Toffanin, C.; Palma, F.D.; Iacono, F.; Magni, L. LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant. Appl. Sci.2023, 13, 7461.
Toffanin, C.; Palma, F.D.; Iacono, F.; Magni, L. LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant. Appl. Sci. 2023, 13, 7461.
Toffanin, C.; Palma, F.D.; Iacono, F.; Magni, L. LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant. Appl. Sci.2023, 13, 7461.
Toffanin, C.; Palma, F.D.; Iacono, F.; Magni, L. LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant. Appl. Sci. 2023, 13, 7461.
Abstract
Activated sludge process is a well known method to treat municipal and industrial waste water. In this complex process, the oxygen concentration in the reactors plays a critical 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 easy-accessible measures collected from a real plant. This dataset covers almost a month. The performances achieved with the proposed LSTM model are compared with the ones obtained with a standard AutoRegressive model with eXogenous input (ARX). Both models catch the oscillation and the overall behaviour (ARX ρ=0.833 , LSTM ρ=0.921), but, while the ARX model fails in reaching the correct amplitude (FIT=41.20%), the LSTM presents satisfactory performance (FIT=60.56%).
Engineering, Industrial and Manufacturing Engineering
Copyright:
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