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Author:

Wang, Ranran (Wang, Ranran.) | Li, Fangyu (Li, Fangyu.) | Yan, Aijun (Yan, Aijun.) (Scholars:严爱军)

Indexed by:

EI Scopus SCIE

Abstract:

The accurate prediction of the nitrogen oxides (NOx) emissions is extremely important for pollutant control in municipal solid waste incineration (MSWI) process. Modular neural network (MNN) provides a support for predicting NOx emissions to overcome the limitations of a single model in nonlinear processes and different climatic conditions. However, the design of sub model for MNN is a challenge. We propose a method based on MNN and adaptive ensemble stochastic configuration network (m-AESCN) in this work. First, fuzzy C-means algorithm decomposes the task into several sub datasets with similar characteristics, and an evaluation function is used to guarantee the optimal decomposition result. Second, for each sub dataset, an adaptive ensemble stochastic configuration network as the sub model of modular neural network. The optimal output for sub model can be obtained by an adaptive weighting method based on variance. Third, in testing phase, a sub model activation method based on cluster centroid is proposed to select a suitable sub model. Besides, the m-AESCN method is validated by the real data of an MSWI plant and shows considerable performance in two different datasets. And the proposed method is compared with several modeling methods, such as stochastic configuration network, support vector regression, random forest and so on. The experimental results under two datasets prove that the maximum improvements in accuracy of m-AESCN are 39.74% and 31.67%, respectively.

Keyword:

Stochastic configuration network Municipal solid waste Nitrogen oxides prediction Modular neural network

Author Community:

  • [ 1 ] [Wang, Ranran]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Fangyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Yan, Aijun]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Wang, Ranran]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Fangyu]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 6 ] [Yan, Aijun]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
  • [ 7 ] [Yan, Aijun]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China
  • [ 8 ] [Li, Fangyu]Beijing Univ Technol, Key Lab Computat Intelligence & Intelligent Syst, Beijing 100124, Peoples R China
  • [ 9 ] [Li, Fangyu]Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 10 ] [Li, Fangyu]Beijing Lab Intelligent Environm Protect, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Yan, Aijun]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;

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Source :

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

ISSN: 0952-1976

Year: 2023

Volume: 127

8 . 0 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

Affiliated Colleges:

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