Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network.

ABSTRACT: Activated sludge models are increasingly being adopted to guide the operation of wastewater treatment plants. Chemical oxygen demand (COD) is an indispensable input for such models. To ensure that the activated sludge mathematical model can adapt to various water quality conditions and minimize prediction errors, it is essential to predict the parameters of the COD components in real-time based on the actual influent COD concentrations. However, conventional methods of determining the components' contributions are too intricate and time-consuming to be really useful. In this study, the chemical oxygen demand in the actual waste water treatment plant was disassembled and analyzed. The research involved determining the proportions of each COD component, assessing the reliability of the measu

SUBMITTER: Wang P 

PROVIDER: S-EPMC11367277 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature