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Wavelet analysis of dynamic behavior in fluidized beds
Alternative TitleChem. Eng. Sci.
Ren, JQ; Mao, QM; Li, JH; Lin, WG
2001-02-01
Source PublicationCHEMICAL ENGINEERING SCIENCE
ISSN0009-2509
Volume56Issue:3Pages:981-988
AbstractWavelet analysis has been used for studying dynamic behavior of fluidized beds, which proved effective in resolution of time series into different scales of components with distinct structure and in identification of transition from the dense phase to the dilute phase. By examining wavelet spectrum functions of various dynamic signals measured from fluidized beds, it is indicated that the signals can be decomposed into three scales of components: micro-scale (particle size), meso-scale (cluster size) and macro-scale (unit size). The principal component method was employed for phase separation from concentration signals measured by the optical probe. In this method, the maximum scale parameter s(0) of the wavelet spectrum function was chosen as the optimum scale parameter. The principal component method can reduce the computation time significantly and remain the benefit offered by the direct method described in our previous publication (Ren & Li, in: L. S. Fan, T. M. Knowlton (Eds.), Fluidization, Vol. IX, Engineering Foundation, New York, 1998, p. 629.). The method was also extended to detect the boundaries of clusters in 2-D digital images acquired from fluidized beds. (C) 2001 Elsevier Science Ltd. All rights reserved.; Wavelet analysis has been used for studying dynamic behavior of fluidized beds, which proved effective in resolution of time series into different scales of components with distinct structure and in identification of transition from the dense phase to the dilute phase. By examining wavelet spectrum functions of various dynamic signals measured from fluidized beds, it is indicated that the signals can be decomposed into three scales of components: micro-scale (particle size), meso-scale (cluster size) and macro-scale (unit size). The principal component method was employed for phase separation from concentration signals measured by the optical probe. In this method, the maximum scale parameter s(0) of the wavelet spectrum function was chosen as the optimum scale parameter. The principal component method can reduce the computation time significantly and remain the benefit offered by the direct method described in our previous publication (Ren & Li, in: L. S. Fan, T. M. Knowlton (Eds.), Fluidization, Vol. IX, Engineering Foundation, New York, 1998, p. 629.). The method was also extended to detect the boundaries of clusters in 2-D digital images acquired from fluidized beds. (C) 2001 Elsevier Science Ltd. All rights reserved.
KeywordDynamic Behavior Wavelet Transform Wavelet Spectrum Function Multi-scale Fluidization
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
URL查看原文
Indexed ByISTP ; SCI
Language英语
WOS KeywordSOLIDS ; FLOW
WOS Research AreaEngineering
WOS SubjectEngineering, Chemical
WOS IDWOS:000167488400032
Citation statistics
Cited Times:61[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Version出版稿
Identifierhttp://ir.ipe.ac.cn/handle/122111/5804
Collection研究所(批量导入)
Affiliation1.Acad Sinica, Chinese Acad Sci, Inst Chem Met, Beijing 100080, Peoples R China
2.Monash Univ, Dept Chem Engn, Clayton, Vic 3168, Australia
Recommended Citation
GB/T 7714
Ren, JQ,Mao, QM,Li, JH,et al. Wavelet analysis of dynamic behavior in fluidized beds[J]. CHEMICAL ENGINEERING SCIENCE,2001,56(3):981-988.
APA Ren, JQ,Mao, QM,Li, JH,&Lin, WG.(2001).Wavelet analysis of dynamic behavior in fluidized beds.CHEMICAL ENGINEERING SCIENCE,56(3),981-988.
MLA Ren, JQ,et al."Wavelet analysis of dynamic behavior in fluidized beds".CHEMICAL ENGINEERING SCIENCE 56.3(2001):981-988.
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