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基于时序关联矩阵的高炉冶炼过程多重关联

蒋珂 蒋朝辉 谢永芳 潘冬 桂卫华

蒋珂, 蒋朝辉, 谢永芳, 潘冬, 桂卫华. 基于时序关联矩阵的高炉冶炼过程多重关联. 自动化学报, 2020, 45(x): 1001−1014 doi: 10.16383/j.aas.c220091
引用本文: 蒋珂, 蒋朝辉, 谢永芳, 潘冬, 桂卫华. 基于时序关联矩阵的高炉冶炼过程多重关联. 自动化学报, 2020, 45(x): 1001−1014 doi: 10.16383/j.aas.c220091
Jiang Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. A multi-correlated time-delay estimation method in the blast furnace ironmaking process based on time-series correlation matrix. Acta Automatica Sinica, 2020, 45(x): 1001−1014 doi: 10.16383/j.aas.c220091
Citation: Jiang Ke, Jiang Zhao-Hui, Xie Yong-Fang, Pan Dong, Gui Wei-Hua. A multi-correlated time-delay estimation method in the blast furnace ironmaking process based on time-series correlation matrix. Acta Automatica Sinica, 2020, 45(x): 1001−1014 doi: 10.16383/j.aas.c220091

基于时序关联矩阵的高炉冶炼过程多重关联

doi: 10.16383/j.aas.c220091
基金项目: 国家重大科研仪器研制项目(61927803),国家自然科学基金(61725306, 61290325),湖南省科技创新计划(2021RC4054),广东省重点领域研发计划(2021B0101200005),中南大学研究生自主探索创新项目(2021zzts0183), 湖南省研究生科研创新项目(CX20210242)资助
详细信息
    作者简介:

    蒋珂:中南大学自动化学院博士研究生. 2019年获中南大学硕士学位. 主要研究方向为数据驱动的工业过程建模和控制, 过程数据分析和机器学习. E-mail: jiangke@csu.edu.cn

    蒋朝辉:中南大学自动化学院教授. 2011年获中南大学博士学位. 主要研究方向为智能传感与检测技术, 图像处理与智能识别, 人工智能与机器学习. 本文通信作者. E-mail: jzh0903@csu.edu.cn

    谢永芳:中南大学自动化学院教授. 1999 年获中南工业大学博士学位. 主要研究方向为分散控制和鲁棒控制, 过程控制, 工业大数据和知识自动化. E-mail: yfxie@csu.edu.cn

    潘冬:中南大学自动化学院讲师. 分别于2015年和2021年获中南大学学士学位和博士学位. 主要研究方向包括红外热成像, 视觉检测和图像处理, 深度学习. E-mail: pandong@csu.edu.cn

    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@csu.edu.cn

A Multi-correlated Time-delay Estimation Method in the Blast Furnace Ironmaking Process Based on Time-series Correlation Matrix

Funds: Supported by National Major Scientific Research Equipment of China (61927803), National Natural Science Foundation of China (61725306, 61290325), Science and Technology Innovation Program of Hunan Province (2021RC4054), Key-area Research and Development Program of Guangdong Province (2021B0101200005), Fundamental Research Funds for the Central Universities of Central South University (2021zzts0183), and Hunan Provincial Innovation Foundation for Postgraduate (CX20210242)
More Information
    Author Bio:

    JIANG Ke Ph.D. candidate at the School of Automation, Central South University. She received her master degree from Central South University in 2019. Her research interest covers data-based modeling and control of industrial process, 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 intelligent sensing and detection technology, image processing and intelligent recognition, artificial intelligence and machine learning. Corresponding author of this paper

    XIE Yong-Fang Professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University of Technology in 1999. His research interest covers decentralized control and robust control, process control, industrial big data and knowledge automation

    PAN Dong Lecturer at the School of Automation, Central South University. He received his bachelor and Ph.D. degrees from Central South University in 2015 and 2021, respectively. His research interest covers infrared thermography, vision-based measurement, image processing and deep learning

    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

  • 摘要: 高炉冶炼过程由炉料传输反应时间和冶炼单元在空间和时间分布上的差异带来的变量时延影响了数据的准确性和真实因果关系, 因此, 有效地估计过程变量间的时延信息并在时序上配准数据是后续过程建模、优化控制与性能评估的核心. 考虑到变量间时延的多重关联性, 提出了一种基于时序关联矩阵的时延参数估计方法. 首先, 根据过程变量的时延参数在时空上重构对应的时序关联矩阵, 并引入灰色关联分析量化时序矩阵的多重关联相关性. 接着, 考虑到穷举所有时序关联矩阵的时间复杂度, 提出了一种双尺度协同搜索策略的动态多群粒子群算法用于快速寻找最优的时延参数, 提出的粒子群算法能兼顾全局探索能力和局部探测能力并跳出局部最优解. 最后, 基于一个数值仿真和某钢铁厂2#高炉的工业实验验证了所提时延参数估计方法的可行性和有效性, 且通过所提方法在时序上重构的数据能有效提高后续硅含量软测量模型性能.
  • 图  1  高炉三维仿真模拟图

    Fig.  1  Three-dimensional simulation diagram of the blast furnace cast field

    图  2  高炉炼铁过程中变时滞问题描述

    Fig.  2  Illustration of variable time-delay problem in the blast furnace ironmaking process

    图  3  基于DMS-PSO-CS算法的时延参数估计框架

    Fig.  3  Time-delay parameter estimation framework based on DMS-PSO-CS algorithm

    图  4  基于DMS-PSO-CS算法时延估计的铁水硅含量预测结果

    Fig.  4  The prediction details of silicon content in molten iron with time-delay estimation based on DMS-PSO-CS algorithm

    图  5  基于PSO算法时延估计的铁水硅含量预测结果

    Fig.  5  The prediction details of silicon content in molten iron with time-delay estimation based on PSO algorithm

    图  6  基于MIC算法时延估计的铁水硅含量预测结果

    Fig.  6  The prediction details of silicon content in molten iron with time-delay estimation based on MIC algorithm

    图  7  基于PCC算法时延估计的铁水硅含量预测结果

    Fig.  7  The prediction details of silicon content in molten iron with time-delay estimation based on PCC algorithm

    图  8  无时延估计的铁水硅含量预测结果

    Fig.  8  The prediction details of silicon content in molten iron without time-delay estimation

    图  9  基于不同算法的寻优迭代曲线

    Fig.  9  Optimization iteration curve based on different algorithm

    表  1  数值仿真中基于不同方法估计的过程变量时延值

    Table  1  The estimated variable time-delay values based on differnent methods in numerical simulation

    变量 PCC MIC PSO DMS-PSO-CS $ \tau ' $
    $ {x_1} $ 1 1 1 1 1
    $ {x_2} $ 0 2 2 2 2
    $ {x_3} $ 2 0 3 3 3
    $ {x_4} $ 1 3 3 4 4
    下载: 导出CSV

    表  2  基于不同方法估计的过程变量时延参数

    Table  2  The estimated process variable time-delay values based on different methods

    变量(单位) PCC MIC PSO DMS-PSO-CS
    富氧率(wt%) 1 5 5 5
    透气性指数($ \rm m^{3}/min \cdot kPa $) 1 1 1 2
    一氧化碳(wt%) 1 2 1 1
    二氧化碳(wt%) 1 1 1 1
    标准风速(m/s) 6 6 2 1
    富氧流量($ \rm m^{3}/s $) 1 3 6 1
    冷风流量($ \rm m^{3}/min $) 6 2 2 2
    鼓风动能(j/s) 1 2 2 3
    炉腹煤气量(t) 6 1 2 2
    炉腹煤气指数 6 2 2 2
    顶压(kPa) 1 3 1 4
    富氧压力(kPa) 2 3 6 6
    冷风压力(kPa) 1 1 6 2
    全压差(kPa) 1 2 2 2
    热风压力(kPa) 1 1 5 2
    实际风速(m/s) 1 3 2 2
    冷风温度(°C) 1 6 1 1
    热风温度(°C) 1 6 2 2
    顶温(°C) 1 5 1 2
    顶温下降管(°C) 1 1 2 6
    阻力系数 1 1 2 1
    鼓风湿度($ \rm g/m^{3} $) 3 6 1 1
    本小时实际喷煤量(t/h) 2 1 1 3
    上小时实际喷煤量(t/h) 1 1 5 5
    铁水温度(°C) 1 1 1 1
    下载: 导出CSV

    表  3  基于不同建模策略下的铁水硅含量软测量模型性能

    Table  3  Soft-sensor model performance of silicon content in molten iron based on different modeling strategies

    序号 建模策略 TrRMSE TrMAE TsRMSE TsMAE 训练时间(s)
    1 SDAE + DM-PSO-CS 0.0715 0.0530 0.0723 0.0542 12.2 $ \times $ 60 + 8
    2 SDAE + PSO 0.0748 0.0565 0.0759 0.0574 12.4 $ \times $ 60 + 10
    3 SDAE + MIC 0.0752 0.0562 0.0765 0.0575 12.3 $ \times $ 60 + 12
    4 SDAE + PCC 0.0763 0.0561 0.0776 0.0573 12.2 $ \times $ 60 + 10
    5 SVR + DM-PSO-CS 0.0775 0.0572 0.0792 0.0588 7
    6 RVFLN + DM-PSO-CS 0.0769 0.0573 0.0782 0.0585 3
    7 SDAE + 无时延估计 0.0826 0.0605 0.0840 0.0613 12.3 $ \times $ 60 + 9
    下载: 导出CSV
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