Optimization pollutants removals from wastewater treatment plant using artificial neural networks

Abstract Water quality, treatment plant management, and environmental concerns all affect how well a sewage performs. Due to the high degree of nonlinearity in as nonuniformity unpredictability influent amount, quality parameters, operational conditions, modelling sludge capacity index activated method municipal wastewater plants is challenging mission. To assess effectiveness al-diwaniyah (WWTP) operation estimate study’s first goal improve WWTP by using artificial neural networks (ANNs). Second, increasing efficiency ANNs model determine best procedure. ANN S were created predict volume (SVI) characteristics. The network’s for predicting SVI consists an input node with six variables, hidden layer five nodes, output one variable, R 2 value 0.965. outcomes show effective right network models are at SVI. This highly helpful tool that operators may use their daily management process dependability WWTP.