基于LabVIEW平台的热镀锌镀层厚度模型研究与应用  

Research and application of coating weight model for hot galvanizing line based on LabVIEW platform

作　　者：费静 张岩 秦大伟 车玉满 孙鹏 姜喆 FEI Jing;ZHANG Yan;QIN Da-wei;CHE Yu-man;SUN Peng;JIANG Zhe(Iron ＆ Steel Research Institute of Angang Group ,Anshan 114009, China)

机构地区：[1]鞍钢集团钢铁研究院

出　　处：《冶金自动化》2018年第3期32-36,41,共6页Metallurgical Industry Automation

基　　金：国家自然科学基金资助项目(50604006);钢铁共性技术协同创新资助项目(2011计划)

摘　　要：针对镀锌生产过程中出现的各种干扰因素对镀层厚度模型精度的影响,以LabVIEW软件为开发平台,完成了热镀锌镀层厚度模型系统的研究和应用。在热镀锌镀层厚度模型设定过程中提出短期自适应和长期自学习模型相结合的模型控制方法,结合卡尔曼算法和指数平滑算法更新模型参数,实现了镀层厚度模型在线自适应控制。生产数据结果表明,镀层厚度模型系统的成功实施提高了热镀锌镀层厚度控制精度,降低了热镀锌镀层厚度偏差,取得了较好的模型控制效果,而且对节约成本、降低镀锌原料消耗具有重要意义。Aiming at the influence of interference factor on coating weight model accuracy during the hot galvanizing process,this paper presents the research and application of coating weight model for hot galvanizing line with LabVIEW software platform. During the configuration of coating weight model,the short term adaption and long term learning methods are introduced combined with Kalman algorithm and exponential smoothing algorithm,which realized the online coating weight self-adaption control. Production results showed that the application of the control system improved the coating thickness control precision and reduced the deviation of coating thickness. Meanwhile,the system application saves the cost and reduces the zinc raw material consumption.

关 键 词：热镀锌 镀层模型 自适应 卡尔曼算法 LABVIEW 

分 类 号：TG174.44[金属学及工艺—金属表面处理]