Non-parametric estimation of particle size distribution from spectral extinction data with PCA approach

He, Zhenzong; Mao, Junkui; Han, Xingsi

doi:10.1016/j.powtec.2017.11.050

Cited by 28 publications

(13 citation statements)

References 26 publications

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“…According to our previous work [23], the optical constant of the aerosol particle in the present study is set as m λ � 1.53 + 0.01i, and the measurement wavelength is set as λ � 0.5 μm. All the simulations were performed on an Intel Core i7-6500 PC by using Matlab 2016.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…For example, the ALS method was applied to study the fractal dimension and size distribution of noncompact soot aggregates [15], the optical constants of the particle [16], the particle size distribution [17,18], and the particle concentration [19][20][21][22]. In the author's previous work [23,24], the ALS method combined with inverse radiative problem technique is successfully applied to study the PSD and optical constants. Moreover, the convergence accuracy and robustness of the ALS method was proved to be better than that of the spectral extinction method.…”

Section: Introductionmentioning

confidence: 99%

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

Mao

et al. 2020

Mathematical Problems in Engineering

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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.

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Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mao

et al. 2020

Mathematical Problems in Engineering

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“…Therefore, the original variables can be replaced by several principal components with highest contribution rates, achieving the purpose of dimension reduction. The detail of PCA approach is available in our previous work (He et al, 2018).…”

Section: Selection Principle Of Temperatures Measurement Signals Basementioning

confidence: 99%

“…According to our previous work (He et al, 2018), the z is determined by letting z V not less than 90%, which denotes the original data which can be represented well with the new z principle components. Therefore, the contribution rate of the TD dimensionless boundary temperatures measurement signals at different dimensionless time  t to the overall z principle components is expressed as:…”

Section:   mentioning

confidence: 99%

Estimation of radiative heat transfer and phase change in participating medium from TD radiation measurement signals with PCA approach

Mao

et al. 2019

JTST

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Time-domain (TD) pulse laser is employed as the measurement laser to solve the inverse problem of radiative heat transfer and phase change in the participating medium. The finite volume method and an improved stochastic particle swarm optimization (ISPSO) algorithm are employed as the direct and inverse problem algorithms, respectively. Moreover, an optimal selection principle of the TD dimensionless boundary temperature measurement signals based on the principle component analysis (PCA) approach is proposed to improve the retrieval accuracy in retrieving the Stefan number St and the conduction to radiation parameter N. Results show that the TD dimensionless boundary temperature measurement signals obtained within dimensionless time selected within [0.05tp*, 0.8tp*] helps with the improvement in the retrieval accuracy. Moreover, compared with the SPSO algorithm, the ISPSO can avoid local optima and improve convergence accuracy, and reasonable results can be obtained even with 5% random measurement errors. As a whole, the present methodology provides a reliable and effective technique to study the inverse problem of radiative heat transfer and phase change in participating medium.

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“…In addition, these methods change the state of the aerosol particles in the atmosphere and may lead to biased results. In contrast, optical measurements are effective in-situ observation methods for quickly obtaining the optical and microphysical properties of the aerosol [23][24][25][26][27]. For example, the widespread ground-based aerosol robotic network (AERONET), as well as the sky radiometer network mainly located in eastern Asia (SKYNET), realize real-time monitoring of atmospheric aerosols by measuring the direct sun and diffuse sky radiance and provide related parameters including aerosol optical depth (AOD), single scattering albedo (SSA), m, and volume size distribution (VSD) [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

et al. 2019

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Detailed knowledge of the complex refractive indices (m) of fine- and coarse-mode aerosols is important for enhancing understanding of the effect of atmospheric aerosol on climate. However, studies on obtaining aerosol modal m values are particularly scarce. This study proposes a method for inferring m values of fine- and coarse-mode aerosol using the inversion products from the AERONET ground-based aerosol robotic network. By identifying the aerosol type, modal m values are constrained and then inferred based on a maximum likelihood method. Numerical tests showed that compared with the reference values, our method slightly overestimates the real parts of the refractive indices (n), but underestimates the imaginary parts (k) by 2.11% ± 11.59% and 8.4% ± 26.42% for fine and coarse modes, respectively. We applied this method to 21 AERONET sites around China, which yielded annual mean m values of (1.45 ± 0.04) + (0.0109 ± 0.0046)i and (1.53 ± 0.01) + (0.0039 ± 0.0011)i for fine- and coarse-mode aerosols, respectively. It is observed that the fine mode n decreased from 1.53 to 1.39 with increasing latitude, while fine mode k values were generally larger than 0.008 over most of China. The coarse-mode n and k ranged from 1.52 to 1.56 and from 0.002 to 0.006, respectively.

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Non-parametric estimation of particle size distribution from spectral extinction data with PCA approach

Cited by 28 publications

References 26 publications

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

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

Estimation of radiative heat transfer and phase change in participating medium from TD radiation measurement signals with PCA approach

Inferring Fine-Mode and Coarse-Mode Aerosol Complex Refractive Indices from AERONET Inversion Products over China

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