Time series clustering method with cluster validation to identify unknown local cell conditions in the aluminum reduction cell. (December 2022)

• Record Type: Journal Article
• Authors: Huang, Zhaoke; Yang, Chunhua; Chen, Xiaofang; Zhou, Xiaojun; Gui, Weihua
• Is Part Of: Computers & industrial engineering. Volume 174(2022)
• Publication Date: 2022-12
• Subjects: Time series clustering -- Cluster validation -- Local cell condition -- Fuzzy c-means -- Aluminum electrolysis; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108790
• Languages: English
• ISSNs: 0360-8352

Abstract

Abstract: It is important to identify local cell conditions in the aluminum reduction cell for cell control. However, in the actual aluminum electrolysis process, local cell conditions are unknown based on traditional measurements. Fortunately, the appearance of anode current signals as a new process variable makes it possible to identify local cell conditions. In this paper, without any prior knowledge on local cell conditions, a new time series clustering method with cluster validation is proposed to identify unknown local cell conditions. First, the nature of anode current signals is analyzed and a new process variable named the pseudo-resistance of an anode–cathode path ( R p s e u d o ) is transformed from the anode current signals, which can provide more insights to the aluminum electrolysis process. Then, a time series clustering algorithm that combines fuzzy c-means with the dynamic time warping distance is used to cluster the anode current signals and R p s e u d o . Finally, a new clustering validity index is designed and a cluster validation process is proposed to determine the optimal number of clusters. Moreover, different anode current signals for different local cell conditions such as normal working, anode deformation, anode effect and anode slippage are deliberately selected to verify the proposed method. The experimental results show that the proposed method can effectively identify local cell conditions. Highlights: How to identify unknown local cell conditions in aluminum reduction cell is studied. Time series clustering analysis of anode current signals is investigated. A cluster validation process based on a new clustering validity index is proposed. It is a significant progress for the monitoring of aluminum electrolysis process.