Sensor availability and costs are nowadays no longer limiting data gathering at wastewater treatment plants (WWTPs). However, one should be aware that a higher amount of measured data gathered does not necessarily imply that also more information is obtained. In this light, this contribution assesses the general applicability and the added value of a structured experimental design approach for planning measurement campaigns at WWTPs, in view of mass-balance-based data reconciliation. To this end, the results from full-scale WWTP case studies available in the literature were compared to those obtained with the developed structured experimental design procedure. Planning measurement campaigns comprises the selection of (additional) measurements to meet a pre-set main goal. The need for a structured experimental design procedure replacing past expert judgment approaches became clear from the fact that three out of five case studies available in the literature failed to meet the main goal and/or performed unnecessary additional measurements. Translating the main goal into specific key variables was found essential in this respect. The general applicability of the procedure was proven with three outcomes. First, the procedure, involving well-defined steps, could be applied to different WWTP layouts. Second, it ensured the fulfilment of various main goals. Third, it provided useful outcomes, i.e., optimal measurement campaigns, which reduced the need for additional measurements (40-70% less) compared to expert knowledge approaches, hence more information could be obtained with less analytical data. Overall, the experimental design procedure proved a fast and useful tool ensuring the success of subsequent mass-balance-based data reconciliation.

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.

This work demonstrates the usefulness of non-linear data reconciliation to evaluate available measurements and estimate unmeasured variables for a full-scale partial nitritation (SHARON) reactor for the treatment of wastewater with high ammonium concentrations. Despite a lack of measured data, the bilinear approach of formulating system constraints allowed to satisfy the requirements for data reconciliation and gross error detection, leading to a balanced data set and the estimation of unmeasured variables.