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 contribution

3 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 publication2018 IEEE International Conference on Industrial Technology (ICIT)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1544-1548
Number of pages5
ISBN (Print)9781509059492
DOIs
StatePublished - May 4 2018

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