Data-Driven Drift Detection in Real Process Tanks: Bridging the Gap between Academia and Practice

Bolette Dybkjær Hansen, Thomas B. Hansen, Thomas B. Moeslund, David Getreuer Jensen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Sensor drift in Wastewater Treatment Plants (WWTPs) reduces the efficiency of the plants and needs to be handled. Several studies have investigated anomaly detection and fault detection in WWTPs. However, these solutions often remain as academic projects. In this study, the gap between academia and practice is investigated by applying suggested algorithms on real WWTP data. The results show that it is difficult to detect drift in the data to a sufficient level due to missing and imprecise logs, ad hoc changes in control settings, low data quality and the equality in the patterns of some fault types and optimal operation. The challenges related to data quality raise the question of whether the data-driven approach for drift detection is the best solution, as this requires a high-quality data set. Several recommendations are suggested for utilities that wish to bridge the gap between academia and practice regarding drift detection. These include storing data and select data parameters at resolutions which positively contribute to this purpose. Furthermore, the data should be accompanied by sufficient logging of factors affecting the patterns of the data, such as changes in control settings.
Original languageEnglish
Article number926
Issue number6
Number of pages20
Publication statusPublished - 16 Mar 2022


  • anomaly
  • data driven
  • detection
  • drift
  • machine learning
  • real data
  • treatment
  • wastewater


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