Based On The Detection And Location Of Modern Signal Processing Aluminum Electrolytic Anode Effect And Achieve

It is important to control the anode effect in industry aluminum electrolysis production. Currently, the main technology to detect the anode effect focuses on monitoring the fluctuation of the slot electric current and electric voltage. Based on the technology, numerous prediction methods are proposed. But this technology suffers from noises of electric current and electric voltage and the performance of this method degrades greatly. J. Xue and H. A. Oyes put forward a new method to detect the anode effect that needs only collecting the status of gas bubbling by monitoring the vibration of anode body. Therefore, it is possible to recognize the bubbling process that is closely related to the anode performance. However, in practice, the environment of industry aluminum electrolysis on different anode body is much different. It means that such a detection for the vibration caused by the bulling is very difficult. This thesis proposes a few methods to analyze the vibration signals of anode body. These methods are able to detect and predict the position of anode body whose anode effect is happening.The main contents of this thesis are as follows:Firstly, the vibration signal of anode is obtained by experiment and analyzed theoretically. It is proved that the anode effect can be predicted and positioned by analyzing the vibration signal of poles.Secondly, the vibration signal of the anode is analyzed and compared from the spectrum techniques, time-frequency techniques and EMD etc. And a reasonable signal processing technique for predicting anode effect is obtained.Thirdly, a BP neural network is adopted to forecast the anode effect. It can be considered as a meaningful step to predict the anode effect by artificial intelligence.Fourthly, a set of software to predict the anode effect is developed under the VC++ platform. This software integrates signal sampling, analyzing, positioning and data management.At the last of this thesis, the summary and review to the research are given. The futuredevelopment of the intelligent prediction on anode effect is also presented.