Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants

Mohammed S. Alharbi; Pei Ying Hong; Taous Meriem Laleg Kirati

doi:10.23919/ACC53348.2022.9867166

Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants

Mohammed S. Alharbi, Pei Ying Hong, Taous Meriem Laleg Kirati

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.

Original language	English (US)
Title of host publication	2022 American Control Conference, ACC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3204-3211
Number of pages	8
ISBN (Electronic)	9781665451963
DOIs	https://doi.org/10.23919/ACC53348.2022.9867166
State	Published - 2022
Event	2022 American Control Conference, ACC 2022 - Atlanta, United States Duration: Jun 8 2022 → Jun 10 2022

Publication series

Name	Proceedings of the American Control Conference
Volume	2022-June
ISSN (Print)	0743-1619

Conference

Conference	2022 American Control Conference, ACC 2022
Country/Territory	United States
City	Atlanta
Period	06/8/22 → 06/10/22

Bibliographical note

Funding Information:
The authors would like to acknowledge the assistance from the KAUST Facilities and Management Utilities Team. This work has been supported by the KAUST, Saudi and Center of Excellence for NEOM research at KAUST, Saudi Arabia flagship project research fund (REI/1/4178-03-01).

Publisher Copyright:
© 2022 American Automatic Control Council.

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.23919/ACC53348.2022.9867166

Cite this

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title = "Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants",

abstract = "The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.",

author = "Alharbi, {Mohammed S.} and Hong, {Pei Ying} and Kirati, {Taous Meriem Laleg}",

note = "Funding Information: The authors would like to acknowledge the assistance from the KAUST Facilities and Management Utilities Team. This work has been supported by the KAUST, Saudi and Center of Excellence for NEOM research at KAUST, Saudi Arabia flagship project research fund (REI/1/4178-03-01). Publisher Copyright: {\textcopyright} 2022 American Automatic Control Council.; 2022 American Control Conference, ACC 2022 ; Conference date: 08-06-2022 Through 10-06-2022",

year = "2022",

doi = "10.23919/ACC53348.2022.9867166",

language = "English (US)",

series = "Proceedings of the American Control Conference",

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Alharbi, MS , Hong, PY & Kirati, TML 2022, Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants. in 2022 American Control Conference, ACC 2022. Proceedings of the American Control Conference, vol. 2022-June, Institute of Electrical and Electronics Engineers Inc., pp. 3204-3211, 2022 American Control Conference, ACC 2022, Atlanta, United States, 06/8/22. https://doi.org/10.23919/ACC53348.2022.9867166

Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants. / Alharbi, Mohammed S.; Hong, Pei Ying; Kirati, Taous Meriem Laleg.
2022 American Control Conference, ACC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 3204-3211 (Proceedings of the American Control Conference; Vol. 2022-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kirati, Taous Meriem Laleg

N1 - Funding Information: The authors would like to acknowledge the assistance from the KAUST Facilities and Management Utilities Team. This work has been supported by the KAUST, Saudi and Center of Excellence for NEOM research at KAUST, Saudi Arabia flagship project research fund (REI/1/4178-03-01). Publisher Copyright: © 2022 American Automatic Control Council.

PY - 2022

Y1 - 2022

N2 - The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.

AB - The quality of the treated wastewater is conditioned by the performance of wastewater treatment processes. However, real-time monitoring of quality variables in wastewater treatment plants (WWTP) is a challenging problem. In this paper, an adaptive online monitoring approach that is based on long short term memory (LSTM) neural network is proposed to estimate the bacterial concentration, mixed liquor suspended solids (MLSS) and mixed liquor volatile suspended solids (MLVSS) in WWTP. Due to the lack of a large dataset and difficulties in measuring quality variables, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is designed to generate synthetic data for training. Tuned hyperparameters are obtained for the proposed method. In addition, the performance is compared with the traditional LSTM using two datasets. Finally, the results indicate that WGAN successfully generates realistic training samples and quality variables are monitored with satisfactory performance.

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Adaptive Neural Network Based Monitoring of Wastewater Treatment Plants

Abstract

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ASJC Scopus subject areas

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