Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (6): 173-184.doi: 10.16088/j.issn.1001-6600.2021092302

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Two Algorithms for Prognosis of DenitrificationConditions of A2/O Technology

XIAO Fei1,2, KANG Zengyan3, WANG Weihong1*   

  1. 1. College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi Xinjiang 830052, China;
    2. College of Water Resources and Architectural Engineering, Tarim University, Aral Xinjiang 843300, China;
    3. China Constrution Third Bureau Installation Engineering Co., Ltd, Wuhan Hubei 430079, China
  • Received:2021-09-23 Revised:2021-11-29 Online:2022-11-25 Published:2023-01-17

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Abstract: With the rapid development of urbanization, water pollution is becoming more serious, while the types of receiving water bodies in urban wastewater treatment plants are becoming increasingly complex, resulting in substandard drainage water quality of wastewater treatment plants. In this paper, the A2/O process of Toutunhe wastewater treatment plant in Urumqi, Xinjiang is taken as the research object. Based on the single-factor test, the Box-Behnken response surface method is used, combined with both BP neural network (RSM-BP) and genetic algorithm-ANN neural network (GA-NN), so as to optimize and predict the nitrogen removal conditions of the A2/O process. The results showed that the factors affecting the TN removal rate were organic load (F/M) > carbon-nitrogen ratio (C/N) > carbon-phosphorus ratio (C/P), while the optimal process conditions optimized by RSM-BP were C/N=8.95, C/P=72.01 and F/M=0.088 d-1. The predicted TN removal rate was 79.12%, the validated value was 77.36%, and the relative error value was 2.275%. The optimal process conditions optimized by GA-NN were C/N=9.00, C/P=72.15 and F/M=0.09 d-1. The predicted value of TN removal rate was 79.25%, the validated value was 78.71%, and the relative error value was 0.686%. The higher TN removal rate was, the smaller error predicted by GA-NN, which indicates that the application of GA-NN algorithm in A2/O process is effective, which can also provide theoretical guidance for the operation of wastewater treatment plant.

Key words: A2/O process, response surface method, genetic algorithm, neural network, total nitrogen

CLC Number: 

  • X703
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