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.

In the pursuit of reducing carbon footprint and in view of increasing energy prices, energy efficiency is more important than ever before. Batch-wise operated aerobic granular sludge reactors consume up to 50% less energy compared to conventional activated sludge systems because pumping energy is reduced and mixing equipment is not needed. Further energy reduction efforts should therefore target aeration energy requirements. The alpha factor is an important factor influencing the oxygen transfer efficiency, however the dynamic behaviour of alpha has hardly been investigated in general and never for an aerobic granular sludge reactor. This study showed that alpha increases during the aeration phase of a cycle due to the influence of different process parameters. Through a data analysis study of 175 batch cycles of the Prototype Nereda® installation in Utrecht over the summer and winter period of 2020–2021, the exchange ratio and temperature were identified as the main influencing factors on the rate of increase of alpha in a batch cycle. A higher exchange ratio was related to a slower increase in alpha over the aeration phase, while a higher temperature was related to a faster increase in alpha. Moreover, alpha was characterized by a same minimal value at the beginning of every aeration phase, which could be explained by the adsorption of soluble biodegradable organic carbon described by a Langmuir adsorption model. Two mathematical models, a decreasing exponential and a first order model, were set up to unravel the dynamic behaviour of alpha. Both models were discussed in view of their practical implications for the design and performance optimization of aerobic granular sludge reactors and other batch-wise operated aerobic wastewater treatment systems.

This study deals with the application of data reconciliation to wastewater treatment processes which are subject to dynamic conditions and therefore do not reach a steady-state behaviour sensu stricto. The SHARON partial nitritation process, which is operated cyclically with alternating aerated and anoxic periods, is studied as an example. The collected data long-term dynamic data set was split up into data subsets corresponding with different pseudo-steady-state operations, which allowed a better gross error detection. Mass balances were set up taking into account off-gas measurements besides liquid phase measurements and including kinetic relations between measurements based on the biological conversions in the reactor. As a result, a higher number of variables could be reconciled, more key variables could be identified, and gross error detection was facilitated. In order to draw conclusions on the process performance in a shorter period of operation, e.g., on the N₂O emission factor, the average value of the whole data set should be used with caution. The strong dependence of infiltrated air on the aeration regime and gross error in grab sampling (magnitude of 20%) had a substantial impact on calculating N₂O emission. It is recommended that the process performance indicators are derived and checked separately for steady state data subsets to guarantee reliable outcomes.

This chapter provides a review of the models available for estimating the production and emission of methane from wastewater collection and treatment systems. The details of a number of mechanistic models as well as the simplified empirical models have been summarized. Their limitations have been identified and general methods for calibration and validation have been presented.

Partial nitration-anammox is a resource-efficient technology for nitrogen removal from wastewater. However, the advantages of this nitrogen removal technology are challenged by the emission of N₂O, a potent greenhouse gas. In this study, a granular sludge one-stage partial nitritation-anammox reactor comprising granules and flocs was run for 337 days in the presence of influent organics to investigate its effect on N removal and N₂O emissions. Besides, the effect of aeration control strategies and flocs removal was investigated as well. The interpretation of the experimental results was complemented with modelling and simulation. The presence of influent organics (1 g COD g^-1 N) helped to suppress NOB and significantly reduced the overall N₂O emissions while having no significant effect on anammox activity. Besides, long-term monitoring of the reactor indicated that constant airflow rate control resulted in more stable effluent quality and less N₂O emissions than DO control. Still, floc removal reduced N₂O emissions at DO control but increased N₂O emissions at constant airflow rate. Furthermore, anammox bacteria could significantly reduce N₂O production during heterotrophic denitrification, likely via competition for NO with heterotrophs. Overall, this study demonstrated that the presence of influent organics together with proper aeration control strategies and floc management could significantly reduce the N₂O emissions without compromising nitrogen removal efficiency during one-stage partial nitritation-anammox processes.

This work shows how more variables can be monitored with a single off-gas sampler on sequentially operated than on continuously fed and aerated reactors and applies the methods to data from a full-scale aerobic granular sludge reactor as a demonstration and to obtain insight in this technology. First, liquid-gas transfer rates were calculated. Oxygen (O₂) absorption and carbon dioxide (CO₂) emission rates showed comparable cyclic trends due to the coupling of O₂ consumption and CO₂ production. Methane (CH₄) emissions showed a stripping profile and nitrous oxide (N₂O) emissions showed two peaks each cycle, which were attributed to different production pathways. Secondly, aeration characteristics were calculated, of which the gradual improvement within cycles was explained by surfactants degradation. Thirdly, liquid phase concentrations were estimated from off-gas measurements via a novel calculation procedure. As such, an average influent CH₄ concentration of 0.7 g·m⁻³ was found. Fourthly, reaction rates could be estimated from off-gas data because no feeding or discharge occurred during reaction phases. The O₂ consumption rate increased with increasing dissolved oxygen and decreased once nitrification was complete. Fifthly, greenhouse gas emissions could be derived, indicating a 0.06% N₂O emission factor. Sixthly, off-gas gave an indication of influent characteristics. The CO₂ emitted per kg COD catabolized corresponded with the TOC/COD ratio of typical wastewater organics in cycles with balanced nitrification and denitrification. High nitrogen removal efficiencies were associated with high catabolized COD/N ratios as estimated from the O₂ absorption. Finally, mass balances could be closed using off-gas O₂ data. As such, an observed yield of 0.27 g COD/g COD was found. All these variables could be estimated with a single sampler because aeration without feeding creates a more homogeneous off-gas composition and simplifies liquid-phase mass balances. Therefore, off-gas analyzers may have a broader application potential for sequentially operated reactors than currently acknowledged.

In municipal wastewater treatment plants, some dissolved methane can enter the aerobic bioreactors. This greenhouse gas originates from sewers and return flows from anaerobic sludge treatment. In well-mixed conventional activated sludge reactors, methane emissions are largely avoided because methane oxidizing bacteria consume a large fraction, even without optimizing for this purpose. In this work, the fate of dissolved methane is studied in aerobic granular sludge reactors, as they become increasingly popular. The influence of the characteristic design and operating conditions of these reactors are studied with a mathematical model with apparent conversion kinetics and stripping: the separation of feeding and aeration in time, a higher substrate transport resistance, a high retention time of granular biomass and a taller water column. Even for a best-case scenario combining an unrealistically low intragranule substrate transport resistance, a high retention time, a tall reactor, an extremely high influent methane concentration and no oxygen limitation, the methane conversion efficiency was only 12% when feeding and aeration were separated in time, which is lower than for continuous activated sludge reactors under typical conditions. A more rigorous model was used to confirm the limited conversion, considering the multi-species and multi-substrate biofilm kinetics, anoxic methane consumers and the high substrate concentration at the bottom during upward plug flow feeding. The observed limited methane conversion is mainly due to the high concentration that accumulates during unaerated feeding phases, which favours stripping more than conversion in the subsequent aeration phase. Based on these findings, strategies were proposed to mitigate methane emissions from wastewater treatment plants with sequentially operated reactors.

This study deals with the effect of aeration control strategies on the nitrogen removal efficiency and nitrous oxide (N₂O) emissions in a partial nitritation–anammox reactor with granular sludge. More specifically, dissolved oxygen (DO) control, constant airflow and effluent ammonium (NH₄⁺) control strategies were compared through a simulation study. Particular attention was paid to the effect of flocs, which are deliberately or unavoidable present besides granules in this type of reactor. When applying DO control, DO setpoints had to be adjusted to the amount of flocs present in the reactor to maintain high nitrogen removal and reduce N₂O emissions, which is difficult to realize in practice because of variable floc fractions. Constant airflow rate control could maintain a good nitrogen removal efficiency independent of the floc fraction in the reactor, but failed in N₂O mitigation. Controlling aeration based on the effluent ammonium concentration results in both high nitrogen removal and relatively low N₂O emissions, also in the presence of flocs. Fluctuations in floc fractions caused significant upsets in nitrogen removal and N₂O emissions under DO control but had less effect at constant airflow and effluent ammonium control. Still, rapid and sharp drops in flocs led to a peak in N₂O emissions at constant airflow and effluent ammonium control. Overall, effluent ammonium control reached the highest average nitrogen removal and lowest N₂O emissions and consumed the lowest aeration energy under fluctuating floc concentrations.

Gas bubbles are introduced in water to absorb or strip volatile substances in a variety of unit operations, for example during (waste)water treatment. To calculate the transfer rate of substances between the liquid phase and the gas phase, different assumptions have been made in literature regarding the gas phase composition and hydraulic pressure, which both vary along the reactor height. In this study, analytical expressions were derived for the total (macroscopic) liquid-gas transfer rate, using either the complete gradients of the mole fraction and pressure (comprehensive approach) or a uniform value, for one or both of them. Simulations with the comprehensive model were performed to understand the effect of the type of volatile substance and of the reactor design and operating conditions on the total liquid-gas transfer rate. These effects were found to be highly interactive and often non-linear. Next, the simulation results of the comprehensive model were compared with those from models that assume either a uniform mole fraction or a uniform pressure in the complete reactor volume. This illustrated that for soluble substances, the mole fraction gradient strongly affects the total liquid-gas transfer rate, while the pressure gradient became only important under operating conditions that promote stripping (i.e., for a high concentration in the liquid phase and low concentration in the inlet gas). For very poorly soluble substances, the pressure became more important under conditions that promote absorption. These results on the importance of the mole fraction and pressure gradients remained equally valid when explicitly considering a typical variation of the volumetric overall transfer coefficient (K_La) along the reactor height. Finally, a simple and fast procedure was made available through a spreadsheet to select appropriate simplifying assumptions in reactor or plant-wide models. By applying the procedure to oxygen (O₂), carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O) and nitrogen gas (N₂) in an aerobic biological wastewater treatment reactor, it was demonstrated that some common simplifications can lead to significant errors, for which corrections were proposed.

Traditional wastewater treatment plants (WWTPs) are increasingly regarded as water resource recovery facilities (WRRFs), reflecting the value of water, nutrients, energy and other resources, besides ensuring the required effluent quality. Resource recovery techniques involve biochemical, physical and physico-chemical processes, and even previously unexploited biological conversions. Biopolymer and bioplastic production also reveal the remarkable potential present in our microbial cultures. Models have demonstrated their usefulness to optimize WWTP operation to achieve better effluent quality at lower costs; they also constitute a useful tool to support the transition of WWTPs into WRRFs that maximize the valorization of products recovered from wastewater. In this paper, the extent to which the new techniques and unit processes applied for resource recovery could be modelled with conventional activated sludge models (ASMs) and additional modelling challenges being faced are discussed while providing recommendations of potential approaches to address current modelling research gaps.

This study deals with the potential and the limitations of dynamic models for describing and predicting nitrous oxide (N₂O) emissions associated with biological nitrogen removal from wastewater. The results of a three-week monitoring campaign on a full-scale partial nitritation reactor were reproduced through a state-of-the-art model including different biological N₂O formation pathways. The partial nitritation reactor under study was a SHARON reactor treating the effluent from a municipal wastewater treatment plant sludge digester. A qualitative and quantitative comparison between experimental data and simulation results was performed to identify N₂O formation pathways as well as for model identification. Heterotrophic denitrifying bacteria and ammonium oxidizing bacteria (AOB) were responsible for N₂O formation under anoxic conditions, whereas under aerated conditions the AOB were the most important N₂O producers. Relative to previously proposed models, hydroxylamine (NH₂OH) had to be included as a state variable in the AOB conversions in order to describe potential N₂O formation by AOB under anoxic conditions. An oxygen inhibition term in the corresponding reaction kinetics was required to fairly represent the relative contribution of the different AOB pathways for N₂O production. Nevertheless, quantitative prediction of N₂O emissions with models remains a challenge, which is discussed.

Multivariate statistical analysis was applied to investigate the dependencies and underlying patterns between N₂O emissions and online operational variables (dissolved oxygen and nitrogen component concentrations, temperature and influent flow-rate) during biological nitrogen removal from wastewater. The system under study was a full-scale reactor, for which hourly sensor data were available. The 15-month long monitoring campaign was divided into 10 sub-periods based on the profile of N₂O emissions, using Binary Segmentation. The dependencies between operating variables and N₂O emissions fluctuated according to Spearman's rank correlation. The correlation between N₂O emissions and nitrite concentrations ranged between 0.51 and 0.78. Correlation >0.7 between N₂O emissions and nitrate concentrations was observed at sub-periods with average temperature lower than 12 °C. Hierarchical k-means clustering and principal component analysis linked N₂O emission peaks with precipitation events and ammonium concentrations higher than 2 mg/L, especially in sub-periods characterized by low N₂O fluxes. Additionally, the highest ranges of measured N₂O fluxes belonged to clusters corresponding with NO₃-N concentration less than 1 mg/L in the upstream plug-flow reactor (middle of oxic zone), indicating slow nitrification rates. The results showed that the range of N₂O emissions partially depends on the prior behavior of the system. The principal component analysis validated the findings from the clustering analysis and showed that ammonium, nitrate, nitrite and temperature explained a considerable percentage of the variance in the system for the majority of the sub-periods. The applied statistical methods, linked the different ranges of emissions with the system variables, provided insights on the effect of operating conditions on N₂O emissions in each sub-period and can be integrated into N₂O emissions data processing at wastewater treatment plants.

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.