摘要
以缩小板坯表面实际温度和目标温度的差异为目标,基于神经网络技术,建立以BP神经网络进行连铸二冷段的温度预测和运用模糊神经网络对二冷段的水量进行实时控制的动态控制模型,模型能及时根据拉速、温度的变化做出水量的动态调整和分配。针对某钢厂2#板坯连铸过程进行了仿真计算和现场应用测试,结果表明:该模型将二冷水量控制问题与铸坯在冷却过程中的温度状态相结合,能很好的响应现场的变化,及时给出二冷段水量的动态调节量。
A dynamic control model for the secondary cooling of slab casting is presented to reduce the difference between the actual temperature and the goal surface temperature of slab. The model, which is based on the BP neural networks for forecasting the temperature and the fuzzy neural networks for dynamically controlling the water in the secondary cooling in the continuous casting, could timely adjust and allocate the water according to the speed and temperature of slab. A series of tests have been conducted based on inputs of the No. 2 slab caster in a steel plant. It has been shown that the model, which integrate the charateristics of water controlling problem in secondary cooling into the temperature status of slab during the cooling process, can control the water in secondary cooling efficiently and dynamically according to the situation of actual production.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第11期37-41,共5页
Journal of Chongqing University
关键词
神经网络
模糊神经网络
板坯连铸
二冷水
动态控制
neural networks
fuzzy-neural networks
continuous casting
secondary cooling
dynamic control
