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

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

Tutkijat

Organisaatiot

  • Universidade Federal de Campina Grande
  • Universidade Federal do Ceara
  • University of Florence
  • Helsinki Region Environmental Services Authority

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut65-80
Sivumäärä16
JulkaisuENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Vuosikerta52
TilaJulkaistu - 1 kesäkuuta 2016
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

ID: 6666834