Dioxin Emission Concentration Modeling Based on Simulation Mechanism and Improved Linear Regression Decision Tree
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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和过程工程先进系统软件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(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厂的边缘侧验证平台对所提方法进行了工业应用验证.Abstract: Municipal solid waste incineration (MSWI) process has been one of the important emission sources of dioxin (DXN) in terms of century posion. Untill now, the evolution mechanism and real-time detection of DXN emission concentration are still unsolved challenges. Existing studies mainly rely on available data to build data-driven modeling, and how to effectively combine the mechanism of combustion process for DXN detection is a problem that is not considered. To solve this problem, this paper proposes DXN emission modelling method based on simulation mechanism and improved linear regression decision tree. First, a numerical simulation model based on coupling fluid dynamic incinerator code (FLIC) and advanced system for process engineering plus (Aspen Plus) software is used to obtain virtual mechanism data with multiple operating conditions. Then, virtual mechanism data is used to construct an improved linear regression decision tree (LRDT) combustion state representation variable CO2, CO, and O2 model. Next, a process mapping model (PMM) model based on multiple input single output LRDT is constructed using real CO2, CO, and O2 as input and DXN as output. Semi-supervised learning and structural transfer learning based on PMM is used to obtain the mechanism mapping models1 (MMM1) model. Finally, the final MMM2 model based on semi-supervised transfer learning is obtained by the structural growth learning of the MMM1 model. The proposed method was validated for industrial application on a hardware-in-loop simulation platform in the laboratory and an edge verification platform at an MSWI factory in Beijing.
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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 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 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 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缘端验证平台
Fig. 14 Edge end 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 值 单位 工业分析 水分 (ar) 38.48 wt% 挥发性 (ar) 41.8 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% 
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表 2 焚烧炉基本情况
Table 2 Basic information about incinerators
参数 值 单位 额定产能 800 t/d 实际产能 624 t/d 炉排 往复式顺推 / 长 × 宽 11 × 12.9 m 速度 8 m/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 %
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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) /
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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 ${\rm{CO}}_2$ 0.2688 0.9702 0.6153 0.8457 CO 0.9519 0.9659 1.8184 0.8751 ${{\rm{O}}_2}$ 0.2811 0.9710 0.6536 0.8446 RDT ${\rm{CO}}_2$ 0.5400 0.8798 0.6237 0.8414 CO 2.2356 0.8117 2.5335 0.7575 ${{\rm{O}}_2}$ 0.6752 0.8325 0.7862 0.7751 RR ${\rm{CO}}_2$ 1.40025 0.1918 1.3945 0.2072 CO 4.9986 0.0586 4.9738 0.0652 ${{\rm{O}}_2}$ 1.4306 0.2481 1.4233 0.2630 MISO LRDT ${\rm{CO}}_2$ 0.4138 0.9294 0.5894 0.8584 CO 1.3046 0.9359 1.7057 0.8901 ${{\rm{O}}_2}$ 0.4282 0.9326 0.5487 0.8905 MIMO LRDT ${\rm{CO}}_2$ 0.1645 0.9357 0.3089 0.8869 CO 0.2220 0.9357 0.2991 0.8867 ${{\rm{O}}_2}$ 0.4056 0.9812 0.5558 0.9747
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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 ${\rm{R}}^2$ PMM 0.0020 0.8021 0.0020 0.8072 MMM1 0.0015 0.8909 0.0015 0.8918 MMM2 0.0014 0.8960 0.0015 0.8965
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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
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