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作者:

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

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摘要:

To improve the treatment effect of effluent ammonia nitrogen in municipal wastewater treatment process, an optimal control method was proposed in this paper. First, the performance index of effluent ammonia nitrogen concentration was analyzed by using the mechanism characteristics. Then, a relationship model with the adaptive kernel function between the performance index and the control variables was established. Next, a particle swarm optimization algorithm was used to obtain the optimal solutions of dissolved oxygen concentration. After that, an adaptive fuzzy neural network controller was designed to complete the tracking control of dissolved oxygen concentration. Finally, the proposed optimal control method was applied to the benchmark simulation model No.1 (BSM1). The results demonstrated that the proposed optimal control method can not only improve the treatment effect of effluent ammonia nitrogen, but also effectively reduce the energy consumption. © 2020, Shanghai Jiao Tong University Press. All right reserved.

关键词:

Adaptive control systems Ammonia Dissolved oxygen Effluents Effluent treatment Energy utilization Fuzzy neural networks Nitrogen Particle swarm optimization (PSO) Process control Wastewater treatment

作者机构:

  • [ 1 ] [Han, Honggui]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Yang, Shiheng]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Zhang, Lu]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Qiao, Junfei]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China

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来源 :

Journal of Shanghai Jiaotong University

ISSN: 1006-2467

年份: 2020

期: 9

卷: 54

页码: 916-923

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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