TY - JOUR
T1 - Simulating and predicting the performance of a horizontal subsurface flow constructed wetland using a fully connected neural network
AU - Li, Pengyu
AU - Zheng, Tianlong
AU - Li, Lin
AU - Lv, Xiuyuan
AU - Wu, Wen Jun
AU - Shi, Zhining
AU - Zhou, Xiaoqin
AU - Zhang, Guangtao
AU - Ma, Yingqun
AU - Liu, Junxin
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12/20
Y1 - 2022/12/20
N2 - 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.
AB - 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.
KW - Decentralized wastewater treatment
KW - Fully connected neural network
KW - Horizontal subsurface flow constructed wetland
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85141274916
U2 - 10.1016/j.jclepro.2022.134959
DO - 10.1016/j.jclepro.2022.134959
M3 - 文章
AN - SCOPUS:85141274916
SN - 0959-6526
VL - 380
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 134959
ER -