Abstract
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 language | English (US) |
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Pages (from-to) | 807-814 |
Number of pages | 8 |
Journal | Journal of Environmental Management |
Volume | 223 |
DOIs | |
State | Published - Oct 1 2018 |
Keywords
- Anomaly detection
- DBNs
- Learning
- OCSVM
- Statistical monitoring
- Wastewater
ASJC Scopus subject areas
- Environmental Engineering
- Waste Management and Disposal
- Management, Monitoring, Policy and Law