Error detection in wastewater treatment plants using mass balances

Abstract: Process data from wastewater treatment plants are often corrupted by errors. These data provide a basis for operating the plant, therefore effort should be made to improve the data quality. Currently, Stockholm Vatten och Avfall uses a method where they quantitatively verify water flow measurement data by comparing it to water level measurements. In this thesis, an alternative approach based on mass balancing to detect errors was evaluated. The aim was to find, implement and evaluate a mass balance based method to detect and locate errors. The objective was to use this method to corroborate the flow verification method used by Stockholm Vatten och Avfall, and to improve flow data from Bromma Wastewater treatment plant. The chosen method consisted of two major steps, gross error detection and data reconciliation. A case study was performed where the method was tested on both simulated data with known added errors, real process data and finally a case where the suggested method was compared to the flow verification method. The results showed that this method was efficient in detecting a gross error when only one flow measurement was erroneous and that the estimation of the error magnitude was good. However, the suggested method was not useful for corroboration of the flow verification method. With the flow verification method, the flow in one filter basin at the time was examined. The suggested method required the combined flow in all 24 filter basins, which made it difficult to compare the two methods. The method has potential to be valuable for error detection in wastewater treatment plants, and to be used as a live tool to detect gross errors.