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城市固废焚烧过程烟气含氧量自适应预测控制

孙剑 蒙西 乔俊飞

孙剑, 蒙西, 乔俊飞. 城市固废焚烧过程烟气含氧量自适应预测控制. 自动化学报, 2023, 49(11): 2338−2349 doi: 10.16383/j.aas.c210935
引用本文: 孙剑, 蒙西, 乔俊飞. 城市固废焚烧过程烟气含氧量自适应预测控制. 自动化学报, 2023, 49(11): 2338−2349 doi: 10.16383/j.aas.c210935
Sun Jian, Meng Xi, Qiao Jun-Fei. Adaptive predictive control of oxygen content in flue gas for municipal solid waste incineration process. Acta Automatica Sinica, 2023, 49(11): 2338−2349 doi: 10.16383/j.aas.c210935
Citation: Sun Jian, Meng Xi, Qiao Jun-Fei. Adaptive predictive control of oxygen content in flue gas for municipal solid waste incineration process. Acta Automatica Sinica, 2023, 49(11): 2338−2349 doi: 10.16383/j.aas.c210935

城市固废焚烧过程烟气含氧量自适应预测控制

doi: 10.16383/j.aas.c210935
基金项目: 国家自然科学基金(62021003, 61890930-5, 61903012), 科技创新2030 —— “新一代人工智能”重大项目(2021ZD0112301, 2021ZD0112302)资助
详细信息
    作者简介:

    孙剑:北京工业大学信息学部博士研究生. 主要研究方向为复杂工业过程数据驱动建模与智能控制. E-mail: sun8927@163.com

    蒙西:北京工业大学信息学部副教授. 主要研究方向为神经网络, 学习系统和工业过程建模与控制. E-mail: mengxi@bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为神经网络, 智能系统和复杂工业过程建模与优化控制. 本文通信作者. E-mail: adqiao@bjut.edu.cn

Adaptive Predictive Control of Oxygen Content in Flue Gas for Municipal Solid Waste Incineration Process

Funds: Supported by National Natural Science Foundation of China (62021003, 61890930-5, 61903012) and National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302)
More Information
    Author Bio:

    SUN Jian Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers data-driven modeling and intelligent control of complex industrial processes

    MENG Xi Associate professor at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers neural networks, learning systems, and industrial process modeling and control

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers neural networks, intelligent systems, and modeling and optimal control of complex industrial processes. Corresponding author of this paper

  • 摘要: 在城市固体废弃物焚烧(Municipal solid waste incineration, MSWI)过程中, 烟气含氧量是影响焚烧效果的重要工艺参数. 由于固废焚烧过程的复杂性, 在实际应用过程中, 难以实现烟气含氧量的有效控制. 面向城市固废焚烧过程烟气含氧量控制的实际需求, 提出一种基于数据驱动的烟气含氧量自适应预测控制方法. 首先, 采用自适应模糊C均值(Fuzzy C-means, FCM)算法辅助确定径向基函数(Radial basis function, RBF)神经网络隐含层神经元个数及初始中心, 建立基于FCM算法的径向基函数神经网络预测模型, 并在控制过程中通过自适应更新策略在线调节预测模型参数; 然后, 利用梯度下降算法求解控制律, 并基于李雅普诺夫理论分析了所提控制方法的稳定性; 最后, 基于城市固废焚烧厂实际数据, 验证了所提控制方法的有效性.
  • 图  1  MSWI炉排炉工艺流程

    Fig.  1  MSWI process with grate furnace

    图  2  烟气含氧量自适应FCM-RBF-MPC策略

    Fig.  2  Adaptive FCM-RBF-MPC strategy of oxygen content in flue gas

    图  3  RBF神经网络结构图

    Fig.  3  RBF neural network framework

    图  4  不同建模方法的烟气含氧量预测效果

    Fig.  4  Prediction effect of oxygen content in flue gas with different modeling methods

    图  5  FCM-RBF-MPC和自适应FCM-RBF-MPC控制效果

    Fig.  5  Control results of FCM-RBF-MPC and adaptive FCM-RBF-MPC

    图  6  FCM-RBF-MPC和自适应FCM-RBF-MPC的操作变量变化情况

    Fig.  6  Changes of manipulated variables of FCM-RBF-MPC and adaptive FCM-RBF-MPC

    图  7  反馈校正误差补偿变化情况

    Fig.  7  Changes of feedback correction error compensation

    表  1  输入/输出变量变化范围

    Table  1  Range of input and output variables

    变量名变量符号变化范围
    干燥炉排一次风流量(km3/h)$x_{p1}$10.75 ~ 14.76
    燃烧炉排1一次风流量(km3/h)$x_{p2}$25.90 ~ 35.56
    燃烧炉排2一次风流量 (km3/h)$x_{p3}$11.25 ~ 15.72
    燃烬炉排一次风流量(km3/h)$x_{p4}$2.27 ~ 5.06
    二次风流量(km3/h)$x_{s}$18.91 ~ 21.84
    烟气含氧量 (%)$y_o $4.6 ~ 7.6
    下载: 导出CSV

    表  2  操作变量与烟气含氧量的皮尔森相关系数

    Table  2  Pearson correlation coefficient between manipulated variables and oxygen content in flue gas

    操作变量名操作变量符号$\gamma$
    干燥炉排一次风流量$x_{p1}$−0.4303
    燃烧炉排1一次风流量$x_{p2} $ 0.3015
    燃烧炉排2一次风流量$x_{p3} $−0.1034
    燃烬炉排一次风流量$x_{p4} $ 0.0697
    二次风流量$x_{s} $ 0.1413
    下载: 导出CSV

    表  3  不同建模方法的烟气含氧量预测评价指标对比

    Table  3  Comparison of prediction evaluation indexes of oxygen content in flue gas with different modeling methods

    模型MAEMAPERMSE
    MLP0.04670.00860.0585
    RBF0.03570.00650.0449
    FCM-RBF0.03070.00560.0417
    下载: 导出CSV

    表  4  不同RBF神经网络预测控制器性能指标对比

    Table  4  Comparison of evaluation indexes of different RBF neural network predictive controllers

    控制器IAEITAEDevmax
    RBF-MPC0.00564.38740.4991
    FCM-RBF-MPC0.00433.00020.4949
    自适应RBF-MPC0.00453.46170.4335
    自适应FCM-RBF-MPC0.00312.41520.4226
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-29
  • 录用日期:  2022-02-10
  • 网络出版日期:  2022-05-08
  • 刊出日期:  2023-11-22

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