Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor.

This work proposes an on-line hybrid intelligent control system based on a genetic algorithm (GA) evolving fuzzy wavelet neural network software sensor to control dissolved oxygen (DO) in an anaerobic/anoxic/oxic process for treating papermaking wastewater. With the self-learning and memory abilitie...

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Publicado en:Journal of Environmental Management Vol. 187; pp. 550 - 560
Autores principales: Ruan, Jujun, Zhang, Chao, Li, Ya, Li, Peiyi, Yang, Zaizhi, Chen, Xiaohong, Huang, Mingzhi, Zhang, Tao
Formato: Artículo
Publicado: Academic Press Inc. Feb2017
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Feb2017
      vid: 187
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      pub: Academic Press Inc.
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        120296736
        10.1016/j.jenvman.2016.10.056
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        atl: Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving FWNN software sensor.
      aug:
        au:
          Ruan, Jujun
          Zhang, Chao
          Li, Ya
          Li, Peiyi
          Yang, Zaizhi
          Chen, Xiaohong
          Huang, Mingzhi
          Zhang, Tao
        affil:
          School of Environmental Science and Engineering, Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology, Sun Yat-Sen University, Guangzhou 510275, PR China
          Department of Water Resources and Environment, Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, PR China
      su:
        Dissolved oxygen in water
        Genetic algorithms
        Neural circuitry
        Papermaking
        Sewage
      sug:
        subj:
          Paper (except Newsprint) Mills
          Dissolved oxygen in water
          Genetic algorithms
          Neural circuitry
          Papermaking
          Sewage
      keyword:
        Dissolved oxygen control
        Genetic algorithm evolving fuzzy wavelet neural network
        Software sensor
        Wastewater treatment process
        Dissolved oxygen control
        Genetic algorithm evolving fuzzy wavelet neural network
        Software sensor
        Wastewater treatment process
      ab: This work proposes an on-line hybrid intelligent control system based on a genetic algorithm (GA) evolving fuzzy wavelet neural network software sensor to control dissolved oxygen (DO) in an anaerobic/anoxic/oxic process for treating papermaking wastewater. With the self-learning and memory abilities of neural network, handling the uncertainty capacity of fuzzy logic, analyzing local detail superiority of wavelet transform and global search of GA, this proposed control system can extract the dynamic behavior and complex interrelationships between various operation variables. The results indicate that the reasonable forecasting and control performances were achieved with optimal DO, and the effluent quality was stable at and below the desired values in real time. Our proposed hybrid approach proved to be a robust and effective DO control tool, attaining not only adequate effluent quality but also minimizing the demand for energy, and is easily integrated into a global monitoring system for purposes of cost management.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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