Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Effluent Total Nitrogen

作     者：Zihao Zhao Zihao Wang Jialuo Yuan Jun Ma Zheling He Yilan Xu Xiaojia Shen Liang Zhu Zihao Zhao;Zihao Wang;Jialuo Yuan;Jun Ma;Zheling He;Yilan Xu;Xiaojia Shen;Liang Zhu

作者机构：Institution of Environment Pollution Control and TreatmentDepartment of Environmental EngineeringZhejiang UniversityHangzhou 310058China Zhejiang Province Key Laboratory for Water Pollution Control and Environmental SafetyHangzhou 310058China Zhejiang Provincial Engineering Laboratory of Water Pollution ControlHangzhou 310058China Haining Water Investment Group Co.Ltd.Haining 314400China Haining Capital Water Co.Ltd.Haining 31440China 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2021年第7卷第2期

页      面：195-202页

核心收录：

学科分类：12[管理学] 082803[工学-农业生物环境与能源工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0828[工学-农业工程] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was funded by the Major Science and Technology Program for Water Pollution Control and Treatment(2017ZX07201003) the National Natural Science Foundation of China(51961125101) the Science and Technology Project of Zhejiang Province(2018C03003) 

主　　题：Feedforward neural network(FFNN) Algorithms Controllable operation parameters Sequencing batch reactor(SBR) Total nitrogen(TN) 

摘      要：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.