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污水处理决策优化控制

栗三一 乔俊飞 李文静 顾锞

栗三一, 乔俊飞, 李文静, 顾锞. 污水处理决策优化控制. 自动化学报, 2018, 44(12): 2198-2209. doi: 10.16383/j.aas.2018.c170257
引用本文: 栗三一, 乔俊飞, 李文静, 顾锞. 污水处理决策优化控制. 自动化学报, 2018, 44(12): 2198-2209. doi: 10.16383/j.aas.2018.c170257
LI San-Yi, QIAO Jun-Fei, LI Wen-Jing, GU Ke. Advanced Decision and Optimization Control for Wastewater Treatment Plants. ACTA AUTOMATICA SINICA, 2018, 44(12): 2198-2209. doi: 10.16383/j.aas.2018.c170257
Citation: LI San-Yi, QIAO Jun-Fei, LI Wen-Jing, GU Ke. Advanced Decision and Optimization Control for Wastewater Treatment Plants. ACTA AUTOMATICA SINICA, 2018, 44(12): 2198-2209. doi: 10.16383/j.aas.2018.c170257

污水处理决策优化控制

doi: 10.16383/j.aas.2018.c170257
基金项目: 

国家自然科学基金 61533002

国家杰出青年科学基金项目 61603009

详细信息
    作者简介:

    栗三一  北京工业大学信息学部博士研究生.主要研究方向为智能优化控制, 神经网络结构设计和优化.E-mail:wslisanyi@126.com

    李文静  博士, 北京工业大学信息学部副教授.主要研究方向为模块化神经网络设计.E-mail:wenjing.li@bjut.edu.cn

    顾锞  博士, 北京工业大学信息学部副教授.主要研究方向为质量感知和机器学习.E-mail:guke@bjut.edu.cn

    通讯作者:

    乔俊飞  博士, 北京工业大学信息学部教授.主要研究方向为智能控制, 神经网络分析与设计.本文通信作者.E-mail:junfeq@bjut.edu.cn

Advanced Decision and Optimization Control for Wastewater Treatment Plants

Funds: 

National Natural Science Foundation of China 61533002

National Science Fund for Distinguished Young Scholars 61603009

More Information
    Author Bio:

     Ph. D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent optimization control, analysis and design of neural networks

     Ph. D., associate professor at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers modular neural networks designing

     Ph. D., associate professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers quality perception and machine learning

    Corresponding author: QIAO Jun-Fei  Ph. D., professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers intelligent control, analysis and design of neural networks. Corresponding author of this paper
  • 摘要: 以抑制出水氨氮浓度、总氮浓度峰值和降低能耗为目标,提出污水处理决策优化控制方法.首先利用神经网络建立出水氨氮和总氮预测模型;其次使用多目标进化算法得到溶解氧浓度和硝态氮浓度设定值;最后,根据出水氨氮和总氮浓度预测结果选择控制策略(优化控制和抑制控制).以仿真基准模型(BSM1)为平台,采用提出的决策优化控制方法进行控制,实验结果表明,该控制方法有效抑制了出水氨氮和总氮浓度峰值,出水超标时间和能耗明显少于所对比决策控制方法.
    1)  本文责任编委 赵千川
  • 图  1  14天不同区域氨氮浓度曲线

    Fig.  1  Ammonia nitrogen concentration curve in different regions within 14 days

    图  2  出水氨氮浓度预测模型

    Fig.  2  Prediction model of $S_{\rm {NH, e}}$

    图  3  出水总氮浓度预测模型

    Fig.  3  Prediction model of $S_{\rm {Ntot, e}}$

    图  4  出水氨氮浓度预测曲线

    Fig.  4  Prediction curve and actual curve of $S_{\rm {NH, e}}$

    图  5  出水总氮浓度预测曲线

    Fig.  5  Prediction curve and actual curve of $S_{\rm {Ntot, e}}$

    图  6  决策优化控制示意图

    Fig.  6  The proposed decision and optimization control system

    图  7  各出水水质浓度变化曲线

    Fig.  7  The change of water quality parameters

    图  8  $S_{\rm {NO, 2}}$设定值及跟踪曲线

    Fig.  8  Optimization and tracking results of $S_{\rm {NO, 2}}$

    图  9  $S_{\rm {O, 5}}$设定值及跟踪曲线

    Fig.  9  Optimization and tracking results of $S_{\rm {O, 5}}$

    图  10  出水TSS、BOD$_5$和COD浓度变化曲线

    Fig.  10  The curves of TSS, BOD$_5$ and COD of effluent

    图  11  决策优化控制与优化控制$S_{\rm {NH, e}}$变化曲线

    Fig.  11  The curves of $S_{\rm {NH, e}}$ with decision and optimization control system and optimization control system

    图  12  决策优化控制与优化控制$S_{\rm {Ntot, e}}$变化曲线

    Fig.  12  The curves of $S_{\rm {Ntot, e}}$ with decision and optimization control system and optimization control system

    图  13  $S_{\rm {O, 5}}$设定值及跟踪曲线

    Fig.  13  Optimization and tracking results of $S_{\rm {O, 5}}$

    图  14  $S_{\rm {NO, 2}}$设定值及跟踪曲线

    Fig.  14  Optimization and tracking results of $S_{\rm {NO, 2}}$

    图  15  $Q_{\rm {a}}$变化曲线

    Fig.  15  The curve of $Q_{\rm {a}}$

    图  16  $K_{\rm {La, 5}}$变化曲线

    Fig.  16  The curve of $K_{\rm {La, 5}}$

    表  1  不同区域到入水氨氮浓度最大时刻滞后时间(h)

    Table  1  The time lag from inflow to different regions (h)

    第二分区 第五分区 出水
    第1天 0.2375 1.1000 4.4375
    第2天 0.2375 1.3250 4.5750
    第3天 0.2375 1.1625 4.6000
    第4天 0.2375 1.3500 4.6875
    第5天 0.2375 1.1500 4.6500
    第6天 0.4875 1.5625 5.2875
    第7天 0.4875 1.6750 5.2750
    第8天 0.2375 1.1250 4.4875
    第9天 0.2375 1.3250 4.5750
    第10天 0.2375 1.1625 4.6000
    第11天 0.2375 1.3500 4.6875
    第12天 0.2375 1.1500 4.6500
    第13天 0.4875 1.5625 5.2875
    第14天 0.4875 1.6750 5.2750
    下载: 导出CSV

    表  2  预测模型测试均方根误差(10次实验平均值)

    Table  2  Test RMSE of prediction model (mean value of ten test results)

    方法 氨氮预测模型RMSE 总氮预测模型RMSE
    决策优化控制 0.4241 0.3506
    Santín[10] 0.9771 0.7515
    下载: 导出CSV

    表  3  SO,5模糊跟踪控制器模糊规则

    Table  3  Fuzzy rules of $S_{\rm {O, 5}}$ fuzzy controller

    E EC
    NB NS ZO PS PB
    NB PB PB PB PM ZO
    NM PB PB PM PS ZO
    NS PM PM PS ZO NS
    ZO PM PS ZO NS NS
    PS PS ZO NS NM NM
    PS ZO NS NM NB NB
    PB ZO NM NB NB NB
    下载: 导出CSV

    表  4  出水水质限制

    Table  4  Effluent quality limits

    水质 上限值(mg/l)
    $S_{\rm {NH, e}}$ 4
    $S_{\rm {Ntot, e}}$ 18
    TSS 30
    BOD$_5$ 10
    COD 100
    下载: 导出CSV

    表  5  不同算法控制效果比较(出水指标、能耗和水质)

    Table  5  Performance comparison for different control algorithms (effluent parameters, $OCI$ and $EQ$)

    算法 EC $OCI$ (kWh/d) $EQ$ (kg poll.Units/d)
    $S_{\rm NH}$ (mg/l) $S_{\rm Ntot}$ (mg/l) TSS (mg/l) BOD$_5$ (mg/l) COD (mg/l)
    F1 2.3037 16.8006 12.6219 2.6763 47.5114 3 909.5 6 080.9
    NSGA2-DLS 2.4389 17.5298 12.6200 2.6791 47.5189 3 688.1 6 203.9
    DPSO[18] 3.2387 14.9184 12.6227 2.6894 47.5503 3 702.3 6 180.3
    APSO[19] 3.1398 14.5995 12.9990 2.7660 48.0766 3 700.4 6 198.7
    ESN[20] 2.8723 15.6014 12.5917 2.6784 47.5114 3 756.8 6 134.5
    下载: 导出CSV

    表  6  不同$S_{\rm{NH, e}}$和$S_{\rm {Ntot, e}}$峰值抑制方法效果对比

    Table  6  Performance comparison for different $S_{\rm {NH, e}}$ and $S_{\rm {Ntot, e}}$ peak suppression methods

    方法 $OCI$
    (KWh/d)
    $EQ$ (kg poll.Units/d) $P$($S_{\rm{NH, e}}$)
    (%)
    $P$($S_{\rm {Ntot, e}}$)
    (%)
    决策优化控制 5 526.7 5 797.1 0 0
    F2 6 268.6 5 350.1 0.74 0.77
    Jeppsson 9 447.24 5 577.97 0.41 1.18
    Nopens 9 348 5 447 0.92 *
    Flores-Alsina 8 024.5 5 022.5 0.2 0.25
    Santín 6 289.59 5 318.95 0.15 0.0046
    下载: 导出CSV
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