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城市污水处理过程动态多目标智能优化控制研究

韩红桂 张璐 卢薇 乔俊飞

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): 1−10 doi: 10.16383/j.aas.c190154
引用本文: 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): 1−10 doi: 10.16383/j.aas.c190154
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): 1−10 doi: 10.16383/j.aas.c190154
Citation: 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): 1−10 doi: 10.16383/j.aas.c190154

城市污水处理过程动态多目标智能优化控制研究

doi: 10.16383/j.aas.c190154
基金项目: 国家自然科学基金(61890931, 61622301, 61533002), 北京自然科学基金(4172005)资助
详细信息
    作者简介:

    韩红桂:北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. 本文通信作者. E-mail: rechardhan@bjut.edu.cn

    张璐:北京工业大学信息学部博士研究生. 2014年获得菏泽学院控制科学与工程学士学位. 主要研究方向为城市污水处理过程多目标智能优化控制. E-mail: zhlulu1991@163.com

    卢薇:中国石化青岛安全工程研究院, 环境保护研究室助理工程师. 主要研究方向为神经网络, 智能系统, 多目标优化. E-mail: luweiwei@emails.bjut.edu.cn

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为城市污水处理过程智能优化控制, 神经网络结构设计与优化. E-mail: junfeq@bjut.edu.cn

Research on Dynamic Multiobjective Intelligent Optimal Control for Municipal Wastewater Treatment Process

Funds: Supported by National Natural Science Foundation of China (61890931, 61622301, 61533002) and Natural Science Foundation of Beijing (4172005)
  • 摘要: 城市污水处理过程(Municipal waster water treatment process, MWWTP)是一个典型的复杂流程工业过程, 其优化运行涉及到多个动态性能指标. 为了实现城市污水处理运行过程的优化控制, 本文提出了一种城市污水处理过程动态多目标智能优化控制方法(Dynamic multiobjective intelligent optimal control, DMIOC). 首先, 建立了一种基于自适应核函数的动态性能指标模型, 实现了城市污水处理关键性能指标的准确描述; 其次, 设计了一种基于自适应飞行参数调整机制的动态多目标粒子群优化算法(Dynamic multiobjective particle swarm optimization, DMOPSO), 可有效平衡粒子的多样性和收敛性, 完成了溶解氧和硝态氮优化设定值的实时获取; 最后, 利用多回路PID控制方法对溶解氧和硝态氮优化设定值进行控制, 实现了城市污水处理过程安全稳定运行. 将提出的DMIOC应用于城市污水处理基准仿真平台, 实验结果显示: DMIOC 能够提高溶解氧和硝态氮的控制效果, 实现城市污水处理过程出水水质达标, 并降低运行成本.
  • 图  1  城市污水处理过程动态多目标智能优化控制架构

    Fig.  1  The scheme of DMIOC for municipal wastewater treatment process

    图  2  不同优化算法的逼近效果

    Fig.  2  The approximation effect of different optimization algorithms

    图  3  平均PE值

    Fig.  3  Average values of PE

    图  4  平均AE值

    Fig.  4  Average values of AE

    图  5  平均EQ值

    Fig.  5  Average values of EQ

    图  6  SO的控制效果

    Fig.  6  Control results of SO

    图  7  SNO的控制效果

    Fig.  7  Control results of SNO

    图  8  $S_{\rm{O}}$$S_{{\rm{NO}}}$的误差值

    Fig.  8  Control errors of $S_{\rm{O}}$ and $S_{{\rm{NO}}}$

    表  1  不同优化算法的性能比较

    Table  1  The approximation effect of different optimization algorithms

    函数 指标 DMOPSO Pccs−AMOPSO Cluster−MOPSO NSGANSGA
    ZDT3 GD 3.982E−03 3.323E−03 1.047E−02 5.834E−03
    std 3.623E−03 9.951E−05 7.052E−02 2.020E−04
    SP 7.025E−02 7.159E−02 7.784E−02 9.222E−02
    std 7.171E−03 4.054E−03 4.362E−02 8.415E−03
    ZDT4 GD 4.147E−03 7.971E−03 3.989E+00 1.655E−02
    std 2.980E−04 1.470E−03 2.616E+00 3.174E−02
    SP 2.645E−02 2.881E−02 7.692E−02 3.838E−02
    std 4.998E−04 5.132E−04 1.931E−02 3.837E−03
    DTLZ2 GD 6.595E−02 6.143E−02 1.265E−01 1.059E−01
    std 7.241E−04 1.898E−03 1.683E−02 8.383E−03
    SP 2.399E−01 3.411E−01 5.762E−01 4.162E−01
    std 9.732E−03 1.242E−02 4.472E−02 3.655E−02
    DTLZ7 GD 1.215E−02 4.277E−02 4.022E−02 1.799E−02
    std 8.600E−04 9.506E−04 2.068E−03 1.294E−03
    SP 3.758E−01 3.988E−01 3.902E−01 4.191E−01
    std 7.593E−03 8.417E−03 1.293E−02 7.961E−03
    下载: 导出CSV

    表  2  不同优化算法的计算时间比较 (s)

    Table  2  Calculation time comparison of different optimization methods (s)

    函数 DMOPSO PccsAMOPSO ClusterMOPSO NSGA
    ZDT3 134 138 141 139
    ZDT4 140 144 142 148
    DTLZ2 249 280 230 287
    DTLZ7 260 287 250 301
    下载: 导出CSV

    表  3  不同优化控制方法优化性能比较

    Table  3  Comparison of optimization performance of different optimal control methods

    方法 PE (kW·h) AE (kW·h) EQ (kg poll)
    DMIOC 249 3630 6616
    clusterMOPSO-OC 254 3691 7291
    pccsAMOPSO-OC 255 3687 7220
    NSGA+PI-OC 243 3694 7214
    下载: 导出CSV

    表  4  不同优化控制方法优化性能比较

    Table  4  Comparison of control performance of different optimal control methods

    方法 PE (kW·h) AE (kW·h) EQ (kg poll)
    DMIOC 0.097 3.08 6616
    clusterMOPSO-OC 0.122 3.23 7291
    pccsAMOPSO-OC 0.012 3.31 13.25
    NSGA+PI-OC 0.100 3.12 7214
    下载: 导出CSV
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  • 收稿日期:  2019-03-13
  • 录用日期:  2019-07-10
  • 网络出版日期:  2021-01-18

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