is a strong greenhouse gas. A simple mass balance model of methane and methane oxidizing bacteria was developed using apparent conversion kinetics (Chapter III) and an expression for the stripping rate of methane with the mean gas phase mole fraction and pressure (Chapter V). As such, Chapter VII acts as a demonstration of a simple model which is fit for use, i.e. to investigate the effect of typical design and operating conditions of aerobic granular sludge reactors on the fate of methane.
Four distinguishing characteristics of aerobic granular sludge reactors were studied which could influence the fate of methane. First, a shift from continuous to sequential aeration and feeding led to a much lower percentage of influent methane that was converted. This was explained by the accumulation of methane during unaerated feeding. The resulting dissolved methane concentration at the start of aeration favours stripping more than conversion.
Secondly, the increased resistance for methane transport due to the granular biomass (Chapter IV) is expected to further lower the methane conversion efficiency. But even for
typical diffusion resistances of flocculent sludge, wash-out of methane oxidizing bacteria was predicted during sequential operation. Thirdly, a long residence time of the biomass, typical for large granules, could increase the methane conversion efficiency marginally, but this effect could be counteracted by the increased intragranule transport resistance of large granules (Chapter IV). Fourthly, a taller reactor gave only a marginal increase of the methane conversion, because methane is very poorly soluble and has a low atmospheric concentration and thus the stripping rate is hardly affected by the reactor height if the volume stays constant (Chapter V). Even for an optimistic value for the intragranule transport resistance, biomass retention, reactor height and a high influent methane concentration (61 g COD.m-3, i.e. the highest value found in literature), the conversion efficiency did not surpass 12%, which is much lower than the values (68-80%) found for continuously aerated systems (Figure 8.6).
Since sequentially operated aerobic granular sludge reactors seem incompatible with high methane conversion efficiencies, other mitigation strategies should be considered.
Continuously operated aerobic granular sludge reactors, which are currently under development, could decrease methane emissions via conversion. Furthermore, methane production in the sewers could be minimized. Head-works and reactors could also be covered to capture methane emissions. The latter may be cheaper than for conventional systems due to the small footprint. Furthermore, dissolved methane could be extracted and separately treated before entering the reactor. To reduce methane emissions during sludge treatment, separate treatment of the reject water via partial nitritation-anammox could oxidize some of the methane that remained dissolved or alternatives to sludge digestion, such as biopolymer extraction, could be considered to minimize methane production.
Figure 8.6. Graphical abstract of Chapter VII on the fate of influent methane.
Perspectives
2.1. Modelling granular sludge reactors
Optimization of aerobic granular sludge reactors
Aerobic granular sludge models could be used to compare the treatment capacity with different aeration strategies that are proposed in literature (Bengtsson et al., 2018b), which stimulate simultaneous nitrification-denitrification, nitritation-denitritation (short-cut nitrogen removal) or intermittent nitrification-denitrification. For this, a biofilm model is probably most adequate, as the control strategy will change the competition for substrates throughout a cycle and therefore also the apparent kinetics (Chapter IV). Additionally, such a model could be used to find aeration strategies that improve the average aeration efficiency over a cycle, taking into account that the alfa-factor gradually increases (Chapter VI). The treatment capacity of two popular plant configurations could be compared: three sequentially operated reactors in parallel versus two reactors and a preceding buffer tank (Pronk et al., 2017). Apparent kinetics may suffice for this application, as the plant configuration is not expected to significantly influence the competition for substrates or microbial population distribution (Chapter IV). A model with a granule size distribution and differential settling could help to find a cycle configuration (in particular the settling time and wasting) that leads to the optimal granule size (distribution). For this application, a simple bioconversion model with only COD removal processes and apparent kinetics could suffice, since the growth of granules is primarily determined by organics conversions. Such a model could also be used to find strategies that speed up the start-up of new reactors, analogous to Su et al. (2013).
The potential of new applications for aerobic granular sludge could also be evaluated via modelling. For example, existing models can be used to quantify the advantages and disadvantages of aerobic granules in continuously operated reactors, which is a technology under development. So far, the advantages of continuous aerobic granular sludge reactors are only hypothesized and the disadvantage of the stronger diffusion limitations is not considered (Kent et al., 2018), while conversion rates are affected more by diffusion at the low substrate concentrations typically present in continuous systems. These effects can be estimated via increased apparent half-saturation coefficients (Chapter IV). Since heat is one of the most environmentally beneficial resources to recover from wastewater (Hao et al., 2019), it would also be valuable to investigate whether the design and operation of aerobic granular sludge reactors allows more heat recovery. It is hypothesized that the reduced plant size compared to conventional plants decreases the heat losses to the environment. This may increase the recoverable heat from the effluent, but the strength of this effect is unknown since no aerobic granular sludge models have included a heat balance model so far (Chapter II), but existing methodologies can be used (Corbala-Robles et al., 2016b).
A different kind of model application would be to test the suggested model predictive control strategy on a reactor to optimize the ammonium removal (Chapter IV). Due to the
sequential reactor operation, a single-equation model can be used for this, because the large uncertainty on the lumped parameters can be compensated via regular automatic recalibration.
Further research could investigate whether similar simple models can be used to predict the nitrate and phosphorus removal via regular recalibration. As such, the control strategy could be extended to ensure a good overall effluent quality, instead of only focussing on ammonium.
Regular recalibration on several plants would result in a large amount of values for apparent kinetic parameters, which could be linked to process conditions via data analysis, e.g. to the influent composition, temperature, batch-size or granule size distribution. Such relationships between process conditions and apparent kinetics could be used for the design of new reactors, considering the local conditions. There may also be opportunities for black-box models, such as artificial neural networks, e.g. to predict the apparent kinetic parameters based on the available data on the current process conditions to improve the predictive control (Gernaey et al., 2004).
To be even more confident in making an appropriate choice between a biofilm model and apparent kinetics for different applications, it would help to search for a reference set of apparent half-saturation coefficients for a complete ASM-type model (i.e. including biomass growth and decay, in contrast with the simpler models suggested for model predictive control).
For this, sensitivity analyses can play a role to determine which apparent half-saturation coefficients can be calibrated from lab-scale experiments and/or full-scale data. If the difference between different plants with similar operational strategies is limited, apparent kinetics can be confidently used for design of new plants, e.g. to determine the required reactor volumes and/or aeration capacity. If the difference between operational strategies (e.g.
intermittent aeration versus constant aeration) is limited, apparent kinetics can also be confidently used to optimize the operation e.g. the aeration and feeding strategy.
Further understanding of granular sludge reactors
There are also more opportunities to gain fundamental insight in the aerobic granular sludge process via models. For example, the effect of the storage compounds on the nitrogen removal, aeration requirements and sludge production can be investigated by artificially removing storage processes in a simulation, as Beun et al. (2001) did for a reactor with an aerated feeding phase. A model could also be used to elucidate the effect of the upward feeding on the reactor performance by comparing a model with a plug flow and a mixed liquid phase. It is expected that the higher concentration of soluble and particulate organics during plug flow benefit these conversions and therefore also the effluent quality. However, it is still not completely clear how the rate of hydrolysis and uptake of organics by PAO are affected by the substrate concentrations, so these kinetics first require further experimental characterisation to correctly quantify this effect of the plug flow. Furthermore, a biofilm model with a granule size distribution could be compared to one that uses a single, average granule
size, to see whether different size classes work synergistically. This would put results from modelling studies that looked for the optimal average granule size in perspective (Chapter II), because different distributions can have the same average.
As there is no fundamental difference between the simultaneous transport and conversions inside flocs, anaerobic, anammox or aerobic granules, the studied changes of apparent kinetics in the latter also occur for the other aggregate types. The difference is only quantitative: apparent kinetics will fluctuate more or less depending on the type of aggregate, type of conversions and type of reactor, due the different dynamics of aggregate formation and break-up and different changes in the degree of competition for substrates in each case.
A similar methodology as in Chapter IV could be used to investigate the apparent half-saturation coefficients of microbial groups in anaerobic and partial nitritation-anammox granules and flocs and the factors that influence them. This may shed more light on why apparent kinetics are more popular for anaerobic granules and flocs. It could be that the typical changes of the microbial population distribution, competition, aggregate size etc. affect the apparent kinetics less or that the fluctuations have less influence on the effluent quality in continuously operated systems. A dedicated experimental verification of the effects of competition on apparent kinetics of aerobic granular sludge would give more insight in the degree of fluctuations in kinetics that can be typically expected within a cycle. For example, the half-saturation coefficient of ammonia oxidizers for oxygen KO2,AOO,app could be determined for sludge that was sampled at different times during an aeration phase. As the internal storage compounds are degraded during the aeration phase, a different degree of heterotrophs will be present during each test. Also the apparent kinetics of the uptake of phosphate and volatile fatty acids by PAO in aerobic granules require dedicated attention, as these were not investigated in Chapter IV.
2.2. Liquid-gas transfer
Existing wastewater treatment modelling platforms, such as the benchmark simulation models (BSM1 and 2), could be easily adapted to include the effects of the gas phase composition and pressure gradients (Eq. 5.7 or Eq. 5.11). It should also be checked whether the results of previous modelling studies with less appropriate assumptions for the gas phase still hold, especially if nitrous oxide or carbon dioxide stripping were calculated with only the atmospheric concentration in the driving force (e.g. Bello et al. (2017), Hellinga et al.
(1996), Massara et al. (2018), Solon et al. (2017), Sötemann et al. (2005) and Vanhooren et al. (2000)), as this resulted in the largest errors.
The derived equations can be used for model-based optimization of the reactor height for minimal greenhouse gas emissions or energy efficient aeration. For example, for a continuously aerated reactor with a known oxygen demand, dissolved oxygen concentration and volume, the required airflow rate could be estimated via Eq. 5.7 and the required gauge
pressure via Eq. 5.5 for a set of reactor heights. The corresponding required blower motor power to supply each airflow rate and pressure can be calculated or found in tables. The example in Figure 8.7 shows that an increase of the water depth from 2 to 3 m leads to a decrease in the required aeration energy because the required air flow rate decreased steeply.
However, for even taller reactors, the aeration energy stays the same because the decrease in the flow rate is compensated by the increasing required pressure in this specific case. The derived equations can also account for the effect of lower atmospheric pressures at high altitudes on the required airflow rate.
Figure 8.7. Required gauge pressure (red dashed line; Eq. 5.5), air flow rate (blue dashed line; Eq. 5.7) and aeration energy for a corresponding blower of the Aerzen Deltablower Generation 5 series in case of a continuously aerated reactor with an oxygen demand of ṁ
L-GO2 = 792.4 kg.d-1, dissolved oxygen set-point CLO2 = 2 g.m-3 and volume V = 705.9 m3 (design example based on Henze et al. (2008)).
Further research could evaluate in which cases the effects of the gradients of the overall transfer coefficient KLai and total molar gas flow rate would significantly influence the total liquid-gas transfer rate. The latter could occur for example in aerobic reactors when nitrogen gas is stripped after a denitrification phase, as the nitrogen gas may significantly increase the molar gas flow along the reactor height when its stripping rate is high. Also in anaerobic reactors, the molar gas flow rate may increase significantly over the height because the gas is completely produced in-situ. Also the effects of gas phase dynamics and non-ideal liquid phase mixing could be assessed. The latter may be particularly important immediately after the filling phase in aerobic granular sludge reactors, because most pollutants and granules are located at the bottom and these are only gradually mixed by the bubbles once aeration starts. Most of these phenomena have already been studied or modelled, but the final effect on the total liquid-gas transfer rate in realistic conditions was not always calculated (Amaral et al., 2018) or the effect was only evaluated for a very specific case (Fiat et al., 2019,
van Dijk et al., 2018). A general theoretical analysis and user-friendly tools, analogous to those presented in Chapter V, can also help others to make assumptions that are fit-for-purpose .
2.3. Off-gas analyses
For off-gas analyses to be widely applied, it should be economically attractive. The capital costs for a device that simultaneously measures the oxygen, carbon dioxide, nitrous oxide and methane content of the off-gas is higher than popular liquid phase sensors, but the operational costs are on the low end (Table 8.1). However, the functions of these sensors are different. Oxygen, pH, ORP, nitrate, ammonium and phosphate sensors can be used to monitor and control the nutrient removal, which is a primary function of a treatment plant, as determined by effluent quality standards. In contrast, an off-gas analyzer can be used to monitor the aeration efficiency, liquid phase concentration and emissions of greenhouse gases, oxygen consumption rate, wastewater characteristics and sludge production, which can mainly fulfil secondary goals, such as energy conservation and minimization of greenhouse gas emissions. Further development of automatic control strategies for nutrient removal based on off-gas analyses would give these analyzers a more direct role in optimizing the primary function of a treatment plant. Then, they could not only complement, but also substitute some liquid phase sensors. For example, a sequentially operated reactor could be operated with a continuous aeration phase, which is automatically terminated when the oxygen consumption rate (Eq. 6.15) drops below a set-point that corresponds with the end of nitrification. It could also be investigated whether the end of nitrification and/or denitrification can be detected with the oxygen and carbon dioxide concentrations directly, so that one does not rely on a dissolved oxygen sensor and estimation of aeration rates. This would be analogous to the widely applied detection of the end of nitrification and/or denitrification based on pH or ORP measurements. Yet, whether and how the off-gas concentration reflect these critical moments is not yet known, since the measurements were performed on a reactor that already had another control strategy which stopped aeration when nitrification was complete.
Further research could also measure nitrous oxide emissions during different operational strategies (e.g. aeration strategies, volume exchange ratios and external carbon addition) on a full-scale reactor to enable minimization. Full-scale measurements appear to be crucial for this, as the emission factor found in this thesis was smaller than most lab-scale studies suggested. Apart from the different wastewater and reactor operation, this may be simply due to the reduced stripping caused by the saturation of the gas phase and increased hydraulic pressure in full-scale reactors (Chapter V). Furthermore, the methods developed in this thesis to derive liquid phase concentrations, conversion rates and sludge production from off-gas analyses require further validation with independent measurement methods. Also the effect of influent constituents on the oxygen transfer remains to be further characterized. It is not yet known which substances exactly cause the greatest decrease of the transfer rate at
the beginning of aeration. Also, their degradation kinetics are largely unknown. These could shine light on possibilities to minimize the aeration energy over a complete cycle by aerating more when most surfactants have already been degraded. Moreover, the effect of an increased biomass aggregation on the oxygen transfer is to be determined. For moving bed biofilm and fixed film activated sludge reactors, a positive impact of the carriers on the oxygen transfer has been observed, so a similar effect may arise with granules (Daigger and Boltz, 2018). From a modelling perspective, off-gas measurements also allow more detailed calibration of liquid-gas transfer parameters (especially the poorly understood alfa-factor) for all measured components.
Table 8.1. Typical costs for gas and liquid phase analyzers.
Measured substance
The results of the simulations could be validated by batch tests on sludge samples from different aerobic granular sludge reactors and flocculent sludge reactors with continuous and sequential operation. As such, it can be seen whether the methane oxidation capacity (in g CH4.d-1.L-1) is indeed generally lower for sequentially operated reactors and aerobic granular sludge compared to continuous systems and flocculent sludge. A mass balance of methane based on a full-scale measuring campaign on these reactors could serve the same purpose. Furthermore, the assumptions made in the model can be checked. For example, by measuring the methane oxidation capacity with nitrate and nitrite as electron donors, it can be verified that these reaction rates are negligible. Also the assumed negligible methane oxidation capacity of the influent can be falsified in a similar manner.