RESUMEN
For the demand of rapid monitoring of pollution gas disorganized emissions in industrial parks, this paper studies the solar fast tracker system of vehicle-mounted SOF-FTIR (Solar Occultation Flux Fourier Transform Infrared Spectroscopy) system, where the spectrometer directly measures the broadband absorption spectrum of solar radiation light. A fast portable solar tracking system based on PSD (position sensitive detector) is designed, the mathematical model of solar spot position on the PSD surface source is established, and the optimal optical design parameters are simulated using the model. The dead-zone integral separation PID (Proportion Integration Differentiation) control algorithm is used to track the trajectory of the solar, and the light spot position model is used to nonlinearly compensate the output of PID control so that the PID controller has the same control precision and response speed in different error areas. Experimental analysis of the solar tracking performance of the vehicle-mounted SOF-FTIR under static and dynamic conditions, as well as the spectral effects on the measurements under static vehicle, constant speed, and turning driving conditions. The remote sensing application experiment of vehicle-mounted SOF-FTIR pollution gas emission flux was carried out in a tire factory in Hefei City, Anhui Province. A vehicle-mounted SOF-FTIR system realized the qualitative and quantitative analysis of the pollution gas at the boundary of the tire plant and calculated the flux of each component pollution gas. The emission flux of pollution gas was highly consistent with the actual pollution distribution of the tire plant. The results show that the positioning accuracy of PSD in the vehicle tracking experiment can also meet SOF-FTIR requirements for solar tracking. The remote sensing system will be useful in the field of atmospheric environment monitoring, and the mobile monitoring of regional pollutant gases based on solar infrared spectroscopy has application value.
RESUMEN
Remote sensing imaging technology is one of the most powerful tools for gas leak monitoring in chemical industrial parks. In the case of leaks, it is necessary to quickly and accurately obtain detailed information of the gas cloud (volume, distribution, diffusion situation and location). This paper proposes a 3-D quantitative reconstruction method for gas clouds. Two scanning Fourier transform infrared (FTIR) remote-sensing imaging systems were used to perform telemetry experiments in a monitored space with a total volume of 314.9 m3, and the released gases were SF6 and CH4. One scanning FTIR remote-sensing imaging system can only measure a 2-D concentration-path-length product (CL) image of a 3-D gas cloud, where each pixel has attitude information of elevation and azimuth. Geometric methods are applied to locate the monitored space and construct a 3-D grid (longitude, latitude, altitude). The optical path length (OPL) sparse matrix of each layer is generated, and the concentration distribution of each layer is reconstructed by the simultaneous algebraic reconstruction technique (SART). The reconstructed results of each layer are stacked into a 3-D gas cloud and displayed on the 3-D Earth software at a set threshold. Three-dimensional leaking gas clouds (CH4, SF6) with geometric information and concentration distribution has been generated through the above processes from measurement, localization to reconstruction and display. On the premise that the gas cloud is completely covered by the field of view of each scanning system, the localization and quantification of the gas cloud is available. Then weighted concentration centers can be calculated from these gas clouds to approximate the leak source. The proposed method effectively extends the online leak monitoring application of the scanning FTIR remote-sensing imaging system.
RESUMEN
The concentration-path-length product (CL) image of the leaking gas cloud measured by the passive Fourier transform infrared (FTIR) scanning remote-sensing imaging system has a low resolution. Gas cloud diffusion is affected by wind speed and direction, which makes it difficult to trace the source of a leakage. Therefore, we propose a method to reconstruct the CL image of the leaking gas cloud applied to the passive FTIR scanning remote-sensing imaging system. First, bicubic interpolation is employed to upsample the low-resolution CL image of gas clouds. Second, the maximum noise-equivalent concentration-path-length (NECL) product is used as a threshold to segment the high-resolution gas cloud image. Third, image morphology processing and the evaluation criteria of the leaking gas cloud are applied to detect the leaking gas cloud. Finally, the high-resolution CL image of the leaking gas cloud is superimposed onto the background image. The effectiveness of the reconstruction method is proven by the SF6 remote-sensing experiment and simulation. The results show that the proposed method should be effectively implemented to reconstruct the high-resolution CL image of the leaking gas cloud. The reconstructed leaking gas cloud plume, as well as the location of the leakage source, are quite obvious. The reconstruction method has been successfully applied to passive FTIR scanning remote-sensing imaging systems, with high accuracy, in real time, and with robustness.
RESUMEN
A PSD-based solar spot position detection system is developed for solar tracking closed-loop control of mobile SOF-FTIR (Solar Occultation Flux method based on Fourier Transform Infrared spectrometer). The positioning error factors of PSD (position sensitive detector) are analyzed in detail. A voltage model for PSD signal conditioning circuit has been established to investigate the noise factors. The model shows that the positioning error is mainly related to PSD dark current and circuit gain. A static voltage deduction calibration method based on genetic algorithm is proposed to eliminate the effect of dark current. The gain ratio between channels is calculated based on the fitting curve slope of discrete position data of PSD center point with different light intensity for circuit gain calibration. The positioning accuracy and precision are greatly enhanced, especially when the light intensity is weak, compared with uncalibrated results. The positioning accuracy of center, middle and edge areas of PSD can reach 0.14%, 0.49%, and 1.09%, respectively, after correction in the range of light intensity voltage from 40 mV to 20 V. The corresponding standard deviations of each region are 0.005, 0.009, and 0.014, respectively. The adjustment methods proposed in this paper improve both measurement accuracy and detection limit. The results demonstrate that the calibrated PSD positioning accuracy can meet the requirements of SOF-FTIR for solar tracking.