Wastewater treatment plant monitoring via a deep learning approach

Fouzi Harrou; Abdelkader Dairi; Ying Sun; Mohamed Senouci

doi:10.1109/ICIT.2018.8352410

Wastewater treatment plant monitoring via a deep learning approach

Fouzi Harrou, Abdelkader Dairi, Ying Sun, Mohamed Senouci

Computer, Electrical and Mathematical Sciences and Engineering

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

8 Scopus citations

Abstract

This paper presents a fault detection method based on an unsupervised deep learning to monitor operating conditions of wastewater treatment plants (WWTPs). This method uses Deep Belief Networks (DBNs) model and one-class support vector machine (OCSVM). Here, DBN model is introduced to account for nonlinear aspects of WWTPs, while OCSVM is employes to reliably detect a fault in WWTP. The developed DBN-OCSVM approach has been tested through practical application on data from a decentralized wastewater treatment plant in Golden, CO, USA. Results show the effectiveness of the developed approach to monitor the WWTP.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1544-1548
Number of pages	5
ISBN (Electronic)	9781509059492
DOIs	https://doi.org/10.1109/ICIT.2018.8352410
State	Published - Apr 27 2018
Event	19th IEEE International Conference on Industrial Technology, ICIT 2018 - Lyon, France Duration: Feb 19 2018 → Feb 22 2018

Publication series

Name	Proceedings of the IEEE International Conference on Industrial Technology
Volume	2018-February

Conference

Conference	19th IEEE International Conference on Industrial Technology, ICIT 2018
Country/Territory	France
City	Lyon
Period	02/19/18 → 02/22/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/ICIT.2018.8352410

Cite this

Harrou, F., Dairi, A., Sun, Y., & Senouci, M. (2018). Wastewater treatment plant monitoring via a deep learning approach. In Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018 (pp. 1544-1548). (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2018-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2018.8352410

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title = "Wastewater treatment plant monitoring via a deep learning approach",

abstract = "This paper presents a fault detection method based on an unsupervised deep learning to monitor operating conditions of wastewater treatment plants (WWTPs). This method uses Deep Belief Networks (DBNs) model and one-class support vector machine (OCSVM). Here, DBN model is introduced to account for nonlinear aspects of WWTPs, while OCSVM is employes to reliably detect a fault in WWTP. The developed DBN-OCSVM approach has been tested through practical application on data from a decentralized wastewater treatment plant in Golden, CO, USA. Results show the effectiveness of the developed approach to monitor the WWTP.",

author = "Fouzi Harrou and Abdelkader Dairi and Ying Sun and Mohamed Senouci",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 19th IEEE International Conference on Industrial Technology, ICIT 2018 ; Conference date: 19-02-2018 Through 22-02-2018",

year = "2018",

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day = "27",

doi = "10.1109/ICIT.2018.8352410",

language = "English (US)",

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Harrou, F, Dairi, A, Sun, Y & Senouci, M 2018, Wastewater treatment plant monitoring via a deep learning approach. in Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Proceedings of the IEEE International Conference on Industrial Technology, vol. 2018-February, Institute of Electrical and Electronics Engineers Inc., pp. 1544-1548, 19th IEEE International Conference on Industrial Technology, ICIT 2018, Lyon, France, 02/19/18. https://doi.org/10.1109/ICIT.2018.8352410

Wastewater treatment plant monitoring via a deep learning approach. / Harrou, Fouzi; Dairi, Abdelkader; Sun, Ying et al.
Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1544-1548 (Proceedings of the IEEE International Conference on Industrial Technology; Vol. 2018-February).

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

TY - GEN

T1 - Wastewater treatment plant monitoring via a deep learning approach

AU - Harrou, Fouzi

AU - Dairi, Abdelkader

AU - Sun, Ying

AU - Senouci, Mohamed

PY - 2018/4/27

Y1 - 2018/4/27

N2 - This paper presents a fault detection method based on an unsupervised deep learning to monitor operating conditions of wastewater treatment plants (WWTPs). This method uses Deep Belief Networks (DBNs) model and one-class support vector machine (OCSVM). Here, DBN model is introduced to account for nonlinear aspects of WWTPs, while OCSVM is employes to reliably detect a fault in WWTP. The developed DBN-OCSVM approach has been tested through practical application on data from a decentralized wastewater treatment plant in Golden, CO, USA. Results show the effectiveness of the developed approach to monitor the WWTP.

AB - This paper presents a fault detection method based on an unsupervised deep learning to monitor operating conditions of wastewater treatment plants (WWTPs). This method uses Deep Belief Networks (DBNs) model and one-class support vector machine (OCSVM). Here, DBN model is introduced to account for nonlinear aspects of WWTPs, while OCSVM is employes to reliably detect a fault in WWTP. The developed DBN-OCSVM approach has been tested through practical application on data from a decentralized wastewater treatment plant in Golden, CO, USA. Results show the effectiveness of the developed approach to monitor the WWTP.

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DO - 10.1109/ICIT.2018.8352410

M3 - Conference contribution

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T3 - Proceedings of the IEEE International Conference on Industrial Technology

SP - 1544

EP - 1548

BT - Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 19th IEEE International Conference on Industrial Technology, ICIT 2018

Y2 - 19 February 2018 through 22 February 2018

ER -

Harrou F, Dairi A, Sun Y, Senouci M. Wastewater treatment plant monitoring via a deep learning approach. In Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1544-1548. (Proceedings of the IEEE International Conference on Industrial Technology). doi: 10.1109/ICIT.2018.8352410

Wastewater treatment plant monitoring via a deep learning approach

Abstract

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Bibliographical note

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