• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Tang, Jian (Tang, Jian.) (学者:汤健) | Xia, Heng (Xia, Heng.) | Aljerf, Loai (Aljerf, Loai.) | Wang, Dandan (Wang, Dandan.) | Ukaogo, Prince Onyedinma (Ukaogo, Prince Onyedinma.)

收录:

EI Scopus SCIE

摘要:

Dioxin (DXN), which is named a "century poison", is emitted from municipal solid waste incineration (MSWI). The first step to effectively control and reduce DXN emissions is the application of soft sensors by utilizing easyto-detect process data. However, DXN samples for data-driven modeling are extremely lacking because of the high cost and long period of measurement. To address the above issue, this work proposes a DXN emission prediction method based on expansion, interpolation, and selection for small-sample modeling, i.e., EIS-SSM, involving three main steps: domain expansion, hybrid interpolation, and virtual sample selection. First, the domain of samples is determined by domain extension, a great number of virtual samples in this domain are generated through hybrid interpolation, and the optimal virtual samples are chosen for virtual sample selection. Afterward, a prediction model for DXN emission is constructed using the optimal samples and raw small samples. Two cases, that is, a benchmark dataset and a DXN dataset from an actual MSWI plant, are applied to implement the proposed method. Results showed that compared with the non-expansion and existing expansion methods, the proposed method exhibits an improved performance by 48.22% and 13.68%, respectively, in the benchmark experiment and by 72.44% and 34.67%, respectively, in the DXN emission prediction experiment. Therefore, the proposed method can substantially improve the prediction of DXN emission from MSWI.

关键词:

Data-driven modeling Dioxin emission Small sample modeling Municipal solid waste incineration Virtual sample selection

作者机构:

  • [ 1 ] [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Xia, Heng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Dandan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Tang, Jian]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 5 ] [Xia, Heng]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 6 ] [Wang, Dandan]Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China
  • [ 7 ] [Aljerf, Loai]Damascus Univ, Fac Sci, Dept Chem, Key Lab Organ Ind, Damascus, Syria
  • [ 8 ] [Ukaogo, Prince Onyedinma]Abia State Univ, Dept Pure & Ind Chem, Analyt Environm Units, Uturu, Nigeria

通讯作者信息:

查看成果更多字段

相关关键词:

来源 :

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING

ISSN: 2213-2929

年份: 2022

期: 5

卷: 10

7 . 7

JCR@2022

7 . 7 0 0

JCR@2022

JCR分区:1

中科院分区:2

被引次数:

WoS核心集被引频次: 42

SCOPUS被引频次: 49

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 1

归属院系:

在线人数/总访问数:550/4955683
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司