摘要
现有热轧带钢层流冷却过程缺少对卷取温度的直接反馈机制,难以将卷取温度控制在一定范围内.将机理模型与案例推理智能技术相结合,提出了由冷却区喷水集管开启阀门总数预设定模型、卷取温度预报模型、前馈补偿模型与反馈补偿模型四个模块组成的混合智能控制方法,并利用某钢厂的实际运行数据进行实验研究.实验结果表明即使在工况条件频繁变化时,提出的层流冷却混合智能控制方法也能够及时、自动调整喷水集管阀门开启总数的设定值,最终将实际卷取温度控制在工艺要求的范围内,从而提高热轧带钢的组织性能.
It is hard to control the coiling temperature in a certain range since the existing laminar cooling process lacks the direct feedback during strip hot rolling. A hybrid intelligent control method was therefore proposed combining the mechanism models with case-based reasoning technology, i.e. , composed of four modeling modules： to preset the number of valves of spray header to be opened in cooling zone, to forecast coiling temperature, to compensate for feedforward, to compensate for feedback. The method proposed was simulated with the actual operating data provided by a steel plant and the results showed that the setting value of the number of spray header valves to be opened can be readjusted automatically and timely according to the changing operation conditions, thus controlling the coiling temperature in the range the technical schedule requires so as to improve the microstructure and mechanical properties of the rolled strip.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第11期1534-1537,共4页
Journal of Northeastern University(Natural Science)
基金
国家重点基础研究发展计划项目(2009CB320601)
国家自然科学基金资助项目(60534010)
国家创新研究群体科学基金资助项目(60521003)
长江学者和创新团队发展计划项目(IRT0421)
关键词
层流冷却
卷取温度
案例推理
混合智能
前馈补偿
反馈补偿
laminar cooling
coiling temperature
case-based reasoning
hybrid intelligence
feedforward compensation
feedback compensation
