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

被引：0
|
作者：
Qiao J.-F. [1 ,2 ]
Guo Z.-H. [1 ,2 ]
Tang J. [1 ,2 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 06期
基金：
国家自然科学基金重大项目; 中国国家自然科学基金;
关键词：
Dioxin (DXN) emission; Intelligent soft-measuring; Municipal solid wastes incineration (MSWI); On-line measurement; Small sample high dimensional data;
D O I：
10.16383/j.aas.c190005
中图分类号：
学科分类号：
摘要：
Incineration has significant advantages in the harmless, reduction and recycling treatment of municipal solid waste (MSW). Dioxins (DXN), a highly toxic and persistent pollutant that is a by-product of the MSW incineration (MSWI) process, is the main cause of the "not in my back yard" effect of incineration plant construction. The industrial status of DXN emission concentration that is difficult to detect real time online has become a bottleneck restricting the optimization of MSWI process operation and municipal environmental pollution control. First, the generation characteristics and emission control strategies of DXN based on a typical MSWI processes are analyzed. Then, the DXN emission concentration detection methods are divided into offline direct detection method, indicator/association online indirect detection method, and soft measurement method in terms of measurement principle, complexity, and time scale. Further, these methods are reviewed in detail. Thirdly, the development stage and correlation of these different methods are addressed, and their respective advantages and disadvantages and complementarity with each other are indicated. Based on the characteristics of MSWI process, the difficulties of DXN emission concentration soft measurement based on process data are summarized. Moreover, it is refined as a class intelligent modeling problem based on small sample high dimensional sparse labeled data. Finally, the future research direction and development prospects of DXN emission concentration intelligent soft measurement are suggested. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
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页码：1063 / 1089
页数：26
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