Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and specia...Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.展开更多
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...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.展开更多
基金Funded by the National Natural Science Foundation of China (No.59838300)
文摘Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.
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)+1 种基金the Teaching and Research Award Program for Excellent Youth Teachers in Higher Education Institu-tions of MOE,China(TRAPOYT)in 2000the Specialized Research Fund for the Doctoral Program of Higher Education of Ministry of Education of China(Grant No.20020532017).
文摘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.
