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

被引:33
|
作者
Guo, Yi-Ming [1 ,2 ]
Liu, Yun-Guo [1 ,2 ]
Zeng, Guang-Ming [1 ,2 ]
Hu, Xin-Jiang [1 ,2 ]
Xu, Wei-Hua [1 ,2 ]
Liu, Yun-Qin [1 ,2 ]
Liu, Si-Mian [1 ,2 ]
Sun, Hong-Song [1 ,2 ]
Ye, Jie [1 ,2 ]
Huang, Hua-Jun [1 ,2 ]
机构
[1] Hunan Univ, Coll Environm Sci & Engn, Changsha 410082, Hunan, Peoples R China
[2] Hunan Univ, Minist Educ, Lab Environm Biol & Pollut Control, Changsha 410082, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Domestic wastewater; SBBR-VFCW; Advanced biological process; Artificial neural network; Dynamic monitor; PERFORMANCE;
D O I
10.1016/j.ecoleng.2013.12.040
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
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 wereplanted 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 thewastewater 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 systemcan treat the wastewater effectively. According to the results of the ANN simulation analysis, the correlation coefficients (R-2) were all higher than 0.99, and the root mean squared errors (RMSE) were lowerthan 0.0782. The concentrations of DO, NH4+-N and TP in the influent exhibited strong impacts on theeffluent. This study reveals that the ANN can efficiently reflect the nonlinear function of each factor, andis suitable for the dynamic monitoring of SBBR-VFCW treatment for wastewater in various conditions. (c) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:18 / 26
页数:9
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