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数据与模型驱动的水泥生料分解率软测量模型

乔景慧 柴天佑

乔景慧, 柴天佑. 数据与模型驱动的水泥生料分解率软测量模型. 自动化学报, 2019, 45(8): 1564-1578. doi: 10.16383/j.aas.c180734
引用本文: 乔景慧, 柴天佑. 数据与模型驱动的水泥生料分解率软测量模型. 自动化学报, 2019, 45(8): 1564-1578. doi: 10.16383/j.aas.c180734
QIAO Jing-Hui, CHAI Tian-You. Data and Model-based Soft Measurement Model of Cement Raw Meal Decomposition Ratio. ACTA AUTOMATICA SINICA, 2019, 45(8): 1564-1578. doi: 10.16383/j.aas.c180734
Citation: QIAO Jing-Hui, CHAI Tian-You. Data and Model-based Soft Measurement Model of Cement Raw Meal Decomposition Ratio. ACTA AUTOMATICA SINICA, 2019, 45(8): 1564-1578. doi: 10.16383/j.aas.c180734

数据与模型驱动的水泥生料分解率软测量模型

doi: 10.16383/j.aas.c180734
基金项目: 

中国博士后科学基金 2015T80268

辽宁省博士科研启动基金 201501082

中国博士后科学基金 2014M561249

流程工业综合自动化国家重点实验室开放课题基金 PAL-N201408

国家自然科学基金 61573249

详细信息
    作者简介:

    柴天佑  中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow, 欧亚科学院院士.1985年获得东北大学博士学位.主要研究方向为自适应控制, 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:

    乔景慧  沈阳工业大学机械工程学院副教授.2012年获东北大学控制理论与控制工程专业博士学位.主要研究方向为复杂工业过程建模与智能控制, 机器学习.本文通信作者.E-mail:qiaojh2002@163.com

Data and Model-based Soft Measurement Model of Cement Raw Meal Decomposition Ratio

Funds: 

China Postdoctoral Science Foundation 2015T80268

Liaoning Province Doctoral Research Foundation 201501082

China Postdoctoral Science Foundation 2014M561249

State Key Laboratory of Synthetical Automation for Process Industries PAL-N201408

National Natural Science Foundation of China 61573249

More Information
    Author Bio:

    Academician of Chinese Academy of Engineering, Professor at Northeastern University, IEEE Fellow, IFAC Fellow, and an Academician of International Eurasian Academy of Sciences. He received his Ph. D. degree from Northeastern University in 1985. His research interest covers adaptive control, intelligent decoupling control, and integrated automation theory, method and technology of industrial process

    Corresponding author: QIAO Jing-Hui Associate professor at the School of Mechanical Engineering, Shenyang University of Technology. He received his Ph. D. degree in control theory and control engineering in 2012 from Northeastern University. His research interest covers modeling and intelligent control for complex industry systems, and machine learning. Corresponding author of this paper
  • 摘要: 水泥生料在分解炉内分解过程的质量指标是生料分解率(Raw meal decomposition ratio,RMDR),由于生料边界条件频繁变化且人工离线检测周期为2小时,致使产品质量指标合格率低且极易造成预热器C5下料管堵塞.为了解决上述问题,本文提出了基于数据与模型驱动的水泥生料分解率软测量模型,由基于Kullback-Leibler(KL)散度密度比的异常值检测、基于机理模型的生料分解率模型、基于层级Sigmoid(S)核函数的生料分解率模型、生料分解率离线检测模型和基于模糊模型的协调因子组成.实际应用结果表明,所提出的模型能够根据当前工况的变化选择正确的子模型,并且使生产远离故障工况.
    1)  本文责任编委 王占山
  • 图  1  生料分解过程工艺流程及控制现状

    Fig.  1  Process flow diagram and current control for raw meal calcination process

    图  2  台时产能与生料分解率关系曲线

    Fig.  2  The relationship curve between the production hourly and raw meal decomposition ratio

    图  3  熟料热耗与生料分解率关系曲线

    Fig.  3  The relationship curve between clinker heat consumption and raw meal decomposition ratio

    图  4  熟料热耗与生料分解率和回转窑耗煤量关系曲线

    Fig.  4  The relationship curve among the clinker heat consumption and raw meal decomposition ratio and feed coal of rotary kiln

    图  5  回转窑负荷率与生料分解率和回转窑转速关系曲线

    Fig.  5  The relationship curve among the load rate and raw meal decomposition ratio and rotary kiln speed

    图  6  台时产能与出均化库生料${\rm CO_2}$的百分含量和预热器C5下料管入回转窑生料${\rm CO_2}$的百分含量关系曲线

    Fig.  6  The relationship curve among the production hourly and percentage of ${\rm CO_2}$ from homogenization and the percentage of ${\rm CO_2}$ in raw material from the preheater C5 tube

    图  7  数据与模型驱动的水泥生料分解率软测量模型

    Fig.  7  The cement raw meal decomposition ratio model based on data and model

    图  8  生料分解率与分解炉温度关系曲线

    Fig.  8  The relationship curve between the raw meal decomposition ratio and calciner temperature

    图  9  生料分解率与生料成分关系曲线

    Fig.  9  The relationship curve between the raw meal decomposition ratio and raw meal components

    图  10  生料成分与生料分解率关系曲线

    Fig.  10  The relationship curve between the raw meal decomposition ratio and raw meal components

    图  11  基于模糊模型的协调因子结构

    Fig.  11  The structure of coordination factor based on fuzzy model

    图  12  误差$E_1$, $E_2$, $E_3$和$E_4$及输出$U$的隶属函数

    Fig.  12  The membership functions of $E_1$, $E_2$, $E_3$ and $U$

    图  13  模糊推理过程

    Fig.  13  The fuzzy inference process

    图  14  基于KL散度密度比的分解炉温度异常值检测

    Fig.  14  Abnormal value detection based on Kullback-Leibler divergence density ratio for calciner temperature

    图  15  三氧化二铝含量正常数据和测试数据

    Fig.  15  Normal data and test data for ${\rm Al_2O_3}$ content

    图  16  基于KL散度密度比的三氧化二铝含量异常值检测

    Fig.  16  Abnormal value detection based on Kullback- Leibler divergence density ratio for ${\rm Al_2O_3}$ content

    图  17  生料分解率模型输出值与离线检测值曲线

    Fig.  17  The curve of model output value and offline detection value for raw meal decomposition ratio

    图  18  生料分解过程

    Fig.  18  The raw meal calcination process

    图  19  生料分解过程主控画面

    Fig.  19  The main picture of raw meal calcination process

    图  20  系统硬件平台

    Fig.  20  The architecture of system hardware platform

    图  21  算法实现控制流程图

    Fig.  21  The flow chart of algorithm realization

    图  22  基于模糊模型的协调因子运行曲线

    Fig.  22  The curve of coordination factor based on fuzzy model

    图  23  生料分解率运行曲线

    Fig.  23  The run curve of raw meal decomposition ratio

    图  24  预热器C5下料管堵塞概率与生料分解率和预热器C5出口温度之间的关系曲线

    Fig.  24  The curve among blocking probability of preheater C5 tube and raw meal decomposition ratio and outlet temperature of preheater C5

    表  1  图 1中各变量及符号的含义

    Table  1  The meaning of variables and symbols in Fig. 1

    变量含义变量含义
    电机 ${\rm C}_i$第$i$个预热器
    $F(t)$生料流量 $TT$温度传感器
    C控制器 $T_{\rm csp}$分解炉温度设定值
    $T_{\rm c5\max}$C5温度最大值 $F_{\rm ref}$生料流量参考值
    $T_{\rm c} (t) $分解炉温度反馈 $T_{\rm c5} (t)$预热器${\rm C}$5温度反馈
    $\Delta u_{\rm c1} (t)$控制器$T_{\rm c1}$输出 $\Delta u_{\rm c2} (t)$控制器$T_{\rm c2}$输出
    $\Delta u_f (t)$控制器$FC$输出 $\gamma _a$分解率实际检测值
    $\Delta u_{\rm c} (t)$控制器输出增量 $\Delta u(t)$控制器输出
    下载: 导出CSV

    表  2  图 7中各变量的含义

    Table  2  The meaning of variables in Fig. 7

    变量含义变量含义
    $T_c (t) $分解炉温度反馈 $w_b^d$输入层与隐含层权值
    $T(t)$分解炉温度滤波 $\zeta _b$S核函数参数
    $\lambda$协调因子 $B^{(1)}$输入变量集
    $\gamma _m$模型输出值 $B^{(2)}$输入变量集滤波值
    $f_\theta(x)$层级S函数输出 $\gamma _{\lambda m}$机理模型输出值
    $M_1$机理模型 $\gamma _{\lambda h}$层级模型输出值
    $M_2$层级模型 $\gamma _a$分解率实际检测值
    $M_3$离线检测模型 $\gamma$分解率输出值
    下载: 导出CSV

    表  3  式(16)中参数的含义

    Table  3  The meaning of variables in (16)

    变量含义变量含义
    $k_0$反应速率常数 $E$反应活化能
    $T$分解炉温度 $R$气体分子常数
    下载: 导出CSV

    表  4  本文所提方法与LS-SVM和RFMPCA-LS-SVM的RMSE对比

    Table  4  The RMSE comparison among the method proposed and LS-SVM and RFMPCA-LS-SVM

    LS-SVMRFMPCA-LS-SVM本文方法
    RMSE1.13261.02351.0198
    下载: 导出CSV

    表  5  基于模糊模型的协调因子参数选择

    Table  5  Parameters selection based on fuzzy model

    参数参数
    $\zeta _{{\text{Target}}}$5$\, \%$$\zeta _{{\text{Tol}}}$15$\, \%$
    $C_{\rm CaSP} (k)$36$\, \%$$C_{\rm FeSP} (k)$3.4$\, \%$
    $C_{\rm SiSP} (k)$13.5$\, \%$$C_{\rm AlSP} (k)$3.55$\, \%$
    $n_1$10$n_2$5
    $n_3$8$n_4$5
    $m$10$l$1
    下载: 导出CSV

    表  6  生料中4种成分变化范围

    Table  6  Range of four components in raw meal

    成分含量($\%$)成分含量($\%$)
    CaO35.5~36.3${\rm SiO_2}$14.5~14.9
    ${\rm Fe_2O_3}$3.7~3.85${\rm Al_2O_3}$3.45~3.62
    下载: 导出CSV
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  • 收稿日期:  2018-11-05
  • 录用日期:  2019-03-25
  • 刊出日期:  2019-08-20

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