Wastewater treatment plant monitoring via a deep learning approach

Fouzi Harrou, Abdelkader Dairi, Ying Sun, Mohamed Senouci

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 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 languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1544-1548
Number of pages5
ISBN (Electronic)9781509059492
DOIs
StatePublished - Apr 27 2018
Event19th IEEE International Conference on Industrial Technology, ICIT 2018 - Lyon, France
Duration: Feb 19 2018Feb 22 2018

Publication series

NameProceedings of the IEEE International Conference on Industrial Technology
Volume2018-February

Conference

Conference19th IEEE International Conference on Industrial Technology, ICIT 2018
Country/TerritoryFrance
CityLyon
Period02/19/1802/22/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

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