环境工程学报2018，Vol.12Issue(1)：119-126,8.DOI:10.12030/j.cjee.201706050
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
Prediction of effluent total nitrogen concentration in a wastewater treatment plant using a particle swarm optimization-support vector regression model
摘要
Abstract
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.关键词
污水处理/数据驱动模型/支持向量回归机/粒子群优化算法Key words
wastewater treatment/data-driven modeling/support vector regression/particle swarm optimization分类
资源环境引用本文复制引用
刘杰,李佟,李军..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[J].环境工程学报,2018,12(1):119-126,8.基金项目
国家水体污染控制与治理科技重大专项(2014ZX07201-001) （2014ZX07201-001）
