电厂烟羽中SO<sub>2</sub>柱浓度的紫外非色散二维成像

doi:10.3964/j.issn.1000-0593(2020)03-0688-06

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Abstract In view of the frequent occurrence of sudden accidents，haze and other pollution phenomena in recent years, the harm scope is wide and the harm degree is deep. Therefore, it is urgent to grasp the region, the scope and the intensity of the pollution, as well as the trend of the pollution diffusion. The two-dimensional fast imaging distribution of pollution sources has an absolute advantage indetermining the location of gas leakage source, identifying emergencies and identifying the scope and impact of pollution. In this paper, the fast imaging measurement about the plume from the power plant in Fengtai is realized by ultraviolet filter based on a planar CCD detector. The concentration of SO₂ obtained by the flue gas on-line monitoring technology is used as the reference concentration. The imaging system is calibrated after the reference concentration is transformed. The calibration result shows that the SO₂ column density presents a linear relationship with the optical intensity, the correlation coefficient is 0.958, and the prerequisite that the imaging theory can be analyzed is satisfied. Considering that the angle of view of the imaging system is small, the background is taken as lens deviates from the plume area upwind. The background intensity indicates that the upwind intensity is uniform and there is no other influence. The 310 nm filter identifying the target gas and the 330 nm filter dispelling the influence of aerosol are used alternately to image for the smoke plume in order to reduce the error of the plume change during the measurement process. At last, the two-dimensional distribution and the sequence diagram of SO₂ slant column density are obtained according to the linear least squares fitting at about 12:30 on May 20, 2017. The results show that SO₂ slant column density is high near the chimney exit, and high SO₂ concentration of inclined column is approximately 1.7×10¹⁷ molec·cm^-2. The figure of slant column density distribution displays visually diffusion trend of SO₂ concentration, showing that SO₂ inclined column density decreases slowly along the axial diffusion of the plume under the direction of the wind, and under the direction of the perpendicular to the plume diffusion, the figure tells that inclined column of SO₂ above the axisis less than its concentration below because of the air buoyancy, fluid dynamics about plume and the direction of wind. However the basic trend of diffusion is that both sides of the SO₂ inclined column perpendicular to the axis decrease quickly. In the 28 meters away from the center of the chimney from downwind direction, the concentration of SO₂ inclined column and gauss curve are taken for fitting, and the fitting coefficient is 0.747, which indicates that the concentration diffusion of SO₂ inclined column in wind direction basically follows gauss diffusion. The plume velocity is about 1.2 m·s^-1 according to the time series diagram about SO₂ column density. The gauss diffusion model is used to verify the feasibility of ultraviolet non-dispersive imaging system on the basis of the known plume SO₂ emissions (9.2 g·s^-1), smoke plume speed (1.2 m·s^-1), smoke plume height (140 m) and the surrounding environment. Comparing the results about the imaging system with the plume model, it is shown that SO₂ slant column density measured and the diffusion trend are consistent with theoretical predictions. This paper for the first time uses rapid imaging method based on filter to image SO₂ slant column density from fixed point pollution source, and finally the distribution and diffusion of SO₂ in plume are obtained successfully. The consistency between measurement results and model simulation indicates that this imaging method is expected to provide measurement basis for quantitative and qualitative assessment of pollution hazards.

Key words：Slant column density; Gauss diffusion; Sulfur dioxide; Filter

Received: 2019-01-10 Accepted: 2019-03-21

ZTFLH:

O433

Corresponding Authors: LI Ang E-mail: angli@aiofm.ac.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)03-0688-06 OR https://www.gpxygpfx.com/EN/Y2020/V40/I03/688