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作者:

Li, Xiaoli (Li, Xiaoli.) (学者:李晓理) | Liu, Quanbo (Liu, Quanbo.) | Wang, Kang (Wang, Kang.)

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Scopus SCIE

摘要:

There has recently been increasing attention paid to sulphur dioxide (SO2) pollution owing to its hazardous effect on both human health and atmospheric environment. To handle this problem, the wet flue gas desulphurization (FGD) system has found wide applications in SO2 emitting industries. Accurate prediction of SO2 emissions in treated flue gas serves the purpose of providing timely operating guidance for the FGD system. However, the wet FGD process is characterized by highly nonlinear dynamics and non-stationarity, which poses significant difficulties and limitations for traditional modeling methods. To address above issues, in this article, an integrated model is proposed to perform SO2 emission forecasting for an FGD process. Our integrated model comprises a multiplicity of techniques, including complete ensemble empirical mode decomposition with adaptive noise stacking ensemble learning (SEL) and permutation-based entropy (PEN). The serves as decomposing SO2 emission signal, then the complexity of each decomposed sub-series is analyzed by PEN and ones with similar scores are combined, finally a stacking-based ensemble learning model which incorporates different types of member models are developed for modeling purposes. The proposed method was validated and evaluated by measurements of a real FGD system in a 600MW coal-fired unit, and experimental results illustrate the superiority of our method.

关键词:

Neural network CEEMDAN-forecasting Ensemble learning Wet flue gas desulfurization Stacking

作者机构:

  • [ 1 ] [Li, Xiaoli]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Quanbo]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Kang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

通讯作者信息:

  • [Liu, Quanbo]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;

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来源 :

INFORMATION TECHNOLOGY AND CONTROL

ISSN: 1392-124X

年份: 2024

期: 3

卷: 53

1 . 1 0 0

JCR@2022

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