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Improving nitrogen removal using a fuzzy neural network-based control system in the anoxic/oxic process |
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| Authors: | Mingzhi Huang Yongwen Ma Jinquan Wan Yan Wang Yangmei Chen Changkyoo Yoo |
| Institution: | 1. Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou, 510275, China 2. College of Environment and Energy, South China University of Technology, Guangzhou, 510640, China 4. The Key Laboratory of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou, 510006, China 3. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, 510640, China 6. College of Environment and Energy, South China University of Technology, Guangzhou, 510006, China 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 |
| 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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