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作者:

Zhang, Lu (Zhang, Lu.) | Zhang, Jiacheng (Zhang, Jiacheng.) | Han, Honggui (Han, Honggui.) (学者:韩红桂) | Qiao, Junfei (Qiao, Junfei.) (学者:乔俊飞)

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EI CSCD

摘要:

To make the effluent total phosphorus reach the real-time standard in wastewater treatment process (WWTP), an effluent total phosphorus control strategy, based on fuzzy neural network (FNN), is proposed to control the biochemical phosphorus in this paper. First, the manipulated variables, based on the mechanism analysis of biochemical phosphorus removal process, were considered as the external carbon (EC) and dissolved oxygen (DO) transfer coefficient. Second, an FNN-based process controller was designed to control the effluent total phosphorus. And a gradient descent algorithm was applied to adjust the parameters of controller. Finally, the proposed FNN-based process controller was tested on the benchmark simulation model No. 1 (BSM1) to evaluate its effectiveness. The results demonstrated that the proposed FNN-based process controller can guarantee the standard discharge of effluent total phosphorus. The results show that the FNN-based effluent total phosphorus controller can ensure that the total effluent total phosphorus is discharged and has a good control effect. © All Right Reserved.

关键词:

Controllers Dissolved oxygen Effluents Effluent treatment Frequency standards Fuzzy neural networks Gradient methods Phosphorus Process control Wastewater treatment

作者机构:

  • [ 1 ] [Zhang, Lu]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Zhang, Lu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Zhang, Lu]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
  • [ 4 ] [Zhang, Jiacheng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 5 ] [Zhang, Jiacheng]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 6 ] [Zhang, Jiacheng]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
  • [ 7 ] [Han, Honggui]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 8 ] [Han, Honggui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 9 ] [Han, Honggui]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
  • [ 10 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 11 ] [Qiao, Junfei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

通讯作者信息:

  • 韩红桂

    [han, honggui]beijing key laboratory of computational intelligence and intelligent system, beijing; 100124, china;;[han, honggui]engineering research center of digital community, ministry of education, beijing; 100124, china;;[han, honggui]faculty of information technology, beijing university of technology, beijing; 100124, china

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来源 :

CIESC Journal

ISSN: 0438-1157

年份: 2020

期: 3

卷: 71

页码: 1217-1225

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