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

Abstract

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.

Keywords

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