2021, Volume 7, Issue 2
Engineering >> 2021, Volume 7, Issue 2 doi: 10.1016/j.eng.2020.07.027
Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Effluent Total Nitrogen
a Institution of Environment Pollution Control and Treatment, Department of Environmental Engineering, Zhejiang University, Hangzhou 310058, China
b Zhejiang Province Key Laboratory for Water Pollution Control and Environmental Safety, Hangzhou 310058, China
c Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou 310058, China
d Haining Water Investment Group Co., Ltd., Haining 314400, China
e Haining Capital Water Co., Ltd., Haining 31440, China
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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 parameters 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.
Keywords
Feedforward neural network (FFNN) ; Algorithms ; Controllable operation parameters ; Sequencing batch reactor (SBR) ; Total nitrogen (TN)
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