基于BP神经网络预测地表水净化装置总氮的去除效果
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摘要
为了模拟预测地表水净化装置脱氮效果,利用水质指标实测数据作为学习样本,选取原水总氮、氨氮、硝氮、COD_(Mn)及装置运行时间等指标作为预测参数,建立了BP神经网络水质预测模型,并运用该模型对净化装置的水质进行预测,同时引入多元线性回归模型作为对比。结果表明,BP神经网络模型预测值的可决系数为0. 985,最大误差为5. 92%,明显优于多元线性回归模型预测效果; BP神经网络模型预测精度较高,预测速度快,能够准确地预测净化装置的总氮去除效果。
A back propagation( BP) artificial neural network model was set up to predict the effect of nitrogen removal using a surface water purification device. The observed data of water quality parameters were used as study sample, and the raw water TN, ammonium nitrogen, nitrate nitrogen, COD_(Mn) and operation time of the device selected as projection parameter in this model. Besides, the multivariate linear regression model was introduced to compare with BP neural network. The results showed that the coefficient of determination of BP artificial neural network model was 0. 985, which stayed at a high level. And the maximum error was 5. 92%. Obviously, BP artificial neural network model was more precise, faster and better than multivariate linear regression model. It could accurately predict the removal effect of TN by purification device.
A back propagation( BP) artificial neural network model was set up to predict the effect of nitrogen removal using a surface water purification device. The observed data of water quality parameters were used as study sample, and the raw water TN, ammonium nitrogen, nitrate nitrogen, COD_(Mn) and operation time of the device selected as projection parameter in this model. Besides, the multivariate linear regression model was introduced to compare with BP neural network. The results showed that the coefficient of determination of BP artificial neural network model was 0. 985, which stayed at a high level. And the maximum error was 5. 92%. Obviously, BP artificial neural network model was more precise, faster and better than multivariate linear regression model. It could accurately predict the removal effect of TN by purification device.
引文
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[6]阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005.
[7]张青,王学雷,张婷,等.基于BP神经网络的洪湖水质指标预测研究[J].湿地科学,2016,14(2):212-218.ZHANG Q,WANG X L,ZHANG T,et al.Prediction of water quality index of Honghu Lake based on back propagation neural network model[J].Wetland Science,2016,14(2):212-218.
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[10]ZOU H X,ZOU X J,XIONG J T,et al.The correlative positioning error compensation of the vision system and the robot mechanism based on BP neural network[J].Key Engineering Materials,2014,621(34):513-518.
[11]HAN H,LI Y,QIAO J.A fuzzy neural network approach for online fault detection in waste water treatment process[J].Computers&Electrical Engineering,2014,40(7):2216-2226.
[12]WANG Z Q,ZHAO C.Study on the fuzzy neural network control used in wastewater treatment[J].Applied Mechanics&Materials,2006,71/72/73/74/75/76/77/78(Suppl 2):3127-3132.
[13]HE G,HUANG C,GUO L,et al.Identification and adjustment of guide rail geometric errors based on BP neural network[J].Measurement Science Review,2017,17(3):135-144.
[14]SINGH G,KANDASAMY J,SHON H K,et al.Measuring treatment effectiveness of urban wetland using hybrid water quality:artificial neural network(ANN)model[J].Desalination&Water Treatment,2011,32(1/2/3):284-290.
[15]李金泽.地表水净化装置在水质净化效果上的预测及装置改造后的仿真模拟研究[D].乌鲁木齐:新疆农业大学,2018.
[16]DUDA R O,HART P E,STORK D G.Pattern classification[M].New York:John Wiley&Sons,2003.
[17]朱庆生,周冬冬,黄伟.BP神经网络样本数据预处理应用研究[J].世界科技研究与发展,2012,34(4):624-626.ZHU Q S,ZHOU D D,HUANG W.Application research of preprocess in BP neural network sample data[J].World Sci-Tech R&D,2012,34(4):624-626.
[18]陈威,艾婵.基于多元线性回归模型的武汉市水资源承载力研究[J].河南理工大学学报(自然科学版),2017,36(1):75-79.CHEN W,AI C.Research on water resources bearing capacity of Wuhan based on multivariate linear regression model[J].Journal of Henan Polytechnic University(Natural Science),2017,36(1):75-79.