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作者:

Han, Hong-Gui (Han, Hong-Gui.) (学者:韩红桂) | Liu, Hong-Xu (Liu, Hong-Xu.) | Liu, Zheng (Liu, Zheng.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (学者:乔俊飞)

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EI Scopus SCIE

摘要:

Fault detection is important in the operation of wastewater treatment process (WWTP). In this paper, to ensure the process safety and effluent qualities, an intelligent fault detection (IFD) method, based on self-organizing type-2 fuzzy-neural-network (SOT2FNN) and intelligent identification method, was developed to detect and identify different types of sludge bulking. The main advantages of IFD are as follows. First, a data-driven framework, based on a data-driven model and an intelligent identification algorithm, was developed to facilitate the fault diagnosis. Second, a SOT2FNN, based on the intensity of information transmission algorithm and adaptive second-order algorithm, was designed to predict the sludge volume index (SW) with high accuracy to provide necessary information for process monitoring. Third, an intelligent identification method, using the target-related identification algorithm (TRIA), was proposed to extract the correlation information to identify the types of sludge bulking. Finally, simulations and experimental examples were provided to confirm the effectiveness of the proposed IFD method.

关键词:

Self-organizing type-2 fuzzy-neural-network Target-related identification algorithm Sludge bulking Sludge volume index Intelligent fault detection method

作者机构:

  • [ 1 ] [Han, Hong-Gui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Han, Hong-Gui]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

通讯作者信息:

  • 韩红桂

    [Han, Hong-Gui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

CONTROL ENGINEERING PRACTICE

ISSN: 0967-0661

年份: 2019

卷: 90

页码: 27-37

4 . 9 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:136

被引次数:

WoS核心集被引频次: 32

SCOPUS被引频次: 32

ESI高被引论文在榜: 0 展开所有

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