Cathode Voltage Distribution Data Analysis And Mining

The cathode of the aluminum electrolytic cell is an important part of the electrolytic cell.At present,most of the researches on cathode are from the perspective of technology,and the analysis and mining of the characteristics of the cathode data is still rare.In this paper,the cathode voltage was analyzed and clustered according to the characteristics of the cathode voltage change and the trend distribution.On this basis,the anomaly detection of the cathode voltage was performed,the abnormal state of the cathode voltage was defined,and the cause of the anomaly was judged by combining with other production measurement data.The analysis results will instruct the process personnel to control the production.The main contents of this article are as follows:1.This paper introduced the acquisition and preprocessing of cathode voltage and other measurement data,and applied multidimensional analysis technology to observe the characteristics and trend of data from multiple perspectives.And correlativity analysis was performed on the cathode voltage and other measurement data to find parameters that were positively related to the cathode voltage.The experimental results showed that the cathode voltage and the cathode steel bar temperature,shell temperature has certain correlation.At the same time,in order to display the distribution and change of the cathode voltage more clearly and intuitively,the cathode voltage was divided into several categories by a clustering algorithm.Observe various cathode voltage changes and make basic judgments on the normal and abnormal state of the cathode voltage.2.To solve the problem of the anomaly detection of cathode voltage,a cathode voltage discrimination algorithm based on time series anomaly detection was proposed.The algorithm uses the sliding window to segment the cathode voltage time series.The length of the sliding window is not fixed and the segmentation error is limited and the least square method is applied in the segment.After the fitting error exceeds the threshold,the segmentation is completed.After segmented,the length,slope and average of each segment are calculated and mapped into a set of spatial objects.Then use local anomaly detection algorithm to detect abnormal patterns based on local anomaly factors and mode length.The experimental results showed that the algorithm can effectively detect the abnormal cathode voltage.3.This paper applied the data analysis method and data mining algorithm mentioned in the article to realize the intelligent analysis system of cathode voltage.The system includes historical data multidimensional analysis module,correlation analysis module,cluster analysis module,and state data visualization module,visualizes the process and results of intelligent analysis,and provides meticulous management for the electrolytic cell.