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.

The aerobic granular sludge (AGS) process treats wastewater with a significantly lower footprint and energy consumption compared to conventional activated sludge systems. Nevertheless, there is still potential for optimizing its performance, and mathematical models are most valuable tools to this end. Aeration energy consumption deserves particular attention, as it is the largest remaining operating cost for AGS systems. Batch-wisely operated reactors show an increasing oxygen transfer efficiency during aeration, which translates into a dynamic alpha factor. However, the dynamic nature of alpha is neglected in current models. The impact of this simplification on the operating performance was addressed for the first time in this study. Through the development of a novel 1-D biofilm reactor model, calibrated to a full-scale AGS plant, it was shown that the alpha dynamics affect both model structure and calibration, as well as the process performance. The description of the dynamic nature of alpha through the empirical relationship with the soluble biodegradable organic carbon required the addition of the state variable representing soluble slowly biodegradable organic carbon (S_CB) to the biokinetic ASM2d model. Simulation results showed that alpha dynamics significantly influences simultaneous nitrification and denitrification and therefore need to be included in mathematical models to optimize AGS process performance. Different process variables such as volume exchange ratio, aeration capacity and granule size can be manipulated to improve reactor design and performance. The practical application of these new insights were discussed regarding the optimization of AGS systems, as well as other batch-wisely operated aerobic wastewater treatment systems.