This study evaluates and models the impacts of employing biofilm carriers in sequencing batch reactors (SBR). A neural network (NN) was used to predict contaminants in the effluent and analyse the importance of operating parameters. With a hydraulic retention time of 7 h, the removal efficiency of chemical oxygen demand (COD), total phosphorous (TP), and total suspended solids (TSS) were 85, 82, and 98.9%, respectively. The removal efficiency of COD, TP, and TSS in our hybrid system was superior to regular single SBR systems. The training procedure of the NN model was successful and almost a perfect match was achieved between predicted values and experimental values. For all models predicting effluent COD, TP, and TSS, the correlation coefficient was higher than 0.99, and mean squared error approached zero. The analysis of input parameters demonstrated that influent concentration is a significant factor in the modelling of effluent characteristics.

中文翻译：

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基于神经网络的市政综合污水处理系统性能评估与建模

这项研究评估和建模在分批批处理反应器（SBR）中使用生物膜载体的影响。神经网络（NN）用于预测废水中的污染物并分析操作参数的重要性。在7 h的水力停留时间下，化学需氧量（COD），总磷（TP）和总悬浮固体（TSS）的去除效率分别为85％，82％和98.9％。在我们的混合系统中，COD，TP和TSS的去除效率优于常规的单SBR系统。NN模型的训练过程是成功的，并且在预测值和实验值之间实现了几乎完美的匹配。对于所有预测出水COD，TP和TSS的模型，相关系数均高于0.99，均方误差接近零。