投审稿入口

冷连轧板形调控模型设定分析与优化

Analysis and optimization of flatness actuators model setting for tandem cold rolling

  • 摘要: 冷轧带钢板形控制技术是钢铁工业领域的核心控制技术之一,其控制效果直接决定了带钢轧后板形质量并影响下游工艺进程。板形执行机构是实现板形控制的关键工艺手段,较低的机构模型精度会影响板形偏差的在线修正能力并易于产生板形缺陷。本文综合考虑轧制力、带钢几何尺寸等关键轧制参数对板形的影响规律,以及不同板形执行机构间的耦合作用机制,建立了包含二次影响系数的弯辊-轧辊倾斜协同设定模型;引入“过喷系数”概念,构建了考虑相邻乳化液喷嘴间部分流量重叠特性的分段冷却流量控制模型;为提升设定模型的精度和计算效率,基于启发式搜索理论制定了设定模型中各系数的寻优设置策略。以某厂1 450 mm UCM 五机架六辊冷连轧机组为应用平台开展工业试验,结果表明,本文开发的板形执行机构设定模型及设定策略可高效、准确地修正在线板形偏差,降低了设定模型的设置难度,并使板形均方根误差命中率提升了约8%。

     

    Abstract: Flatness control technology for cold-rolled strip is one of the core control technologies in the iron and steel industry. Its control effectiveness directly determines the post-rolling flatness quality and influences subsequent downstream processes. Flatness actuators are the key technological means for achieving flatness control. Lower accuracy in the actuator models can impair the online correction capability for flatness deviations and easily lead to flatness defects. This paper comprehensively considers the influence patterns of key rolling parameters, such as rolling force and strip geometric dimensions, on strip flatness, as well as the coupling mechanisms among different flatness actuators. Based on this, a roll bending-roll tilting coordinated setup model incorporating quadratic influence coefficients is established. The concept of an "overspray coefficient" is introduced to construct a segmented cooling flow control model that accounts for the partial flow overlap characteristics between adjacent emulsion nozzles. To enhance the accuracy and computational efficiency of the setup model, an optimization strategy for setting the coefficients in the setup model is developed based on heuristic search theory. Industrial trials are conducted using a 1 450 mm UCM five-stand six-high tandem cold rolling mill as the application platform. The results indicate that the flatness actuator setup model and strategy developed in this paper can efficiently and accurately correct online flatness deviations. They reduce the setup complexity of the model and improve the hit rate of flatness root mean square error by approximately 8%.

     

/

返回文章
返回