Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

  • Authors:

  • Huang Mingzhi;Wan Jinquan;Ma Yongwen;Wang Yan;Li Weijiang;Sun Xiaofei

  • Affiliations:

  • College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China;College of Environmental Science and Engineering, South China University of Technology, Guangzhou 510006, People's Republic of China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

The present work is part of a global development of reliable real-time control and supervision tools applied to wastewater pollution removal processes. In these processes, oxygen is a key substrate in animal cell metabolism and its consumption is thus a parameter of great interest for the monitoring. In this paper, an integrated neural-fuzzy process controller was developed to control aeration in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP). In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proves to be very effective in modeling the aeration performs better than artificial neural networks (ANN). For comparing between operation with and without the fuzzy neural controller, an aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective DO control tool, easy to integrate in a global monitoring system for cost managing.