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Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process

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Due to the inherent complexity, uncertainty, and posterity in operating a biological wastewater treatment process, it is difficult to control nitrogen removal in the biological wastewater treatment process. In order to cope with this problem and perform a cost-effective operation, an integrated neural-fuzzy control system including a fuzzy neural network (FNN) predicted model for forecasting the nitrate concentration of the last anoxic zone and a FNN controller were developed to control the nitrate recirculation flow and realize nitrogen removal in an anoxic/oxic (A/O) process. In order to improve the network performance, a self-learning ability embedded in the FNN model was emphasized for improving the rule extraction performance. The results indicate that reasonable forecasting and control performances had been achieved through the developed control system. The effluent COD, TN, and the operation cost were reduced by about 14, 10.5, and 17 %, respectively.

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This research has been supported by National Natural Science Foundation of China (No. 51208206), Guangdong Provincial Department of Science (No. 2012A032300015), and State key laboratory of Pulp and Paper Engineering in China (201213). The authors are thankful to the anonymous reviewers for their insightful comments and suggestions.

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Correspondence to Mingzhi Huang or Jinquan Wan.

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Responsible editor: Michael Matthies

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Huang, M., Ma, Y., Wan, J. et al. Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process. Environ Sci Pollut Res 21, 12074–12084 (2014). https://doi.org/10.1007/s11356-014-3092-4

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  • Fuzzy neural network
  • Nitrate recirculation
  • Hybrid algorithm
  • A/O process
  • Nitrogen removal