On-line measurement of the size distribution of particles in a gas–solid two-phase flow through acoustic sensing and advanced signal analysis

Guo, Miao, Yan, Yong, Hu, Yonghui, Sun, Duo, Qian, Xiangchen, Han, Xiaojuan (2014) On-line measurement of the size distribution of particles in a gas–solid two-phase flow through acoustic sensing and advanced signal analysis. Flow Measurement and Instrumentation, 40 . pp. 169-177. ISSN 0955-5986. (doi:10.1016/j.flowmeasinst.2014.08.001) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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http://dx.doi.org/10.1016/j.flowmeasinst.2014.08.0...

Abstract

Acoustic emission (AE) technology is a promising approach to non-intrusively measure the size distribution of particles in a pneumatic suspension. This paper presents an experimental study of the AE sensing technology coupled with signal processing algorithms for on-line particle sizing. The frequency characteristics of the AE signals under different experimental conditions are studied and compared. Initially, the characteristics of the background noise and AE signals are compared in the frequency domain for different air velocities and particle feeding rates. Through short-term energy analysis the working features of the suction unit and the vibration feeder are revealed. To find the effective characteristic frequency band of the AE signals, a multiple scanning and accumulation method assisted with a Savitzky–Golay smoothing filter is used to denoise the power spectra of the signals. Wavelet analysis is also deployed to denoise the signals. The denoising performance of different wavelet parameters (wavelet function, decomposition level and thresholding) is compared in terms of signal-to-noise ratio and signal smoothness. Finally, particle size is predicted through a neural network with energy fraction extracted through wavelet analysis. Experimental results demonstrate that the relative error of the particle sizing system is no greater than 23%.

Item Type: Article
DOI/Identification number: 10.1016/j.flowmeasinst.2014.08.001
Uncontrolled keywords: Acoustic emission; Gas–solid flow; Particle size distribution; Wavelet transform; Energy fraction
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 09 Dec 2014 12:01 UTC
Last Modified: 29 May 2019 13:51 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/45828 (The current URI for this page, for reference purposes)
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