INVERSION OF PARTICLE SIZE DISTRIBUTION BY SPECTRAL EXTINCTION TECHNIQUE USING THE ATTRACTIVE AND REPULSIVE PARTICLE SWARM OPTIMIZATION ALGORITHM

Abstract

The particle size distribution (PSD) plays an important role in environmental pollution detection and human health protection, such as fog, haze and soot. In this study, the Attractive and Repulsive Particle Swarm Optimization (ARPSO) algorithm and the basic PSO were applied to retrieve the PSD. The spectral extinction technique coupled with the Anomalous Diffraction Approximation (ADA) and the Lambert-Beer Law were employed to investigate the retrieval of the PSD. Three commonly used monomodal PSDs, i.e. the Rosin-Rammer (R-R) distribution, the normal (N-N) distribution, the logarithmic normal (L-N) distribution were studied in the dependent model. Then, an optimal wavelengths selection algorithm was proposed. To study the accuracy and robustness of the inverse results, some characteristic parameters were employed. The research revealed that the ARPSO showed more accurate and faster convergence rate than the basic PSO, even with random measurement error. Moreover, the investigation also demonstrated that the inverse results of four incident laser wavelengths showed more accurate and robust than those of two wavelengths. The research also found that if increasing the interval of the selected incident laser wavelengths, inverse results would show more accurate, even in the presence of random error.

Dates

  • Submission Date2014-03-19
  • Revision Date2014-06-08
  • Acceptance Date2014-07-30
  • Online Date2014-09-06

DOI Reference

10.2298/TSCI140319103Q

References

  1. Qi, H., Ruan, L. M., Wang, S. G., Shi, M., Zhao, H., Application of multi-phase particle swarm optimization technique to retrieve the particle size distribution, Chinese Optics Letters, 6 (2008), 5, pp. 346-349
  2. Qin, S., Cai, X. S., Indirect measurement of the intensity of incident light by the light transmission fluctuation method, Optics letters, 36 (2011), 20, pp. 4068-4070
  3. Tang, H., Sun, X. G., Yuan, G. B., Calculation method for particle mean diameter and particle size distribution function under dependent model algorithm, Chinese Optics Letters, 5 (2007), 1, pp. 31-33
  4. Arias, M. L., Frontini, G. L., Particle size distribution retrieval from elastic light scattering measurements by a modified regularization method, Particle & Particle Systems Characterization, 23 (2006), 5, pp. 374-380
  5. Tang, H., Lin, J. Z., Retrieval of spheroid particle size distribution from spectral extinction data in the independent mode using PCA approach, Journal of Quantitative Spectroscopy and Radiative Transfer, 115 (2012), pp. 78-92
  6. Hulst, H. C., Light scattering: by small particles, Courier Dover Publications, New York, USA, 1957
  7. Zhao, J. Q., Hu, Y. Q., Bridging technique for calculating the extinction efficiency of arbitrary shaped particles, Applied Optics, 42 (2003), 24, pp. 4937-4945
  8. Sun, X. G., Tang, H., Yuan, G. B., Anomalous diffraction approximation method for retrieval of spherical and spheroidal particle size distributions in total light scattering, Journal of Quantitative Spectroscopy and Radiative Transfer, 109 (2008), 1, pp. 89-106
  9. Tang, H., Optimal wavelength selection algorithm of non-spherical particle size distribution based on the light extinction data, Thermal Science, 16 (2012), 5, pp. 1353-1357
  10. Ye, M., Wang, S. M., Lu, Y., Hu, T., Zhu, Z., Xu, Y. Q., Inversion of particle-size distribution from angular light-scattering data with genetic algorithms, Applied Optics, 38 (1999), 12, pp. 2677-2685
  11. Gugliotta, L. M., Stegmayer, G. S., Clementi, L. A., Gonzalez, V. D., Minari, R. J., Leiza, J. R., Vega, J. R., A neural network model for estimating the particle size distribution of dilute latex from multiangle dynamic light scattering measurements, Particle & Particle Systems Characterization, 26 (2009), 12, pp. 41-52
  12. Qi, H., Zhang, B., Ren, Y. T., Ruan, L. M., Tan, H. P., Retrieval of spherical particle size distribution using ant colony optimization algorithm, Chinese Optics Letters, 11 (2013), 11, pp. 112901 (1-5)
  13. Kennedy, J., Eberhart, R., Particle swarm optimization, Proceedings of IEEE international conference on neural networks, IEEE Press, Perth, Australia, 1995, Vol. 4, pp. 1942-1948
  14. Qi, H., Ruan, L. M., Zhang, H. C., Wang, Y. M., Tan, H. P., Inverse radiation analysis of a one-dimensional participating slab by stochastic particle swarm optimizer algorithm, International journal of thermal sciences, 46 (2007), 7, pp. 649-661
  15. Lee, K. H., Baek, S. W., Kim, K. W., Inverse radiation analysis using repulsive particle swarm optimization algorithm, International Journal of Heat and Mass Transfer, 51 (2008), pp. 2772-2783
  16. Riget, J., Vesterstrøm, J. S., A diversity-guided particle swarm optimizer-the ARPSO, Report No. 2002-02, University of Aarhus, Aarhus, Denmark, 2002
  17. Tang, H., Retrieval of spherical particle size distribution with an improved tikhonov iteration method, Thermal Science, 16 (2012), 5, pp. 1400-1404
  18. He Z. Z., Qi H., Wang Y. Q., Ruan L. M., Inverse estimation of the spheroidal particle size distribution using Ant Colony Optimization algorithms in multispectral extinction technique, Optics Communications, 328 (2014), pp. 8-22
  19. Zhao, J. Q., Li, J. N., Analytical transform techniques to retrieve non-spherical particle size distribution, Journal of Quantitative Spectroscopy and Radiative Transfer, 129 (2013), pp. 287-297
  20. Ruan, L. M., Yu, Q. Z., Tan, H. P., A transmission method for the determination of the radiation properties of small ash particles, Journal of Harbin Institute of Technology, 2 (1994), pp. 10-14