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基于仿真机理和改进回归决策树的二噁英排放建模

夏恒 汤健 余文 乔俊飞

夏恒, 汤健, 余文, 乔俊飞. 基于仿真机理和改进回归决策树的二噁英排放建模. 自动化学报, 2024, 50(8): 1001−1019 doi: 10.16383/j.aas.c230625
引用本文: 夏恒, 汤健, 余文, 乔俊飞. 基于仿真机理和改进回归决策树的二噁英排放建模. 自动化学报, 2024, 50(8): 1001−1019 doi: 10.16383/j.aas.c230625
Xia Heng, Tang Jian, Yu Wen, Qiao Jun-Fei. Dioxin emission concentration modeling based on simulation mechanism and improved linear regression decision tree. Acta Automatica Sinica, 2024, 50(8): 1001−1019 doi: 10.16383/j.aas.c230625
Citation: Xia Heng, Tang Jian, Yu Wen, Qiao Jun-Fei. Dioxin emission concentration modeling based on simulation mechanism and improved linear regression decision tree. Acta Automatica Sinica, 2024, 50(8): 1001−1019 doi: 10.16383/j.aas.c230625

基于仿真机理和改进回归决策树的二噁英排放建模

doi: 10.16383/j.aas.c230625
基金项目: 国家自然科学基金 (62073006, 62173120, 62373017)资助
详细信息
    作者简介:

    夏恒:北京工业大学信息学部博士研究生. 主要研究方向为城市固废焚烧过程二噁英排放预测与控制, 树结构深/宽度学习结构设计与优化. E-mail: xiaheng@emails.bjut.edu.cn

    汤健:北京工业大学信息学部教授. 主要研究方向为小样本数据建模和城市固废处理过程智能控制.本文通信作者. E-mail: freeflytang@bjut.edu.cn

    余文:墨西哥国立理工大学高级研究中心自动化部教授. 主要研究方向为复杂工业过程建模与控制, 机器学习. E-mail: yuw@ctrl.cinvestav.mx

    乔俊飞:北京工业大学信息学部教授. 主要研究方向为污水处理过程智能控制和神经网络结构设计与优化. E-mail: junfeiq@bjut.edu.cn

Dioxin Emission Concentration Modeling Based on Simulation Mechanism and Improved Linear Regression Decision Tree

Funds: Supported by National Natural Science Foundation of China (62073006, 62173120, 62373017)
More Information
    Author Bio:

    XIA Heng Ph.D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers dioxin emission prediction and control of the municipal solid waste incineration process, and structure design and optimization of tree-structured deep/ broad learning

    TANG Jian Professor at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and intelligent control of municipal solid waste treatment process. Corresponding author of this paper

    YU Wen Professor in the Departamento de Control Automatico, Centro de Investigation de Estudios Avanzados, National Polytechnic Institute México. His research interest covers modeling and control of the complex industrial process, and machine learning

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

  • 摘要: 城市固废焚烧(Municipal solid waste incineration, MSWI)过程是“世纪之毒”二噁英(Dioxin, DXN)的重要排放源之一. 截止目前为止, DXN的演化机理和实时检测仍是尚未解决的难题. 现有研究主要基于离线化验数据构建数据驱动模型, DXN的检测未有效结合燃烧过程机理. 针对该问题, 本文提出基于仿真机理和改进线性回归决策树(Linear regression decision tree, LRDT)的DXN排放建模. 首先, 采用基于床层固废燃烧模拟软件 (Fluid dynamic incinerator code, FLIC)和过程工程先进系统软件(Advanced system for process engineering plus, Aspen plus)耦合的数值仿真模型, 获取蕴含多运行工况的虚拟机理数据; 接着, 利用虚拟机理数据构建基于改进LRDT的CO2、CO和O2燃烧状态表征变量模型; 最后, 以真实CO2、CO、O2作为输入和以DXN真值作为输出, 构建多入单出LRDT的过程映射模型(Process mapping model, PMM), 再利用该模型进行半监督学习和结构迁移得到机理映射模型1 (Mechanism mapping models1, MMM1), 进一步通过结构增量学习获得基于半监督迁移学习的MMM2模型. 在实验室的半实物平台和北京某MSWI厂的边侧验证平台对所提方法进行了工业应用验证.
  • 图  1  MSWI过程流程图

    Fig.  1  Process flow of MSWI process

    图  2  虚实数据驱动的建模策略

    Fig.  2  Modeling strategy driven by virtual and real data

    图  3  Aspen plus 模型示意图

    Fig.  3  Aspen plus model diagram

    图  4  MIMO LRDT结构图

    Fig.  4  MIMO LRDT structure chart

    图  5  树形结构转换图

    Fig.  5  Tree structure transformation diagram

    图  6  基于基准工况的固相燃烧结果图

    Fig.  6  Solid phase combustion results based on benchmark conditions

    图  7  虚拟机理数据中的输入/输出关系

    Fig.  7  Input/output relation in virtual mechanism data

    图  8  虚拟机理数据异常值去除前后的结果

    Fig.  8  Results of before and after removal of outliers from virtual mechanism data

    图  9  PMM模型在DXN 数据中的应用结果

    Fig.  9  Application results of PMM model in DXN data

    图  10  伪标记数据曲线

    Fig.  10  Pseudo-labeled data curve

    图  11  基于伪标记机理数据的模型测试曲线

    Fig.  11  Model testing curves based on pseudolabeled mechanical data

    图  12  MSWI过程半实物平台图

    Fig.  12  Hardware-in-loop simulation platform of MSWI process

    图  13  MSWI过程半实物仿真平台DXN检测软件界面

    Fig.  13  DXN testing software interface of hardware-in-loop simulation platform of MSWI process

    图  14  北京某MSWI 厂的基于安全隔离采集设备的边侧验证平台

    Fig.  14  Edge verification platform with secure isolation acquisition equipment at an MSWI factory in Beijing

    表  1  MSW成分分析

    Table  1  Analysis of MSW components

    分析项 单位
    工业分析 水分 (ar) 38.48 wt%
    挥发性 (ar) 41.80 wt%
    固定碳 (ar) 6.56 wt%
    灰烬 (ar) 13.16 wt%
    元素分析 C (daf) 64.31 wt%
    H (daf) 9.91 wt%
    N (daf) 24.93 wt%
    S (daf) 0.51 wt%
    O (daf) 0.34 wt%
    下载: 导出CSV

    表  2  焚烧炉基本情况

    Table  2  Basic information about incinerators

    参数单位
    额定产能800t/d
    实际产能624t/d
    炉排往复式顺推/
    长 × 宽11 × 12.9m
    速度8m/h
    一次风量65400${\rm{m}}^3$/h
    二次风量7500${\rm{m}}^3$/h
    一次风温度200
    一次风在干燥段的风量分布比例24.31%
    一次风在燃烧一段的风量分布比例43.35%
    一次风在燃烧二段的风量分布比例19.27%
    一次风在燃烬段的风量分布比例13.07%
    下载: 导出CSV

    表  3  正交实验参数信息

    Table  3  Orthogonal experimental parameter information

    10因素5水平 5因素5水平
    参数 因素 单位 水平-1 水平-2
    操作参数 炉排速度 m/h 7, 7.5, 8, 8.5, 9 −0.1
    给料量 t/h 24.2, 24.7 25.2 25.7 26.2 +0.1
    第1区域进风 ${\rm{m}}^3$/h 16080, 16440, 16800, 17160, 17520 +1.8
    第2区域进风 ${\rm{m}}^3$/h 28620, 29280, 29940, 30600, 31260 +3.2
    第3区域进风 ${\rm{m}}^3$/h 12660, 12960, 13260, 13560, 13860 +1.4
    第4区域进风 ${\rm{m}}^3$/h 8640, 8820, 9000, 9180, 9360 +1
    微观参数 颗粒大小 mm 15, 20, 25, 30, 35 /
    颗粒混合系数 / 2${\rm{e}}{-6}$, 3${\rm{e}}{-6}$, 4${\rm{e}}{-6}$, 5${\rm{e}}{-6}$, 6${\rm{e}}{-6}$ /
    组分参数 水分含量 % 48, 49.75, 51.5, 53.25, 55 /
    C : H : O 比率 % (58 : 7.5 : 33), (59 : 7.5 : 32), (60 : 7.5 : 31), (61 : 7.5 : 30), (62 : 7.5 : 29) /
    下载: 导出CSV

    表  4  机理数据的不同方法性能比较结果

    Table  4  Results of performance comparison between different methods of mechanism data

    方法目标值训练集测试集
    RMSE${\rm{R}}^2$RMSE${\rm{R}}^2$
    DT${\rm{CO}}_2$0.26880.97020.61530.8457
    CO0.95190.96591.81840.8751
    ${{\rm{O}}_2}$0.28110.97100.65360.8446
    RDT${\rm{CO}}_2$0.54000.87980.62370.8414
    CO2.23560.81172.53350.7575
    ${{\rm{O}}_2}$0.67520.83250.78620.7751
    RR${\rm{CO}}_2$1.400250.19181.39450.2072
    CO4.99860.05864.97380.0652
    ${{\rm{O}}_2}$1.43060.24811.42330.2630
    MISO LRDT${\rm{CO}}_2$0.41380.92940.58940.8584
    CO1.30460.93591.70570.8901
    ${{\rm{O}}_2}$0.42820.93260.54870.8905
    MIMO LRDT${\rm{CO}}_2$0.16450.93570.30890.8869
    CO0.22200.93570.29910.8867
    ${{\rm{O}}_2}$0.40560.98120.55580.9747
    下载: 导出CSV

    表  5  基于伪标记机理数据的模型统计结果

    Table  5  Model statistical results based on pseudo-labeling mechanism data

    方法训练集测试集
    RMSE${\rm{R}}^2$RMSE${\rm{R}}^2$
    PMM0.00200.80210.00200.8072
    MMM10.00150.89090.00150.8918
    MMM20.00140.89600.00150.8965
    下载: 导出CSV

    6  缩写词说明

    6  Abbreviation description

    缩写词 英文全称 中文全称
    MSWI Municipal solid waste incineration 城市固废焚烧
    DXN Dioxin 二噁英
    SNCR Selective non-catalytic reduction 选择性非催化还原
    FLIC Fluid dynamic incinerator code 床层固废燃烧模拟软件
    Aspen Plus Advanced system for process engineering plus 过程工程先进系统
    LRDT Linear regression decision tree 线性回归决策树
    PMM Process mapping model 过程映射模型
    MMM1 Mixed-driven models1 混合驱动模型1
    DD Data-driven 数据驱动
    MD Mechanism-driven 机理驱动
    MIMO Multiple-in multiple-out 多入多出
    SNCR Selective non-catalytic reduction 选择性非催化还原
    PICs Products of incomplete combustion 不完全燃烧产物
    LHV Lower heat value 低热值
    PA Primary airflow 一次风量
    FC Feeding capacity 给料量
    QAF Quartile abnormal filter 四分位异常滤波
    DCS Distributed control system 集散控制系统
    CmHn Hydrocarbons 碳氢化合物
    MISO Multiple-input and single-output 多输入单输出
    CART Classification and regression tree 分类回归树
    MSE Mean squared error 均方误差
    MMM Mechanism mapping model 机理映射模型
    DT Decision tree 决策树
    RDT Random decision tree 随机决策树
    RR Ridge regression 岭回归
    SVR Support vector regression 支持向量回归
    BPNN Back-propagation neural network BP神经网络
    RF Random forest 随机森林
    XGBoost Extreme gradient boost 极端梯度提升
    GBDT Gradient boosting decision tree 梯度提升决策树
    RMSE Root mean square error 均方根误差
    ${\rm{R}}^2$ Coefficient of determination 决定系数
    下载: 导出CSV
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  • 收稿日期:  2023-10-10
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