Abstract
The small-angle optical particle counter (OPC) can detect particles with strong light absorption. At the same time, it can ignore the properties of the detected particles and detect the particle size singly and more accurately. Reasonably improving the resolution of the low pulse signal of fine particles is key to improving the detection accuracy of the small-angle OPC. In this paper, a new adaptive filtering method for the small-angle scattering signals of particles is proposed based on the recursive least squares (RLS) algorithm. By analyzing the characteristics of the small-angle scattering signals, a variable forgetting factor (VFF) strategy is introduced to optimize the forgetting factor in the traditional RLS algorithm. It can distinguish the scattering signal from the stray light signal and dynamically adapt to the change in pulse amplitude according to different light absorptions and different particle sizes. To verify the filtering effect, small-angle scattering pulse extraction experiments were carried out in a simulated smoke box with different particle properties. The experiments show that the proposed VFF-RLS algorithm can effectively suppress system stray light and background noise. When the particle detection signal appears, the algorithm has fast convergence and tracking speed and highlights the particle pulse signal well. Compared with that of the traditional scattering pulse extraction method, the resolution of the processed scattering pulse signal of particles is greatly improved, and the extraction of weak particle scattering pulses at a small angle has a greater advantage. Finally, the effect of filter order in the algorithm on the results of extracting scattering pulses is discussed.
Highlights
The mass concentration of particulate matter is often used as an important numerical index to evaluate regional air quality
According to the 2020 EPA certification list published by the American Environmental Monitoring Technical Information Center (AMTIC) [6], the main methods for detecting the mass concentration of particles are the manual reference method, the manual equivalence method, and the automatic equivalence method
We propose a novel method for online extraction of small-angle scattering pulse signals from particles based on the variable forgetting factor (VFF) recursive least squares (RLS) algorithm
Summary
The mass concentration of particulate matter is often used as an important numerical index to evaluate regional air quality. Some studies have found that the number concentration of particles in some environments is positively correlated with the concentration of bioaerosol [1]. Xie et al showed that air pollution, such as PM2.5 and PM10, is positively correlated with the mortality of individuals infected with. According to the 2020 EPA certification list published by the American Environmental Monitoring Technical Information Center (AMTIC) [6], the main methods for detecting the mass concentration of particles are the manual reference method, the manual equivalence method, and the automatic equivalence method. The most widely used is the automatic equivalence method, including the tapered element oscillating microbalance (TEOM) method [7], the β-ray method [8], and the light scattering method [9,10]. Compared with other measurement methods, the light scattering method has many advantages, including fast measurement speed, high precision, good repeatability, online and real-time non-contact measurement [11]
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