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数据和知识驱动的城市污水处理过程多目标优化控制

韩红桂 张琳琳 伍小龙 乔俊飞

韩红桂, 张琳琳, 伍小龙, 乔俊飞. 数据和知识驱动的城市污水处理过程多目标优化控制. 自动化学报, 2021, 47(x): 1−9 doi: 10.16383/j.aas.c210098
引用本文: 韩红桂, 张琳琳, 伍小龙, 乔俊飞. 数据和知识驱动的城市污水处理过程多目标优化控制. 自动化学报, 2021, 47(x): 1−9 doi: 10.16383/j.aas.c210098
Han Hong-Gui, Zhang Lin-Lin, Wu Xiao-Long, Qiao Jun-Fei. Data-knowledge driven multiobjective optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(x): 1−9 doi: 10.16383/j.aas.c210098
Citation: Han Hong-Gui, Zhang Lin-Lin, Wu Xiao-Long, Qiao Jun-Fei. Data-knowledge driven multiobjective optimal control for municipal wastewater treatment process. Acta Automatica Sinica, 2021, 47(x): 1−9 doi: 10.16383/j.aas.c210098

数据和知识驱动的城市污水处理过程多目标优化控制

doi: 10.16383/j.aas.c210098
基金项目: 国家重点研发项目(2018YFC1900800-5), 国家自然科学基金(61890930-5, 61903010, 62021003), 北京市卓越青年科学家计划项目(BJJWZYJH01201910005020), 北京市教育委员会科技计划重点项目(KZ202110005009)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

    张琳琳:北京工业大学信息学部博士研究生. 主要研究方向为城市污水处理过程智能优化控制. E-mail: zhangllsy@163.com

    伍小龙:北京工业大学信息学部讲师. 主要研究方向为城市污水处理过程智能特征建模与智能控制. E-mail: lewis_wxl@sina.com

    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

Data-Knowledge Driven Multiobjective Optimal Control for Municipal Wastewater Treatment Process

Funds: Supported by National Key Research and Development Project (2018YFC1900800-5), National Science Foundation of China (61890930-5, 61903010, 62021003), Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), and Beijing Natural Science Foundation (KZ202110005009)
More Information
    Author Bio:

    HAN Hong-Gui Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal wastewater treatment process, structure design and optimization of neural networks. Corresponding author of this paper

    ZHANG Lin-Lin Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers intelligent optimal control of municipal wastewater treatment process

    WU Xiao-Long Lecturer at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent feature modeling and intelligent control of municipal wastewater treatment process

    QIAO Jun-Fei Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimal control of municipal wastewater treatment process, structure design and optimization of neural networks

  • 摘要: 城市污水处理过程优化控制是降低能耗的有效手段, 然而, 如何提高出水水质的同时降低能耗依然是当前城市污水处理过程面临的挑战. 围绕上述挑战, 文中提出了一种数据和知识驱动的多目标优化控制(Data-knowledge driven multiobjective optimal control, DK-MOC)方法. 首先, 建立了出水水质、能耗以及系统运行状态的表达关系, 获得了运行过程优化目标模型. 其次, 提出了一种基于知识迁徙学习的动态多目标粒子群优化算法, 实现了控制变量优化设定值的自适应求解. 最后, 将提出的DK-MOC应用于城市污水处理过程基准仿真模型1 (Benchmark simulation model no. 1, BSM1). 结果表明该方法能够实时获取控制变量的优化设定值, 提高了出水水质, 并且有效降低了运行能耗.
  • 图  1  DK-MOC的流程图

    Fig.  1  The flow chart of DK-MOC

    图  2  控制变量设定值求解步骤

    Fig.  2  The solution procedure of set values of control variables

    图  3  干旱天气下出水水质建模结果

    Fig.  3  The modeling results of effluent quality in dry weather

    图  4  干旱天气下能耗建模结果

    Fig.  4  The modeling results of energy consumption in dry weather

    图  5  干旱天气下平均出水水质值

    Fig.  5  Average values of effluent quality in dry weather

    图  6  干旱天气下平均能耗值

    Fig.  6  Average values of energy consumption in dry weather

    图  7  干旱天气下溶解氧浓度跟踪性能

    Fig.  7  Tracking performance of SO in dry weather

    图  8  干旱天气下硝态氮浓度跟踪性能

    Fig.  8  Tracking performance of SNO in dry weather

    图  9  干旱天气下硝态氮和溶解氧浓度跟踪误差

    Fig.  9  Tracking errors of SNO and SO in dry weather

    表  1  干旱天气下能耗和出水水质的测量精度

    Table  1  The measurement accuracy of energy consumption and effluent quality in dry weather

    模型 EC EQ
    RMSE PA RMSE PA
    AKF 0.0082 98.71% 0.0079 97.06%
    GA-ANN 0.0123 93.36% 0.0124 95.52%
    LSSVM 0.0115 96.21% 0.0139 94.31%
    AFNN 0.0079 98.99% 0.0070 98.73%
    下载: 导出CSV

    表  2  阴雨天气下能耗和出水水质的测量精度

    Table  2  The measurement accuracy of energy consumption and effluent quality in rainy weather

    模型 EC EQ
    RMSE PA RMSE PA
    AKF 0.0093 96.60% 0.0833 97.37%
    GA-ANN 0.0146 95.72% 0.1002 94.32%
    LSSVM 0.0138 95.74% 0.0940 96.76%
    AFNN 0.0089 98.88% 0.0776 98.89%
    下载: 导出CSV

    表  3  DK-MOC的出水水质结果

    Table  3  Effluent quality results obtained by DK-MOC

    水质指标 干旱天气 阴雨天气 排放标准
    SNH/mg·L−1 3.44 3.89 <4
    Ntot/mg·L−1 17.41 17.01 <18
    TSS/mg·L−1 12.57 13.51 <30
    COD/mg·L−1 47.75 46.53 <100
    BOD/mg·L−1 2.71 2.93 <10
    下载: 导出CSV

    表  4  不同优化控制策略的比较结果

    Table  4  Comparison results of different optimal control strategies

    优化控制策略 干旱天气 阴雨天气
    EC
    kW·h
    EQ
    Kg Poll.units
    EC
    kW·h
    EQ
    Kg Poll.units
    AFNN+NSGAII+PID 4015 8867 4969 9049
    AFNN+CMOPSO+PID 4770 8915 4869 9293
    AFNN+DMOPSO+PID 3848 7695 3870 7944
    AFNN+KT-DSOPSO+PID 3727 9062 3892 9479
    DK-MOC 3641 7179 3821 8106
    下载: 导出CSV
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  • 收稿日期:  2021-02-10
  • 录用日期:  2021-05-28
  • 网络出版日期:  2021-07-28

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