Prediction of effluent total nitrogen concentration in a wastewater treatment plant using a particle swarm optimization-support vector regression model [基于改进支持向量回归机的污水处理厂出水总氮预测模型] - 文章详情页

作者：

Liu, J. (Liu, J..) | Li, T. (Li, T..) | Li, J. (Li, J..)

收录：

Scopus PKU CSCD

摘要：

A　particle　swarm　optimization　(PSO)-support　vector　regression　(SVR)　was　built　based　on　small　sample　and　applied　it　to　predict　effluent　total　nitrogen　concentration　in　a　wastewater　treatment　plant.　The　analysis　of　prediction　accuracies　indicated　that　the　mean　relative　error　(MRE)　is　1.836%,　the　coefficient　of　determination　(R2)　is　67.76%　as　well　as　the　root　mean　square　error　(RMSE)　is　0.693　9.　In　addition,　the　accuracy　of　the　PSO-SVR　model　was　analyzed　by　comparison　with　the　multivariable　linear　regression　(MLR)　model　and　the　BP　neural　network　(BP-ANN).　The　results　indicated　that　the　PSO-SVR　model　is　better　than　MLR　and　BP-ANN　in　prediction　of　effluent　total　nitrogen　concentration　in　a　wastewater　treatment　plant.　Therefore,　it　is　feasible　and　effective　to　predict　effluent　total　nitrogen　concentration　in　a　wastewater　treatment　plant　by　using　PSO-SVR　model,　which　provides　the　method　to　modeling　the　process　of　wastewater　treatment.　©　2018,　Science　Press.　All　right　reserved.

关键词：

Data-driven modeling; Particle swarm optimization; Support vector regression; Wastewater treatment

作者机构：

[ 1 ] [Liu, J.]College of Environmental Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
[ 2 ] [Li, T.]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Li, T.]Beijing Drainage Group Co. Ltd., Beijing, 100044, China
[ 4 ] [Li, J.]College of Architecture and Civil Engineering, Beijing University of Technology, Beijing, 100124, China

通讯作者信息：

[Li, T.]College of Architecture and Civil Engineering, Beijing University of TechnologyChina

电子邮件地址：

ltong@bdc.cn

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