Online Prediction Method of Molten Aluminium Height in Electrolytic Cell Based on Extreme Learning Machine with Kernel Function | Zendy

Bin Cao | Zendy; Jiarui Cui | Zendy; Qing Li | Zendy; Minggang Wang | Zendy; Xiangquan Li | Zendy; Qun Yan | Zendy

Open Access

Online Prediction Method of Molten Aluminium Height in Electrolytic Cell Based on Extreme Learning Machine with Kernel Function

Author(s) -

Bin Cao,

Jiarui Cui,

Qing Li,

Minggang Wang,

Xiangquan Li,

Qun Yan

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/9980194

Subject(s) - aluminium , anode , kernel (algebra) , voltage , cathode , extreme learning machine , function (biology) , support vector machine , approximation error , process (computing) , algorithm , computer science , materials science , engineering , artificial intelligence , metallurgy , mathematics , chemistry , artificial neural network , electrical engineering , electrode , combinatorics , evolutionary biology , biology , operating system

An online prediction method of molten aluminium height is proposed based on extreme learning machine with kernel function (K-ELM). Firstly, relevant variables that can be measured online related to aluminium liquid fluctuations were obtained by analyzing the mechanism model of aluminium liquid fluctuations. Then, the online prediction method of molten aluminium height is proposed based on kernel function and ELM, which just use the anode-cathode voltage and the anode rod current data. Finally, the data collection and experiment of 3 sets of anode rods in the 200 kA series aluminium electrolytic cells are carried out on-site. The results show that the maximum absolute error is only 0.25 cm and relative error is less than 1.4%, which satisfied the production site requirements. Compared with existing methods, it has certain advantages in real-time and prediction accuracy and meets the real-time and accuracy requirements of the actual production process on-site.

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