摘要
介绍了带钢热连轧机组温度模型以及目前常用的温度模型自学习方法.针对目前常用的温度模型自学习方法的不足,提出了一种分区补偿法用于温度模型自学习,该方法按一定的分配系数将终轧温度偏差分配到各冷却区段,温度偏差分配系数可以根据各机架轧制力情况进行调节,所以在保证终轧温度预测精度的同时,也提高了轧制力的预测精度.这种新型的温度模型自学习方法被成功地应用于天津荣程750 mm精轧机组,取得了较好的应用效果.
The temperature model of hot strip finishing mills in tandem was discussed, as well as the conventional self-learning strategy relevant to it. To rise above the defects in the conventional self-learning strategy, a zonal compensation method is applied to temperature model self-learning, where the finish rolling temperature errors are assigned to each cooling zone according to certain temperature distribution coefficients, which can be readjusted according to the rolling force of each and every stand . In such a way the predicted precision of not only the rolling force but also the finish rolling temperature can be improved well. The new temperature model self-learning strategy has successfully been applied in Tianjin RockCheck 750 mm hot strip plant.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第3期369-372,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金重点资助项目(50534020)
关键词
热轧带钢
温度模型
模型自学习
轧制力
终轧温度
hot strip rolling
temperature model
model self-learning
rolling force
finish rolling temperature
