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高炉铁水质量鲁棒正则化随机权神经网络建模

李温鹏 周平

李温鹏, 周平. 高炉铁水质量鲁棒正则化随机权神经网络建模. 自动化学报, 2020, 46(4): 721-733. doi: 10.16383/j.aas.2018.c170670
引用本文: 李温鹏, 周平. 高炉铁水质量鲁棒正则化随机权神经网络建模. 自动化学报, 2020, 46(4): 721-733. doi: 10.16383/j.aas.2018.c170670
LI Wen-Peng, ZHOU Ping. Robust Regularized RVFLNs Modeling of Molten Iron Quality in Blast Furnace Ironmaking. ACTA AUTOMATICA SINICA, 2020, 46(4): 721-733. doi: 10.16383/j.aas.2018.c170670
Citation: LI Wen-Peng, ZHOU Ping. Robust Regularized RVFLNs Modeling of Molten Iron Quality in Blast Furnace Ironmaking. ACTA AUTOMATICA SINICA, 2020, 46(4): 721-733. doi: 10.16383/j.aas.2018.c170670

高炉铁水质量鲁棒正则化随机权神经网络建模

doi: 10.16383/j.aas.2018.c170670
基金项目: 

国家自然科学基金项目 61890934

国家自然科学基金项目 61790572

国家自然科学基金项目 61290323

中央高校基本科研业务费项目 N180802003

辽宁省‘兴辽英才计划’项目 XLYC1907132

矿冶过程自动控制技术国家(北京市)重点实验室开放课题 BGRIMM-KZSKL-2017-04

详细信息
    作者简介:

    李温鹏  东北大学硕士研究生. 2016年获得烟台大学学士学位.主要研究方向为数据驱动建模与控制, 机器学习算法.E-mail:weepenli@163.com

    通讯作者:

    周平   东北大学教授.分别于2003年, 2006年, 2013年获得东北大学学士学位、硕士学位和博士学位.主要研究方向为工业过程运行反馈控制, 数据驱动建模与控制.本文通信作者.E-mail: zhouping@mail.neu.edu.cn

  • 本文责任编委 贺威

Robust Regularized RVFLNs Modeling of Molten Iron Quality in Blast Furnace Ironmaking

Funds: 

National Natural Science Foundation of China 61890934

National Natural Science Foundation of China 61790572

National Natural Science Foundation of China 61290323

Fundamental Research Funds for the Central Universities N180802003

Liaoning Revitalization Talents Program XLYC1907132

State Key Laboratory of Process Automation in Mining & Metallurgy (Beijing) BGRIMM-KZSKL-2017-04

More Information
    Author Bio:

    LI Wen-Peng   Master student at Northeastern University. He received his bachelor degree from YanTai University in 2016. His research interest covers data-driven modeling and control, and machine learning algorithm

    Corresponding author: ZHOU Ping  Professor at Northeastern University. He received his bachelor degree, master degree, and Ph.D. degree from Northeastern University in 2003, 2006, and 2013, respectively. His research interest covers operation feedback control of industrial process, data-driven modeling and control. Corresponding author of this paper
  • Recommended by Associate Editor HE Wei
  • 摘要: 高炉炼铁过程运行优化与控制依赖于可靠、稳定的难测铁水质量(Molten iron quality, MIQ)指标模型.针对现有MIQ建模方法的不足, 本文提出一种新型的数据驱动鲁棒正则化随机权神经网络(Random vector functional-link networks, RVFLNs)算法, 用于实现MIQ指标在线估计的鲁棒建模.首先, 为了提高建模效率和降低计算复杂度, 采用数据驱动典型相关性分析方法从众多变量中提取与MIQ相关性最强的变量作为建模输入变量; 其次, 由于传统RVFLNs网络的输出权值由最小二乘估计获得, 易受离群数据影响而鲁棒性差, 引入基于Gaussian分布加权的M估计技术, 提出新型鲁棒RVFLNs算法建立多元MIQ指标的鲁棒模型; 同时, 在鲁棒加权后的最小二乘损失函数基础上, 进一步引入${L_1}$和${L_2}$两个正则化项以构成优化目标函数的Elastic net, 用于稀疏化RVFLNs网络的输出权值矩阵, 解决RVFLNs网络多重共线性和过拟合的问题.最后, 基于某大型高炉工业数据, 进行充分数据实验, 结果表明所提方法具有更高的建模与估计精度以及较强的鲁棒性能.
    Recommended by Associate Editor HE Wei
    1)  本文责任编委 贺威
  • 图  1  高炉炼铁工艺示意图

    Fig.  1  Diagram of a typical BF ironmaking process

    图  2  建模误差RMSE与输入权值倍数和输入偏置倍数之间的关系

    Fig.  2  The relationship between the modeling RMSE and the input weight multiple and the input bias multiple

    图  3  建模误差RMSE与正则化系数之间的关系曲线

    Fig.  3  The relation curve between the modeling RMSE and the regularization coefficient

    图  4  不同离群点比例时铁水质量估计RMSE箱形图

    Fig.  4  The box diagram of the Estimation RMSE of MIQ indices with different methods at each of the outlier contamination rates

    图  5  不同离群幅值时铁水质量估计RMSE箱形图

    Fig.  5  The box diagram of the Estimation RMSE of MIQ indices with different methods when the amplitudes of the outliers are increased

    图  6  离群比例为20%和离群幅值步长$a$为2时, 不同RVFLNs建模方法铁水质量指标建模效果

    Fig.  6  Modeling results of MIQ indices with different methods when outlier contamination rate is twenty percent and amplitude is two

    图  7  离群比例为20%和离群幅值步长$a$为2时, 不同RVFLNs建模方法铁水质量指标估计效果

    Fig.  7  Estimation results of MIQ indices with different methods when outlier contamination rates is twenty percent and amplitude is two

    图  8  不同RVFLNs建模方法铁水质量估计误差PDF曲线

    Fig.  8  PDF curve of MIQ estimation error with different methods

    图  9  不同RVFLNs建模方法铁水质量估计时网络输出权值为0的数量曲线

    Fig.  9  The curve of the number of output weights with '0' value of MIQ estimation error with different methods

    表  1  典型相关系数的显著性检验

    Table  1  Significance test of canonical correlation coefficient

    典型变量 显著性检验指标
    Wilk's Chi-SQ DF Sig.
    1 0.337 299.701 72 0
    2 0.527 176.485 51 0
    3 0.754 77.786 32 0
    4 0.958 11.678 15 0.703
    下载: 导出CSV

    表  2  高炉本体参数典型变量的标准化系数

    Table  2  Standardized canonical coefficients of BF body variables

    影响变量 典型变量 变量权值
    1 2 3 4
    冷风流量 14.821 2.609 $-3.502$ $-24.047$ 11.95769
    送风比 $-0.669 $ 1.803 0.658 3.633 1.695912
    热风压力 0.724 2.664 $-2.139$ -0.209 2.885878
    压差 2.749 1.384 $ -0.527$ 2.382 2.655439
    顶压富氧率 $-0.06$ $ -0.712$ 0.95 -0.022 0.865848
    透气性 5.292 5.441 $-6.701$ 10.201 9.263463
    阻力系数 0.801 0.268 $ -4.063$ 8.006 2.505639
    热风温度 0.587 $-0.469$ $ -1.356$ $-0.268$ 1.23674
    富氧流量 11.697 $ -4.429$ 0.07 1.748 9.493758
    富氧率 -5.751 3.556 $ -4.229$ $-4.556$ 7.362393
    设定喷煤量 $-0.931$ 3.284 4.027 $ -0.233$ 4.222921
    鼓风湿度 0.533 0.805 1.932 $-0.465$ 1.654862
    理论燃烧温度 $-3.408$ 2.774 5.055 1.685 5.906544
    标准风速 $-2.222$ $ -0.705$ 2.385 6.454 2.824337
    实际风速 $ -0.224 $ 0.023 0.55 $-0.767$ 0.401351
    鼓风动能 $-1.85 $ $-1.067$ $-0.255$ $-0.002$ 1.815443
    炉腹煤气量 $ -14.106 $ $-6.874$ 0.208 15.053 12.34763
    炉腹煤气指数 0.292 0.651 $-0.006$ $ -0.198$ 0.535663
    下载: 导出CSV
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  • 收稿日期:  2017-11-23
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