A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model
Abstract By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear 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.
Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model
Abstract By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear 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.
Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model
Li, Xiaodong (author) / Zeng, Guangming (author) / Huang, Guohe (author) / Li, Jianbing (author) / Jiang, Ru (author)
2007-07-01
5 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2008
Model-Based Construction of Wastewater Treatment Plant Influent Data for Simulation Studies
| DOAJ | 2024