基于GA-BP的NiFe_2O_4基金属陶瓷阳极优化设计

Optimization designs of NiFe_2O_4 cermet inert anodes based on GA-BP hybrid neural net work

采用BP神经网络对铝电解NiFe2O4基金属陶瓷惰性阳极的电解腐蚀过程进行了系统辨识。建立了以Al2O3质量浓度、电解温度、分子比、面积比和电流密度为输入,腐蚀率为输出的网络模型。在材料的设计中,采用了GA-BP优化方法,BP网络参与GA迭代计算时对个体的评价。应用结果表明,NiFe2O4基金属陶瓷惰性阳极的电解腐蚀率预测结果与实测值吻合;优化设计的结果与实验值很接近。

The corrosion processes of 5% Ni-NiFe2O4 inert anodes were recognized by back propagation neural net works and the prediction model was presented. The structures of neural net work include four input nodes, alumina concentration, bath temperature, cryolitic ratio, and area ratio of cathode to anode, current density, and one output node, corrosion rate. The hybrid neural network, genetic algorithms and back propagation neural networks, were applied when optimizing the design of the trial parameters. Some trial strategies were deduced by the hybrid model. The application and experimental results shows that, the neural prediction values of the corrosion rate of NiFe2O4 inert anodes fit in with the trial values, and the hybrid neural network model has guidance signification for material design.