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Development of a Novel Feedforward Neural Network Model Based on controllable parameters for Predicting Effluent Total Nitrogen

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Engineering 2021年第2期7卷 195-202页

作者： Zihao Zhao Zihao Wang Jialuo Yuan Jun Ma Zheling He Yilan Xu Xiaojia Shen Liang Zhu Institution of Environment Pollution Control and Treatment Department of Environmental EngineeringZhejiang UniversityHangzhou 310058China Zhejiang Province Key Laboratory for Water Pollution Control and Environmental Safety Hangzhou 310058China Zhejiang Provincial Engineering Laboratory of Water Pollution Control Hangzhou 310058China Haining Water Investment Group Co. Ltd.Haining 314400China Haining Capital Water Co. Ltd.Haining 31440China

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 *** 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 *** with previous models,this model has two main characteristics:①Superficial 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,and②the model prediction accuracy is improved on the basis of a feedforward neural network(FFNN)with algorithm *** 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 parameters and key control *** 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.

关键词： Feedforward neural network(FFNN) Algorithms controllable operation parameters Sequencing batch reactor(SBR) Total nitrogen(TN)

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