Statistical monitoring of a wastewater treatment plant: A case study

Fouzi Harrou*, Abdelkader Dairi, Ying Sun, Mohamed Senouci

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


The efficient operation of wastewater treatment plants (WWTPs) is key to ensuring a sustainable and friendly green environment. Monitoring wastewater processes is helpful not only for evaluating the process operating conditions but also for inspecting product quality. This paper presents a flexible and efficient fault detection approach based on unsupervised deep learning to monitor the operating conditions of WWTPs. Specifically, this approach integrates a deep belief networks (DBN) model and a one-class support vector machine (OCSVM) to separate normal from abnormal features by simultaneously taking advantage of the feature-extraction capability of DBNs and the superior predicting capacity of OCSVM. Here, the DBN model, which is a powerful tool with greedy learning features, accounts for the nonlinear aspects of WWTPs, while OCSVM is used to reliably detect the faults. The developed DBN-OCSVM approach is tested through a practical application on data from a decentralized WWTP in Golden, CO, USA. The results from the DBN-OCSVM are compared with two other detectors: DBN-based K-nearest neighbor and K-means algorithms. The results show the capability of the developed strategy to monitor the WWTP, suggesting that it can raise an early alert to the abnormal conditions.

Original languageEnglish (US)
Pages (from-to)807-814
Number of pages8
JournalJournal of Environmental Management
StatePublished - Oct 1 2018


  • Anomaly detection
  • DBNs
  • Learning
  • Statistical monitoring
  • Wastewater

ASJC Scopus subject areas

  • Environmental Engineering
  • Waste Management and Disposal
  • Management, Monitoring, Policy and Law


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