Abstract The problem of effluent total nitrogen (TN) at most of the wastewater treatment plants (WWTPs) in China is important for meeting the related water quality standards, even under the condition of high energy consumption. To achieve better prediction and control of effluent TN concentration, an efficient prediction model, based on controllable operation parameters, was constructed in a sequencing batch reactor process. Compared with previous models, this model has two main characteristics: ① superficial gas velocity and anoxic time are controllable operation parameters and are selected as the main input parameters instead of dissolved oxygen to improve the model controllability, and ② the model prediction accuracy is improved on the basis of a feedforward neural network (FFNN) with algorithm optimization. The results demonstrated that the FFNN model was efficiently optimized by scaled conjugate gradient, and the performance was excellent compared with other models in terms of the correlation coefficient (R). The optimized FFNN model could provide an accurate prediction of effluent TN based on influent water quality and key control parameters. This study revealed the possible application of the optimized FFNN model for the efficient removal of pollutants and lower energy consumption at most of the WWTPs.

中文翻译：

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基于可控参数预测出水总氮的新型前馈神经网络模型的开发

摘要 我国大部分污水处理厂（WWTPs）出水总氮（TN）问题对于满足相关水质标准至关重要，即使在高能耗条件下也是如此。为了更好地预测和控制出水TN浓度，在序批式反应器工艺中构建了基于可控操作参数的高效预测模型。与以往模型相比，该模型主要有两个特点：①表观气速和缺氧时间为可控运行参数，选择代替溶解氧作为主要输入参数，提高模型可控性；②提高了模型预测精度。具有算法优化的前馈神经网络 (FFNN) 的基础。结果表明，FFNN模型通过缩放共轭梯度进行了有效优化，并且在相关系数（R）方面与其他模型相比性能优异。优化后的 FFNN 模型可以根据进水水质和关键控制参数对出水 TN 进行准确预测。这项研究揭示了优化的 FFNN 模型在大多数污水处理厂有效去除污染物和降低能源消耗的可能应用。