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面向城市固废焚烧过程的二噁英排放浓度检测方法综述

乔俊飞 郭子豪 汤健

乔俊飞, 郭子豪, 汤健. 面向城市固废焚烧过程的二噁英排放浓度检测方法综述. 自动化学报, 2020, 46(6): 1063−1089 doi: 10.16383/j.aas.c190005
引用本文: 乔俊飞, 郭子豪, 汤健. 面向城市固废焚烧过程的二噁英排放浓度检测方法综述. 自动化学报, 2020, 46(6): 1063−1089 doi: 10.16383/j.aas.c190005
Qiao Jun-Fei, Guo Zi-Hao, Tang Jian. Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: a survey. Acta Automatica Sinica, 2020, 46(6): 1063−1089 doi: 10.16383/j.aas.c190005
Citation: Qiao Jun-Fei, Guo Zi-Hao, Tang Jian. Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: a survey. Acta Automatica Sinica, 2020, 46(6): 1063−1089 doi: 10.16383/j.aas.c190005

面向城市固废焚烧过程的二噁英排放浓度检测方法综述

doi: 10.16383/j.aas.c190005
基金项目: 科学技术部国家重点研究发展计划(2018YFC1900800-5, 2018YFC1900801), 国家自然科学基金 ( 61573364, 61873009, 61703089), 国家自然科学基金重大项目(61890930-5), 北京市自然科学基金(4192009), 矿冶过程自动控制技术国家重点实验室及矿冶过程自动控制技术北京市重点实验室(BGRIMM-KZSKL-2018-06)资助
详细信息
    作者简介:

    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, 神经网络结构设计与优化. E-mail: junfeq@bjut.edu.cn

    郭子豪:北京工业大学信息学部硕士研究生. 主要研究方向为高维小样本数据的特征建模, 固废处理过程难测参数软测量. E-mail: miller94@163.com

    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, 城市固废处理过程智能控制. 本文通信作者. E-mail: freeflytang@bjut.edu.cn

Dioxin Emission Concentration Measurement Approaches for Municipal Solid Wastes Incineration Process: A Survey

Funds: Supported by National Key Research and Development Program of the Ministry of Science and Technology of China (2018YFC1900800-5, 2018YFC1900801) , National Natural Science Foundation of China (61573364, 61873009, 61703089, 61890930–5), Beijing Natural Science Foundation (4192009), and National Key Laboratory of Process Automation in Mining and Metallurgy and Beijing Key Laboratory of Process Automation in Mining and Metallurgy (BGRIMM-KZSKL-2018-06)
  • 摘要: 焚烧在城市固体废物(Municipal solid wastes, MSW)的无害化、减量化和资源化处理方面优势显著. MSW焚烧(MSW incineration, MSWI)过程副产品之一的剧毒持久性污染物二噁英(Dioxins, DXN)是造成焚烧建厂“邻避效应”的主要原因. DXN排放浓度难以在线实时检测的工业现状已成为制约MSWI过程运行优化与城市环境污染控制的瓶颈. 首先, 结合典型MSWI过程分析DXN的生成特性与排放控制策略; 接着, 将DXN排放浓度检测方法从测量原理、复杂度和时间尺度等视角分为离线直接检测法、指示物/关联物在线间接检测法和软测量法并进行综述; 然后, 对不同方法的发展阶段和关联性进行分析, 指出各自的优劣性和相互间的互补性, 结合MSWI过程特点归纳基于过程数据进行DXN排放浓度软测量的难点, 并将其提炼为一类面向小样本高维稀疏标记数据的智能建模问题; 最后, 指出进行DXN排放浓度智能软测量的未来研究方向和发展前景.
  • 图  1  MSMI过程示意图

    Fig.  1  MSMI process diagram

    图  2  中国2010年 ~ 2016年MSW处理能力统计和2016年MSW处理比例

    Fig.  2  Statistical results of MSW processing capacity in China mainland from 2010 to 2016 and the proportion of MSW processing in 2016

    图  3  与DXN具有类似结构和特性的DDT生态食物链积聚图

    Fig.  3  Schematic diagram of the accumulation of DDT ecological food chain with similar structure and characteristics as DXN

    图  4  PCDDs与PCDFs分子结构图

    Fig.  4  Schematic diagram of the molecular structure of PCDDs and PCDFs

    图  5  基于炉排炉的MSWI工艺流程图

    Fig.  5  Schematic diagram of MSWI process based on grate furnace

    图  6  MSWI过程DXN生成区域示意图

    Fig.  6  DXN generation area diagram of MSWI process

    图  7  DXN排放浓度检测方法分类图

    Fig.  7  Schematic diagram of DXN emission concentration detection method classification

    图  8  DXN离线直接检测法分类与检测步骤汇总

    Fig.  8  DXN offline direct detection method classification and detection steps summary diagram

    图  9  基于指示物/关联物的DXN在线间接检测法

    Fig.  9  Indirect detection method of DXN based on indicator/associated substance

    图  10  低挥发性有机氯(LVOCL)在线测量系统示意图

    Fig.  10  Schematic diagram of on-line measurement system for low volatile organic chlorine (LVOCL)

    图  11  文献[142]用于构建DXN排放浓度模型的输入输出采样点示意图

    Fig.  11  Input and output sampling point diagram for constructing DXN emission concentration model in [142]

    图  12  不同DXN检测方法间的关系

    Fig.  12  Relation among different DXN detection methods

    图  13  文献[165]给出的仿真结果与设计数据的比较曲线

    Fig.  13  Comparison comparison curves between simulation results and design data given in [165]

    图  14  虚拟样本空间与真实样本空间的关系示意图

    Fig.  14  Schematic diagram of the relation between virtual sample space and real sample space

    图  15  机理特性分析、数值模拟和软测量模型间的关系

    Fig.  15  Relation among mechanism characteristic analysis, numerical simulation and soft sensor model

    表  1  DXN生物检测法优缺点的对比[87]

    Table  1  Comparison of advantages and disadvantages of DXN bioassay detection method[87]

    方法预处理检测周期 (天)检测成本 (美元)灵敏度 (pg/g)实验室投入 (美元)
    酶活力诱导生物法简便31 000~1 2001.000200 000
    酶免疫法简便2200~9000.500200 000
    荧光素酶法简便1200~9000.025200 000
    下载: 导出CSV

    表  2  DXN检测标准的发展历程

    Table  2  Development of DXN detection standards

    序号标准号描述年份国家文献
    1EPA 1613 (美) 同位素稀释法测定八氯二噁英及其呋喃1994年10月美国[100]
    2EPA 23 (美) MSWI过程中多氯化二苯并二噁英的测定1995年12月美国[101]
    3EPA 8280 (美) 多氯化二苯并二噁英和呋喃的分析测定, 高分辨气相低分辨质谱法1996年12月美国[102]
    4EPA 1668A (美) 高分辨气质联用测定水、土壤、沉积物、生物体和组织中的多氯联苯1999年12月美国[103]
    5JIS K0311 (日) 排入空气中的PCDDs、PCDFs以及Co-PCBs的气相色谱–质谱联用检测 方法1999年9月日本[104]
    6EN1948-1 (欧盟) DXN排放统一检测标准2006年欧盟[105]
    7HJ/T77-2001 (中) 多氯代二苯并二噁英和多氯代二苯并呋喃的测定, 同位素稀释高分辨毛 细管气相色谱–高分辨质谱法2001年中国[106]
    8HJ 77.3-2008 (中) 固体废物二噁英类的测定, 同位素稀释, 高分辨气相色谱–高分辨质谱法2008年中国[107]
    9HJ/T 365-2007 (中) 规定危险废物焚烧处置设施二噁英排放监测技术要求2007年中国[108]
    10GB 18485-2001 (中) 生活垃圾焚烧污染控制标准2001年中国[109]
    11GB 18485-2014 (中) 生活垃圾焚烧污染控制标准2014年中国[110]
    注: 中国的HJ/T 77-2001标准改进于美国的EPA 1613, HJ 77.3-2008改进于美国的EPA 8280.
    下载: 导出CSV

    表  3  DXN检测方法统计

    Table  3  DXN detection method statistics

    类别名称方法简述优点和缺点侧重点年份与文献
    离线直接检测法色谱法首先对样本进行采集、提取与净化、同位素标记、色谱柱分离, 然后与检测器联用进行定性与定量分析优点: 可分离DXN类物质组分
    和准确度量
    缺点: 周期长、费用高、对操
    作人员与设备要求高
    DXN类物质的超痕量分析1993[146]
    DXN类物质萃取方法1994[147]
    MSWI过程DXN排放浓度检测1992[148]
    空气中DXN浓度的检测1989[149], 1996[150]
    论述DXN的提取方法1995[151], 1996[152]
    检测土壤中的DXN1994[153]
    激光质谱法基于激光波长选择性电离, 再采用飞行时间质谱仪进行质量选择优点: 快速、高灵敏度
    缺点: 前期准备过程复杂
    指出DXN同类物具有独特光谱结构和较窄带宽2010[88]
    简述激光质谱法原理1998[89]
    激光质谱法在环境监测中的应用2001[90]
    可移动式激光质谱仪对MSWI过程中产生的DXN排放浓度进行检测1996[154]
    生物法酶活力诱导生物法: 通过特殊受体芳香烃测量DXN毒性优点: 周期短、成本低、大量样品可同时测定
    缺点: 仅能测总体的毒性当量
    简述酶活力诱导生物法1985[155], 1989[156]
    简述酶活力诱导生物法在国内DXN检测中的应用1996[91]
    酶免疫分析法: 单克隆或复合克隆抗体与DXN同类物高度结合的特性, 建立竞争抑制酶免疫方法优点: 分析简便、易操作、测定周期较短
    缺点: 不能测出DXN同类物的具体量值、需测定标准曲线、样品量大时误差较大
    简述酶免疫分析法1987[157], 1997[92], 1999[93]
    简述酶免疫法在国内DXN检测方面的应用1997[158]
    提出酶免疫法与光谱法联合使用2006[94]
    与酶活力免疫法进行比较, 其准确性较高2001[95]
    荧光素酶法: 利用基因工程, 重组染色体配体复合物, 进一步合成荧光素优点: 灵敏度高、检测时间短
    缺点: 无法测出DXN同类物的具体量值
    简述荧光素酶法1984[96]
    荧光素酶法与其他生物法的比较2001[97], 2001[98]
    对MSWI过程DXN的排放进行检测, 并与色谱法比较2011[99]
    免疫法基于DXN类抗体获得样本毒性当量, 计算出DXN含量优点: 操作简便, 检测仪器要求低
    缺点: 抗体制作复杂, DXN同类物检测种类有限
    采用单克隆抗体对DXN进行检测1980[159]
    采用单克隆与血清蛋白结合, 缩短检测时间1986[160]
    指示物/
    关联物在
    线间接检
    测法
    基于氯苯与DXN映射关系进行检测研究多种氯苯与DXN之间的映射关系优点: 检测周期短
    缺点: 不够稳定, 映射模型本身存在误差, 存在时间滞后性
    六氯苯与DXN映射关系1985[114]
    五氯苯与DXN映射关系2006[115]
    多氯联苯与DXN映射关系1994[116]
    其他氯苯与DXN映射关系1996[118], 1999[121], 2001[122], 2002[117], 2005[48], 2005[119], 2010[123], 2012[51], 2016[124], 2017[120], 2018[39]
    基于多环芳烃与DXN映射关系进行检测研究多环芳烃与DXN映射关系同上多环芳烃与DXN映射关系2003[125], 2006[126], 2009[127]
    实时在线跟踪多环芳烃的质谱仪1999[128]
    基于氯酚与DXN映射关系进行检测研究氯酚与DXN映射关系同上氯酚与DXN之间的映射关系2002[117], 1999[129]
    氯苯和氯酚与DXN映射关系2000[131], 2001[130], 2016[133]
    氯苯与氯酚与DXN之间的映射关系的精度对比1987[132], 2005[48]
    其他指示物与DXN映射关系进行检测研究其他指示物与DXN映射关系同上 有机卤素化合物与DXN之间的映射关系2010[134]
    松针表面DXN浓度检测2018[135]
    低挥发性有机氯与DXN之间的映射关系2010[136]
    软测量法回归分析法构建线性映射关系模型优点: 周期短, 成本低;
    缺点: 无法描述非线性映射关系
    温度与DXN之间的回归模型1989[137]
    焚烧尾气CO含量与DXN之间的回归模型2002[138], 2002[117]
    过量空气与DXN之间的回归模型1989[139]
    烟气处理设备前的烟气与DXN之间的回归模型2013[140]
    神经网络构建非线性单模型优点: 周期短, 成本低;
    缺点: 基于小样本的神经网络模型稳定性差, 泛化能力差
    欧美研究结构收集的焚烧炉数据, 单神经网络模型1995[59], 2018[141]
    中国实验规模的焚烧炉, 单神经网络与集成神经网络模型2008[62], 2012[63]
    中国台湾地区焚烧炉, 单神经网络模型2003[142]
    遗传编程构建非线性模型优点: 周期短, 检测成本低;
    缺点: 泛化能力差, 计算复杂度高
    欧美研究结构收集的3种类型焚烧炉数据, 基于遗传编程构建非线性模型2000[60]
    支持向量机构建非线性模型优点: 周期短, 成本低;
    缺点: 泛化能力差, 样本有限
    未进行特征选择
    中国华南地区某焚烧炉, 单模型2017[143]
    欧美研究结构收集的焚烧炉数据, 选择性集成模型2019[144]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-03
  • 录用日期:  2019-06-06
  • 网络出版日期:  2020-07-10
  • 刊出日期:  2020-07-10

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