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基于类脑模块化神经网络的污水处理过程关键出水参数软测量

蒙西 乔俊飞 韩红桂

蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量. 自动化学报, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497
引用本文: 蒙西, 乔俊飞, 韩红桂. 基于类脑模块化神经网络的污水处理过程关键出水参数软测量. 自动化学报, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497
MENG Xi, QIAO Jun-Fei, HAN Hong-Gui. Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks. ACTA AUTOMATICA SINICA, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497
Citation: MENG Xi, QIAO Jun-Fei, HAN Hong-Gui. Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks. ACTA AUTOMATICA SINICA, 2019, 45(5): 906-919. doi: 10.16383/j.aas.2018.c170497

基于类脑模块化神经网络的污水处理过程关键出水参数软测量

doi: 10.16383/j.aas.2018.c170497
基金项目: 

国家自然科学基金 61622301

国家自然科学基金 61533002

北京市自然科学基金项目 4172005

详细信息
    作者简介:

    蒙西  北京工业大学博士研究生.主要研究方向为类脑计算, 神经网络结构优化设计.E-mail:mengxi@emails.bjut.edu.cn

    韩红桂  北京工业大学教授.主要研究方向为污水处理工艺复杂建模与控制, 神经网络分析与设计.E-mail:rechardhan@sina.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.本文通信作者.E-mail:junfeq@bjut.edu.cn

Soft Measurement of Key Effluent Parameters in Wastewater Treatment Process Using Brain-like Modular Neural Networks

Funds: 

National Natural Science Foundation of China 61622301

National Natural Science Foundation of China 61533002

Beijing Natural Science Foundation 4172005

More Information
    Author Bio:

    Ph. D. candidate at the Faculty of Information Technology, Beijing University of Technology. Her research interest covers brain-like computing and optima design of neural networks

    Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers modelling and control in waste water treatment process, analysis and design of neural networks

    Corresponding author: QIAO Jun-Fei 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: 针对城市污水处理过程关键出水参数难以实时检测的问题,文中提出了一种基于类脑模块化神经网络(Brain-like modular neural network,BLMNN)的关键出水参数软测量方法.首先,基于互信息和专家知识进行任务分解,分析关键出水参数的相关变量,获取各出水参数的辅助变量.其次,通过模拟大脑皮层模块化分区结构,构建软测量子模型对各水质参数进行同步测量,降低软测量模型复杂度的同时保证了其精度.最后,通过基于实际数据的仿真实验验证了所提出方法的准确性和有效性.
    1)  本文责任编委 贺威
  • 图  1  BLMNN软测量模型策略图

    Fig.  1  Strategy diagram of BLMNN soft-measurement model

    图  2  类脑模块化神经网络结构图

    Fig.  2  Structure of brain-like modular neural network

    图  3  IErrCor RBF网络学习能力与结构的关系

    Fig.  3  Relationship between the learning ability and structure of IErrCor RBF network

    图  4  IErrCor RBF网络实际输出与期望输出对比

    Fig.  4  Prediction results of IErrCor RBF network

    图  5  IErrCor RBF网络预测误差曲线

    Fig.  5  Prediction errors of IErrCor RBF network

    图  6  出水BOD软测量子模型性能与结构间的关系

    Fig.  6  Relationship between structure and performance of the effluent BOD soft-measurement sub-model

    图  7  BLMNN模型出水BOD软测量测试结果

    Fig.  7  Testing results of BLMNN soft-measurement model on the effluent BOD

    图  8  BLMNN模型出水BOD软测量测试误差

    Fig.  8  Testing errors of BLMNN soft-measurement model on the effluent BOD

    图  9  出水TP软测量子模型性能与结构间的关系

    Fig.  9  Relationship between structure and performance of the effluent TP soft-measurement sub-model

    图  10  BLMNN模型出水TP软测量测试结果

    Fig.  10  Testing results of BLMNN soft-measurement model on the effluent TP

    图  11  BLMNN模型出水TP软测量测试误差

    Fig.  11  Testing errors of BLMNN soft-measurement model on the effluent TP

    图  12  不同算法对出水TP和出水BOD软测量测试结果

    Fig.  12  Testing results of different soft-measurement models on the effluent TP and effluent BOD

    表  1  软测量模型候选变量

    Table  1  Candidate variables of the soft-measurement model

    变量名单位变量名单位
    进水PH曝气池DOmg/L
    出水PH进水NH$_{4} $-Nmg/L
    进水SSmg/L出水NH$_{4} $-Nmg/L
    出水SSmg/L进水色度(稀释)倍数
    进水BODmg/L出水色度(稀释)倍数
    出水BODmg/L进水总氮mg/L
    进水CODmg/L出水总氮mg/L
    出水CODmg/L进水TPmg/L
    进水石油类mg/L出水TPmg/L
    出水石油类mg/L进水水温
    曝气池SVmg/L出水水温
    曝气池MLSSmg/L
    注:固体悬浮物浓度(Suspended solids, SS); 污泥沉降比(Settling velocity, SV); 混合悬浮固体浓度(Mixed liquid suspended solid, MLSS); 溶解氧(Dissolved oxygen, DO).
    下载: 导出CSV

    表  2  软测量模型变量相关性分析

    Table  2  Correlation analysis between variables

    变量名与出水BOD相关性变量名与出水TP相关性
    进水TP0.5419出水NH$_{4} $-N0.4461
    出水NH$_{4} $-N0.4484出水温度0.4272
    进水BOD(曝气池SV)0.3806 (0.2492)进水TP0.3308
    出水石油类0.3703DO0.2516
    曝气池MLSS0.2737出水油类0.1890
    进水油类0.2644进水油类0.1558
    下载: 导出CSV

    表  3  Mackey-Glass时间序列预测结果对比

    Table  3  Performance comparison of different algorithms on Mackey-Glass prediction

    算法训练时间(s)训练RMSE测试RMSE隐含层神经元数
    GDFNN87.12*/0.0118*11*
    GPFNN56.14*/0.0107*9*
    SVR0.1690.06100.063817
    IELM0.2330.03900.0365500
    PSO-RBF859.6*/0.0208*12*
    APSO-RBF832.7*/0.0135*11*
    IErrCor-RBF5.6830.00840.00845
    注: *表示参考原文给出的结果; /表示原文未给出结果.
    下载: 导出CSV

    表  4  不同算法对出水BOD和出水TP软测量统计结果对比

    Table  4  Testing results of different soft measurement models on the effluent TP and effluent BOD

    待测变量算法平均训练时间(s)平均测试时间(s)测试RMSE测试APE平均模型结构
    最大值最小值平均值最大值最小值平均值
    出水BODRBF12.19130.01111.32730.35870.73400.08940.02160.0441106
    GAP-RBF0.60870.03270.92660.81630.87410.05020.04740.048617
    IELM0.06730.03340.57460.43190.48310.03760.02980.0337400
    SVR0.01030.00190.47730.21240.28260.05570.02510.034841
    APSO-RBF211.64120.00460.51960.42700.45980.03270.02910.031213
    SCNN0.23610.00120.35760.23840.28450.02340.01490.017657
    BLMNN6.61940.00130.29920.22860.23670.01790.01450.016310
    出水TPRBF12.19130.01110.14170.04300.06970.22400.05410.0969106
    GAP-RBF1.00270.05550.19510.10760.14820.23310.14130.184334
    IELM0.08680.06530.09450.06540.07830.1290.09670.1105400
    SVR0.01250.00340.14220.05800.09500.22540.08510.143055
    APSO-RBF222.63490.005190.08220.05560.07110.11680.07590.103820
    SCNN0.21500.00230.09930.06940.08220.10840.08080.096442
    BLMNN2.70320.00080.04260.03540.03940.05650.04880.05245
    下载: 导出CSV
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  • 收稿日期:  2017-09-01
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