Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model  

作　　者：LI Xiaodong ZENG Guangming HUANG Guohe LI Jianbing JIANG Ru 

机构地区：[1]College of Environmental Science and Engineering,Hunan University,Changsha 410082,China

出　　处：《Frontiers of Environmental Science & Engineering》2007年第3期334-338,共5页环境科学与工程前沿（英文）

基　　金：The work was supported by the Natural Science Foundation of China for Distinguished Young Scholars(Grant Nos.50225926 and 50425927);the National High-Tech Research and Development(863)Program of China(Grant No.2004AA649370);the Teaching and Research Award Program for Excellent Youth Teachers in Higher Education Institu-tions of MOE,China(TRAPOYT)in 2000;the Specialized Research Fund for the Doctoral Program of Higher Education of Ministry of Education of China(Grant No.20020532017).

摘　　要：By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.

关 键 词：wastewater treatment plant(WWTP) influent quantity short-term forecasting time series chaos neural network model 

分 类 号：X70[环境科学与工程—环境工程]