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| Neural network prediction and control model for ammonia oxidizing process under low DO concentration |
| FENG Hong-li1, LIU Xiu-hong2, YANG Qing1, HUANG Si-ting1, CUI Bin1, ZHOU Tong1, YANG Yu-bing1, ZHOU Xue-yang1 |
1. Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing 100022;
2. School of Environment & Natural Resources, Renmin University of China, Beijing 100872 |
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Abstract Under low dissolved oxygen (DO) concentration, the neural network prediction method was applied in SBR for treating domestic wastewater. The neural network control model was built to predict and control the ammonia oxidizing process. The model was divided into two parts. In the first part with the correlation coefficient (R value) of 0.9985, the end of ammonia oxidization was predicted based on the on-line pH variations. In the second part with R value of 0.9083, the ammonia concentration was real-time predicted based on the on-line pH variations. The results showed that the model with high prediction accuracy, good controllability, better adaptability and stability, can not only benefit for achieving and stabilizing short-cut, but also promote the application of anaerobic ammonium oxidation for treating domestic wastewater.
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Received: 31 March 2016
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