LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant

Abstract

The activated sludge process is a well-known method used to treat municipal and industrial wastewater. In this complex process, the oxygen concentration in the reactors plays a key role in the plant efficiency. This paper proposes the use of a Long Short-Term Memory (LSTM) network to identify an input–output model suitable for the design of an oxygen concentration controller. The model is identified from easily accessible measures collected from a real plant. This dataset covers almost a month of data collected from the plant. The performances achieved with the proposed LSTM model are compared with those obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models capture the oscillation frequencies and the overall behavior (ARX Pearson correlation coefficient ρ = 0.833 , LSTM ρ = 0.921), but, while the ARX model fails to reach the correct amplitude (index of fitting FIT = 41.20%), the LSTM presents satisfactory performance (FIT = 60.56%).