Manage Sewer In-Line Storage Control Using Hydraulic Model and Recurrent Neural Network

This paper described manage sewer in-line storage control for the city of Drammen, Norway. The purpose of the control is to use the free space of the pipes to reduce overflow at the wastewater treatment plant (WWTP). This study combined the powerful sides of the hydraulic model and neural networks. A detailed hydraulic model was developed to identify which part of the sewer system have more free space. Subsequently, the effectiveness of the proposed control solution was tested. Simulation results showed that intentionally control sewer with free space could significantly reduce overflow at the WWTP. At last, in order to enhance better decision making and give enough response time for the proposed control solution, Recurrent Neural Network (RNN) was employed to forecast flow. Three RNN architectures, namely Elman, NARX (nonlinear autoregressive network with exogenous inputs) and a novel architecture of neural networks, LSTM (Long Short-Term Memory), were compared. The LSTM exhibits the superior capability for time series prediction.