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Engineering >> 2021, Volume 7, Issue 9 doi: 10.1016/j.eng.2020.11.012

A Cell Condition-Sensitive Frequency Segmentation Method Based on the Sub-Band Instantaneous Energy Spectrum of Aluminum Electrolysis Cell Voltage

a School of Automation, Central South University, Changsha 410083, China
b Key Laboratory of Intelligent Computing & Information Processing, Ministry of Education, Xiangtan University, Xiangtan 411105, China c School of Metallurgy and Environment, Central South University, Changsha 410083, China

Received: 2020-07-12 Revised: 2020-10-16 Accepted: 2020-11-23 Available online: 2021-07-28

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Abstract

Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell. A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage. In this paper, the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters. Based on the qualitative analysis of the cell voltage, the sub-band instantaneous energy spectrum (SIEP) is first proposed, which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions. Ultimately, a cell condition-sensitive frequency segmentation method is given. The proposed frequency segmentation method divides the effective frequency band into the [0, 0.001] Hz band of low-frequency signals and the [0.001, 0.050] Hz band of low-frequency noise, and subdivides the low-frequency noise into the [0.001, 0.010] Hz band of metal pad abnormal rolling and the [0.01, 0.05] Hz band of sub-lowfrequency noise. Compared with the instantaneous energy spectrum based on empirical mode decomposition, the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage. The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information, thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.

 

SupplementaryMaterials

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