Anode effect forecast in aluminum electrolysis based on an extreme learning machine

被引：0

作者：

Zhang, Hai-Gang ^{[1
]}

Zhang, Sen ^{[1
]}

Cao, Bin ^{[2
]}

机构：

[1] School of Automation and Electronic Engineering, University of Science and Technology Beijing, Beijing,100083, China

[2] Guiyang Aluminum Magnesium Design & Research Institute, Guiyang,550081, China

来源：

Gongcheng Kexue Xuebao/Chinese Journal of Engineering | 2015年 / 37卷

关键词：

Learning algorithms - Aluminum - Anodes - Learning systems - Electrolytic cells - Knowledge acquisition - Forecasting - Electrolysis - Machine learning - Cells - Statistics;

D O I：

10.13374/j.issn2095-9389.2015.s2.007

中图分类号：

学科分类号：

摘要：

A cell resistance forecast model is established based on an extreme learning machine (ELM) algorithm. Considering a harsh environment in aluminum electrolysis, there exist outliers in measured data. These outliers may affect the performance of an ordinary ELM algorithm, and even destroy the well-trained model. This paper introduces a modified extreme learning machine algorithm subject to the outliers. Based on the distributed measurement of current through the anode rod, the cell resistance can be calculated and the cell resistance at the next time point can be forecast. Then the cell resistance is made to contact with the anode effect and the alarm criterion is set. Real industrial data have been applied to verify the effectiveness and accuracy of this proposed scheme. © All right reserved.

引用

页码：40 / 46

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