摘要
以密闭鼓风炉熔炼过程为研究对象 ,采用自适应组合集成技术将神经网络NN和灰色理论有机结合的方法建立了密闭鼓风炉透气性预测模型。仿真和工业应用结果表明 :所提出的模型能很好地实现密闭鼓风炉透气性的预测 ,并能使铅锌产量得到显著的提高。
Based on gray theory and neural network, the breathing capacity predication model of an imperial blast furnace was constructed by self-adaptive integration technology according to the melting process in an imperial blast furnace. Simulation and industry application results show that the predictive model can realize accurate predication of the breathing capacity for imperial blast furnace, which leads to a notable increase of plumbum and zinc output.
出处
《中国有色金属学报》
EI
CAS
CSCD
北大核心
2003年第5期1306-1310,共5页
The Chinese Journal of Nonferrous Metals
基金
国家 8 63高科技计划资助项目 (2 0 0 1AA4110 40 )
关键词
炼锌
炼铅
神经网络
灰色理论
密闭鼓风炉
透气性
预测模型
自适应模糊组合
imperial blast furnace melting process
breathing capacity
predictive model
neural networks
gray theory
self-adaptive fuzzy combination
