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Accurately predicting the performance outcomes of electrolytic cells holds paramount significance for subsequent production and decision-making within the aluminum electrolysis industry. Presently, the level of automation in electrolytic cell performance prediction methods is limited, heavily relying on manual assessments, which fail to accurately assess the stability of cell conditions and other related issues. This paper leverages knowledge graphs and graph convolutional neural networks for predicting electrolytic cell performance. Firstly, an electrolytic cell parameter knowledge graph is constructed to facilitate the structured processing of production data. Secondly, the graph convolutional neural network model is improved to enhance the performance of electrolyzer performance prediction by aggregating neighbourhood information through four layers of attention. Finally, validation is conducted through smelting experiments. Experimental results demonstrate that the proposed prediction method exhibits a high degree of accuracy, offering valuable insights for practical applications.
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