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Simulating and predicting the performance of a horizontal subsurface flow constructed wetland using a fully connected neural network

  • Pengyu Li
  • , Tianlong Zheng
  • , Lin Li
  • , Xiuyuan Lv
  • , Wen Jun Wu
  • , Zhining Shi
  • , Xiaoqin Zhou
  • , Guangtao Zhang
  • , Yingqun Ma
  • , Junxin Liu
  • CAS - Research Center for Eco-Environmental Sciences
  • University of Chinese Academy of Sciences
  • Hohai University
  • Chinese Academy of Environmental Planning
  • University of South Australia
  • University of Science and Technology Beijing
  • University of Macau

科研成果: 期刊稿件文章同行评审

25 引用 (Scopus)

摘要

Constructed wetland systems, as an engineered ecological system, are being increasingly employed for wastewater treatment. However, owing to the complex incentives for pollutant removal in ecological treatment systems, it is challenging to simulate and optimize the operation of constructed wetlands to advance ecological wastewater treatment systems. In this study, a horizontal subsurface flow constructed wetland (HSCW) system was constructed and applied to a rural wastewater treatment system. Reeds (Phragmites australis) were planted in the HSCW to remove pollutants from the wastewater. Further, a fully connected neural network (FCNN) was designed based on the Adam optimization algorithm with weather conditions, quality, and quantity of influent and effluent as input to simulate and predict the performance of the HSCW. The results of the FCNN simulation analysis showed that the relative errors of the simulated concentrations of CODcr, NH4+-N, total nitrogen (TN), and total phosphorus (TP) for the FCNN model were 8.07 ± 10.73%, 18.34 ± 17.75%, 9.90 ± 11.91%, and 9.47 ± 10.98%, respectively. The mean absolute errors (MAEs) of CODcr, NH4+-N, TN, and TP for the FCNN model were 2.17, 1.06, 1.21, and 0.54, respectively. The root-mean-squared errors (RMSEs) of CODcr, NH4+-N, TN, and TP for the FCNN model were 3.91, 2.05, 2.22, and 0.80, respectively. The correlation coefficients (R2) of CODcr, NH4+-N, TN, and TP for the model were 0.99, 0.91, 0.92, and 0.82, respectively. These results indicate that the model performed well. Sensitivity analysis results also showed that temperature, solar radiation intensity, and rainfall had a strong impact on the model accuracy. This study verifies that an artificial neural network can effectively reflect the nonlinear function of each factor and is suitable for simulating HSCW treatment for wastewater under various conditions, providing a new optimization method for wastewater ecological treatment systems.

源语言英语
文章编号134959
期刊Journal of Cleaner Production
380
DOI
出版状态已出版 - 20 12月 2022

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 7 - 经济适用的清洁能源
    可持续发展目标 7 经济适用的清洁能源

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