2.624

2020影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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, 2022, 48(x): 1−13 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, 2022, 48(x): 1−13 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, 2022, 48(x): 1−13 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, 2022, 48(x): 1−13 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, 62073006), 科技创新2030——“新一代人工智能”重大项目(2021ZD0112301, 2021ZD0112302), 国家重点研发计划(2019YFC1906004-2), 北京市自然科学基金 (4212032) 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, 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, 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, 62073006), National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302, 2019YFC1906004-2), Beijing Natural Science Foundation (4212032)
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: 在城市固废焚烧过程中, 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, 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, 文中提出了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RBF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, 并基于李亚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
    干燥段一次风流量xp110.75~14.76 km3/h
    燃烧1段一次风流量xp225.90~35.56 km3/h
    燃烧2段一次风流量xp311.25~15.72 km3/h
    燃烬段一次风流量xp42.27~5.06 km3/h
    二次风流量xs18.91~21.84 km3/h
    烟气含氧量yo4.6~7.6%
    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r
    干燥段一次风流量xp1−0.4303
    燃烧1段一次风流量xp20.3015
    燃烧2段一次风流量xp3−0.1034
    燃烬段一次风流量xp40.0697
    二次风流量xs0.1413
    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
    MLP网络0.04670.00860.0585
    RBF网络0.03570.00650.0449
    FCM-RBF网络0.03070.00560.0417
    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
  • [1] Maalouf A, Mavropoulos A, El-Fadel M. Global municipal solid waste infrastructure: delivery and forecast of uncontrolled disposal. Waste Management and Research, 2020, 38(9): 1028-1036 doi: 10.1177/0734242X20935170
    [2] 中华人民共和国统计局. 中国统计年鉴. 北京: 中国统计出版社, 2019

    National Bureau of Statistics of the People's Republic of China. China Statistical Yearbook. Beijing: China Statistics Press, 2019
    [3] 龙文, 梁昔明, 龙祖强. 基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制. 中南大学学报(自然科学版), 2012, 43(3): 980-985

    Long Wen, Liang Xi-Ming, Long Zu-Qiang. O2 content in flue gas of boilers predictive control based on hybrid PSO and LSSVM. Journal of Central South University (Science and Technology), 2012, 43(3): 980-985
    [4] Zhang Y J, Jia Y, Chai T Y, et al. Data-driven PID controller and its application to pulp neutralization process. IEEE Transactions on Control Systems Technology, 2017, 26(3): 828-841
    [5] Moura J P, Neto J V F, Rêgo P H M. A neuro-fuzzy model for online optimal tuning of PID controllers in industrial system applications to the mining sector. IEEE Transactions on Fuzzy Systems, 2019, 28(8): 1864-1877
    [6] Nath U M, Dey C, Mudi R K. Desired characteristic equation based PID controller tuning for lag-dominating processes with real-time realization on level control system. IEEE Control Systems Letters, 2021, 5(4): 1255-1260 doi: 10.1109/LCSYS.2020.3030173
    [7] Li D P, Han H G, Qiao J F. Observer-based adaptive fuzzy control for nonlinear state-constrained systems without involving feasibility conditions. IEEE transactions on cybernetics, doi: 10.1109/TCYB.2021.3071336
    [8] Nguyen A T, Taniguchi T, Eciolaza L, et al. Fuzzy Control Systems: Past, Present and Future. IEEE Computational Intelligence Magazine, 2019, 14(1): 56-68 doi: 10.1109/MCI.2018.2881644
    [9] Shen D. Iterative learning control with incomplete information: a survey. IEEE/CAA Journal of Automatica Sinica, 2018, 5(5): 1-17 doi: 10.1109/JAS.2018.7511201
    [10] 赵钢, 李斌. 燃气锅炉燃烧效率优化控制仿真研究. 计算机仿真, 2017, 34(1): 376-379 doi: 10.3969/j.issn.1006-9348.2017.01.081

    Zhao Gang, Li Bin. Simulation on the optimal control of gas boiler combustion efficiency. Computer Simulation, 2017, 34(1): 376-379 doi: 10.3969/j.issn.1006-9348.2017.01.081
    [11] Zhang R D, Cao Z X, Li P, et al. Design and implementation of an improved linear quadratic regulation control for oxygen content in a coke furnace. IET Control Theory and Applications, 2014, 8(14): 1303-1311 doi: 10.1049/iet-cta.2013.1023
    [12] Thai S M, Wilcox S J, Chong A Z S, et al. Combustion optimisation of stoker fired boiler plant by neural networks. Journal of the Energy Institute, 2008, 81(3): 171-176. doi: 10.1179/174602208X339131
    [13] 席裕庚, 李德伟, 林姝. 模型预测控制-现状与挑战. 自动化学报, 2013, 39(3): 222-236 doi: 10.1016/S1874-1029(13)60024-5

    Xi Yu-Geng, Li De-Wei, Lin Shu. Model predictive control — status and challenges. Acta Automatica Sinica, 2013, 39(3): 222−236 doi: 10.1016/S1874-1029(13)60024-5
    [14] Karamanakos P, Liegmann E, Geyer T, et al. Model predictive control of power electronic systems: methods, results, and challenges. IEEE Open Journal of Industry Applications, 2020, 1: 95-114 doi: 10.1109/OJIA.2020.3020184
    [15] 何德峰, 丁宝苍, 于树友. 非线性系统模型预测控制若干基本特点与主题回顾. 控制理论与应用, 2013, 30(3): 273-287 doi: 10.7641/CTA.2013.20737

    He De-Feng, Ding Bao-Cang, Yu Shu-You. Review of fundamental properties and topics of model predictive control for nonlinear systems. Control Theory and Applications, 2013, 30(3): 273-287 doi: 10.7641/CTA.2013.20737
    [16] Zhang R D, Jin Q B. Design and Implementation of hybrid modeling and PFC for oxygen content regulation in a coke furnace. IEEE Transactions on Industrial Informatics, 2018, 14(6): 2335-2342 doi: 10.1109/TII.2018.2815717
    [17] Huang X Y, Wang J C, Zhang L W, et al. Data-driven modelling and fuzzy multiple-model predictive control of oxygen content in coal-fired power plant. Transactions of the Institute of Measurement and Control, 2017, 39(11): 1631-1642 doi: 10.1177/0142331216644498
    [18] Wang P, Yang C H, Tian X M, et al. Adaptive nonlinear model predictive control using an on-line support vector regression updating strategy. Chinese Journal of Chemical Engineering, 2014, 22(7): 774-781 doi: 10.1016/j.cjche.2014.05.004
    [19] Xiao H Z, Chen C L P. Incremental updating multi-robot formation using nonlinear model predictive control method with general projection neural network. IEEE Transactions on Industrial Electronics, 2019, 66(6): 4502-4512 doi: 10.1109/TIE.2018.2864707
    [20] 陈进东, 潘丰. 基于在线支持向量回归的非线性模型预测控制方法. 控制与决策, 2014, 29(3): 460-464

    Chen Jin-Dong, Pan Feng. Online support vector regression-based nonlinear model predictive control. Control and Decision, 2014, 29(3): 460-464
    [21] Vatankhah B, Farrokhi M. Nonlinear adaptive model predictive control of constrained systems with offset-free tracking behavior. Asian Journal of Control, 2018, 20(1): 1-13 doi: 10.1002/asjc.1548
    [22] Wu Z, Rincon D, Christofides P D. Real-time adaptive machine-learning-based predictive control of nonlinear processes. Industrial and Engineering Chemistry Research, 2020, 59: 2275-2290 doi: 10.1021/acs.iecr.9b03055
    [23] Lu J W, Zhang S K, Hai J, et al. Status and perspectives of municipal solid waste incineration in China: A comparison with developed regions. Waste Management, 2017, 69: 170-186 doi: 10.1016/j.wasman.2017.04.014
    [24] Tan S T, Ho W S, Hashim H, et al. Energy, economic and environmental (3E) analysis of waste-to-energy (WTE) strategies for municipal solid waste (MSW) management in Malaysia. Energy Conversion and Management, 2015, 102: 111-120 doi: 10.1016/j.enconman.2015.02.010
    [25] 乔俊飞, 郭子豪, 汤健. 面向城市固废焚烧过程的二噁英排放浓度检测方法综述. 自动化学报, 2020, 46(6): 1063-1089

    Qiao Jun-Fei, Guo Zi-Hao, Tang Jian. Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: a survey. Acta Automatica Sinica, 2020, 46(6): 1063-1089
    [26] Xie S W, Xie Y F, Huang T W, et al. Generalized predictive control for industrial processes based on neuron adaptive splitting and merging rbf neural network. IEEE Transactions on Industrial Electronics, 2019, 66(2): 1192-1202 doi: 10.1109/TIE.2018.2835402
    [27] Zhou P, Guo D W, Chai T Y. Data-driven predictive control of molten iron quality in blast furnace ironmaking using multi-output LS-SVR based inverse system identification. Neurocomputing, 2018, 308: 101-110 doi: 10.1016/j.neucom.2018.04.060
    [28] Qiao J F, Meng X, Li W J, et al. A novel modular RBF neural network based on a brain-like partition method. Neural Computing and Applications, 2018, 32: 899-911
    [29] Ren M, Liu P Y, Wang Z H, et al. A self-adaptive fuzzy c-means algorithm for determining the optimal number of clusters. Computational Intelligence and Neuroscience, 2016: 2647389
    [30] Li Y, Yu F. A new validity function for fuzzy clustering. In: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing, Wuhan, China: IEEE, 2009. 462-465
    [31] 任俊超, 刘丁, 万银. 基于混合集成建模的硅单晶直径自适应非线性预测控制. 自动化学报, 2020, 6(5): 1004-1016

    Ren Jun-Chao, Liu Ding, Wan Yin. Hybrid integrated modeling based adaptive nonlinear predictive control of silicon single crystal diameter. ACTA AUTOMATICA SINICA, 2020, 6(5): 1004-1016
    [32] Han H G, Wu X L, Qiao J F. Real-time model predictive control using a self-organizing neural network. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(9): 1425-1436 doi: 10.1109/TNNLS.2013.2261574
    [33] Noriega J R, Wang H. A direct adaptive neural-network control for unkonwn nonlinear systems and its application. IEEE Transactions on Neural Networks, 1998, 9(1): 27-34 doi: 10.1109/72.655026
    [34] Lu C H. Wavelet fuzzy neural networks for identification and predictive control of dynamic systems. IEEE Transactions on Industrial Electronics, 2011, 58(7): 3046-3058. doi: 10.1109/TIE.2010.2076415
    [35] Hou Z S, Xiong S S. On model-free adaptive control and its stability analysis. IEEE Transactions on Automatic Control, 2019, 64(11): 4555-4569. doi: 10.1109/TAC.2019.2894586
    [36] Han H G, Liu H X, Li J M, et al. Cooperative fuzzy-neural control for wastewater treatment process. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5971-5981. doi: 10.1109/TII.2020.3034335
  • 加载中
计量
  • 文章访问数:  17
  • HTML全文浏览量:  7
  • 被引次数: 0
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

目录

    /

    返回文章
    返回