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基于运动轨迹和径向距离的高炉料面堆积形状建模方法

蒋朝辉 周科 桂卫华 曹婷 潘冬 朱既承

蒋朝辉, 周科, 桂卫华, 曹婷, 潘冬, 朱既承. 基于运动轨迹和径向距离的高炉料面堆积形状建模方法. 自动化学报, 2023, 49(6): 1155−1169 doi: 10.16383/j.aas.c220768
引用本文: 蒋朝辉, 周科, 桂卫华, 曹婷, 潘冬, 朱既承. 基于运动轨迹和径向距离的高炉料面堆积形状建模方法. 自动化学报, 2023, 49(6): 1155−1169 doi: 10.16383/j.aas.c220768
Jiang Zhao-Hui, Zhou Ke, Gui Wei-Hua, Cao Ting, Pan Dong, Zhu Ji-Cheng. A modeling method of blast furnace burden surface accumulation shape based on the motion trajectory and radial distance. Acta Automatica Sinica, 2023, 49(6): 1155−1169 doi: 10.16383/j.aas.c220768
Citation: Jiang Zhao-Hui, Zhou Ke, Gui Wei-Hua, Cao Ting, Pan Dong, Zhu Ji-Cheng. A modeling method of blast furnace burden surface accumulation shape based on the motion trajectory and radial distance. Acta Automatica Sinica, 2023, 49(6): 1155−1169 doi: 10.16383/j.aas.c220768

基于运动轨迹和径向距离的高炉料面堆积形状建模方法

doi: 10.16383/j.aas.c220768
基金项目: 国家重大科研仪器研制项目(61927803), 国家自然科学基金基础科学中心项目(61988101), 湖南省科技创新计划(2021RC4054), 国家自然科学基金青年基金(62103206), 中国博士后科学基金(2021M701804)资助
详细信息
    作者简介:

    蒋朝辉:中南大学自动化学院教授. 2011年获中南大学博士学位. 主要研究方向为智能传感与检测技术, 图像处理与智能识别和人工智能与机器学习. E-mail: jzh0903@csu.edu.cn

    周科:中南大学自动化学院博士研究生. 2018年获重庆大学自动化专业学士学位. 主要研究方向为复杂过程建模与优化控制. 本文通信作者. E-mail: zhouke95@csu.edu.cn

    桂卫华:中国工程院院士, 中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程建模, 优化与控制应用和故障诊断与分布式鲁棒控制. E-mail: gwh@csu.edu.cn

    曹婷:鹏城实验室全职博士后. 2013年和2018年获浙江大学学士及博士学位. 主要研究方向为应用于复杂工业环境的智能检测技术, 图像处理和点云处理. E-mail: caot@pcl.ac.cn

    潘冬:中南大学自动化学院讲师. 2015年和2021年获中南大学自动化学士学位和控制科学与工程博士学位. 主要研究方向为红外热成像, 视觉检测, 图像处理和深度学习. E-mail: pandong@csu.edu.cn

    朱既承:中南大学自动化学院博士研究生. 2020年获得湖南科技大学自动化学士学位. 主要研究方向为工业过程建模与控制和机器学习. E-mail: 224601041@csu.edu.cn

A Modeling Method of Blast Furnace Burden Surface Accumulation Shape Based on the Motion Trajectory and Radial Distance

Funds: Supported by National Major Scientific Research Equipment of China (61927803), National Natural Science Foundation of China Basic Science Center Project (61988101), Science and Technology Innovation Program of Hunan Province (2021RC4054), National Natural Science Foundation for Young Scholars of China (62103206), and Postdoctoral Science Foundation of China (2021M701804)
More Information
    Author Bio:

    JIANG Zhao-Hui Professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 2011. His research interest covers intelligent sensing and detection technology, image processing and intelligent recognition, artificial intelligence and machine learning

    ZHOU Ke Ph.D. candidate at the School of Automation, Central South University. He received his bachelor degree in automatic control from Chongqing University in 2018. His research interest covers modeling and optimal control of complex industrial processes. Corresponding author of this paper

    GUI Wei-Hua Academician of Chinese Academy of Engineering, and professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His research interest covers complex industrial process modeling, optimization and control applications, fault diagnosis and distributed robust control

    CAO Ting Full-time postdoctor researcher at Peng Cheng Laboratory. She received her bachelor and the Ph.D. degrees from Zhejiang University in 2013 and 2018, respectively. Her research interest covers intelligent detection technology, image processing and point cloud processing applied to complex industrial environment

    PAN Dong Lecturer at the School of Automation, Central South University. He received his bachelor degree in automatic and the Ph.D. degree in control science and engineering from Central South University in 2015 and 2021, respectively. His research interest covers infrared thermography, vision-based measurement, image processing and deep learning

    ZHU Ji-Cheng Ph.D. candidate at the School of Automation, Central South University. He received his bachelor degree in automation from Hunan University of Science and Technology in 2020. His research interest covers industrial process modeling and control, and machine learning

  • 摘要: 高炉料面形貌是反映煤气流分布和煤气利用率的关键指标, 研究高炉料面炉料堆积形状数学建模方法对实现高炉精准布料控制和“双碳”战略在钢铁行业落地具有重要意义. 针对高炉多环布料情况下料面堆积形状预测难的问题, 本文提出了一种基于炉料运动轨迹和径向移动距离的高炉料面炉料堆积形状建模方法. 首先, 提出了一种与炉料初始状态和溜槽状态相关的炉料运动轨迹建模方法, 获取炉料从节流阀至料面的炉料运动轨迹, 并确定炉料在炉喉空区的内轨迹曲线和外轨迹曲线. 然后, 基于炉料运动轨迹和初始料面形状, 以体积守恒原则为约束, 提出了一种基于炉料径向移动距离的高炉料面炉料堆积形状数学建模方法, 获取炉料在料面的堆积形状. 最后, 基于某钢铁厂2# 高炉的尺寸建立离散单元法 (Discrete element method, DEM) 仿真模型, 模型仿真结果验证了所提方法的准确性和有效性.
  • 图  1  高炉炉顶炉料运动过程示意图

    Fig.  1  Schematic diagram of the moving process of burden flow on the blast furnace top

    图  2  坐标变换过程示意图

    Fig.  2  Schematic diagram of the coordinate transformation process

    图  3  炉料与溜槽碰撞前后速度关系示意图

    Fig.  3  Schematic diagram of the velocity relationship between the burden flow and chute collision

    图  4  炉料在溜槽上位置示意图

    Fig.  4  Schematic diagram of the position of the burden flow on the chute

    图  5  炉料在料面落点位置和速度分布示意图

    Fig.  5  Schematic diagram of the position and velocity distribution of the burden flow on the burden surface

    图  6  炉料堆积过程示意图

    Fig.  6  Schematic diagram of burden flow accumulation

    图  7  料面堆积迭代流程图

    Fig.  7  Iterative flow chart of burden surface accumulation

    图  8  DEM仿真几何模型

    Fig.  8  The geometry size used in the DEM simulation

    图  9  传感器安装位置与料面堆积截面

    Fig.  9  Installation positions of sensors and accumulation section of burden surface

    图  10  基于DEM仿真的高炉料面堆积形状

    Fig.  10  The accumulation shape of blast furnace burden surface based on DEM simulation

    图  11  多环布料料面形状堆积更新过程

    Fig.  11  The update process of burden surface accumulation shape of the multi-loop charging

    图  12  基于DEM仿真和数学模型预测的料面堆积形状

    Fig.  12  The burden surface accumulation shape based on DEM simulation and mathematical model

    图  13  基于DEM仿真和数学模型预测的料面堆积形状绝对误差

    Fig.  13  The absolute error of burden surface accumulation shape based on DEM simulation and mathematical model

    表  1  DEM仿真中的粒子属性

    Table  1  The particle properties used in DEM simulation

    参数数值
    焦炭属性半径(m)0.04 ~ 0.06
    泊松比(P−P)0.22
    剪切模量(Pa)2.2×107
    密度(kg/m3)1050
    恢复系数碰撞恢复系数(P−P; P−C)0.18; 0.2
    静摩擦系数(P−P; P−C)0.56; 0.41
    滚动摩擦系数(P−P; P−C)0.15; 0.09
    (P−P和P−C分别表示粒子与粒子、粒子与溜槽接触)
    下载: 导出CSV

    表  2  仿真中高炉布料操作参数

    Table  2  The charging operation parameters in the simulation

    布料环环1环2环3环4环5
    溜槽转速(°/s)6060606060
    溜槽倾角(°)4139363330
    旋转圈数43321
    下载: 导出CSV

    表  3  基于数学模型的料面高度预测性能

    Table  3  The charging operation parameters in the burden furnace simulation

    布料环环1环2环3环4环5合计轮廓点数
    料面轮廓点1419253134123
    RMSE0.04290.02400.02140.02590.03760.0308
    $\Delta h \le 0.08$m$H(n)$1419253132121
    ${\rm{HR}}$100%100%100%100%94.12%98.37%
    $\Delta h \le 0.06$m$H(n)$1119253132116
    ${\rm{HR}}$78.57%100%100%96.77%91.18%94.31%
    下载: 导出CSV
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  • 收稿日期:  2022-10-04
  • 录用日期:  2023-02-10
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