Wavelet transform analysis of pressure fluctuation signals in a pressurized bubble column

Park, Soung Hee; Kang, Yong; Kim, Sang Done

doi:10.1016/s0009-2509(01)00223-8

Cited by 27 publications

(8 citation statements)

References 8 publications

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“…Power spectrum density (PSD) diagrams can be obtained using Fourier transformation to handle collected pressure signals. In these diagrams, the peak value represents the periodic component or random variable component and the peak frequency indicates the presence of gas bubbles of corresponding frequency . Besides, the amplitude of pressure fluctuation shows the size of gas bubbles .…”

Section: Resultsmentioning

confidence: 99%

Characterization on hydrodynamic behavior in liquid‐containing gas‐solid fluidized bed reactor

et al. 2012

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in Wiley Online Library (wileyonlinelibrary.com).In many industrial processes involving gas-solid fluidized bed rectors, the addition of a liquid phase significantly alters the hydrodynamics. To fully characterize the hydrodynamics in the fluidized bed, pressure and acoustic measuring techniques were applied to study the behavior of gas bubbles and particles. A camera was used to take pictures to verify the pressure and acoustic results. During the liquid-addition process, the pressure technique captured the bubble size variation and bubble motion while the acoustic technique reflected particle motion and particle size growth. Hurst and V-statistics analyses of acoustic emission were used for the first time to detect periodic behavior during the injection process. The new break formation and change trend of V max were used as the criteria to judge occurrence of abnormal fluidization states, such as agglomeration and gas channeling formation. These measurement techniques are beneficial in the elimination of adverse effects caused by the addition of liquid.

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Section: Resultsmentioning

confidence: 99%

Characterization on hydrodynamic behavior in liquid‐containing gas‐solid fluidized bed reactor

et al. 2012

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“…In this method, by differentiation of measured time series, contribution due to white noise moves toward the finer scales and this process distributes more energy to finer scales. Scalewise power versus scale (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) was first plotted. Thresholding scale level was identified as 2, therefore wavelet coefficients of scales 1 and 2 were set to be zero in the reconstruction of the denoised pressure fluctuation signal.…”

Section: Resultsmentioning

confidence: 99%

“…Fourier transform and inverse Fourier transform establish one-to-one relationship between the time domain and the frequency domain [1]. Fourier analysis provides good resolution in frequency domain and some intrinsic features of flow dynamics can definitely be revealed using Fourier analysis, as there exist similarities between Fourier analysis and chaos analysis [2].…”

Section: Nomenclaturementioning

confidence: 99%

“…The other is that the window size may be carried out for every single spectral component without losing any information. The fine and coarse resolution components from wavelet decomposition capture the fine and coarse features in the signals, respectively [1]. Wavelet coefficients also provide a measure of the signal energy in various regions of the time-frequency plane.…”

Section: Nomenclaturementioning

confidence: 99%

“…The number of peaks (D4 and D5) and peak amplitude may provide information about the bubble frequency [24,25] and bubble diameter [26]. Frequency content of objects could be obtained from wavelet coefficients [1], where smooth coefficients mainly captured the objects of low frequency oscillations (large bubbles) in the pressure signal, and the detail coefficients captured the ones of higher frequency (small bubbles) over the time. Furthermore, wavelet analysis confirms that multi-scale flow behavior is a common phenomenon in gas-solids two-phase flow systems, as reported by Wu et al [27].…”

Section: Characterization Of Multiscale Flow Behavior Using Wavelet Dmentioning

confidence: 99%

See 2 more Smart Citations

Multiresolution analysis of pressure fluctuations in a gas–solids fluidized bed: Application to glass beads and polyethylene powder systems

Kantzas

Bellehumeur

et al. 2007

Chemical Engineering Journal

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Wavelet Analysis of Particle Concentration Signals in an Acoustic Bubbling Fluidized Bed

Chen

Guo

2008

Chem Eng & Technol

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The time series of fluid catalytic cracking (FCC) particle concentrations were measured by an optical fiber probe under conditions of different sound pressure levels and sound frequencies in an acoustic bubbling fluidized bed (f 140 mm × 1600 mm). The results show that the minimum fluidization velocity had a minimum value when the sound wave frequency was 150 Hz. Under the same sound frequency, the fluidization velocity decreased as the sound pressure level increased. The particle concentration signals in an acoustic fluidized bed were also analyzed by means of wavelet analysis. On the basis of discrete wavelet transform, an original signal was resolved into five detailed scale signals. By using wavelet energy analysis, it was found that the peak frequency of the scale 3 or 4 detail wavelet signals represents the bubbling frequency and the peak amplitude for the bubble size. The results indicate that the bubbling frequency and bubble size decreased with increasing sound pressure level at a given frequency. In addition they decreased with increasing sound frequency ranging from 50-150 Hz, but further increased with increasing sound frequency ranging from 150-500 Hz.

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Wavelet transform analysis of pressure fluctuation signals in a pressurized bubble column

Cited by 27 publications

References 8 publications

Characterization on hydrodynamic behavior in liquid‐containing gas‐solid fluidized bed reactor

Characterization on hydrodynamic behavior in liquid‐containing gas‐solid fluidized bed reactor

Multiresolution analysis of pressure fluctuations in a gas–solids fluidized bed: Application to glass beads and polyethylene powder systems

Wavelet Analysis of Particle Concentration Signals in an Acoustic Bubbling Fluidized Bed

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