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基于KPLS鲁棒重构误差的高炉燃料比监测与异常识别

周平 刘记平 梁梦圆 张瑞垚

周平, 刘记平, 梁梦圆, 张瑞垚. 基于KPLS鲁棒重构误差的高炉燃料比监测与异常识别. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c180579
引用本文: 周平, 刘记平, 梁梦圆, 张瑞垚. 基于KPLS鲁棒重构误差的高炉燃料比监测与异常识别. 自动化学报, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c180579
Zhou Ping, Liu Ji-Ping, Liang Meng-Yuan, Zhang Rui-Yao. KPLS Robust reconstruction error based monitoring and anomaly identification of fuel ratio in blast furnace ironmaking. Acta Automatica Sinica, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c180579
Citation: Zhou Ping, Liu Ji-Ping, Liang Meng-Yuan, Zhang Rui-Yao. KPLS Robust reconstruction error based monitoring and anomaly identification of fuel ratio in blast furnace ironmaking. Acta Automatica Sinica, 2020, 46(x): 1−11 doi: 10.16383/j.aas.c180579

基于KPLS鲁棒重构误差的高炉燃料比监测与异常识别

doi: 10.16383/j.aas.c180579
基金项目: 国家自然科学基金项目(61890934, 61790572), 辽宁省'兴辽英才计划'项目(XLYC1907132), 中央高校基本科研业务费项目(N180802003), 矿冶过程自动控制技术国家(北京市)重点实验室开放课题资助(BGRIMM-KZSKL-2017-04)
详细信息
    作者简介:

    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等. 本文通信作者.E-mail: zhouping@mail.neu.edu.cn

    刘记平:于2017年获得东北大学硕士学位. 主要研究方向为数据驱动质量监测.E-mail: m18842388402@163.com

    梁梦圆:东北大学硕士研究生, 期间获国家奖学金. 于2016年获得东北大学秦皇岛分校大学学士学位. 主要研究方向为数据驱动质量监测.E-mail: liangmy1994@163.com

    张瑞垚:东北大学硕士研究生. 于2018年获得东北大学学士学位. 主要研究方向为数据驱动质量监测.E-mail: 1870768@stu.neu.edu.cn

KPLS Robust Reconstruction Error Based Monitoring and Anomaly Identification of Fuel Ratio in Blast Furnace Ironmaking

Funds: Supported by National Natural Science Foundation of China (61890934, 61790572), Liaoning Revitalization Talents Program (XLYC1907132), and Fundamental Research Funds for the Central Universities (N180802003). This work was also supported by the State (Beijing) Key Laboratory of Process Automation in Mining & Metallurgy (BGRIMM-KZSKL-2017-04)
  • 摘要: 作为钢铁冶金制造的核心工序, 高炉炼铁是典型的高能耗过程, 其运行能耗约占钢铁总能耗的50%以上, 这 其中80%的能耗是焦炭和煤粉等燃料消耗. 因此, 对表征高炉燃料消耗的燃料比参数进行监测, 并尽可能早地 识别影响燃料比异常波动的关键因素, 对于高炉炼铁过程的节能降耗具有重要意义. 本文针对先验故障知识少的 高炉燃料比监测与异常识别难题, 提出一种基于核偏最小二乘(Kernel partial least squares, KPLS)鲁棒重构误差的故障识别方法. 该方法首先 建立过程变量与监测变量的KPLS监测模型, 然后根据非线性映射空间的协方差矩阵和核空间Gram矩阵之间的关系, 反向估计原始空间变量的正常估值. 为了增强算法的鲁棒性, 采用迭代去噪算法减少异常数据对原始空间正常估值的影响. 通过利用原始空间正常估值和真实值来构造故障识别指标, 并给出故障识别指标的控制限. 基于实际工业数据的高炉数据试验表明所提方法不仅可以监测出正常工况下影响燃料比异常变化的潜在因素, 还可识别出异常工况下影响燃料比异常变化的关键因素, 具有很好的工程应用前景.
  • 图  1  高炉炼铁过程能耗示意图

    Fig.  1  Schematic diagram of energy consumption in blast furnace ironmaking process

    图  2  故障1的KPLS监测图

    Fig.  2  KPLS monitoring chart for fault 1

    图  4  故障2的KPLS监测图

    Fig.  4  KPLS monitoring chart for fault 2

    图  6  故障3的KPLS监测图

    Fig.  6  KPLS monitoring chart for fault 3

    图  3  故障1的故障变量识别图

    Fig.  3  Fault variable identification map of fault 1

    图  5  故障2的故障变量识别图

    Fig.  5  Fault variable identification map of fault 2

    图  7  故障3的故障变量识别图

    Fig.  7  Fault variable identification map of fault 3

    图  8  高炉燃料比监测曲线

    Fig.  8  Blast furnace fuel ratio monitoring curve

    图  9  高炉燃料比休哈顿图及残差图

    Fig.  9  Blast furnace fuel ratio Hughton diagram and residual map

    图  10  鼓风湿度异常时高炉燃料比异常识别曲线

    Fig.  10  Blast furnace fuel ratio anomaly identification curve when blast humidity is abnormal

    图  11  高炉操作调节关联图

    Fig.  11  Association diagram of blast furnace operation adjustment

    图  12  管道行程异常工况时高炉燃料比异常识别曲线

    Fig.  12  Abnormal identification curve of blast furnace fuel ratio in abnormal pipeline condition

    表  1  部分过程变量控制限与故障指标值的差值

    Table  1  The value of the control limit is reduced to the value of the fault index for part process variables.

    过程变量 时间 $(1h)$
    $T$ 290 $T$ 291 $T$ 292 $T$ 293 $T$ 294 $T$ 295 $T$ 296 $T$ 297 $T$ 298 $T$ 299 $T$ 300
    焦炭负荷 0.618 0.692 0.692 0.692 0.095 -0.394 -0.306 -0.397 -18.397 -0.676 -5.217
    球团 0.847 0.847 0.847 0.880 -0.533 -0.577 -0.581 -0.245 -0.611 -1.366 -0.552
    烧结比 1.025 1.025 1.024 1.024 -1.464 -1.545 -1.557 -1.556 -1.599 -1.508 -1.504
    球团比 0.869 0.871 0.870 0.870 -0.730 -0.793 -0.798 -0.801 -0.837 -0.768 -0.758
    顶压风量比 0.372 -0.744 -0.744 -0.744 -1.818 -2.467 -1.816 -0.286 -0.722 -3.198 -0.731
    标准风速 0.027 -0.654 -0.502 -0.654 0.861 0.790 0.861 0.909 0.909 0.184 0.068
    鼓风动能 0.087 -0.569 0.156 -0.217 0.906 0.901 0.906 0.902 0.906 0.638 0.673
    炉腹煤气指数 0.080 -0.771 -0.309 -0.771 0.933 0.929 0.933 0.864 0.932 0.694 0.690
    下载: 导出CSV
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