Application of Multistep Inversion Method for Online Monitoring Aerosol Particle Size Distribution and Aerosol Concentration

Abstract

Aerosol concentration in the flow is usually time varying, and aerosol particle size distribution (PSD) is considered to be unchanged, which increases the difficulty of the measurement of aerosol PSD and concentration online. To solve these problems, a kind of multistep inversion method based on the angular light-scattering (ALS) signals is proposed. First, the aerosol PSD is estimated using shuffled frog-leaping algorithms (SFLAs) from relative ALS signals. Then, with aerosol PSD as priori information, the aerosol concentration is obtained by the Kalman filter (KF) algorithm, widely used in the real-time control system of industrial facilities for its ability of fast predictions. The result reveals that the performance of the improved SFLA is better than that of the original SFLA in solving the aerosol PSD. Moreover, in studying the aerosol concentration, more accurate results can be obtained with larger standard deviation of process noise or smaller standard deviation of measurement noise, while decreasing sampling time interval can improve the accuracy of retrieval results and reduce time delay to a certain degree. So, to improve retrieval accuracy, the noise should be controlled, and appropriate sampling time interval should be selected. All the numerical simulations confirm that the methodology provides effective and reliable results in real-time estimating.