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基于知识与AW-ESN融合的烧结过程FeO含量预测

方怡静 蒋朝辉 黄良 桂卫华 潘冬

方怡静, 蒋朝辉, 黄良, 桂卫华, 潘冬. 基于知识与AW-ESN融合的烧结过程FeO含量预测. 自动化学报, 2024, 50(2): 282−294 doi: 10.16383/j.aas.c211013
引用本文: 方怡静, 蒋朝辉, 黄良, 桂卫华, 潘冬. 基于知识与AW-ESN融合的烧结过程FeO含量预测. 自动化学报, 2024, 50(2): 282−294 doi: 10.16383/j.aas.c211013
Fang Yi-Jing, Jiang Zhao-Hui, Huang Liang, Gui Wei-Hua, Pan Dong. FeO content prediction in sintering process based on fusion of data-knowledge and AW-ESN. Acta Automatica Sinica, 2024, 50(2): 282−294 doi: 10.16383/j.aas.c211013
Citation: Fang Yi-Jing, Jiang Zhao-Hui, Huang Liang, Gui Wei-Hua, Pan Dong. FeO content prediction in sintering process based on fusion of data-knowledge and AW-ESN. Acta Automatica Sinica, 2024, 50(2): 282−294 doi: 10.16383/j.aas.c211013

基于知识与AW-ESN融合的烧结过程FeO含量预测

doi: 10.16383/j.aas.c211013
基金项目: 国家自然科学基金(61773406, 61927803), 中南大学中央高校基本科研任务业务费专项资金(2020zzts572), 长沙市自然科学基金(kq2202075)资助
详细信息
    作者简介:

    方怡静:中南大学自动化学院博士研究生. 2016年和2019年分别获得中南大学学士学位和硕士学位. 主要研究方向为数据驱动的工业过程建模和控制, 工业过程数据分析和机器学习. E-mail: yijingfang@csu.edu.cn

    蒋朝辉:中南大学自动化学院教授. 2011年获得中南大学博士学位. 主要研究方向为光电信息感知与图像处理, 人工智能与工业虚拟现实, 智能优化控制. 本文通信作者. E-mail: jzh0903@csu.edu.cn

    黄良:中南大学自动化学院硕士研究生. 主要研究方向为工业过程建模与优化控制. E-mail: huangliangcsu@163.com

    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, 故障诊断与分布式鲁棒控制. E-mail: gwh@outlook.com

    潘冬:中南大学自动化学院讲师. 2021年获得中南大学控制科学与工程专业博士学位. 主要研究方向为红外机器视觉, 图像处理, 工业过程检测和控制. E-mail: panda@csu.edu.cn

FeO Content Prediction in Sintering Process Based on Fusion of Data-Knowledge and AW-ESN

Funds: Supported by National Natural Science Foundation of China (61773406, 61927803), Central South University Central University Basic Scientific Research Task Business Expenses Special Funds (2020zzts572), and Changsha Natural Science Foundation Project (kq2202075)
More Information
    Author Bio:

    FANG Yi-Jing Ph.D. candidate at the School of Automation, Central South University. She received her bachelor and master degrees from Central South University in 2016 and 2019, respectively. Her research interest covers data-based modeling and control of industrial process, industrial process data analysis, and machine learning

    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 photoelectric information perception and image processing, artificial intelligence and industrial virtual reality, and intelligent optimization control. Corresponding author of this paper

    HUANG Liang Master student at the School of Automation, Central South University. His main research interest is modeling and optimal control of industrial process

    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

    PAN Dong Lecturer at the School of Automation, Central South University. He received his Ph.D. degree in control science and engineering from Central South University in 2021. His research interest covers infrared computer vision, image processing, and detection and control in industrial process

  • 摘要: 氧化亚铁(FeO)含量是衡量烧结矿强度和还原性的重要指标, 烧结过程FeO含量的实时准确预测对于提升烧结质量、优化烧结工艺具有重要意义. 然而烧结过程热状态参数缺失、过程参数波动频繁给FeO含量的高精度预测带来巨大的挑战, 为此, 提出一种基于知识与变权重回声状态网络融合(Fusion of data-knowledge and adaptive weight echo state network, DK-AWESN)的烧结过程FeO含量预测方法. 首先, 针对烧结过程热状态参数缺失的问题, 建立烧结料层最高温度分布模型, 实现基于料层温度分布特征的FeO含量等级划分; 其次, 针对烧结过程参数波动频繁的问题, 提出基于核函数高维映射的多尺度数据配准方法, 有效抑制离群点的影响, 提升建模数据的质量; 最后, 针对烧结过程数据驱动模型缺乏机理认知致使模型预测精度不高的问题, 将过程数据中提取得到的FeO含量等级知识与AW-ESN (Adaptive weight echo state network)结合, 建立DK-AWESN模型, 有效提升复杂工况下FeO含量的预测精度. 现场工业数据试验表明, 所提方法能实时准确地预测烧结过程FeO含量, 为烧结过程的智能化调控提供实时有效的FeO含量反馈信息.
  • 图  1  烧结过程示意图

    Fig.  1  Schematic diagram of sintering process

    图  2  料层全时空最高温度分布

    Fig.  2  Maximum temperature distribution of sinter bed in whole time and space

    图  3  料层全时空温度分布图

    Fig.  3  Temperature distribution of sinter bed in whole time and space

    图  4  各参数隶属函数

    Fig.  4  Membership function of each parameter

    图  5  基于DK-AWESN的FeO含量预测方法框图

    Fig.  5  Schematic of FeO content prediction method based on DK-AWESN

    图  6  ESN和AW-ESN预测值与实际值对比

    Fig.  6  Comparison between predicted values and actual values of ESN and AW-ESN

    图  7  AW-ESN和DK-AWESN预测值与实际值对比

    Fig.  7  Comparison between predicted values and actual values of AW-ESN and DK-AWESN

    图  8  ESN和DK-ESN预测值与实际值对比

    Fig.  8  Comparison between predicted values and actual values of ESN and DK-ESN

    图  9  不同方法的预测误差对比

    Fig.  9  Comparison of prediction errors of different methods

    图  10  不同方法预测值和实测值的散点图

    Fig.  10  Scatter plot of predicted values and measured values by different methods

    表  1  反应速率计算参数表

    Table  1  Reaction rate parameters

    参数名称符号
    指前因子$ k $$6.89 \times {10^5} \sim 8.3 \times {10^5}\;{{\rm{s}}^{ - 1} }$
    反应活化能$ E $${ {125.61 \sim 137.16\;{\rm{kJ} } } \mathord{\left/ {\vphantom { {125.61 \sim 137.16\;{\rm{kJ} } } { {\rm{mol} } } } } \right. } {{\rm{mol}}} }$
    比例系数$ R $$8.314\;{\rm{kJ}}/({\rm{mol}} \cdot {\rm{K}})$
    抽风负压$ P $${\text{1} }{\text{.2} } \times {\text{1} }{ {\text{0} }^4}\;{\rm{Pa} }$
    料前氧分压${P_{ {{\rm{O}}_2}1} }$${\text{0} }{\text{.21} }\;{\rm{P} }$
    料后氧分压${P_{ {{\rm{O}}_2}2} }$$0.09 \sim 0.11\;{\rm{P}}$
    总氧气扩散系数${D_{ {{\rm{O}}^2} } }$$ {\text{2}}.03 \times {10^{ - 5}}{T^{1.87}} $
    雷诺数$ Re $$ {\text{2}} \times {\text{1}}{{\text{0}}^3} \sim 3.5 \times {10^4} $
    燃料孔隙率$ {\varepsilon _c} $$ {\text{0}}{\text{.39}} $
    有效孔隙率$ B $$ 0.15 $
    施密特数$Sc$$ {\text{0}}{{.6 \sim 2}}{\text{.5}} $
    氧气浓度${C_{ {{\rm{O}}_2} } }$9.735%
    下载: 导出CSV

    表  2  FeO含量等级推理结果与实际值对比

    Table  2  Comparison of the inference results with measured values of FeO content

    序号料层最高温度 (℃)料层高度 (mm)燃料比 (%)全铁 (%)推理结果化验数据
    11 278.55749.1294.18761.874正常正常 (8.96)
    21 127.32736.3834.49660.899正常正常 (9.27)
    31 158.76761.9184.49261.854正常正常 (9.06)
    41 211.36718.5364.33161.715正常正常 (9.47)
    51 274.22717.2534.16260.706偏小偏小 (7.37)
    $\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $$\vdots $
    5581 160.56649.5794.56163.755正常正常 (9.47)
    5591 176.76650.8644.30560.802偏大偏大 (10.60)
    6001 308.99710.7114.28661.067正常正常 (8.16)
    下载: 导出CSV

    表  3  各过程参数的灰色关联度

    Table  3  The grey relational degree of process parameters

    序号变量名称关联度序号变量名称关联度
    1风箱废气温度0.80311空支流量0.559
    2烧结机机速0.79812CMgO0.557
    3料层高度0.77813透气性0.549
    4烧结终点0.73714返矿比0.539
    5$ {\rm{C}}_{{\rm{SiO}}_2}$0.73315风箱负压0.527
    6碱度0.70316CCaO0.459
    7燃料配比0.66917烟道压力0.337
    8环冷机速度0.64118风机入口温度0.327
    9点火温度0.61819混一温度0.271
    10煤支流量0.57420圆辊速度0.249
    下载: 导出CSV

    表  4  模型输入变量

    Table  4  The input variables of the model

    序号变量名称序号变量名称
    1风箱废气温度9空支流量
    2烧结机机速10CMgO
    3料层高度11透气性
    4烧结终点12返矿比
    5${\rm{C}}_{{\rm{SiO}}_2} $13风箱负压
    6碱度14燃料配比
    7环冷机速度15点火温度
    8煤支流量
    下载: 导出CSV

    表  5  储备池规模对DK-AWESN性能的影响

    Table  5  Influence of reservoir size on the performance of DK-AWESN

    储备池规模训练时间 (s) 测试 NRMSE
    平均值标准差
    5021.8210.4250.0332
    10021.8320.3710.0258
    15021.8400.3320.0254
    20021.8410.3010.0218
    25021.8500.3430.0262
    30021.8640.3990.0246
    35021.8660.4350.0321
    40021.8910.4820.0326
    下载: 导出CSV

    表  6  各模型的预测性能指标比较

    Table  6  Comparison of prediction performance indicators for different algorithms

    性能指标ESNDK-ESNAW-ESNDK-AWESN
    MAE0.3510.2540.2980.251
    RMSE0.4200.3160.3450.301
    HR (%)70.0083.3378.3386.67
    下载: 导出CSV
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