An integrated treatment of domestic wastewater using sequencing batch biofilm reactor combined with vertical flow constructed wetland and its artificial neural network simulation study

详细信息    查看全文

• 作者：Yi-Ming Guo^a ; ^b ; Yun-Guo Liu^a ; ^b ; ^{hdhjguo@163.com" class="auth_mail} ; Guang-Ming Zeng^a ; ^b ; Xin-Jiang Hu^a ; ^b ; Wei-Hua Xu^a ; ^b ; Yun-Qin Liu^a ; ^b ; Si-Mian Liu^a ; ^b ; Hong-Song Sun^a ; ^b ; Jie Ye^a ; ^b ; Hua-Jun Huang^a ; ^b
• 关键词：Domestic wastewater ; SBBR-VFCW ; Advanced biological process ; Artificial neural network ; Dynamic monitor
• 刊名：Ecological Engineering
• 出版年：March, 2014
• 年：2014
• 卷：64
• 期：Complete
• 页码：18-26
• 全文大小：1969 K

文摘

In this study, a sequencing batch biofilm reactor combined with a vertical flow constructed wetland (SBBR-VFCW) system was constructed and applied to the wastewater treatment. Thalia dealbatas were planted in the VFCW. The artificial neural network (ANN) was used to simulate and predict the performance of SBBR-VFCW. The results showed that when the concentrations of COD, NH₄⁺-N, TN and TP in the wastewater were 200.22 mg/L, 48.11 mg/L, 48.11 mg/L and 6.11 mg/L respectively, the removal efficiencies were 97.0%, 98.5%, 91.5% and 88.5%, correspondingly, which indicated that the SBBR-VFCW system can treat the wastewater effectively. According to the results of the ANN simulation analysis, the correlation coefficients (R²) were all higher than 0.99, and the root mean squared errors (RMSE) were lower than 0.0782. The concentrations of DO, NH₄⁺-N and TP in the influent exhibited strong impacts on the effluent. This study reveals that the ANN can efficiently reflect the nonlinear function of each factor, and is suitable for the dynamic monitoring of SBBR-VFCW treatment for wastewater in various conditions.