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

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Abstract

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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Acknowledgments

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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Authors and Affiliations

  1. Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou, 510275, China

    Mingzhi Huang

  2. College of Environment and Energy, South China University of Technology, Guangzhou, 510640, China

    Yongwen Ma, Jinquan Wan, Yan Wang & Yangmei Chen

  3. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, 510640, China

    Jinquan Wan

  4. The Key Laboratory of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China

    Yongwen Ma

  5. Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do, 446701, Korea

    Changkyoo Yoo

  6. College of Environment and Energy, South China University of Technology, Guangzhou, 510006, China

    Jinquan Wan

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  1. Mingzhi Huang
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  2. Yongwen Ma
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  3. Jinquan Wan
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  4. Yan Wang
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  5. Yangmei Chen
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  6. Changkyoo Yoo
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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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  • DOI: https://doi.org/10.1007/s11356-014-3092-4

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