Data evaluation for wastewater treatment plants

Abstract

While nowadays a lot of measurements are conducted at wastewater treatment plants, data reliability could further be improved, e.g., through data reconciliation. This study demonstrated the added value of data reconciliation to improve data quality in a full-scale wastewater treatment plant. Also, the effect of the mass balance setting (linear and bilinear mass balances) was quantitatively evaluated, considering data sets with missing measurements and with gross errors. The improvement in the precision of the key variables was higher with bilinear mass balances (40–80 %) compared to the linear setting (0–70 %). Besides, it delivered a higher number of improved key variables, especially when flow measurements were limited (minimum improved variables of 15 and 0, respectively). Bilinear mass balances were also more efficient in gross error detection and played a crucial role in cross-validation based on flow measurements, resulting in lower incorrectly-identified gross errors. Overall, it is recommended to use bilinear mass balances.