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基于宽度混合森林回归的城市固废焚烧过程二噁英排放软测量

夏恒 汤健 崔璨麟 乔俊飞

夏恒, 汤健, 崔璨麟, 乔俊飞. 基于宽度混合森林回归的城市固废焚烧过程二噁英排放软测量. 自动化学报, 2022, 48(x): 1−23 doi: 10.16383/j.aas.c220012
引用本文: 夏恒, 汤健, 崔璨麟, 乔俊飞. 基于宽度混合森林回归的城市固废焚烧过程二噁英排放软测量. 自动化学报, 2022, 48(x): 1−23 doi: 10.16383/j.aas.c220012
Xia Heng, Tang Jian, Cui Can-Lin, Qiao Jun-Fei. Soft sensing method of dioxin emission in municipal solid waste incineration process based on broad hybrid forest regression. Acta Automatica Sinica, 2022, 48(x): 1−23 doi: 10.16383/j.aas.c220012
Citation: Xia Heng, Tang Jian, Cui Can-Lin, Qiao Jun-Fei. Soft sensing method of dioxin emission in municipal solid waste incineration process based on broad hybrid forest regression. Acta Automatica Sinica, 2022, 48(x): 1−23 doi: 10.16383/j.aas.c220012

基于宽度混合森林回归的城市固废焚烧过程二噁英排放软测量

doi: 10.16383/j.aas.c220012
基金项目: 国家自然科学基金(62073006, 62173120, 62021003), 北京市自然科学基金资助项目(4212032, 4192009)和科技创新2030-“新一代人工智能”重大项目(2021ZD0112301, 2021ZD0112302)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. 主要研究方向为小样本数据建模和城市固废焚烧过程二噁英排放预测. 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

    崔璨麟:北京工业大学信息学部硕士研究生. 主要研究方向为城市固废焚烧过程风险预警. E-mail: cuicanlin@emails.bjut.edu.cn

    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

Soft Sensing Method of Dioxin Emission in Municipal Solid Waste Incineration Process Based on Broad Hybrid Forest Regression

Funds: Supported by National Natural Science Foundation of China(62073006, 62173120, 62021003), Beijing Natural Science Foundation (4212032, 4192009) and National Key Research and Development Program of China (2021ZD0112301, 2021ZD0112302)
More Information
    Author Bio:

    XIA Heng Ph. D. candidate at the Faculty of Information Technology, Beijing University of Technology. His research interest covers small sample data modeling and dioxin emission prediction of the municipal solid waste incineration process

    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

    CUI Can-Lin Master student at the Faculty of Information Technology, Beijing University of Technology. His main research interest is risk warning of municipal solid waste incineration process

    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: 二噁英是城市固废焚烧过程排放的痕量有机污染物. 受限于相关技术的复杂度和高成本, 二噁英排放浓度检测的大时滞已成为制约城市固废焚烧过程优化控制的关键因素之一. 虽然具有低成本、快响应、高精度等特点的数据驱动软测量模型能够有效解决上述问题, 但二噁英建模方法必须要契合数据的小样本、高维度特性. 对此, 提出了由特征映射层、潜在特征提取层、特征增强层和增量学习层组成的宽度混合森林回归软测量方法. 首先, 构建由随机森林和完全随机森林构成的混合森林组进行高维特征映射; 其次, 依据贡献率对全联接混合矩阵进行潜在特征提取, 采用信息度量准则保证潜在有价值信息的最大化传递和最小化冗余, 降低模型的复杂度和计算消耗; 然后, 基于所提取潜在信息训练特征增强层以增强特征表征能力; 最后, 通过增量式学习策略构建增量学习层后采用Moore-Penrose伪逆获得权重矩阵. 在基准数据集和城市固废焚烧过程二噁英数据集上的实验结果表明了方法的有效性和优越性.
  • 图  1  城市固废焚烧工艺流程图

    Fig.  1  Process flow chart of municipal solid waste incineration process

    图  2  宽度混合森林回归建模策略图

    Fig.  2  Modeling strategy of broad hybrid forest regression

    图  3  RF和CRF的建模过程

    Fig.  3  Modeling process of RF and CRF

    图  4  基准数据集中潜在特征的贡献率曲线

    Fig.  4  Contribution rate curves of latent features of benchmark datasets

    图  5  基准数据集潜在特征与真值的互信息值

    Fig.  5  Mutual information values of latent features and true values of benchmark datasets

    图  6  基准数据集训练误差收敛曲线

    Fig.  6  Training error convergence curve of benchmark datasets

    图  7  NIR数据集的拟合曲线

    Fig.  7  Fitting curves of NIR dataset

    图  9  RB数据集的拟合曲线

    Fig.  9  Fitting curves of RB dataset

    图  8  CT数据集的拟合曲线

    Fig.  8  Fitting curves of CT dataset

    图  10  DXN数据潜在特征的贡献率曲线

    Fig.  10  Contribution rate curve of latent feature of DXN dataset

    图  11  DXN数据潜在特征与真值的互信息值

    Fig.  11  Mutual information value of latent feature and true value of DXN dataset

    图  12  DXN训练误差收敛曲线

    Fig.  12  Convergence curves of DXN training error

    图  13  DXN数据集中的拟合曲线

    Fig.  13  Fit curves of DXN dataset

    图  16  超参数敏感性分析曲线

    Fig.  16  Sensitivity analysis curves of super parameter

    表  1  符号说明

    Table  1  Symbol description

    缩写词中文全称英文全称
    MSWI城市固废焚烧Municipal solid waste incineration
    DXN二噁英Dioxin
    HRGC/HRMS高分辨色谱质谱联用High-resolution chromatography combined with high-resolution mass spectrometry
    NN神经网络Neural network
    PCDDs/PCDFs多氯二苯并二恶英/多氯二苯并呋喃Polychlorinated dibenzo-p-dioxins/Polychlorinated dibenzofurans
    BPNN反向传播神经网络Back-propagation neural network
    SVR支持向量回归Support vector regression
    SEN选择性集成Selective emsemble
    RF随机森林Random forest
    DFR深度森林回归Deep forest regression
    BLS宽度学习系统Broad learning system
    BLS-NN神经网络宽度学习系统Broad learning system neural network
    BHFR宽度混合森林回归Broad hybrid forest regression
    CRF完全随机森林Completely random forest
    MSW城市固废Municipal solid waste
    TEQ毒性当量Toxic equivalent quangtity
    G 1烟气1Gas 1
    $\text{NO}_{x}$氮氧化物Nitrogen oxides
    HCL氯化氢Hydrogen chloride
    HF氟化氢Hydrogen fluoride
    $\text{SO}_2$二氧化硫Sulfur dioxide
    PbPlumbum
    HgMercury
    CdCadmium
    G 2烟气2Gas 2
    G 3烟气3Gas 3
    RSM随机子空间法Random subspace method
    PCA主成分分析Principal components analysis
    MI互信息Mutual information
    CTCT切片在轴轴数据上的相对位置The relative location of ct slices on the axial axis data
    RB住宅建筑数据Residential building data
    NIR橙汁近红外光谱数据The orange juice near infrared spectra data
    RMSE均方根误差Root mean square error
    MAE平均绝对误差Mean absolute error
    $\text{R}^{2}$决定系数Coefficient of determination
    DFR-clfc基于跨层全连接的深度森林回归Deep forest regression based on cross-layer full connection
    下载: 导出CSV

    表  2  实验数据统计结果

    Table  2  Statistical results of experimental datasets

    数据集实际样本量实际样本量特征维数
    总数训练集验证集测试集
    NIR218109552727700
    CT583117592929291
    RB372124623131106
    DXN141141713535116
    下载: 导出CSV

    表  3  NIR数据集实验结果

    Table  3  Experimental results of NIR dataset

    方法数据集RMSEMAE$\text{R}^{2}$
    平均值方差平均值方差平均值方差
    RF训练集5.7142$\times {10} ^{0}$2.1347$\times {10} ^{-2}$3.8799$\times {10} ^{0}$9.4469$\times {10} ^{-3}$8.1131$\times {10} ^{-1}$9.2887$\times {10} ^{-5}$
    验证集1.3522$\times {10} ^{1}$1.3660$\times {10} ^{-2}$8.3125$\times {10} ^{0}$7.2473$\times {10} ^{-3}$2.6715$\times {10} ^{-1}$1.6048$\times {10} ^{-4}$
    测试集1.0925$\times {10} ^{1}$3.2313$\times {10} ^{-2}$8.1093$\times {10} ^{0}$1.7493$\times {10} ^{-2}$3.0986$\times {10} ^{-1}$5.1625$\times {10} ^{-4}$
    DFR训练集5.8836$\times {10} ^{0}$1.4331$\times {10} ^{-3}$4.1004$\times \text{10}^{0}$6.1766$\times {10} ^{-4}$8.0007$\times \text{10}^{-1}$6.5971$\times {10} ^{-6}$
    验证集1.3585$\times {10} ^{1}$3.7257$\times {10} ^{-3}$8.3057$\times {10} ^{0}$1.5854$\times {10} ^{-3}$2.6036$\times {10} ^{-1}$4.4251$\times {10} ^{-5}$
    测试集1.0842$\times {10} ^{1}$1.4742$\times {10} ^{-3}$8.0306$\times {10} ^{0}$1.8258$\times {10} ^{-3}$3.2046$\times {10} ^{-1}$2.3176$\times {10} ^{-5}$
    DFR-clfc训练集5.7742$\times {10} ^{0}$1.7160$\times {10} ^{-2}$4.0139$\times {10} ^{0}$9.6271$\times {10} ^{-3}$8.0735$\times {10} ^{-1}$7.6485$\times {10} ^{-5}$
    验证集1.3569$\times {10} ^{1}$3.1520$\times {10} ^{-3}$8.3265$\times {10} ^{0}$1.3592$\times {10} ^{-3}$2.6219$\times {10} ^{-1}$3.7318$\times {10} ^{-5}$
    测试集1.0793$\times {10} ^{1}$2.5668$\times {10} ^{-3}$7.9746$\times {10} ^{0}$3.3925$\times {10} ^{-3}$3.2664$\times {10} ^{-1}$3.9879$\times {10} ^{-5}$
    BLS-NN训练集7.4226$\times {10} ^{-1}$3.7588$\times {10} ^{-1}$4.6530$\times {10} ^{-1}$1.5602$\times {10} ^{-1}$9.9476$\times {10} ^{-1}$3.2486$\times {10} ^{-5}$
    验证集7.8288$\times {10} ^{3}$4.9487$\times {10} ^{7}$2.5450$\times {10} ^{3}$4.6180$\times {10} ^{6}$$-$4.3402$\times {10} ^{5}$7.2220$\times {10} ^{11}$
    测试集6.4945$\times {10} ^{3}$1.4866$\times {10} ^{7}$2.1689$\times {10} ^{3}$1.5496$\times {10} ^{6}$$-$3.2544$\times {10} ^{5}$8.9669$\times {10} ^{10}$
    BHFR训练集2.8931$\times {10} ^{0}$5.5126$\times {10} ^{-1}$2.1335$\times {10} ^{0}$3.3004$\times {10} ^{-1}$9.4864$\times {10} ^{-1}$6.0585$\times {10} ^{-4}$
    验证集1.3782$\times {10} ^{1}$1.7263$\times {10} ^{0}$9.2584$\times {10} ^{0}$6.5031$\times {10} ^{-1}$2.3224$\times {10} ^{-1}$2.1525$\times {10} ^{-2}$
    测试集9.9505$\times {10} ^{0}$5.6804$\times {10} ^{-1}$7.3675$\times {10} ^{0}$2.4515$\times {10} ^{-1}$4.2455$\times {10} ^{-1}$7.3749$\times {10} ^{-3}$
    下载: 导出CSV

    表  5  RB数据集实验结果

    Table  5  Experimental results of RB dataset

    方法数据集RMSEMAE$\text{R}^{2}$
    平均值方差平均值方差平均值方差
    RF训练集5.3243$\times {10} ^{1}$3.7463$\times {10} ^{0}$4.2919$\times {10} ^{1}$2.4224$\times {10} ^{0}$8.4698$\times {10} ^{-1}$1.2156$\times {10} ^{-4}$
    验证集1.3211$\times {10} ^{2}$6.6035$\times {10} ^{0}$8.1855$\times {10} ^{1}$2.9950$\times {10} ^{0}$5.3824$\times {10} ^{-1}$3.2140$\times {10} ^{-4}$
    测试集7.5697$\times {10} ^{1}$4.3622$\times {10} ^{0}$6.0986$\times {10} ^{1}$2.7466$\times {10} ^{0}$7.0126$\times {10} ^{-1}$2.7457$\times {10} ^{-4}$
    DFR训练集4.8752$\times {10} ^{1}$1.4944$\times {10} ^{-1}$3.9200$\times {10} ^{1}$8.4396$\times {10} ^{-2}$8.7185$\times {10} ^{-1}$4.1370$\times {10} ^{-6}$
    验证集1.2600$\times {10} ^{2}$1.6487$\times {10} ^{-1}$7.8890$\times {10} ^{1}$1.1939$\times {10} ^{-1}$5.8012$\times {10} ^{-1}$7.3315$\times {10} ^{-6}$
    测试集7.4221$\times {10} ^{1}$3.1170$\times {10} ^{-1}$6.0387$\times {10} ^{1}$2.4493$\times {10} ^{-1}$7.1299$\times {10} ^{-1}$1.8723$\times {10} ^{-5}$
    DFR-clfc训练集3.0978$\times {10} ^{1}$2.6657$\times {10} ^{1}$2.4856$\times {10} ^{1}$1.7056$\times {10} ^{1}$9.4690$\times {10} ^{-1}$3.4386$\times {10} ^{-4}$
    验证集1.1830$\times {10} ^{2}$4.9405$\times {10} ^{0}$7.2901$\times {10} ^{1}$2.7676$\times {10} ^{0}$6.2977$\times {10} ^{-1}$1.9820$\times {10} ^{-4}$
    测试集6.9427$\times {10} ^{1}$1.7460$\times {10} ^{0}$5.5570$\times {10} ^{1}$2.1741$\times {10} ^{0}$7.4879$\times {10} ^{-1}$9.2839$\times {10} ^{-5}$
    BLS-NN训练集9.4877$\times {10} ^{-4}$4.8003$\times {10} ^{-6}$4.3563$\times {10} ^{-4}$7.7161$\times {10} ^{-7}$1.0000$\times {10} ^{0}$1.1561$\times {10} ^{-18}$
    验证集5.0098$\times {10} ^{2}$1.3099$\times {10} ^{5}$2.6163$\times {10} ^{2}$2.6950$\times {10} ^{4}$$-$8.9285$\times {10} ^{0}$1.6631$\times {10} ^{2}$
    测试集5.2093$\times {10} ^{2}$1.5354$\times {10} ^{5}$2.8934$\times {10} ^{2}$3.2539$\times {10} ^{4}$$-$2.0737$\times {10} ^{1}$9.3226$\times {10} ^{2}$
    BHFR训练集8.4893$\times {10} ^{0}$6.4125$\times {10} ^{-1}$6.4970$\times {10} ^{0}$3.1874$\times {10} ^{-1}$9.9608$\times {10} ^{-1}$5.6404$\times {10} ^{-7}$
    验证集9.9275$\times {10} ^{1}$2.2922$\times {10} ^{0}$5.4880$\times {10} ^{1}$1.0407$\times {10} ^{0}$7.3930$\times {10} ^{-1}$6.3926$\times {10} ^{-5}$
    测试集4.5645$\times {10} ^{1}$1.7188$\times {10} ^{0}$3.2284$\times {10} ^{1}$9.0105$\times {10} ^{-1}$8.9137$\times {10} ^{-1}$3.8975$\times {10} ^{-5}$
    下载: 导出CSV

    表  4  CT数据集实验结果

    Table  4  Experimental results of CT dataset

    方法数据集RMSEMAE$\text{R}^{2}$
    平均值方差平均值方差平均值方差
    RF训练集1.5839$\times {10} ^{0}$1.2389$\times {10} ^{-2}$1.2676$\times {10} ^{0}$6.1900$\times {10} ^{-3}$9.7215$\times {10} ^{-1}$1.4862$\times {10} ^{-5}$
    验证集1.8907$\times {10} ^{0}$2.2323$\times {10} ^{-2}$1.4978$\times {10} ^{0}$1.4923$\times {10} ^{-2}$9.5761$\times {10} ^{-1}$4.2488$\times {10} ^{-5}$
    测试集2.2138$\times {10} ^{0}$3.6173$\times {10} ^{-2}$1.8254$\times {10} ^{0}$2.1898$\times {10} ^{-2}$9.5706$\times {10} ^{-1}$5.3991$\times {10} ^{-5}$
    DFR训练集4.1046$\times {10} ^{0}$1.3520$\times {10} ^{-3}$3.4579$\times {10} ^{0}$9.5891$\times {10} ^{-4}$8.1383$\times {10} ^{-1}$1.1086$\times {10} ^{-5}$
    验证集4.1864$\times {10} ^{0}$1.2940$\times {10} ^{-3}$3.5658$\times {10} ^{0}$1.4003$\times {10} ^{-3}$7.9338$\times {10} ^{-1}$1.2646$\times {10} ^{-5}$
    测试集4.8124$\times {10} ^{0}$2.1197$\times {10} ^{-3}$4.2219$\times {10} ^{0}$1.4647$\times {10} ^{-3}$7.9851$\times {10} ^{-1}$1.4815$\times {10} ^{-5}$
    DFR-clfc训练集3.7411$\times {10} ^{0}$4.1351$\times {10} ^{-2}$3.1392$\times {10} ^{0}$3.2494$\times {10} ^{-2}$8.4491$\times {10} ^{-1}$2.8032$\times {10} ^{-4}$
    验证集3.8251$\times {10} ^{0}$3.9698$\times {10} ^{-2}$3.2193$\times {10} ^{0}$3.5889$\times {10} ^{-2}$8.2708$\times {10} ^{-1}$3.1969$\times {10} ^{-4}$
    测试集4.3811$\times {10} ^{0}$4.9342$\times {10} ^{-2}$3.8122$\times {10} ^{0}$4.5453$\times {10} ^{-2}$8.3262$\times {10} ^{-1}$2.8693$\times {10} ^{-4}$
    BLS-NN训练集1.0447$\times {10} ^{-7}$4.7677$\times {10} ^{-15}$4.1145$\times {10} ^{-8}$5.7001$\times {10} ^{-16}$1.0000$\times {10} ^{0}$7.4604$\times {10} ^{-32}$
    验证集2.5527$\times {10} ^{0}$6.9348$\times {10} ^{-1}$1.7606$\times {10} ^{0}$2.0416$\times {10} ^{-1}$9.1542$\times {10} ^{-1}$3.2479$\times {10} ^{-3}$
    测试集2.9466$\times {10} ^{0}$1.3019$\times {10} ^{0}$1.8882$\times {10} ^{0}$4.4686$\times {10} ^{-1}$9.1371$\times {10} ^{-1}$3.5944$\times {10} ^{-3}$
    BHFR训练集4.4458$\times {10} ^{-1}$8.4237$\times {10} ^{-3}$3.7023$\times {10} ^{-1}$4.5597$\times {10} ^{-3}$9.9773$\times {10} ^{-1}$1.6195$\times {10} ^{-6}$
    验证集9.5728$\times {10} ^{-1}$1.3354$\times {10} ^{-2}$7.4450$\times {10} ^{-1}$6.1644$\times {10} ^{-3}$9.8905$\times {10} ^{-1}$1.0451$\times {10} ^{-5}$
    测试集9.3108$\times {10} ^{-1}$1.2802$\times {10} ^{-2}$6.7034$\times {10} ^{-1}$1.2237$\times {10} ^{-2}$9.9235$\times {10} ^{-1}$5.1917$\times {10} ^{-6}$
    下载: 导出CSV

    表  6  DXN数据集实验结果

    Table  6  Experimental results of DXN dataset

    方法数据集RMSEMAE$\text{R}^{2}$
    平均值方差平均值方差平均值方差
    RF训练集1.1159$\times {10} ^{-2}$5.7497$\times {10} ^{-8}$9.0221$\times {10} ^{-3}$4.0684$\times {10} ^{-8}$8.5346$\times {10} ^{-1}$3.9360$\times {10} ^{-5}$
    验证集2.0051$\times {10} ^{-2}$1.8026$\times {10} ^{-7}$1.4677$\times {10} ^{-2}$8.2255$\times {10} ^{-8}$5.0196$\times {10} ^{-1}$4.3515$\times {10} ^{-4}$
    测试集1.6922$\times {10} ^{-2}$1.6150$\times {10} ^{-7}$1.3548$\times {10} ^{-2}$8.9520$\times {10} ^{-8}$5.9001$\times {10} ^{-1}$3.7817$\times {10} ^{-4}$
    DFR训练集1.1493$\times {10} ^{-2}$8.7413$\times {10} ^{-9}$9.4568$\times {10} ^{-3}$4.6626$\times {10} ^{-9}$8.4463$\times {10} ^{-1}$6.3663$\times {10} ^{-6}$
    验证集2.0735$\times {10} ^{-2}$9.7835$\times {10} ^{-9}$1.5780$\times {10} ^{-2}$1.1121$\times {10} ^{-8}$4.6759$\times {10} ^{-1}$2.5813$\times {10} ^{-5}$
    测试集1.7791$\times {10} ^{-2}$1.7308$\times {10} ^{-8}$1.4608$\times {10} ^{-2}$1.5235$\times {10} ^{-8}$5.4701$\times {10} ^{-1}$4.5066$\times {10} ^{-5}$
    DFR-clfc训练集8.0852$\times {10} ^{-3}$2.9078$\times {10} ^{-6}$6.6040$\times {10} ^{-3}$2.0819$\times {10} ^{-6}$9.1986$\times {10} ^{-1}$1.1887$\times {10} ^{-3}$
    验证集2.0187$\times {10} ^{-2}$1.4562$\times {10} ^{-7}$1.5626$\times {10} ^{-2}$2.3355$\times {10} ^{-8}$4.9520$\times {10} ^{-1}$3.6404$\times {10} ^{-4}$
    测试集1.7025$\times {10} ^{-2}$1.5755$\times {10} ^{-7}$1.4068$\times {10} ^{-2}$6.0233$\times {10} ^{-8}$5.8501$\times {10} ^{-1}$3.7843$\times {10} ^{-4}$
    BLS-NN训练集1.2924$\times {10} ^{-9}$1.5756$\times {10} ^{-18}$9.5358$\times {10} ^{-10}$7.2150$\times {10} ^{-19}$1.0000$\times {10} ^{0}$8.2358$\times {10} ^{-29}$
    验证集6.8845$\times {10} ^{-2}$7.0040$\times {10} ^{-4}$5.3153$\times {10} ^{-2}$3.3474$\times {10} ^{-4}$$-$5.6928$\times {10} ^{0}$3.7799$\times {10} ^{1}$
    测试集7.8396$\times {10} ^{-2}$6.7692$\times {10} ^{-4}$6.0922$\times {10} ^{-2}$4.1785$\times {10} ^{-4}$$-$8.7153$\times {10} ^{0}$4.7630$\times {10} ^{1}$
    BHFR训练集6.0665$\times {10} ^{-3}$1.6330$\times {10} ^{-8}$3.9665$\times {10} ^{-3}$8.4708$\times {10} ^{-9}$9.5669$\times {10} ^{-1}$3.3481$\times {10} ^{-6}$
    验证集2.1551$\times {10} ^{-2}$3.5181$\times {10} ^{-8}$1.2384$\times {10} ^{-2}$3.5083$\times {10} ^{-8}$4.2484$\times {10} ^{-1}$9.8731$\times {10} ^{-5}$
    测试集1.6189$\times {10} ^{-2}$2.2474$\times {10} ^{-8}$1.1226$\times {10} ^{-2}$1.0102$\times {10} ^{-8}$6.2491$\times {10} ^{-1}$4.8607$\times {10} ^{-5}$
    下载: 导出CSV

    表  7  BHFR超参数及其区间设置

    Table  7  Super parameters and interval setting of BHFR

    模型超参数符号NIRCTRBDXN
    决策树叶节点最小样
    本数
    ${N_{{\rm{samples}}}}$$[1,55]$$[1,59]$$[1,62]$$[1,71]$
    RSM特征选择数量${M_{{\rm{RSM}}}}$$[1,700]$$[1,291]$$[1,103]$$[1,116]$
    决策树的
    数量
    ${M_{{\rm{tree}}}}$$[5,100]$$[5,100]$$[5,100]$$[5,100]$
    混合森林组的数量${N_{{\rm{Forest}}}}$$[5,100]$$[5,100]$$[5,100]$$[5,100]$
    潜在特征贡献率阈值$\eta $$[0.7,1]$$[0.4,1]$$[0.71,1]$$[0.3,1]$
    互信息阈值$\zeta $[0.65, 0.75][0.75, 0.85][0.72, 0.8][0.68, 0.76]
    正则化参数$\lambda$[1−10, 110][1−10, 110][1−10, 110][1−10, 110]
    下载: 导出CSV

    AI  DXN数据集的过程变量信息

    AI  The process variable information for DXN datasets

    序号过程变量单位
    1燃烧室左侧温度1
    2燃烧室左侧温度2
    3燃烧室右侧温度3
    4燃烧室左侧温度4
    5燃烧室右侧温度5
    6燃烧室右侧温度6
    7燃烬段炉排顶端气温左
    8燃烬段炉排顶端气温右
    9干燥炉排温度左入口
    10干燥炉排温度左出口
    11干燥炉排温度右入口
    12干燥炉排温度右出口
    13干燥段与燃烧段炉排的炉墙左侧内温度
    14干燥段与燃烧段炉排的炉墙左侧外温度
    15干燥段与燃烧段炉排的炉墙右侧内温度
    16干燥段与燃烧段炉排的炉墙右侧外温度
    17燃烧炉排1-1左内侧温度
    18燃烧炉排1-1左外侧温度
    19燃烧炉排1-1右内侧温度
    20燃烧炉排1-1右外侧温度
    21燃烧炉排1-2左内侧温度
    22燃烧炉排1-2左外侧温度
    23燃烧炉排1-2右内侧温度
    24燃烧炉排1-2右外侧温度
    25燃烧炉排2-1左内侧温度
    26燃烧炉排2-1左外侧温度
    27燃烧炉排2-1右内侧温度
    28燃烧炉排2-1右外侧温度
    29燃烧炉排2-2左内侧温度
    30燃烧炉排2-2左外侧温度
    31燃烧炉排2-2右内侧温度
    32燃烧炉排2-2右外侧温度
    33二次燃烧室出口温度左
    34二次燃烧室出口温度右
    35一次空预器出口空气温度
    36燃烧炉排入口空气温度
    37干燥炉排入口空气温度
    38二次空预器出口空气温度
    39炉墙左侧冷却风出口温度
    40炉墙右侧冷却风出口温度
    41炉排左侧冷却风出口温度
    42炉排右侧冷却风出口温度
    43一级过热器出口蒸汽温度
    44二级过热器出口蒸汽温度
    45三级过热器出口蒸汽温度
    46省煤器出口给水温度
    47反应器入口温度
    48布袋A入口温度
    49布袋B入口温度
    50流化风机出口温度
    51Fgd出口烟气温度A
    52Fgd出口烟气温度
    53烟道入口烟气温度
    54干燥炉排左1空气流量km3N/h(1)
    55干燥炉排右1空气流量km3N/h(1)
    56干燥炉排左2空气流量km3N/h(1)
    57干燥炉排右2空气流量km3N/h(1)
    58燃烧炉排左1-1空气流量km3N/h(1)
    59燃烧炉排右1-1空气流量km3N/h(1)
    60燃烧炉排左1-2空气流量$\text{km}^{3}\text{N/h}^{(1)}$
    61燃烧炉排右1-2空气流量km3N/h(1)
    62燃烧炉排左2-1空气流量km3N/h(1)
    63燃烧炉排右2-1空气流量$\text{km}^{3}\text{N/h}^{(1)}$
    64燃烧炉排左2-2空气流量km3N/h(1)
    65燃烧炉排右2-2空气流量km3N/h(1)
    66燃烬炉排左空气流量km3N/h(1)
    67燃烬炉排右空气流量$\text{km}^{3}\text{N/h}^{(1)}$
    68二次风流量km3N/h(1)
    69省煤器No.2给水流量t/h
    70省煤器No.1给水流量t/h
    71一级过热器冷却水流量t/h
    72二级过热器冷却水流量t/h
    73锅炉出口主蒸汽流量t/h
    74烟道入口烟气流量km3N/h(1)
    75混合器水流量Akg/h
    76混合器水流量Bkg/h
    77尿素溶剂供应流量L/h
    78石灰储仓给料量kg/h
    79活性炭储仓给料量kg/h
    80一次风机出口空气压力kPa
    81二次风机出口空气压力kPa
    82省煤器出口压力kPa
    83烟道入口烟气压力Pa
    84汽包压力MPa
    85三级过热器出口蒸汽压力MPa
    86Fgd出口烟气压力Pa
    87布袋压差AkPa
    88布袋压差BkPa
    89进料器左内侧速度%
    90进料器左外侧速度%
    91进料器右内侧速度%
    92进料器右外侧速度%
    93干燥炉排左内侧速度%
    94干燥炉排左外侧速度%
    95干燥炉排右内侧速度%
    96干燥炉排右外侧速度%
    97燃烧炉排1左内侧速度%
    98燃烧炉排1左外侧速度%
    99燃烧炉排1右内侧速度%
    100燃烧炉排1右外侧速度%
    101燃烧炉排2左内侧速度%
    102燃烧炉排2左外侧速度%
    103燃烧炉排2右内侧速度%
    104燃烧炉排2右外侧速度%
    105燃烬炉排左侧速度%
    106燃烬炉排右侧速度%
    107汽包水位mm
    108烟气净化系统入口烟气分析-$\text{o}_2$%
    109出口烟气分析-$\text{o}_2$%
    110出口烟气分析-灰尘mg/m3N(2)
    111出口烟气分析-$\text{no}_\text{x}$mg/m3N(2)
    112出口烟气分析-s$\text{o}_2$mg/m3N(2)
    113出口烟气分析-hclmg/m3N(2)
    114出口烟气分析-comg/m3N(2)
    115出口烟气分析c$\text{o}_2$%
    116出口烟气分析-hfmg/m3N(2)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-05
  • 录用日期:  2022-03-04
  • 网络出版日期:  2022-08-09

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