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高炉炼铁过程多元铁水质量非线性子空间建模及应用

宋贺达 周平 王宏 柴天佑

宋贺达, 周平, 王宏, 柴天佑. 高炉炼铁过程多元铁水质量非线性子空间建模及应用. 自动化学报, 2016, 42(11): 1664-1679. doi: 10.16383/j.aas.2016.c150819
引用本文: 宋贺达, 周平, 王宏, 柴天佑. 高炉炼铁过程多元铁水质量非线性子空间建模及应用. 自动化学报, 2016, 42(11): 1664-1679. doi: 10.16383/j.aas.2016.c150819
SONG He-Da, ZHOU Ping, WANG Hong, CHAI Tian-You. Nonlinear Subspace Modeling of Multivariate Molten Iron Quality in Blast Furnace Ironmaking and Its Application. ACTA AUTOMATICA SINICA, 2016, 42(11): 1664-1679. doi: 10.16383/j.aas.2016.c150819
Citation: SONG He-Da, ZHOU Ping, WANG Hong, CHAI Tian-You. Nonlinear Subspace Modeling of Multivariate Molten Iron Quality in Blast Furnace Ironmaking and Its Application. ACTA AUTOMATICA SINICA, 2016, 42(11): 1664-1679. doi: 10.16383/j.aas.2016.c150819

高炉炼铁过程多元铁水质量非线性子空间建模及应用

doi: 10.16383/j.aas.2016.c150819
基金项目: 

辽宁省教育厅科技项目 L20150186

国家自然科学基金 61473064, 61290323, 61333007

>中央高校基本科研业务费项目 N130108001

详细信息
    作者简介:

    宋贺达 东北大学硕士研究生.2013年获得东北大学学士学位.主要研究方向为数据驱动建模与控制, 机器学习算法.E-mail:1401124@stu.neu.edu.cn

    王宏 教授, 国家特聘专家, IET 和InstMC Fellow, 长江学者讲座教授, 英国曼彻斯特大学教授.主要研究方向为非高斯随机系统的随机分布控制, 故障检测与诊断, 非线性控制,基于数据的复杂系统建模.E-mail:hong.wang@manchester.ac.uk

    柴天佑 中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow.1985年获得东北大学博士学位.主要研究方向为自适应控制, 多变量智能解耦控制, 流程工业综合自动化理论、方法与技术. E-mail:tychai@mail.neu.edu.cn

    通讯作者:

    周平 东北大学副教授. 分别于2003年, 2006 年, 2013 年获得东北大学学士、硕士和博士学位. 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

Nonlinear Subspace Modeling of Multivariate Molten Iron Quality in Blast Furnace Ironmaking and Its Application

Funds: 

General Project on Scienti¯c Research for the Education Department of Liaoning Province L20150186

Supported by National Natural Science Foundation of China 61473064, 61290323, 61333007

Fundamental Research Funds for the Central Universities N130108001

More Information
    Author Bio:

    Master student at Northeastern University. He received his bachelor degree from Northeastern University in 2013. His research inter-est covers data-driven modeling and control and machine learning algorithm.E-mail:

    IET, InstMC fel-low, professor at the Control System Centre, Univer-sity of Manchester, Manchester, UK since 2002. His re-search interest covers stochastic distribution control of non-Gaussian stochastic system, fault detection and diagnosis,nonlinear control, and data-based modeling for complex systems.E-mail:

    Academician of Chinese Academy of Engineering, pro-fessor at Northeastern University, IEEE Fellow, IFAC Fel-low. He received his Ph. D. degree from Northeastern Uni-versity in 1985. His research interest covers adaptive con-trol, intelligent decoupling control, and integrated automa-tion theory, method and technology of industrial process.). E-mail:

    Corresponding author: ZHOU Ping Associate professor at Northeastern University. He received his bachelor, master and Ph. D. degrees from Northeast-ern University in 2003, 2006 and 2013, respectively. His research interest covers operation feedback control of in-dustrial process, data-driven modeling and control. Corre-sponding author of this paper.E-mail:zhouping@mail.neu.edu.cn
  • 摘要: 高炉炼铁是一个物理化学反应复杂、多相多场耦合的大滞后、非线性动态系统,其关键工艺指标——铁水质量参数的检测、建模和控制一直是冶金工程和自动控制领域的难题.本文提出一种面向控制的数据驱动高炉炼铁多元铁水质量非线性子空间建模方法.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与相关性分析相结合的方法提取与铁水质量相关性最强的关键可控变量作为建模的输入变量;同时,为了更好地反映高炉非线性动态特性,将相关输入输出变量的时序和时滞关系在建模过程进行考虑;最后,采用基于最小二乘支持向量机(Least square support vector machine,LS-SVM)的非线性Hammerstein系统子空间辨识方法建立数据驱动的多元铁水质量非线性状态空间模型.同时,将核函数表示的模型非线性特性用多项式函数拟合,在仅损失很小模型精度的前提下大大降低模型的计算复杂度.基于实际数据的工业试验验证了所提建模方法的准确性、有效性和先进性.
  • 图  1  高炉炼铁过程示意图

    Fig.  1  diagram of a typical BF ironmaking process

    图  2  多元铁水质量参数非线性子空间建模策略

    Fig.  2  Strategy diagram of multivariable nonlinear dynamic modeling for molten iron quality

    图  3  多项式拟合非线性

    Fig.  3  Polynomial ¯tting for nonlinearity

    图  4  基于非线性子空间辨识的多元铁水质量模型在多项式拟合非线性特性前后的建模结果

    Fig.  4  Modeling results of molten iron quality prediction with and without polynomial ¯tting

    图  5  模型在多项式拟合非线性特性前后对多元铁水质量参数的实际估计效果

    Fig.  5  Estimation results of molten iron quality prediction with and without polynomial ¯tting

    图  6  不同建模方法对多元铁水质量的实际估计效果对比

    Fig.  6  Estimation results of molten iron quality prediction with di®erent models

    图  7  不同模型估计误差自相关函数

    Fig.  7  Autocorrelation function of estimating error of di®erent models

    图  8  不同建模方法的估计值与实际值散点图

    Fig.  8  Scatter diagram of estimated and actual value by di®erent models

    图  9  多元铁水质量预测控制结果

    Fig.  9  Predictive control results of molten iron quality parameters

    图  10  多元铁水质量预测控制控制量曲线

    Fig.  10  Control input curves of predictive control of molten iron quality parameters

    表  1  典型相关分析结果

    Table  1  The results of canonical correlation analysis

    0.563[Si]-0.453[P]-0.638[Si]+0.899[P]--0.857[Si]-0.368[P]-
    0.447[S]+0.287MIT0.986[S]-0.518MIT 0.801[S]-0.398MIT
    送风比-1.103 -3.254 0.716
    热风压力0.587 -1.109 -1.493
    顶压0.974 -0.911 -0.320
    压差0.495 -7.118 2.506
    顶压风量-0.948 -0.794 3.185
    透气性-2.687 -4.104 -2.782
    阻力系数-2.702 6.501 -5.392
    热风温度-5.375 14.185 4.230
    富氧流量4.914 -4.014 -0.054
    富氧率-7.284 8.123 -0.377
    设定喷煤量0.998 -7.335 -3.443
    鼓风湿度0.096 0.208 -0.071
    理论燃烧温度3.789 -12.762 -5.639
    标准风速-0.439 -3.953 -0.717
    实际风速1.816 2.624 3.444
    鼓风动能-0.438 -4.662 -2.728
    炉腹煤气量-1.306 14.932 4.029
    炉腹煤气指数-0.176 -1.503 -0.335
    下载: 导出CSV

    表  2  两种模型对各个铁水质量指标估计的均方根误差和计算时间比较

    Table  2  Comparison between two models in RMSE for molten iron quality prediction and computation time

    RMSETime (s)
    [Si] (%) [P] (%) [S] (%) MIT (±C)
    Kernel 0.0633 0.0032 0.0021 6.2388 0.01123
    Polynomial 0.0664 0.0031 0.0022 6.6907 0.00061
    下载: 导出CSV

    表  3  多元铁水质量估计值均方根误差比较

    Table  3  RMSE for molten iron quality prediction

    RMSE
    [Si] (%) [P] (%) [S] (%) MIT (±C)
    N-SM 0.0664 0.0031 0.0022 6.6907
    L-SM 0.1584 0.0107 0.0073 8.4801
    M-LS-SVR 0.0811 0.0058 0.0051 8.0037
    RLS-ARMA 0.0721 0.0060 0.0044 7.2992
    下载: 导出CSV
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  • 收稿日期:  2015-12-04
  • 录用日期:  2016-04-18
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