Volume 9 Issue 5
Sep.  2019
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Article Navigation >  Journal of Environmental Engineering Technology  > 2019  >  9(5): 573-578
LIN Jiamin, CHEN Jinliang, LIN Jingjing, LI Xuanji, MA Cong, ZHANG Zhiqiang, SHEN Liang. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261
Citation: LIN Jiamin, CHEN Jinliang, LIN Jingjing, LI Xuanji, MA Cong, ZHANG Zhiqiang, SHEN Liang. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261
shu

The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model

doi: 10.12153/j.issn.1674-991X.2019.03.261
  • 1.

    College of Chemistry, Xiamen University, Xiamen 361005, China

  • 2.

    Xiamen Water Environment Technology Co. Ltd., Xiamen 361009, China

  • 3.

    Liaoning Northern Environmental Testing Technology Co. Ltd., Shenyang 110161, China

More Information
  • Corresponding author: MA Cong E-mail: mc@xmwaterenv.com; SHEN Liang E-mail: shenliang@xmu.edu.cn
  • Received Date: 2018-11-16
  • Publish Date: 2019-09-20
    Abstract
  • Total nitrogen in effluent is one of the critical indicators for evaluating the performance of wastewater treatment plants. A BP neural network model was developed to simulate the present nitrogen removal system for wastewater treatment, and an autoregressive integrated moving average (ARIMA) model was creatively applied to realize the short-term prediction of future effluent. The results showed that the simulation average relative error of BP model on training set was 15.9%, and that on test set was 16.5%,which revealed that the stability of model prediction was poor. The average error of the ARIMA model for predicting the total nitrogen value in the coming week was around 4.41%, which showed high prediction accuracy. The combination of the two models could help fast and efficient on-line detection of wastewater treatment plants.

     

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