A stepwise experimental design procedure to obtain reliable data from wastewater treatment plants (WWTPs) was developed. The proposed procedure aims at determining sets of additional measurements (besides available ones) that guarantee the identifiability of key process variables, which means that their value can be calculated from other, measured variables, based on available constraints in the form of linear mass balances. Among all solutions, i.e. all possible sets of additional measurements allowing the identifiability of all key process variables, the optimal solutions were found taking into account two objectives, namely the accuracy of the identified key variables and the cost of additional measurements. The results of this multi-objective optimization problem were represented in a Pareto-optimal front. The presented procedure was applied to a full-scale WWTP. Detailed analysis of the relation between measurements allowed the determination of groups of overlapping mass balances. Adding measured variables could only serve in identifying key variables that appear in the same group of mass balances. Besides, the application of the experimental design procedure to these individual groups significantly reduced the computational effort in evaluating available measurements and planning additional monitoring campaigns. The proposed procedure is straightforward and can be applied to other WWTPs with or without prior data collection.

In aerobic granular sludge (AGS), the growth of nitrite oxidizing bacteria (NOB) can be uncoupled from the nitrite supply of ammonia oxidizing bacteria (AOB). Besides, unlike for conventional activated sludge, Nitrobacter was found to be the dominant NOB and not Nitrospira. To explain these experimental observations, two possible pathways have been put forward in literature. The first one involves the availability of additional nitrite from partial denitrification (nitrite-loop) and the second one consists of mixotrophic growth of Nitrobacter in the presence of acetate (ping-pong). In this contribution, mathematical models were set up to assess the possibility of these pathways to explain the reported observations. Simulation results revealed that both pathways influenced the nitrifier distribution in the granules. The nitrite-loop pathway led to an elevated NOB/AOB ratio, while mixotrophic growth of Nitrobacter guaranteed their predominance among the NOB population. Besides, mixotrophic growth of Nitrobacter could lead to NO emission from AGS. An increasing temperature and/or a decreasing oxygen concentration led to an elevated NOB/AOB ratio and increased NO emissions.

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Data reconciliation was applied to a full-scale SHARON partial nitritation process. Adding off-gas analysis allowed to identify more key variables, facilitated gross error detection and led to more reliable information on N2O emissions.