Fault Detection Based on Acoustic Emission鈥揈arly Agglomeration Recognition System in Fluidized Bed Reactor

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• 作者：Yefeng Zhou ; Kezeng Dong ; Huang Zhengliang ; Jingdai Wang ; Yongrong Yang
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2011
• 出版时间：July 20, 2011
• 年：2011
• 卷：50
• 期：14
• 页码：8476-8484
• 全文大小：976K
• 年卷期：v.50,no.14(July 20, 2011)
• ISSN：1520-5045

文摘

Agglomeration is one of the most challenging problems due to overheating of the particles in fluidized bed reactors (FBRs). Therefore, it is an urgent task to develop a reliable and sensitive method, which can help accurately detect the agglomeration in an early stage. In this study, acoustic emission鈥揺arly agglomeration recognition system (AE-EARS) has been put forward for fault detection. Based on acoustic emission signals, the attractor comparison method was developed for prewarning the agglomeration in lab-scale and pilot-scale apparatus. The results concluded from this study demonstrated that the statistical characteristic S acts more sensitively to small agglomeration when compared with the malfunction coefficients C_D2 and C_K2, and other traditional measurement techniques (such as 纬 ray, temperature, and pressure difference). Besides, model optimization based on AE-EARS can help to select the criterion and improve the rate of false alarm. The analysis methods based on AE-EARS can warn the agglomeration earlier, faster, and more accurately. Especially the S value based on the attractor comparison, can be used as an indicator for 鈥渆arly recognition鈥? which enjoys a broad prospect in industrial application.