Gas-Solid Fluidized Bed Agglomeration Monitoring Technique

With the rapid development of polyethylene industry, gas-solid fluidized bed reactor(FBR) technology attracts more and more attention. Although fluidized bed has many advantages, the agglomeration problem has become one of the most challenging problems in the fluidized bed. The traditional agglomeration monitoring methods are differential pressure method and temperature method. However, these two methods can not accurately, timely find fluidized bed agglomeration fault. Other agglomeration monitoring methods, such as electrostatic measurement and radiation measurement, also have drawbacks. The electrostatic measurement need transform the existing fluidized bed apparatus. The radiation measurement equipment is expensive and may even affect human health. Therefore, this paper decided to use passive acoustic emission techniques to detect fluidized bed agglomeration fault. The acoustic emission test system was established. The piezoelectric ceramic acoustic sensor was used to monitor the acoustic signals that the material particles strike the inner wall of the fluidized bed. Meanwhile it converts the acoustic signals into charge signals. The charge signals transport through the long shielded cable without signal distortion and finally were sampled by the sound card which was called by writing Qt programs. The samples acoustic signal data were stored by using the QSqlite database in Qt.In this paper, the source of the acoustic signal on the fluidized bed wall was analyzed. It is found that the acoustic signal mainly comes from the collisions of the material and the inner wall of the fluidized bed. In order to monitor the fluidized bed agglomeration fault, the Agglomeration Early Warning System (AEWS) was proposed based on the frequency model. The voiceprint features were extracted by wavelet packet decomposition and PCA dimensionality reduction. The scale of the wavelet packet decomposition was determined by observing the changes of the power spectral gravity center. The fluidized bed agglomeration criterion was established based on the Support Vector Data Description (SVDD) method.Compared with the traditional agglomeration monitor, the experimental results have shown that the agglomeration can be recognized45-60minutes earlier by AEWS. At the same time it was found that when the fluidized bed was under the normal condition, the AEWS was not sensitive to the disturbance of other chemical variables. Compared with the traditional fluidized bed agglomeration monitoring methods, it shows that the AEWS can recognize the agglomeration faster and more accurate. In order to minimize the false alarm rate, both moving time window method and minimizing value method were analyzed. The experimental results show that moving time window method was better than the other. So the AEWS select the former. In addition to the fluidized bed, the other polymeric chemical device may also have agglomeration fault, which indicates that AEWS has a broad application prospect.