摘要
基于铜闪速熔炼过程是典型的高温、多相多组分复杂生产过程,熔炼时,闪速炉内发生激烈而迅速的化学反应,冰铜品位、冰铜温度和渣中铁硅比是铜熔炼过程的关键三大工艺参数,在线检测时存在成本高、滞后大,实现困难等问题,在分析影响工艺参数因素的基础上,提出一种基于BP神经网络的三大工艺参数预测方法,通过收集现场生产数据,挖掘其中隐含的工艺参数信息,建立预测模型。仿真结果表明,这三大工艺参数的最大绝对误差分别为0.630,6.680和0.051,最大相对误差分别为1.16%,0.55%和3.40%,说明模型预测结果与实际生产数据较吻合,该预测模型可用来指导实际生产操作,并可用于铜闪速熔炼过程参数优化。
Copper flash smelting is a complex industrial process with multiple phases and multiple components at high temperature. During the process of smelting, the reactions occur fiercely and rapidly in flash furnace, and matte grade, matte temperature and mass rate of Fe and SiO2 are the three key technology parameters during the process of copper flash smelting. The measurement method of these parameters is not only hard to be detected on-line, but also has time-delaying and costs a lot. A back propagation neural network prediction model was presented to predict these parameters, and its simulation experiment was given. The simulative results show that the biggest absolute error of the three parameters are 0.630, 6.680 and 0.051 and the biggest relative error are 1.16%, 0.55% and 3.40%, respectively. These results indicate that the prediction results of the model is in accordance with the practical data very well, and thus the model can be used in the parameter optimization for copper flash smelting and can be used to optimize parameters in the practical production.
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第3期523-527,共5页
Journal of Central South University:Science and Technology
基金
国家自然科学重点基金资助项目(60634020)
国家自然科学基金资助项目(60574030)
湖南省自然科学基金资助项目(06FD007)
国家发改委专项基金资助项目(2004-1113-17)
中国博士后科学基金资助项目(20060400885)
关键词
铜闪速熔炼
BP神经网络
预测模型
copper flash smelting
back propagation neural network
prediction model
