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

Author:

Toffanin Chiara1ORCID,Di Palma Federico2ORCID,Iacono Francesca1ORCID,Magni Lalo2ORCID

Affiliation:

1. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy

2. Department of Civil and Architecture Engineering, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy

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%).

Funder

Azienda Servizi Mortara S.p.A

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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