Feature extraction and early warning of agglomeration in fluidized bed reactors based on an acoustic approach
Feature extraction and early warning of agglomeration in fluidized bed reactors based on an acoustic approach
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基于声学方法的流化床反应器团聚特征提取与预警
DOI：
10.1016/j.powtec.2015.04.009
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发表时间：
2015-07
影响因子：
5.2
通讯作者：
Wu Haiyan
中科院分区：
文献类型：
--
作者：
Lin Weiguo;Wang Xiaodong;Wang Fenwei;Wu Haiyan
本文报道了声发射技术在乙烯聚合流化床反应器中颗粒团聚监测中的应用。比较了正常和团聚信号之间的功率谱质心的偏移，并证实了在团聚条件下声学信号的能量分布发生了变化。在此基础上，对声信号进行小波包分解，将各子带的能量比作为声纹特征。随后，主成分分析（PCA）被引入到降低特征向量的维数。此外，基于正态信号，利用支持向量数据描述（SVDD）建立集聚预警模型，避免了因集聚样本不足而导致的描述精度下降。最后，设计了一个合适的报警率（AR）参数，以解决聚合过程中团聚现象缺乏可重复性和随机性而导致的误报问题。根据中试工厂的实验结果，所提出的结块预警方法可以比传统的压力和温度监测方法提前20-50分钟发出预警。
The potential use of audible acoustic emissions for monitoring particle agglomeration in ethylene polymerization fluidized bed reactors was investigated by the authors and reported in this paper. The offset in the power spectral centroid between normal and agglomeration signals was compared, and the energy distribution of the acoustic signals was confirmed to change under agglomeration conditions. On this basis, the acoustic signals were decomposed by wavelet packet decomposition (WPD) and the energy ratios of every sub-band were set as the voiceprint. Subsequently, principal component analysis (PCA) was introduced to reduce the dimensionality of the feature vector. Furthermore, based on normal signals, an agglomeration warning model could be created by support vector data description (SVDD) to avoid the decrease in description accuracy caused by the lack of agglomeration samples. Finally, a proper alarm rate (AR) parameter was designed to solve the problem of false alarms caused by the lack of repeatability and haphazardness of agglomeration in polymerization. According to the experimental results in a pilot plant, the proposed early-warning approach for agglomeration could provide a warning 20–50 min in advance of the traditional pressure and temperature monitoring methods.
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DOI：
--
发表时间：
1999
期刊：
--
影响因子：
--
作者：
D. Tax;R. Duin
通讯作者：
D. Tax;R. Duin
影响因子：
4.2
作者：
Yefeng Zhou;Zhengliang Huang;Congjing Ren;Jingdai Wang;Yongrong Yang
通讯作者：
Yefeng Zhou;Zhengliang Huang;Congjing Ren;Jingdai Wang;Yongrong Yang
影响因子：
7.5
作者：
Tax, DMJ;Duin, RPW
通讯作者：
Duin, RPW
DOI：
10.1016/j.ijmultiphaseflow.2009.06.010
发表时间：
2009-11
影响因子：
3.8
作者：
N. Salehi-Nik;R. Sotudeh-Gharebagh;N. Mostoufi;R. Zarghami;M. Mahjoob
通讯作者：
N. Salehi-Nik;R. Sotudeh-Gharebagh;N. Mostoufi;R. Zarghami;M. Mahjoob
影响因子：
5.8
作者：
Whitaker, M;Baker, GR;Collins, MP
通讯作者：
Collins, MP