Adaptive control of feed in the Hall-Héroult cell using a neural network
Adaptive control of feed in the Hall-Héroult cell using a neural network
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DOI：
10.1007/s11837-010-0028-4
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发表时间：
2010-02
期刊：
影响因子：
2.6
通讯作者：
K. D. Boadu;F. K. Omani
中科院分区：
文献类型：
--
作者：
K. D. Boadu;F. K. Omani
提出了一种线性神经网络用于铝电解槽中氧化铝浓度的估计。采用模拟140 kA Center-Break Hall-Héroult电解槽的电解液电阻/氧化铝浓度数据作为输入向量，训练一个由6个约束和6个自由度构成的双层神经网络。模拟和真实的数据测试使用派生的估计算法的结果。此外，神经网络与扩展卡尔曼滤波器进行了比较，并显示出具有上级性能的估计问题。最后，该论文声称神经算法对由于电池事件（如轻敲和阳极变化）引起的电阻变化具有鲁棒性。
A linear neural network is proposed for estimating alumina concentration in an aluminum reduction cell. Bath resistance/alumina concentration data from a simulated 140 kA Center-Break Hall-Héroult cell were used as input vectors to train a two-layer neural network constructed with six constraints and six degrees of freedom. Results from simulated and real data tests using the derived estimation algorithm are presented. Also, neural network is compared with extended Kalman filter and shown to have a superior performance in the estimation problem. Finally, the paper claims robustness for the neural algorithm against changes in resistance due to cell events like tapping and anode change.