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.
作者
李春华
胡文
叶春
李金泽
魏伟伟
LI Chunhua;HU Wen;YE Chun;LI Jinze;WEI Weiwei(National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences,Beijing 100012,China;School of Geographic and Environmental Science,Guizhou Normal University,Guiyang 550025,China;School of Architecture,Yanching Institute of Technology,Langfang 065201,China)
出处
《环境工程技术学报》
CAS
2018年第6期651-655,共5页
Journal of Environmental Engineering Technology
基金
国家水体污染控制与治理科技重大专项(2017ZX07203-005)
关键词
地表水净化
脱氮
效果预测
BP神经网络
surface water purification
nitrogen removal
effect prediction
BP neural network
