Agglomeration Detection in Horizontal Stirred Bed Reactor Based on Autoregression Model by Acoustic Emission Signals
Agglomeration Detection in Horizontal Stirred Bed Reactor Based on Autoregression Model by Acoustic Emission Signals
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DOI：
10.1021/ie202497f
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发表时间：
2012-09
影响因子：
4.2
通讯作者：
Yefeng Zhou;Zhengliang Huang;Congjing Ren;Jingdai Wang;Yongrong Yang
中科院分区：
文献类型：
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作者：
Yefeng Zhou;Zhengliang Huang;Congjing Ren;Jingdai Wang;Yongrong Yang
用于生产聚烯烃的卧式搅拌床反应器(HSBR)中出现的结块现象会对反应器的运行效率产生负面影响，有时还会导致装置的意外停产。本文提出了一种基于声发射(AE)技术的自回归(AR)模型，以建立声发射信号与HSBR中团聚现象之间的定性关系。在这种方法中，声发射信号的频率随不同尺寸的颗粒撞击反应堆壁而不同。冷模实验发现，在实验室规模的HSBR中加入团聚体后，AR功率谱发生波动，同时低频段能量比和声发射信号的方差不断上升。此外，基于声发射的AR模型也被成功地应用于工业HSBR装置中的结块检测，表明该方法能够以环保的方式监测结块，并具有较好的精度。
Agglomeration occurring in horizontal stirred bed reactors (HSBR) for polyolefin production has negative impacts on the efficiency of the reactor operation and may sometimes lead to unscheduled shutdown of the plant. In this paper, an autoregression (AR) model based on acoustic emission (AE) technique has been proposed to establish the qualitative relationship between AE signals and agglomeration in the HSBR. In this method, the frequency of AE signal varies with particles of different sizes striking the reactor walls. From the cold model experiments, it was found that AR power spectrum became fluctuant after the addition of agglomerations into laboratorial scale HSBR, and meanwhile the low frequency band energy ratio and the variance of AE signals kept rising. Furthermore, this AE-based AR model was also successfully applied to detect the agglomeration in an industrial HSBR unit, showing that the method could monitor agglomerations in an environmentally friendly manner and with fairly good accuracy.