Huimin Lu, Zuxian Qiu, Keming Fang, Fuming Wang, and Yanruo Hong, Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network, J. Univ. Sci. Technol. Beijing, 6(1999), No. 2, pp. 107-110.
Cite this article as:
Huimin Lu, Zuxian Qiu, Keming Fang, Fuming Wang, and Yanruo Hong, Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network, J. Univ. Sci. Technol. Beijing, 6(1999), No. 2, pp. 107-110.
Huimin Lu, Zuxian Qiu, Keming Fang, Fuming Wang, and Yanruo Hong, Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network, J. Univ. Sci. Technol. Beijing, 6(1999), No. 2, pp. 107-110.
Citation:
Huimin Lu, Zuxian Qiu, Keming Fang, Fuming Wang, and Yanruo Hong, Current Efficiency of Low Temperature Aluminum Electrolysis Studied by Neural Network, J. Univ. Sci. Technol. Beijing, 6(1999), No. 2, pp. 107-110.
Metallurgy School, University of Science and Technology Beijing, Beijing 100083, China
Department of Nonferrous Metallurgy, Northeastern University, Shenyang 110006, China
中文摘要
A prediction model for Current Efficiency (CE) of low temperature aluminum electrolysis (LTAE) with the low molar ratio electrolyte of Na3AIF6-AIF3-CaF2-MgF2-LiF-Al2O3 system was investigated based on artificial neural network principles. The nonlinear mapping between CE of LATE and various electrolytic conditions was obtained from a number of experimental data and used to predictCE of LATE. The trained neural networks possessed high precision and resulted in a good predicting effect. As a result, artificial neural networks as a new cooperating and predicting technology provide a new approach to the further studies on low temperature aluminum electrolysis.
A prediction model for Current Efficiency (CE) of low temperature aluminum electrolysis (LTAE) with the low molar ratio electrolyte of Na3AIF6-AIF3-CaF2-MgF2-LiF-Al2O3 system was investigated based on artificial neural network principles. The nonlinear mapping between CE of LATE and various electrolytic conditions was obtained from a number of experimental data and used to predictCE of LATE. The trained neural networks possessed high precision and resulted in a good predicting effect. As a result, artificial neural networks as a new cooperating and predicting technology provide a new approach to the further studies on low temperature aluminum electrolysis.
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