Adaptive data-derived anomaly detection in the activated sludge process of a large-scale wastewater treatment plant

Henri Haimi*, Michela Mulas, Francesco Corona, Stefano Marsili-Libelli, Paula Lindell, Mari Heinonen, Riku Vahala

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)65-80
Number of pages16
JournalENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume52
DOIs
Publication statusPublished - 1 Jun 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Adaptive process monitoring
  • Anomaly detection
  • Principal component analysis
  • Wastewater treatment

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