2.765

2022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

基于自组织递归小波神经网络的污水处理过程多变量控制

苏尹 杨翠丽 乔俊飞

苏尹, 杨翠丽, 乔俊飞. 基于自组织递归小波神经网络的污水处理过程多变量控制. 自动化学报, 2024, 50(6): 1199−1209 doi: 10.16383/j.aas.c220679
引用本文: 苏尹, 杨翠丽, 乔俊飞. 基于自组织递归小波神经网络的污水处理过程多变量控制. 自动化学报, 2024, 50(6): 1199−1209 doi: 10.16383/j.aas.c220679
Su Yin, Yang Cui-Li, Qiao Jun-Fei. Multivariate control of wastewater treatment process based on self-organized recurrent wavelet neural network. Acta Automatica Sinica, 2024, 50(6): 1199−1209 doi: 10.16383/j.aas.c220679
Citation: Su Yin, Yang Cui-Li, Qiao Jun-Fei. Multivariate control of wastewater treatment process based on self-organized recurrent wavelet neural network. Acta Automatica Sinica, 2024, 50(6): 1199−1209 doi: 10.16383/j.aas.c220679

基于自组织递归小波神经网络的污水处理过程多变量控制

doi: 10.16383/j.aas.c220679
基金项目: 国家自然科学基金 (61890930-5, 62021003, 61973010), 国家重点研发计划(2021ZD0112302) 资助
详细信息
    作者简介:

    苏尹:嘉兴大学信息科学与工程学院讲师. 2023年获得北京工业大学控制科学与工程专业博士学位. 主要研究方向为基于神经网络的城市污水处理过程预测及过程控制. E-mail: suy@zjxu.edu.cn

    杨翠丽:北京工业大学信息学部副教授. 2008年获得中国石油大学(东营)工学学士学位, 2010年获得天津大学理学硕士学位, 2014年获得香港城市大学博士学位. 主要研究方向为计算智能, 污水处理过程的建模与控制. E-mail: clyang5@bjut.edu

    乔俊飞:北京工业大学信息学部教授. 分别于1992年和1995年获得辽宁工业大学控制工程学士和硕士学位, 1998年获得东北大学博士学位. 主要研究方向为神经网络, 智能系统, 自适应系统和过程控制. 本文通信作者. E-mail: adqiao@bjut.edu.cn

Multivariate Control of Wastewater Treatment Process Based on Self-organized Recurrent Wavelet Neural Network

Funds: Supported by National Natural Science Foundation of China (61890930-5, 62021003, 61973010) and National Key Research and Development Program of China (2021ZD0112302)
More Information
    Author Bio:

    SU Yin Lecturer at the College of Information Science and Engineering, Jiaxing University. She received her Ph.D. degree in control science and engineering from Beijing University of Technology in 2023. Her research interest covers neural network-based urban wastewater treatment process prediction and process control

    YANG Cui-Li Associate professor at the Faculty of Information Technology, Beijing University of Technology. She received her bachelor degree from China University of Petroleum (Dongying) in 2008, master degree from Tianjin University in 2010, and Ph.D. degree from City University of Hong Kong, Hong Kong, China, in 2014. Her research interest covers computational intelligence, and modeling and control for wastewater treatment process

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. He received his bachelor and master degrees in control engineering from Liaoning Technical University in 1992 and 1995, respectively, and his Ph.D. degree from Northeastern University in 1998. His research interest covers neural networks, intelligent systems, self-adaptive systems, and process control. Corresponding author of this paper

  • 摘要: 污水处理过程(Wastewater treatment process, WWTP)是一个包含多个生化反应的复杂过程, 具有非线性和动态特性. 因此, 实现污水处理过程的精准控制是一项挑战. 为解决这个问题, 提出一种基于自组织递归小波神经网络(Self-organized recurrent wavelet neural network, SRWNN)的污水处理过程多变量控制. 首先, 针对污水处理过程的动态特性, 根据小波基的激活强度设计一种自组织机制来动态调整递归小波神经网络控制器的结构, 提高控制的性能. 然后, 采用结合自适应学习率的在线学习算法, 实现控制器的参数学习. 此外, 通过李雅普诺夫稳定性定理证明此控制器的稳定性. 最后, 采用基准仿真平台进行仿真验证, 实验结果表明, 此控制方法可以有效提高污水处理过程的控制绝对误差积分(Integral of absolute error, IAE)和积分平方误差(Integral of squared error, ISE)的精度.
  • 图  1  活性污泥法

    Fig.  1  Activated sludge method

    图  2  控制框图

    Fig.  2  Control block diagram

    图  3  SRWNN结构图

    Fig.  3  The structure of SRWNN

    图  4  控制流程图

    Fig.  4  The flow chart of control

    图  5  不同小波函数时DO控制结果

    Fig.  5  Control results of DO under different wavelet functions

    图  6  SRWNN小波节点变化图

    Fig.  6  Change of SRWNN wavelet node

    图  7  晴天工况下DO控制结果

    Fig.  7  Control results of DO under sunny condition

    图  8  晴天工况下NO控制结果

    Fig.  8  Control results of NO under sunny condition

    图  9  阴雨工况下DO控制结果

    Fig.  9  Control results of DO under cloudy and rain conditions

    图  10  阴雨工况下NO控制结果

    Fig.  10  Control results of NO under cloudy and rain conditions

    图  11  $K_{La5}$变化曲线

    Fig.  11  The change curves of $K_{La5}$

    图  12  $Q_a$变化曲线

    Fig.  12  The change curves of $Q_a$

    图  13  SRWNN小波节点变化图

    Fig.  13  Change of SRWNN wavelet node

    图  14  晴天工况下DO控制结果

    Fig.  14  Control results of DO under sunny condition

    图  15  晴天工况下NO控制结果

    Fig.  15  Control results of NO under sunny condition

    图  16  阴雨工况下DO控制结果

    Fig.  16  Control results of DO under cloudy and rain conditions

    图  17  阴雨工况下NO控制结果

    Fig.  17  Control results of NO under cloudy and rain conditions

    图  18  $K_{La5}$变化曲线

    Fig.  18  The change curves of $K_{La5}$

    图  19  $Q_a$变化曲线

    Fig.  19  The change curves of $Q_a$

    表  1  不同控制方法在恒定设定值时的性能比较

    Table  1  Performance comparison of different control methods at constant set-point

    工况控制器No.DONO
    IAEISEDEV_MAXIAEISEDEV_MAX
    晴天SRWNN35.66×10−41.63×10−60.00870.00367.61×10−50.0114
    RWNN50.00173.26×10−50.05260.00203.06×10−50.0540
    NNOMC100.0390*5.31×10−4*0.0725*0.0490*7.18×10−4*0.1630*
    RARFNNC40.0073*1.61×10−4*0.0104*0.0126*2.83×10−4*0.1050*
    DRFNNC60.0079*1.82×10−4*0.0154*0.0085*3.25×10−4*0.0176*
    阴雨SRWNN40.00411.75×10−40.10420.01019.80×10−40.1291
    RWNN50.00512.21×10−40.14340.01171.40×10−30.2244
    PID0.00161.90×10−30.20380.03178.23×10−30.3233
    注: “$*$”表示原文中的结果, “—”表示无相应数据.
    下载: 导出CSV

    表  2  不同控制方法在变化设定值时的性能比较

    Table  2  Performance comparison of different control methods at changed set-point

    工况控制器No.DONO
    IAEISEDEV_MAXIAEISEDEV_MAX
    晴天SRWNN30.00673.68×10−60.01560.00611.64×10−40.0067
    RWNN50.00872.62×10−40.11560.01262.30×10−30.1116
    PID0.01272.38×10−30.10380.02714.90×10−30.2184
    阴雨SRWNN30.00471.10×10−40.05380.00653.18×10−40.1527
    RWNN50.00691.92×10−40.06440.00884.58×10−40.1781
    RFNNC0.0240*2.40×10−3*0.08630.0260*1.00×10−3*0.1881*
    注: “$*$”表示原文中的结果, “—”表示无相应数据.
    下载: 导出CSV
  • [1] Tang W Z, Pei Y S, Zheng H, Zhao Y, Shu L M, Zhang H. Twenty years of China's water pollution control: Experiences and challenges. Chemosphere, 2022, 295: Article No. 133875 doi: 10.1016/j.chemosphere.2022.133875
    [2] 杨翠丽, 武战红, 韩红桂, 乔俊飞. 城市污水处理过程优化设定方法研究进展. 自动化学报, 2020, 46(10): 2092−2108

    Yang Cui-Li, Wu Zhan-Hong, Han Hong-Gui, Qiao Jun-Fei. Perspectives on optimal setting methods for municipal wastewater treatment processes. Acta Automatica Sinica, 2020, 46(10): 2092−2108
    [3] Lizarralde I, Fernández-Arévalo T, Brouckaert C, Vanrolleghem P, Ikumi D S, Ekama G A, et al. A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 2015, 74: 239−256 doi: 10.1016/j.watres.2015.01.031
    [4] 潘南全. 基于前馈控制模型的生化反应池曝气控制优化. 工控制计算机, 2015, 28(1): 58−60

    Pang Nan-Quan. Optimization aeration control of reaction tank based on feed forward control model. Industrial Control Computer, 2015, 28(1): 58−60
    [5] Du S L, Yan Q S, Qiao J F. Event-triggered PID control for wastewater treatment plants. Journal of Water Process Engineering, 2022, 47: Article No. 102765 doi: 10.1016/j.jwpe.2020.101659
    [6] 曾春霞, 董宗哲, 何涛. 模糊代数PID控制在污水处理溶解氧控制系统的应用. 化工自动化及仪表, 2021, 48(6): 528−534 doi: 10.3969/j.issn.1000-3932.2021.06.003

    Zeng Chun-Xia, Dong Zong-Zhe, He Tao. Application of fuzzy algebra PID control in dissolved oxygen control system of wastewater treatment. Control and Instruments in Chemical Industry, 2021, 48(6): 528−534 doi: 10.3969/j.issn.1000-3932.2021.06.003
    [7] 刘锁清, 刘少虹, 李军红, 彭伟娟. 基于模糊自整定PID串级控制的废水处理PH值控制. 自动化技术与应用, 2019, 38(2): 22−27 doi: 10.3969/j.issn.1003-7241.2019.02.006

    Liu Suo-Qing, Liu Shao-Hong, Li Jun-Hong, Peng Wei-Juan. Wastewater treatment PH value control based on fuzzy self-tuning PID cascade control. Techniques of Automation and Applications, 2019, 38(2): 22−27 doi: 10.3969/j.issn.1003-7241.2019.02.006
    [8] Hoang B L, Tien D N, Luo F, Nguyen P H. Dissolved oxygen control of the activated sludge wastewater treatment process using Hedge Algebraic control. In: Proceedings of the 7th International Conference on Biomedical Engineering and Informatics. Dalian, China: IEEE, 2014. 827−832
    [9] 许进超, 杨翠丽, 乔俊飞, 马士杰. 基于自组织模糊神经网络溶解氧控制方法研究. 智能系统学报, 2018, 13(6): 905−912 doi: 10.11992/tis.201801019

    Xu Jin-Chao, Yang Cui-Li, Qiao Jun-Fei, Ma Shi-Jie. Dissolved oxygen concentration control method based on self-organizing fuzzy neural network. CAAI Transactions on Intelligent Systems, 2018, 13(6): 905−912 doi: 10.11992/tis.201801019
    [10] Wang D, Ha M M, Qiao J F. Data-driven iterative adaptive critic control toward an urban wastewater treatment plant. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7362−7369 doi: 10.1109/TIE.2020.3001840
    [11] Nawaz A, Arora A S, Yun C M, Lee J J, Lee M. Development of smart AnAmmOx system and its agile operation and decision support for pilot-scale WWTP. Soft Computing Techniques in Solid Waste and Wastewater Management. Amsterdam: Elsevier, 2021. 7423−7454
    [12] 韩红桂, 张璐, 卢薇, 乔俊飞. 城市污水处理过程动态多目标智能优化控制研究. 自动化学报, 2021, 47(3): 620−629

    Han Hong-Gui, Zhang Lu, Lu Wei, Qiao Jun-Fei. Research on dynamic multiobjective intelligent optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(3): 620−629
    [13] 乔俊飞, 韩改堂, 周红标. 基于知识的污水生化处理过程智能优化方法. 自动化学报, 2017, 43(6): 1038−1046

    Qiao Jun-Fei, Han Gai-Tang, Zhou Hong-Biao. Knowledge-based intelligent optimal control for wastewater biochemical treatment process. Acta Automatica Sinica, 2017, 43(6): 1038−1046
    [14] Vega P, Revollar S, Francisco M, Martín J M. Integration of set point optimization techniques into nonlinear MPC for improving the operation of WWTPs. Computers and Chemical Engineering, 2014, 68: 78−95 doi: 10.1016/j.compchemeng.2014.03.027
    [15] Han H G, Liu H X, Li J M, Qiao J F. Cooperative fuzzy-neural control for wastewater treatment process. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5971−5981 doi: 10.1109/TII.2020.3034335
    [16] El-Sousy F F M, Abuhasel K A. Adaptive nonlinear disturbance observer using a double-loop self-organizing recurrent wavelet neural network for a two-axis motion control system. IEEE Transactions on Industry Applications, 2018, 54(1): 764−786 doi: 10.1109/TIA.2017.2763584
    [17] 王桐, 邱剑彬, 高会军. 随机非线性系统基于事件触发机制的自适应神经网络控制. 自动化学报, 2019, 45(1): 226−233

    Wang Tong, Qiu Jian-Bin, Gao Hui-Jun. Event-triggered adaptive neural network control for a class of stochastic nonlinear systems. Acta Automatica Sinica, 2019, 45(1): 226−233
    [18] Liu Y J, Li J, Tong S C, Chen C L P. Neural network control-based adaptive learning design for nonlinear systems with full-state constraints. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(7): 1562−1571 doi: 10.1109/TNNLS.2015.2508926
    [19] Lee C H, Chang H H. Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems. Neural Computing and Applications, 2014, 24(5): 1035−1045 doi: 10.1007/s00521-012-1326-2
    [20] Lin C H. A novel hybrid recurrent wavelet neural network control of permanent magnet synchronous motor drive for electric scooter. Turkish Journal of Electrical Engineering and Computer Sciences, 2014, 22(4): 1056−1075
    [21] 张伟, 乔俊飞, 李凡军. 溶解氧浓度的直接自适应动态神经网络控制方法. 控制理论与应用, 2015, 32(1): 115−121 doi: 10.7641/CTA.2014.40311

    Zhang Wei, Qiao Jun-Fei, Li Fan-Jun. Direct adaptive dynamic neural network control for dissolved oxygen concentration. Control Theory & Applications, 2015, 32(1): 115−121 doi: 10.7641/CTA.2014.40311
    [22] El-Sousy F F M, Abuhasel K A, Self-organizing recurrent fuzzy wavelet neural network-based mixed $ {H_2/H_\infty } $ adaptive tracking control for uncertain two-axis motion control system. In: Proceedings of the IEEE Industry Applications Society Annual Meeting. Addison, USA: IEEE, 2015. 1−14
    [23] Han H G, Zhang L, Hou Y, Qiao J F. Nonlinear model predictive control based on a self-organizing recurrent neural network. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2): 402−415 doi: 10.1109/TNNLS.2015.2465174
    [24] Ku C C, Lee K Y. Diagonal recurrent neural networks for dynamic systems control. IEEE Transactions on Neural Networks, 1995, 6(1): 144−156 doi: 10.1109/72.363441
    [25] 韩广, 乔俊飞, 薄迎春. 溶解氧浓度的前馈神经网络建模控制方法. 控制理论与应用, 2013, 30(5): 585−591 doi: 10.7641/CTA.2013.20773

    Han Guang, Qiao Jun-Fei, Bo Ying-Chun. Feedforward neural network modeling and control for dissolved oxygen concentration. Control Theory & Applications, 2013, 30(5): 585−591 doi: 10.7641/CTA.2013.20773
    [26] Qiao J F, Han G T, Han H G, Chai W. Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network. International Journal of Intelligent Computing and Cybernetics, 2017, 10(2): 94−110 doi: 10.1108/IJICC-12-2016-0069
    [27] Qiao J F, Han G T, Han H G, Yang C L, Chai W. Decoupling control for wastewater treatment process based on recurrent fuzzy neural network. Asian Journal of Control, 2019, 21(3): 1270−1280 doi: 10.1002/asjc.1844
    [28] 韩改堂, 乔俊飞, 韩红桂. 基于自适应递归模糊神经网络的污水处理控制. 控制理论与应用, 2016, 33(9): 1252−1258 doi: 10.7641/CTA.2016.50965

    Han Gai-Tang, Qiao Jun-Fei, Han Hong-Gui. Wastewater treatment control method based on adaptive recurrent fuzzy neural network. Control Theory and Applications, 2016, 33(9): 1252−1258 doi: 10.7641/CTA.2016.50965
  • 加载中
图(19) / 表(2)
计量
  • 文章访问数:  160
  • HTML全文浏览量:  63
  • PDF下载量:  51
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-08-29
  • 网络出版日期:  2024-03-28
  • 刊出日期:  2024-06-20

目录

    /

    返回文章
    返回