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基于知识的污水生化处理过程智能优化方法

乔俊飞 韩改堂 周红标

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. doi: 10.16383/j.aas.2017.c170088
引用本文: 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. doi: 10.16383/j.aas.2017.c170088
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. doi: 10.16383/j.aas.2017.c170088
Citation: 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. doi: 10.16383/j.aas.2017.c170088

基于知识的污水生化处理过程智能优化方法

doi: 10.16383/j.aas.2017.c170088
基金项目: 

国家杰出青年科学基金项目 61225016

国家自然科学基金 61533002

详细信息
    作者简介:

    韩改堂 北京工业大学博士研究生.主要研究方向为计算智能与智能系统, 神经网络结构设计和优化.E-mail:hangaitang@emails.bjut.edu.cn

    周红标 北京工业大学博士研究生.主要研究方向为神经网络结构设计与优化, 多目标优化和过程控制系统.E-mail:hyitzhb@163.com

    通讯作者:

    乔俊飞 北京工业大学教授.主要研究方向为智能控制, 神经网络分析与设计.E-mail:junfeq@bjut.edu.cn

Knowledge-based Intelligent Optimal Control for Wastewater Biochemical Treatment Process

Funds: 

National Natural Science Funds for Distinguished Young Scholar 61225016

National Natural Science Foundation of China 61533002

More Information
    Author Bio:

    Ph. D. candidate at Beijing University of Technology. His research interest covers computational intelligence and intelligent systems, analysis and design of neural networks

    Ph. D. candidate at Beijing University of Technology. His research interest covers analysis and design of neural networks, multi-objective optimization, and process control systems

    Corresponding author: QIAO Jun-Fei Professor at Beijing University of Technology. His research interest covers intelligent control, analysis and design of neural networks. Corresponding author of this paper
  • 摘要: 针对污水处理过程控制能耗过大和水质超标严重等问题,本文提出一种基于知识的污水生化处理过程智能优化控制方法.该方法通过记忆多目标智能优化算法的动态处理信息,建立环境变量参数与最优解之间的知识模型.优化算法利用知识库中非支配解的引导,结合定向局部区域寻优以及随机全局寻优策略,提高了算法的收敛性,获取了更高质量的解.最后基于国际通用平台BSM1进行实验验证.结果表明,与其他优化算法相比,该方法能够在保证出水水质的前提下产生更少的能量消耗.
    1)  本文责任编委 郭戈
  • 图  1  污水处理系统工艺布局图

    Fig.  1  Wastewater treatment system process layout

    图  2  基于KBMOPSO的多目标优化控制方法

    Fig.  2  Multiple objective optimization control system based on KBMOPSO

    图  3  KBMOPSO程序流程图

    Fig.  3  The program flow chart of KBMOPSO

    图  4  FNN结构图

    Fig.  4  The structure of FNN

    图  5  晴朗天气入水流量

    Fig.  5  Influent flow in dry weather

    图  6  晴朗天气入水组分

    Fig.  6  Influent component in dry weather

    图  7  案例数

    Fig.  7  The case numbers

    图  8  能耗建模效果

    Fig.  8  Model performance of EC

    图  9  出水水质建模效果

    Fig.  9  Model performance of EQ

    图  10  Pareto最优解集及最优折衷解确定

    Fig.  10  Pareto optimal solutions and identify of optimal

    图  11  设定值优化结果及跟踪效果

    Fig.  11  Optimization and tracking results of the set point values

    图  12  出水水质参数变化情况

    Fig.  12  The change of water quality parameters

    表  1  参数描述

    Table  1  Parameters description

    符号描述
    Do_set溶解氧设定值
    Sno_set硝态氮设定值
    CT控制器
    MI测量设备
    Z0入水组分
    Za内回流组分
    Zr外回流组分
    Zw污泥组分
    Ze出水组分
    Q0入水流量
    Qa内回流
    Qr外回流
    Qw污泥流
    Qe出水流量
    下载: 导出CSV

    表  2  不同算法性能比较

    Table  2  Performance comparison for difierent algorithm

    SO, 5SNO, 2AEPEEnergyUp/DownFinesUp/Down
    PID[8]2*1*841.1*86.2*927.3*-5129.5-
    DDAOC[8]1.5799*1.087*758.2*89.8*848.0*↓8.50%*5185.6↑1.79%*
    Hopfleld[9]--814.863.4878.2↓5.30%*--
    SOOC[14]--852.653.8906.4↓2.25%*--
    DMOOC[14]--849.230.4879.6↓5.14%*5440.9↑6.07%*
    MOPSO1.50121.077840.435.7876.1↓5.52%5409.1↑5.17%
    KBMOPSO1.39991.294763.2102.7865.9↓6.62%5092.4↓0.7%
     *表示参考了原文给出的结果
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-02-20
  • 录用日期:  2017-03-30
  • 刊出日期:  2017-06-20

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