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.

中文翻译：

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基于核函数极限学习机的电解槽铝液高度在线预测方法

提出了一种基于核函数极限学习机(K-ELM)的铝液高度在线预测方法。首先，通过分析铝液波动的机理模型，得到与铝液波动相关的可在线测量的相关变量。然后，提出了基于核函数和ELM的铝液高度在线预测方法，该方法仅利用阳极-阴极电压和阳极棒电流数据。最后，对200 kA系列铝电解槽中的3组阳极棒进行了现场数据采集和实验。结果表明，最大绝对误差仅为0.25 cm，相对误差小于1.4%，满足生产现场要求。与现有方法相比，