摘要
以缩小连铸二冷区板坯表面实际温度和目标温度的差异为目标,建立了板坯连铸二次冷却智能控制模型.该模型采用支持向量机(SVM)实现板坯表面目标温度的动态设定,采用对角递归神经网络(DRNN)实现板坯表面温度的预测,采用T-S模糊递归神经网络实现二次冷却水动态调整与分配.通过对某钢厂板坯连铸过程进行仿真计算和现场试验,结果表明:该模型将二次冷却水水量控制问题与板坯在冷却过程中的温度状态相结合,实现了连铸二次冷却动态优化控制,有利于提高板坯的质量.
An intelligent control model of secondary cooling in continuous slab casting was presented to reduce the difference between actual temperature and target temperature at the surface of slabs during secondary cooling. The model dynamically sets the target temperature at the surface of slabs with support vector machine, forecasts the surface temperature of slabs with diagonal recurrent neural network, and dynamically controls and distributes the water flow of secondary cooling with T-S fuzzy recurrent neural network. Simulation calculation and field test were performed on the process of continuous slab casting in a steel plant. It is shown that the model integrates the problem of controlling the water flow of secondary cooling with the temperature state of slabs during the cooling process, can achieve the dynamic optimum control of secondary cooling and improve the quality of slabs.
出处
《北京科技大学学报》
EI
CAS
CSCD
北大核心
2009年第10期1322-1327,共6页
Journal of University of Science and Technology Beijing
基金
"十一五"国家科技支撑计划资助项目(No.2006BAE03A06)
国家自然科学基金资助项目(No.60705017)
关键词
二次冷却
智能控制
支持向量机
对角递归神经网络
模糊递归神经网络
secondary cooling
intelligent control
support vector machine
diagonal recurrent neural network
fuzzy recurrent neural network
