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Author:

Han, Honggui (Han, Honggui.) | Qin, Chenhui (Qin, Chenhui.) | Sun, Haoyuan (Sun, Haoyuan.) | Yang, Hongyan (Yang, Hongyan.) | Qiao, Junfei (Qiao, Junfei.)

Indexed by:

EI Scopus SCIE

Abstract:

The phenomenon of sludge bulking, which can be classified as slight or serious abnormal operating conditions according to sludge volume index (SVI), is a widespread problem in wastewater treatment process. In this article, a piecewise sliding-mode control (PSMC) strategy is developed to overcome the adverse effects and furthermore improve the operating performance for wastewater treatment process under different operating conditions of sludge bulking. First, a soft sensing model of SVI based on fuzzy neural network is established. Then, it can online determine the state of sludge and whether sludge bulking has occurred. Second, in view of the specific operating condition of sludge, a piecewise sliding-mode controller is designed. Then, the sludge bulking can be eliminated by regulating the concentration of dissolved oxygen and nitrate nitrogen. Third, the boundary condition of PSMC is evaluated. Then, the stability of the control system is proved on the basis of ensuring the boundary condition of PSMC. Finally, the performance of PSMC is verified in the benchmark simulation platform. The results further demonstrate the effectiveness of the proposed control method.

Keyword:

Fuzzy neural networks sludge bulking piecewise sliding-mode control Sliding mode control Multiple operating conditions Nitrogen Microorganisms Process control Recycling Wastewater treatment wastewater treatment process

Author Community:

  • [ 1 ] [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing Key Lab Computat Intelligence & Intelligen, Fac Informat Technol,Engn Res Ctr Digital Communit, Beijing 100021, Peoples R China
  • [ 2 ] [Qin, Chenhui]Beijing Univ Technol, Beijing Lab Intelligent Environm Protect, Beijing 10021, Peoples R China

Reprint Author's Address:

  • [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing Key Lab Computat Intelligence & Intelligen, Fac Informat Technol,Engn Res Ctr Digital Communit, Beijing 100021, Peoples R China;;

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Source :

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

ISSN: 1551-3203

Year: 2023

Issue: 3

Volume: 19

Page: 2876-2885

1 2 . 3 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:19

Cited Count:

WoS CC Cited Count: 6

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

Affiliated Colleges:

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