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Author:

Liu, Zheng (Liu, Zheng.) | Han, Honggui (Han, Honggui.) | Qiao, Junfei (Qiao, Junfei.) | Ma, Zeyu (Ma, Zeyu.)

Indexed by:

EI Scopus SCIE

Abstract:

Neural network control has been developed into an efficient strategy to guarantee the safe and steady operation of wastewater treatment process (WWTP). However, due to the complex mechanism and serious damage of sludge bulking in WWTP, it is significant for neural network control to achieve the timely self-helaing of operation. Therefore, the goal of this article is to devise a knowledge-guided adaptive neuro-fuzzy self-healing control (KG-ANFSHC) for sludge bulking. The originality of KG-ANFSHC is threefold. First, a knowledge evaluation strategy is introduced to consider the correlation and differentiation between the normal operation condition and sludge bulking to obtain available information. Then, the proposed strategy can provide a guide for control to take remedial actions. Second, a KG-ANFSHC based on a knowledge transfer mechanism, which makes full use of knowledge and data to dynamically adjust its parameters, is designed to eliminate the sludge bulking. Then, KG-ANFSHC can timely and precisely regulate manipulated variables to realize the self-healing of operation. Third, the Lyapunov stability theorem is employed to ensure the stability of KG-ANFSHC. Then, the proof of stability can assist its effective application. Finally, the proposed control is applied to Benchmark Simulation Model No. 2 to verify its advantages. Several results demonstrate that KG-ANFSHC can own satisfying self-healing performance to guarantee the operation recovered from sludge bulking.

Keyword:

stability analysis knowledge transfer mechanism (KTM) knowledge-guided adaptive neuro-fuzzy self- healing control (KG-ANFSHC) sludge bulking Knowledge evaluation strategy (KES)

Author Community:

  • [ 1 ] [Liu, Zheng]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Minist Educ,Beijing, Beijing 100022, Peoples R China
  • [ 2 ] [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Minist Educ,Beijing, Beijing 100022, Peoples R China
  • [ 3 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Minist Educ,Beijing, Beijing 100022, Peoples R China
  • [ 4 ] [Liu, Zheng]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 5 ] [Han, Honggui]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 6 ] [Qiao, Junfei]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China
  • [ 7 ] [Ma, Zeyu]Beijing OriginWater Technol Co Ltd, Dept Res & Dev, Beijing 100097, Peoples R China

Reprint Author's Address:

  • [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Engn Res Ctr Digital Community,Minist Educ,Beijing, Beijing 100022, Peoples R China;;[Han, Honggui]Beijing Univ Technol, Beijing Lab Urban Mass Transit, Beijing 100022, Peoples R China;;

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Source :

IEEE TRANSACTIONS ON FUZZY SYSTEMS

ISSN: 1063-6706

Year: 2024

Issue: 5

Volume: 32

Page: 3226-3236

1 1 . 9 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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