基于自适应模糊神经网络的铜闪速熔炼渣含Fe/SiO₂模型研究

来源期刊：有色金属科学与工程2011年第1期

论文作者：曾青云 汪金良 张传福

文章页码：5 - 8

关键词：铜；闪速熔炼；模糊控制；神经网络；仿真

Key words：copper; flash smelting; fuzzy control, neural network; simulating

摘    要：

基于Sugeno型自适应模糊神经网络系统(ANFIS)及利用某闪速炼铜厂生产实践的稳定数据，建立了网络结构为3输入、单输出、隶属度函数个数为[5 3 5]的闪速炼铜过程的渣含Fe/SiO₂模型．结果显示其训练数据平均绝对误差为0.0055,相对误差为1.4%：仿真检验数据平均绝对误差为0.028，相对误差为2.9%，表明所建立的模型预测值与生产操作数据基本吻合，该模型对铜熔炼过程的最优化具有参考价值，可以代替现有的静态配料模型用于工业在线计算机控制．

Abstract: The Fe/SiO₂ in Slag model of copper flash smelting process, which has the net- structure of 3 in-put ,single out-put data, and the membership functions are [5 3 5] , was developed based on Adaptive Fuzzy Inference System and the practical data from one Copper smelter. The results indicate the average absolute error of train samples is 0.0055 and the relative error percentage is l.4%. The simulation results show that the average absolute error is 0.028%,and the relative error percentage is 2.9%. It means that the simulative results accord to the practical data. Thus, the model has good reference value on process optimized control of copper smelting. It also can be used in industrial online control to replace the model of static mixture.