摘要
高炉料面形状是指导高炉布料决策的重要依据.本文在已开发的新型雷达检测手段的基础上将雷达检测数据和布料机理进行融合.首先根据牛顿运动规律计算炉料颗粒从料流调节阀至料面的运动过程,然后根据体积约束原则建立料面形状模型,最后通过高斯过程回归模型将机理模型和摆动雷达测量得到的料面信息融合,建立了基于雷达数据和机理模型双驱动的高炉料面形状模型.仿真结果表明,本文提出的数据融合的方法在结合雷达检测数据和机理模型的基础上能够更好的拟合出高炉料面形状,可以为高炉稳定运行,节能减排提供可靠指导.
The burden profile plays an essential role in the decision-making for the blast furnace burden distribution.This paper combines the radar measurement data with the mechanism analysis of burden distribution process based on the developed new radar measurement. First, according to the Newtonian mechanics, this paper calculate the law of burden particles motion from the flow control gate to the burden profile. Then, the burden profile model is established under the volume constraint principle. Finally, the Gaussian process regression model is used to fuse the mechanism-based burden profile model with the swing radar measurement data. The blast furnace burden profile model based on radar data and mechanism model is established. The simulation results show that the proposed data-fusion method has better fitness to the burden profile based on the radar measurement and mechanism model. Besides, the proposed method is helpful to the stable operation, energy conservation and emission reduction for the blast furnace.
作者
张森
李酉
陈先中
尹怡欣
ZHANG Sen;LI You;CHEN Xian-zhong;YIN Yi-xin(School of Automation and Electrical,University of Science and Technology Beijing,Beijing 100083,China;Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education,University of Science and Technology Beijing,Beijing 100083,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2020年第5期978-986,共9页
Control Theory & Applications
基金
国家自然科学基金项目(61673056,61671055,61671054)
北京市自然科学基金项目(4182038,4182039)资助.
关键词
料面形状
高炉布料
雷达检测
机理模型
高斯过程回归
burden profile
blast furnace burden distribution
radar measurement
mechanism model
Gaussian process regression
