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

机译:序贯生物膜反应器与垂直流人工湿地联合处理生活污水及其人工神经网络模拟研究。

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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 (R~2) 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.
机译:在这项研究中,定序生物膜反应器与垂直流人工湿地(SBBR-VFCW)系统相结合,被构建并应用于废水处理。塔利亚·塔巴巴塔(Talia Dealbatas)种植在VFCW中。人工神经网络(ANN)用于模拟和预测SBBR-VFCW的性能。结果表明,当废水中COD,NH4〜+ -N,TN和TP的浓度分别为200.22 mg / L,48.11 mg / L,48.11 mg / L和6.11 mg / L时,去除效率为97.0%分别为98.5%,91.5%和88.5%,表明SBBR-VFCW系统可以有效处理废水。根据人工神经网络的分析结果,相关系数(R〜2)均大于0.99,均方根误差(RMSE)小于0.0782。进水中DO,NH4〜+ -N和TP的浓度对出水影响很大。这项研究表明,人工神经网络可以有效地反映每个因素的非线性函数,并且适合于在各种条件下对SBBR-VFCW处理废水进行动态监测。

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