Adaptive data-derived anomaly detection in the activated sludge process of a large-scale wastewater treatment plant
This work examines real-time anomaly detection and isolation in a full-scale wastewater treatment application. The Viikinmäki plant is the largest municipal wastewater treatment facility in Finland. It is monitored with ample instrumentation, though their potential is not yet fully exploited. One re...
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| Published in | Engineering applications of artificial intelligence Vol. 52; pp. 65 - 80 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.06.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0952-1976 1873-6769 |
| DOI | 10.1016/j.engappai.2016.02.003 |
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| Abstract | This work examines real-time anomaly detection and isolation in a full-scale wastewater treatment application. The Viikinmäki plant is the largest municipal wastewater treatment facility in Finland. It is monitored with ample instrumentation, though their potential is not yet fully exploited. One reason that prevents the use of the instrumentation in plant control is the occasional insufficient measurement performance. Therefore, we investigate an intelligent anomaly detection system for the activated sludge process in order to motivate a more efficient use of sensors in the process operation. The anomaly detection methodology is based on principal component analysis. Because the state of the process fluctuates, moving-window extensions are used to adapt the analysis to the time-varying conditions. The results show that both instrument and process anomalies were successfully detected using the proposed algorithm and the variables responsible for the anomalies correctly isolated. We also demonstrate that the proposed algorithm represents a convenient improvement for supporting the efficient operation of wastewater treatment plants.
•Anomaly detection is investigated in a biological process of a full-scale WWTP.•The aim is to design a system motivating an efficient use of sensors in the operation.•The proposed intelligent anomaly detection system is used for real-time monitoring.•Adaptive techniques are used to adjust to the time-varying process conditions.•Instrument and process anomalies are successfully detected with the proposed system. |
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| AbstractList | This work examines real-time anomaly detection and isolation in a full-scale wastewater treatment application. The Viikinmaeki plant is the largest municipal wastewater treatment facility in Finland. It is monitored with ample instrumentation, though their potential is not yet fully exploited. One reason that prevents the use of the instrumentation in plant control is the occasional insufficient measurement performance. Therefore, we investigate an intelligent anomaly detection system for the activated sludge process in order to motivate a more efficient use of sensors in the process operation. The anomaly detection methodology is based on principal component analysis. Because the state of the process fluctuates, moving-window extensions are used to adapt the analysis to the time-varying conditions. The results show that both instrument and process anomalies were successfully detected using the proposed algorithm and the variables responsible for the anomalies correctly isolated. We also demonstrate that the proposed algorithm represents a convenient improvement for supporting the efficient operation of wastewater treatment plants. This work examines real-time anomaly detection and isolation in a full-scale wastewater treatment application. The Viikinmäki plant is the largest municipal wastewater treatment facility in Finland. It is monitored with ample instrumentation, though their potential is not yet fully exploited. One reason that prevents the use of the instrumentation in plant control is the occasional insufficient measurement performance. Therefore, we investigate an intelligent anomaly detection system for the activated sludge process in order to motivate a more efficient use of sensors in the process operation. The anomaly detection methodology is based on principal component analysis. Because the state of the process fluctuates, moving-window extensions are used to adapt the analysis to the time-varying conditions. The results show that both instrument and process anomalies were successfully detected using the proposed algorithm and the variables responsible for the anomalies correctly isolated. We also demonstrate that the proposed algorithm represents a convenient improvement for supporting the efficient operation of wastewater treatment plants. •Anomaly detection is investigated in a biological process of a full-scale WWTP.•The aim is to design a system motivating an efficient use of sensors in the operation.•The proposed intelligent anomaly detection system is used for real-time monitoring.•Adaptive techniques are used to adjust to the time-varying process conditions.•Instrument and process anomalies are successfully detected with the proposed system. |
| Author | Marsili-Libelli, Stefano Haimi, Henri Mulas, Michela Corona, Francesco Lindell, Paula Heinonen, Mari Vahala, Riku |
| Author_xml | – sequence: 1 givenname: Henri surname: Haimi fullname: Haimi, Henri email: henri.haimi@aalto.fi organization: Department of Built Environment, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland – sequence: 2 givenname: Michela surname: Mulas fullname: Mulas, Michela organization: Department of Built Environment, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland – sequence: 3 givenname: Francesco surname: Corona fullname: Corona, Francesco organization: Department of Information and Computer Science, Aalto University, School of Science, P.O. Box 15400, FI-00076 Aalto, Finland – sequence: 4 givenname: Stefano surname: Marsili-Libelli fullname: Marsili-Libelli, Stefano organization: Department of Information Technology, University of Florence, Via S. Marta 3, 50139 Florence, Italy – sequence: 5 givenname: Paula surname: Lindell fullname: Lindell, Paula organization: HSY Helsinki Region Environmental Services Authority, P.O. Box 100, FI-00066 HSY, Finland – sequence: 6 givenname: Mari surname: Heinonen fullname: Heinonen, Mari organization: HSY Helsinki Region Environmental Services Authority, P.O. Box 100, FI-00066 HSY, Finland – sequence: 7 givenname: Riku surname: Vahala fullname: Vahala, Riku organization: Department of Built Environment, Aalto University, School of Engineering, P.O. Box 15200, FI-00076 Aalto, Finland |
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| SubjectTerms | Activated sludge process Adaptive process monitoring Algorithms Anomalies Anomaly detection Artificial intelligence Expert systems Fluctuation Instrumentation Principal component analysis Wastewater treatment |
| Title | Adaptive data-derived anomaly detection in the activated sludge process of a large-scale wastewater treatment plant |
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