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Author:

Wang, Ranran (Wang, Ranran.) | Yan, Aijun (Yan, Aijun.) | Tang, Jian (Tang, Jian.) (Scholars:汤健)

Indexed by:

CPCI-S EI

Abstract:

To accurately predict the nitrogen oxides (NOx) emissions concentration dating the municipal solid waste incineration process, a prediction method based on the Bagging ensemble and stochastic configuration network (SCN) is proposed, and the relevant variables of the municipal solid waste incineration process are used to predict NOx concentration in this paper. Firstly, the Bootstrap sampling method is used to generate several different training subsets, and multiple SCN base models are trained tinder different subsets. The average value of the output results of every base model is taken as the final output. Finally, the actual historical data of a solid waste treatment plant in Beijing are used to verily the model and compared with the single SCN, random vector function link network (RVFLN), Bagging-RVFLN. The experimental results show that the proposed method has high accuracy and can accurately predict the NOx emissions concentration in MSW incineration process.

Keyword:

ensemble learning municipal solid waste NOx emissions stochastic configuration network

Author Community:

  • [ 1 ] [Wang, Ranran]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Yan, Aijun]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Tang, Jian]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Wang, Ranran]Minist Educ, Engn Res Ctr Digital Commun, Beijing 100124, Peoples R China
  • [ 5 ] [Yan, Aijun]Minist Educ, Engn Res Ctr Digital Commun, Beijing 100124, Peoples R China
  • [ 6 ] [Wang, Ranran]Beijing Univ Technol, Beijing, Peoples R China
  • [ 7 ] [Yan, Aijun]Beijing Univ Technol, Beijing, Peoples R China
  • [ 8 ] [Tang, Jian]Beijing Univ Technol, Beijing, Peoples R China
  • [ 9 ] [Yan, Aijun]Beijing Lab Urban Mass Transit, Beijing 100124, Peoples R China

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Source :

2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC

ISSN: 1948-9439

Year: 2023

Page: 355-359

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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