Fourier Transform (FFT) is adopted for signal processing. Automobile emission CO and CO2 concentration can be accurately obtained by appropriately adjusting sampling period and the light source modulation frequencies, the system SNR is improved and the detection limit is reduced. The experimental results show that the detector’s data processor has 3% accuracy and stability which can meet the measurement and analysis of automobile emission CO and CO2 concentration.
The emissions of NOX from Cement plant or Coal-fired power plant have serious pollution to the environment. In recent years, Selective Catalytic Reduction (SCR) is an effective means of reducing the emissions of NOX by injecting ammonia into the combustion flue gas, which ideally reacts with the NOX to produce harmless components (H2O and N2). The efficiency of SCR is determined by monitoring the ammonia slip of the flue exhaust outlet, excess ammonia injection can cause ammonia slip, which not only destroy the plant, but also increase the operating costs. In addition, ammonia is also pollution gases as NOX. The flue gas at the measurement point is high temperature, vibrate and high particle density processes in Cement plant primarily, such harsh conditions coupled with the highly reactive nature of ammonia, so it is difficult to reliable extractive low level analysis. The paper describes an in-situ Tunable Diode Laser analyzer for measuring ammonia slip in the combustion flue gas after SCR in Cement Plant or Coal-fired power plant. A correlation filtering algorithm is developed to select high-quality spectral absorption signal, which improve the accuracy of concentration inversion of analyzer. The paper also includes field test data on an actual Cement plant all day, and we compare the ammonia slip and NOX emissions of flue gas during actual production process, the results indicate that the measured values of the ammonia slip and NOX emissions present a good correlation and comply with the principle of SCR.
Hydrogen cyanide gas leakage may exist in the petrochemical industry, smelting plant, and other industrial processes, causing serious harm to the environment, and even threatening the safety of personnel. So the continuous detection of HCN gas plays an important role in the prevention of risk in production process and storage environment that existing hydrogen cyanide gas. The Tunable Diode Laser Technology (TDLAS) has advantages of non-contact, high sensitivity, high selectivity, and fast response time, etc., which is one of the ideal method of gas detection technologies and can be used to measure the hydrogen cyanide concentration. This paper studies the HCN detection system based on TDLAS technology, selects the absorption lines of hydrogen cyanide in 6539.12cm-1, and utilizes the center wavelength of 1.529μm distributed feedback (DFB) laser as a light source. It is discussed in detail on technical requirements of a high frequency modulated laser signal detection circuit, including noise level, gain, and bandwidth. Based on the above theory, the high frequency modulation preamplifier circuit and main amplifier circuit are designed for InGaAs photoelectric detector. The designed circuits are calculation analyzed with corresponding formula and simulation analyzed based on the Multisim software.
The main ingredient of mash gas is alkenes, and methane is the most parts of mash gas and ethane is a small portion of it. Fast, accurate, real-time measurement of methane and ethane concentration is an important task for preventing coal mining disaster. In this research, a monitoring system with tunable diode laser absorption spectroscopy (TDLAS) technology has been set up for simultaneous measurement of methane and ethane, and a DFB laser at wavelength of 1.653μm was used as the laser source. The absorption spectroscopy information of methane and ethane, especially the characteristic of the spectrum peak positions and relative intensity were determined by available spectral structures from previous study and available database. Then, the concentration inversion algorithm method based on the spectral resolution and feature extraction was designed for methane and ethane synchronous detection. At last, the continuously experimental results obtained by different concentration of methane and ethane sample gases with the multiple reflection cell and the standard distribution system. In this experiment, the standard distribution system made with the standard gas and two high precision mass flow meters of D07 Sevenstar series whose flow velocity is 1l/min and 5l/min respectively. When the multiple reflection cell work stably, the biggest detection error of methane concentration inversion was 3.7%, and the biggest detection error of ethane was 4.8%. So it is verified that this concentration inversion algorithm works stably and reliably. Thus, this technology could realize the real-time, fast and continuous measurement requirement of mash gas and it will provide the effective technical support to coal mining production in safety for our country.
With the increasing number of vehicles, the harm from NO to the environment becomes more and more prominent. So the monitoring of the NO concentration of the vehicle exhaust emissions is very important to assess the emission levels. In this paper, the NO detection system designing for vehicle exhaust emissions based on the non-dispersive ultraviolet principle (NDUV) has been researched. The technical indexes of the two-way modulation UV signal detection circuit are discussed in detail. And then a precision detection circuit is designed, which is composed of a trans-impedance amplifier and a lock-in amplifier, with which the output of the UV photoelectric detector can be amplified to a suitable voltage range, and the DC noise of the pre-stage amplifier is effectively removed by the lock-in amplifier. An experimental system was set up to test the designed circuit. To ensure the consistency of the two channels, the method of exchange calibration was adopted in the test. It’s drawn that the designed circuit is of high SNR, measuring accuracy and a large dynamic range from the test results. The NO concentration detection limit of vehicle emissions can reach 1ppm, and the detection precision is ±15ppm.
High nitrogen fertilizer input is the main manner to maintain the high-yield crops in farmland in China. The average application quantity of nitrogen fertilizer in China is significantly higher than some developed countries in the world. However, the nitrogen fertilizer utilization efficiency is very low. Thus, high sensitivity sensing and on-line monitoring ammonia concentration were needed to quickly acquire the soil nutrient information and to get the nitrogen fertilizer utilization efficiency. A high sensitivity ammonia concentration sensor used in farmland has been developed based on Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology, high frequency modulation technique and long optical path technique. TDLAS is a method to obtain the spectroscopy of gas molecule single absorption line in the characteristic absorption spectrum region as the characteristic of the distributed feed back (DFB) laser with narrow line width and tunability. A sensor array formed with three ammonia concentration sensors by distributed sensing technique was used for ammonia volatilization experiment in a wide range of farmland. It was verified that the performance consistency of the three ammonia sensors was good and the sensor array realized the regional ammonia concentration monitoring. Continuous measurement results showed that the ammonia concentration influenced by the volatile source location, wind direction, weather and other factors, and it was positively correlated with the ammonia volatilization rate. The ammonia sensor array is suitable for continuously ammonia volatilization monitoring in a wide range of farmland environment with its high sensitivity, rapid response time without gas sampling.
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