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基于粒度聚类的转炉炼钢氧气消耗量预测

阳青锋 赖旭芝 杜胜 胡杰 陈略峰 吴敏

阳青锋, 赖旭芝, 杜胜, 胡杰, 陈略峰, 吴敏. 基于粒度聚类的转炉炼钢氧气消耗量预测. 自动化学报, 2024, 50(1): 132−142 doi: 10.16383/j.aas.c230333
引用本文: 阳青锋, 赖旭芝, 杜胜, 胡杰, 陈略峰, 吴敏. 基于粒度聚类的转炉炼钢氧气消耗量预测. 自动化学报, 2024, 50(1): 132−142 doi: 10.16383/j.aas.c230333
Yang Qing-Feng, Lai Xu-Zhi, Du Sheng, Hu Jie, Chen Lue-Feng, Wu Min. Converter steelmaking oxygen consumption prediction based on granularity clustering. Acta Automatica Sinica, 2024, 50(1): 132−142 doi: 10.16383/j.aas.c230333
Citation: Yang Qing-Feng, Lai Xu-Zhi, Du Sheng, Hu Jie, Chen Lue-Feng, Wu Min. Converter steelmaking oxygen consumption prediction based on granularity clustering. Acta Automatica Sinica, 2024, 50(1): 132−142 doi: 10.16383/j.aas.c230333

基于粒度聚类的转炉炼钢氧气消耗量预测

doi: 10.16383/j.aas.c230333
基金项目: 高等学校学科创新引智计划资助项目(B17040), 国家自然科学基金(62303431), 湖北省自然科学基金(2015CFA010, 2021CFB145, 2022CFB582), 中国博士后科学基金(2023M733306) 资助
详细信息
    作者简介:

    阳青锋:中国地质大学(武汉) 自动化学院硕士研究生. 主要研究方向为复杂工业过程控制与状态监测. E-mail: yangqingfeng@cug.edu.cn

    赖旭芝:中国地质大学(武汉) 自动化学院教授. 主要研究方向为非线性系统控制, 智能系统和机器人控制. 本文通信作者. E-mail: laixz@cug.edu.cn

    杜胜:中国地质大学(武汉) 自动化学院教授. 主要研究方向为过程控制, 智能控制和计算智能. E-mail: dusheng@cug.edu.cn

    胡杰:中国地质大学(武汉) 自动化学院教授. 主要研究方向为过程控制, 计算智能和人工智能. E-mail: hujie@cug.edu.cn

    陈略峰:中国地质大学(武汉) 自动化学院教授. 主要研究方向为智能系统, 模式识别和计算智能. E-mail: chenluefeng@cug.edu.cn

    吴敏:中国地质大学(武汉) 自动化学院教授. 主要研究方向为过程控制, 鲁棒控制和智能系统. E-mail: wumin@cug.edu.cn

Converter Steelmaking Oxygen Consumption Prediction Based on Granularity Clustering

Funds: Supported by the 111 Project (B17040), National Natural Science Foundation of China (62303431), Natural Science Foundation of Hubei Province (2015CFA010, 2021CFB145, 2022CFB582), and China Postdoctoral Science Foundation (2023M733306)
More Information
    Author Bio:

    YANG Qing-Feng Master student at the School of Automation, China University of Geosciences. His research interest covers control and condition monitoring of complex industrial processes

    LAI Xu-Zhi Professor at the School of Automation, China University of Geosciences. Her research interest covers nonlinear system control, intelligent systems, and robot control. Corresponding author of this paper

    DU Sheng Professor at the School of Automation, China University of Geosciences. His research interest covers process control, intelligent control, and computational intelligence

    HU Jie Professor at the School of Automation, China University of Geosciences. His research interest covers process control, computational intelligence, and artificial intelligence

    CHEN Lue-Feng Professor at the School of Automation, China University of Geosciences. His research interest covers intelligent systems, pattern recognition, and computational intelligence

    WU Min Professor at the School of Automation, China University of Geosciences. His research interest covers process control, robust control, and intelligent systems

  • 摘要: 转炉炼钢是钢铁企业的主要耗氧工序, 预测转炉炼钢的氧气消耗量对氧气系统合理调度、保证生产安全具有重要意义. 考虑到转炉冶炼工况多、钢种数据粒度不统一, 提出一种基于粒度聚类的转炉炼钢氧气消耗量预测方法. 首先, 利用孤立森林异常检测法剔除历史数据库中的异常数据; 接着, 采用皮尔逊相关性分析和互信息相关系数选取相关影响因子, 对不同钢种数据进行信息粒化, 实现数据特征提取和维度统一, 使用模糊C均值(Fuzzy C-means, FCM) 划分工况并建立不同工况下的氧气消耗量预测子模型; 最后, 利用企业的实际生产数据进行实验, 验证所提方法的准确性和有效性.
  • 图  1  转炉冶炼过程

    Fig.  1  Converter steelmaking process

    图  2  基于粒度聚类的氧气消耗量预测方案

    Fig.  2  Oxygen consumption prediction scheme based on granularity clustering

    图  3  孤立树结构

    Fig.  3  Isolation tree structure

    图  4  基于信息粒化的钢种模糊聚类

    Fig.  4  Fuzzy clustering of steel types based on information granulation

    图  5  剔除异常值前后箱线图对比

    Fig.  5  Comparison of box plots before and after removing outliers

    图  6  不同聚类数目的${D_b} $值

    Fig.  6  $ {D_b}$ values for different numbers of clusters

    图  7  基于单一模型的氧气消耗量预测结果

    Fig.  7  Oxygen consumption prediction results based on a single model

    图  8  基于不同组合模型的氧气消耗量预测结果

    Fig.  8  Oxygen consumption prediction results based on different combination models

    表  1  符号说明

    Table  1  Symbol description

    符号含义符号含义
    ${C_1}$铁水重量${C_2}$废钢重量
    ${C_3}$石灰重量${C_{\rm{Si}}}$铁水中化学元素Si的含量
    ${C_{\rm{Mn}}}$铁水中化学元素Mn的含量${C_{\rm{P}}}$铁水中化学元素P的含量
    ${C_{\rm{S}}}$铁水中化学元素S的含量${C_{\rm{Cu}}}$铁水中化学元素Cu的含量
    ${C_{\rm{As}}}$铁水中化学元素As的含量${C_{\rm{Sn}}}$铁水中化学元素Sn的含量
    ${C_{\rm{Ti}}}$铁水中化学元素Ti的含量${C_{\rm{V}}}$铁水中化学元素V的含量
    ${C_{\rm{Pb}}}$铁水中化学元素Pb的含量${C_{\rm{Zn}}}$铁水中化学元素Zn的含量
    ${C_{\rm{Cr}}}$铁水中化学元素Cr的含量${C_{\rm{Ni}}}$铁水中化学元素Ni的含量
    ${C_{\rm{Nb}}}$铁水中化学元素Nb的含量${C_{\rm{Mo}}}$铁水中化学元素Mo的含量
    ${C_{\rm{Sb}}}$铁水中化学元素Sb的含量${C_{\rm{W}}}$铁水中化学元素W的含量
    ${Y}$氧气消耗量$S({X})$样本${X}$的异常得分
    ${\rho _{\rm{pcc}}}$皮尔逊相关系数值${\rho _{\rm{NMI}}}$归一化互信息相关系数值
    ${X_A}$A钢种${\tilde X_A}$A钢种信息粒
    ${r_1}$均方根误差 (Root mean square error, RMSE)${r_2}$平均绝对误差 (Mean absolute error, MAE)
    ${r_3}$平均绝对百分比误差 (Mean absolute percentage error, MAPE)${r_4}$最大绝对百分比误差 (Maximum absolute percentage error, MaxAPE)
    ${D_b}$Davies-Bouldin指数
    下载: 导出CSV

    表  2  相关参数与吹氧量的${\rho _{\rm{pcc}}}$和${\rho _{\rm{NMI}}}$ 值

    Table  2  ${\rho _{\rm{pcc}}}$ and ${\rho _{\rm{NMI}}}$ values between relevant parameters and oxygen blowing amount

    过程参数$C_1$$C_2$$C_3$$C_{\rm{Si}}$$C_{\rm{Mn}}$
    ${\rho _{\rm{pcc}}}$0.2810.5100.3400.0540.180
    ${\rho_{\rm{NMI}}}$0.7760.8200.8310.8640.856
    过程参数$C_{\rm{P}}$$C_{\rm{S}}$$C_{\rm{Cu}}$$C_{\rm{As}}$$C_{\rm{Sn}}$
    ${\rho _{\rm{pcc}}}$0.0160.1170.1200.1020.012
    ${\rho_{\rm{NMI}}}$0.7580.8120.7660.7570.710
    过程参数$C_{\rm{Ti}}$$C_{\rm{V}}$$C_{\rm{Pb}}$$C_{\rm{Zn}}$$C_{\rm{Cr}}$
    ${\rho _{\rm{pcc}}}$0.0250.1020.0530.0150.022
    ${\rho_{\rm{NMI}}}$0.8510.8410.5420.5540.770
    过程参数$C_{\rm{Ni}}$$C_{\rm{Nb}}$$C_{\rm{Mo}}$$C_{\rm{Sb}}$$C_{\rm{W}}$
    ${\rho _{\rm{pcc}}}$0.0190.0190.0460.0360.030
    ${\rho_{\rm{NMI}}}$0.7180.5350.4720.3140.609
    下载: 导出CSV

    表  3  不同模型预测误差

    Table  3  Prediction errors of different models

    预测模型${r_1}$${r_2}$${r_3}$${r_4}$
    Elman575.60465.414.850.19
    IG-FCM-Elman565.11415.354.350.24
    ELM480.12359.383.740.17
    IG-FCM-ELM471.48352.713.680.24
    RF417.17324.693.360.13
    IG-FCM-RF400.04313.113.240.12
    SVR415.40321.543.330.16
    IG-FCM-SVR407.64304.743.160.14
    下载: 导出CSV
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  • 收稿日期:  2023-06-08
  • 录用日期:  2023-09-08
  • 网络出版日期:  2023-12-25
  • 刊出日期:  2024-01-29

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