Wastewater treatment plant monitoring via a deep learning approach
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-05-04
Print Publication Date2018-02
Permanent link to this recordhttp://hdl.handle.net/10754/630406
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AbstractThis 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.
CitationHarrou F, Dairi A, Sun Y, Senouci M (2018) Wastewater treatment plant monitoring via a deep learning approach. 2018 IEEE International Conference on Industrial Technology (ICIT). Available: http://dx.doi.org/10.1109/ICIT.2018.8352410.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582
Conference/Event name19th IEEE International Conference on Industrial Technology, ICIT 2018