Current water resource recovery facility (WRRF) models only consider local concentration variations caused by inadequate mixing to a very limited extent, which often leads to a need for (rigorous) calibration. The main objective of this study is to visualize local impacts of mixing by developing an integrated hydrodynamic-biokinetic model for an aeration compartment of a full-scale WRRF. Such a model is able to predict local variations in concentrations and thus allows judging their importance at a process level. In order to achieve this, full-scale hydrodynamics have been simulated using computational fluid dynamics (CFD) through a detailed description of the gas and liquid phases and validated experimentally. In a second step, full ASM1 biokinetic model was integrated with the CFD model to account for the impact of mixing at the process level. The integrated model was subsequently used to evaluate effects of changing influent and aeration flows on process performance. Regions of poor mixing resulting in non-uniform substrate distributions were observed even in areas commonly assumed to be well-mixed. The concept of concentration distribution plots was introduced to quantify and clearly present spatial variations in local process concentrations. Moreover, the results of the CFD-biokinetic model were concisely compared with a conventional tanks-in-series (TIS) approach. It was found that TIS model needs calibration and a single parameter set does not suffice to describe the system under both dry and wet weather conditions. Finally, it was concluded that local mixing conditions have significant consequences in terms of optimal sensor location, control system design and process evaluation.
Water resource recovery facility (WRRF) modelling, and activated sludge modelling in particular, has been increasingly applied in the last couple of decades. Activated sludge models (ASM) have proven to be a useful tool for process evaluation, design and optimization (Fenu et al. 2010; Hauduc et al. 2013). However, mostly in current modelling efforts, a simplified approach tanks-in-series (TIS) modelling is employed. In TIS modelling, detailed spatial variations in substrate and electron acceptor concentrations in bioreactors (stemming from design characteristics and operational conditions) are typically not taken into account, even though it is plausible that they could have a significant impact on model predictions. Therefore, these models could be unsuitable to evaluate the detailed impact of certain design parameters (such as tank geometry, number and type of propellers, aerator system design, on the performance of the biological process), as well as different operational strategies, if this belongs to the goal of the modelling study. Computational fluid dynamics (CFD) is a modelling method that can be applied to visualise the extent of local hydraulic conditions as a function of design parameters and operational strategies. Currently, this modelling framework is mainly used for basic hydraulic design and troubleshooting. However, when integrated with biokinetic models, it becomes a powerful tool to gain detailed insight into the impact of local mixing conditions in the reactor on overall process performance and thus to be used for developing next generation of flow sheet models (Laurent et al. 2014).
The potential of CFD to improve biokinetic model predictions has already been shown in various studies. It was demonstrated that improved systemic model structures can be generated by incorporating knowledge from CFD (Alex et al. 2002, 1999). The application potential of combined CFD and ASM models in both pilot- and full-scale systems was evaluated by Glover et al. (2006), concluding that it can provide a more accurate description of system oxygenation capacity. Le Moullec et al. (2011) used CFD results to create a compartmental model for a pilot plant reactor showing better results than the conventional systemic approach (i.e. TIS) and requiring less computational power than CFD. The potential impact of sensor location in a mixing-limited system on controller performance was demonstrated through the use of a CFD model by Rehman et al. (2015), while detailed aeration modelling has been performed in terms of oxygen mass transfer at full scale by Fayolle et al. (2007) to show the impact of different operational conditions on mixing. However, it is important to note that these previous efforts did not entail complete ASM-CFD integration at full scale along with detailed modelling of the aeration system, but were limited to either laboratory scale or to only aeration modelling without complete ASM integration. An integrated CFD-ASM model will quantify the impact of local concentration gradients on the overall performance and such that more accurate and objective oriented compartmental models can be developed.
The ASM models are based on a Monod formulation (Monod 1942), which has been extensively used for modeling biological processes in wastewater treatment. In Monod's formulation, microbial growth (and thus substrate consumption) largely depends on local substrate and electron acceptor concentrations (dissolved oxygen (DO), ammonium, etc.) and these dependencies are described by half saturation indices or K-values (Henze et al. 1987; Arnaldos et al. 2015). The K-values are usually experimentally measured or calibrated for different substrates in the context of different biological processes. However, it is observed that the K-values show high degree of variability and are not consistent between different publications (Arnaldos et al. 2015). These differences are normally explained by diffusional or biological limitations in different systems. However, Arnaldos et al. (2015) pointed out that advective limitations (arising due to incomplete mixing), that might be present in a system, are not explicitly taken into account while measuring or calibrating K-values. While the biokinetic models have uncertainties regarding the K-values, the conventional TIS modelling approach also has its limitation in terms of its inability to take spatial heterogeneities into account. Therefore, by using TIS approach, there is always a tendency of over calibration of kinetic parameters by correcting errors which are induced by hydrodynamic limitations. Hence, there is a need of detailed hydrodynamic modelling to be included to correctly calibrate and partly avoid the unnecessary and erroneous calibration efforts.
The main objectives of this paper are therefore (1) to provide an evidence that incomplete mixing leads to local heterogeneities in species concentrations and (2) to demonstrate the applicability and added value of integrated CFD-ASM models for process analysis and evaluation for full-scale systems (i.e. knowledge buildup). In order to achieve this principal objective, first, the hydrodynamics of the aeration compartment of a full-scale plant were simulated using CFD and validated experimentally. In a second step, the CFD model was integrated with an ASM to describe the impact of local mixing conditions on biological process performance. The integrated model was then used to analyze consequences at the process level of changing influent and aeration flows. Moreover, we introduced the concept of concentration distribution plots (CDPs) in order to quantify the heterogeneity of species concentrations. In addition, a simple TIS model was also included in the paper to evaluate the consequences of using conventional methods. Henceforth, the impact of K-values on concentrations and process rates was briefly discussed. The usefulness of employing this integrated CFD-ASM approach in terms of determining sensor location, control system design and process evaluation was briefly addressed as well.
MATERIALS AND METHODS
First, geometry of the aeration tank was created in a commercial tool AutoCAD® (Autodesk) taking into account all the details which can influence reactor hydrodynamics (Figure 1(b)). One of the most significant improvements to the model as compared to previously reported studies was detailed modelling of the aerators. 196 plate aerators (diffusers) are installed at the bottom of the reactor. Practically, it is not feasible to model individual pores on plates. Therefore, in this study, the aeration plates were modelled individually such that each plate represented total area of the pores on a plate keeping geometric symmetry with real plates. Next, meshing using a commercial mesh generator tool (ICEM CFD, ANSYS) was performed. The mesh was kept as structured as possible with 90% of the cells being hexahedrons. Different meshes (0.7, 1.5 and 2.5 million cells) were generated to check for grid independence. The 1.5 and 2.5 million cells mesh provided best match with velocity measurements but the 1.5 million cells mesh was used for all the simulations due to its reduced computational demand. For a steady state solution, the 1.5 million cells mesh took about 3–4 h to converge as compared to 5–7 h for the 2.5 million cells mesh. Subsequently, CFD simulations for the hydrodynamics part were performed in FLUENT (v14.5) (ANSYS).
The mixture model (Eulerian two-fluid model) was used for modeling multi-phase (gas/liquid) flow because the calculation of movement of individual bubbles is difficult in a full-scale plant, whereas this model depicts average movement of a secondary phase (bubbles) (Versteeg & Malalasekera 2007). The relative velocity of the phases was modeled by solving an extra equation for the slip velocity. Turbulence was modeled with Realizable k-ε model and the drift velocity model was incorporated to model the dispersion of bubbles by turbulent flow. The drift velocity is important for better prediction of the gas hold-up (Talvy et al. 2007). Moreover, standard wall functions were used for the flow near the walls (Manninen et al. 1996). Development of these models (Ishii & Hibiki 2011) is considered standard so the model details are not described here. However, governing equations of momentum and drift and slip velocity are provided in the Appendix (mixture model) (available with the online version of this paper). Initially, standard water and air properties (density, viscosity, etc.) at 20 °C were used but later during the validation procedure a more realistic sludge density based on suspended solids concentration (De Clercq 2003) was implemented. The effect of gravity was also incorporated in the solution of the Navier-Stokes equations. The free top surface of the reactor was modelled using degassing boundary condition for the gas phase and the symmetry boundary condition for the liquid phase. The mixing propellers were modelled by inducing a constant source of momentum at their corresponding location in the reactor. The momentum source was calculated based on the propellers' energy consumption and their geometrical design (i.e. blade size and type) (Atif et al. 2010).
In order to validate the hydrodynamics, velocity measurements with the help of an Acoustic Doppler Current Profiler (ADCP) (Teledyne RD) were performed at two bridges (B1 & B2) (Figure 1(a)). Measurements at a third bridge were too disturbed by aeration and thus could not be used for validation purposes. Time-averaged axial and tangential velocity components at different depths were measured at three radial locations at each bridge (A, B & C). At each location for every measurement, 15 min of data were collected and each measurement was repeated three times to reduce the errors induced by the dynamic inflow.
Here, J is the local mass flux, KL the mass transfer coefficient and a the interfacial area. KL is calculated using the classical penetration theory (Higbie 1935) based on the diffusion coefficient of oxygen in water at 20 °C, and the interfacial area a is based on bubble size and local volume fractions (Fayolle et al. 2007). The coalescence and breakage phenomenon of the bubbles was not considered and thus a constant bubble size was assumed; an assumption that will be relaxed in the future. The membrane aerators are designed to provide a bubble size of about 3 mm.
Simulations were performed sequentially such that the simulations for biokinetic equations were performed on top of the ‘frozen’ converged steady state hydrodynamic solution (at selected liquid and gas flow rates) to reduce the convergence time. Computational requirements to achieve convergence increased significantly from a 3–4 h to 8–10 h after including all the biokinetic equations for the steady-state solution (using eight parallel processors). It is important to mention that convergence time for the next simulation was significantly reduced to 4–6 h when the solution was initialized from a previous converged steady state solution. The influent flowrate and composition is collected from the available plant historical data to use as inlet boundary condition and is provided in the form of ASM1 components in Appendix 1 (available with the online version of this paper). As the inlet data to outer ring is not directly measured so it was borrowed from the Eindhoven WWTP model developed by Amerlinck (2015).
Several simulations using the CFD-ASM model were carried out to investigate impact of different realistic combinations of liquid and air flow rates on local distribution of substrates and thus process performance. The combination of two influent liquid flow rates and two air flow rates have been simulated to produce a total of four scenarios. The low inflow rate was selected to be Eindhoven's WRRF average dry weather flow (1,876 m3/h referred to as scenario ‘L’), while the high inflow rate was double that (scenario ‘2 L’). Similarly, the low gas flow rate was also chosen to be the process base case (2,000 Nm3/h referred to ‘G’) and the high gas flow rate was three times that (‘3G’) representing the aeration rate at high peak load conditions. These scenarios were selected based on investigation of long-term flow dynamics in the plant.
A simple and standalone TIS model for the bioreactor (only outer ring) of the WRRF was developed in order to draw a comparison between TIS and CFD modelling. The reactor was divided into six tanks based on reactor's configuration and is shown in Appendix 2a. Each of the tanks was named according to its configuration and function. In the Appendix 2a, Tin is the section having the inlet, Tan1 is thanoxic section before winter package, TW is the winter package section, Tan2 is the anoxic section between summer and winter packages, TS is the summer package and Tout is the section having the outlet and recycle pumps. Steady-state simulations were then performed in the modelling and simulation platform WEST (MikebyDHI) with the default K-values and same influent composition and flowrates as of the CFD simulations. The respective plant layout in WEST is shown in Appendix 2b. (Appendix 2 is available with the online version of this paper.)
RESULTS AND DISCUSSION
The results from CFD hydrodynamic and CFD-biokinetic model are discussed separately in the following sections. Moreover, the comparison between TIS and CFD results is also presented and described.
CFD hydrodynamic model results
In order to further investigate mixing behaviour in the reactor, velocity vector plots in a vertical cross section of the reactor (in the aerated region) are shown for two different aeration rates (Figure 3(c) and 3(d)) (it should be noted that all the vertical cross section plots are taken from the same location indicated in Figure 1). The L-G case (Figure 3(c)) clearly shows macromixing patterns with one major (in the middle) and one minor (near the inner wall) ‘dead’ zone in the cross section that will entail lower mass transfer rates. As can be seen, in the L-3G case (Figure 3(d)), the minor dead zone disappears due to increased aeration rate. The implication of the results shown is that non-ideal mixing is ubiquitous throughout the reactor, including in the aerated region, where oxygen has to be transferred efficiently to liquid phase. These regions are averaged out when using simpler models (such as the TIS modelling); consequently, these models do not completely reflect heterogeneity of the reactor in terms of mixing and aeration. These findings are similar to observations made in previous studies (Gresch et al. 2011a, 2011b). Given this, it is now important to investigate what the implications are of this non-ideal mixing on local process rates and, hence, local component concentrations and what consequences this has with regard to current modelling approaches to account for mixing and the need for model calibration.
CFD-biokinetic model results
The variations across the width eventually lead to differences in concentrations near walls and in bulk flow. The outlet of the reactor is located at the wall and this heterogeneity would ultimately cause difference in ammonium and dissolved oxygen concentrations at the reactor outlet and in bulk flow. In terms of dissolved oxygen, a sensor used as input for a DO controller can lead to significantly different controller behaviour depending on its location (exactly at the outlet or in the bulk flow). The importance of sensor location has already been demonstrated by Rehman et al. (2015). However, simple TIS approach can only take into account the variations along the flow by using more number of tanks and assumes complete mixing within each tank. The TIS models are unable to distinguish between different lateral locations of the sensor, therefore, could lead to misleading conclusions and, hence, decisions based on those.
Figure 4(c) and 4(d) show the impact of the flow patterns in a vertical cross section of the aerated region shown in Figure 3(c) and 3(d) on the local concentrations of dissolved oxygen and ammonium for the base case (L-G). These regions are considered to be well-mixed, whereas Figure 4(c) and 4(d) show a clear heterogeneity in dissolved oxygen and ammonium concentrations. The variations in dissolved oxygen concentration originate from the fact that aeration is causing a flow pattern, which creates significant recirculation. The latter creates a region within its core (dead zone) that becomes isolated from the bulk flow and thus leads to less mass transfer (from the gas to liquid phase). Therefore, the dissolved oxygen concentration is not uniform and consequently results in local variation in ammonium concentration since it is subject to an aerobic process. Similarly, these variations also affect other process variables, as well, such as nitrate and organic carbon concentrations (Appendix 3, available with the online version of this paper).
Equation (4) shows that the autotrophic growth rate depends on dissolved oxygen and ammonia concentrations, therefore, the local concentrations impact process rate locally. Appendix 4 (available online) shows the impact of concentrations shown in Figure 4(c) and 4(d) on local autotrophic growth rate in a vertical slice of the reactor in the aerated zone. As can be seen, there are major areas with significantly reduced dissolved oxygen concentrations (Figure 4(c)) leading to anoxic conditions and, hence, limiting aerobic growth of autotrophic biomass. Therefore, it is evident that local mixing is limiting the process rate locally. However, as these details are ignored in a TIS model, reduced/enhanced process rates need to be achieved by altering half saturation indices.
Concentration distribution plots
The CDPs for the DO concentrations in two different sections of the reactor and under different conditions are shown in Figure 7. A step change (ΔC) of 0.1 mg/L is used for all these plots. Figure 7(a) shows the CDP for DO in the aerated and non-aerated sections of the reactor. It should be noted that the curves are merely extrapolated by joining the points which correspond to mid-points of each step change. For example, the first point in Figure 7(a) (for non-aerated CDP) represent the step change 0–0.1 mg/L, thus it is plotted at 0.05 mg/L and its y-axis value reads 0.37 (volume fraction). It can now be stated that 37% of the non-aerated region has a concentration between 0 and 0.1 mg/L. Moreover, Figure 7(a) shows that, in the non-aerated section, the DO concentration ranges between 0 and 0.6 mg/L. However, the aerated section has a dissolved oxygen concentration range between 0 and 1.4 mg/L and does not exceed 1.4 mg/L. The CDPs clearly show more heterogeneity in the aerated section as compared to the non-aerated one, which could be expected but has now been quantified.
Figure 7(b) shows CDPs for dissolved oxygen in the aerated section but for the two different scenarios (i.e. L-G and 2 L-3G). The variation in dissolved oxygen distributions due to changes in operational conditions is evident. In the case of the 2 L-3G scenario, the CDP is extensively spread and, thus, the use of a completely mixed assumption will not be a good estimate of the entire tank behaviour under such conditions. Depending on the final objective of the modelling study this loss of detail could be acceptable or, on the contrary, could bring about incomplete and/or incorrect process conclusions or lead to significant calibration efforts.
In order to investigate the actual impact of heterogeneous concentrations on the process rates, Appendix 4 shows distributions for the process rate (autotrophic growth rate). A step change of 0.005 (×10−4 kg/m3·s) is used for these distributions. It can be seen that the distribution of autotrophic growth rate is more non-uniform for the aerated section as compared to the non-aerated section. For the aerated section, the growth rate CDP spreads between 0.018 and 0.07 (×10−4kg/m3·s), whereas, in the non-aerated section it is between 0 and 0.02 (×10−4kg/m3·s). Similar observations can be made for the different scenarios in Appendix 6b (available online). The CDPs in the case of 2 L-3G show that the growth rate distribution is more non-uniform as compared to the L-G case. These findings are similar to the findings in Figure 7 with respect to non-uniformity among the different sections of the reactor as well as among the different operational conditions.
Comparison between the TIS and CFD modelling
In the L-G case, it can be seen that TIS model predicted lower DO concentrations compared to CFD model. This is due to the fact that the TIS assumed complete mixing in each tank and thus overestimated oxygen consumption resulting in lower DO concentrations. However, the CFD model took into account mixing limitations and hence resulted in higher DO concentrations. The trend in DO concentrations is, however, similar in both models until the aerated tank (TS). Further downstream in the outlet region (Tout), the DO concentrations decrease to 0.25 mg/L in the case of the TIS model but increase to 0.75 mg/L in the case of the CFD model. This can be understood by considering the underlying flow patterns and mixing limitations inside the reactor shown in Figure 4. Figure 3(b) shows that the gas holdup is pushed in the direction of bulk flow and hence a high DO concentration is observed in the outlet region (Figure 4(a)). This results in a higher average CFD DO concentration in Figure 8 in the Tout. However, the TIS model is unable to take into account the impact of local hydrodynamics and thus predicts low DO concentration. Similarly, ammonium concentrations are also lower for the TIS model due to the assumption of complete mixing. It is also important to note that the TIS model predicts 0.52 mg/L ammonium in the Tout which is eventually considered effluent concentration, whereas the CFD model predicts 0.9 mg/L ammonium concentration in Tout and 0.82 mg/L in effluent (i.e. average over the outlet of the bioreactor).
In the 2 L-3G case, the difference between the TIS and the CFD average values is even larger than in the L-G case. This is because the TIS model ignored the impact of operational conditions on the hydrodynamics but the CDP distributions (Figure 7(b)) for the 2 L-3G case displayed a wider distribution of the DO concentrations compared to the L-G case and, hence, a larger degree of heterogeneity. Therefore, it verifies the obvious that larger heterogeneity leads to larger deviation from the complete mixing assumption. Moreover, again the TIS model predicted lower DO concentrations compared to the CFD model except for the aerated region. In the aerated region (TS), it could be due to the impact of bulk flow, as well as due to mixing limitations caused by the dead zones. It can be noticed that, for the CFD model, the DO concentration in the outlet and inlet regions is quite high compared to the other regions due to this.
These results show that the heterogeneities observed in the CFD-biokinetic model are hugely averaged out in the TIS model. For example, for L-G case, the TIS model predicted 0.37 mg/L DO concentration in the aerated region; however, Figure 7 shows that the DO concentration in the aerated region ranges between 0 and 1.4 mg/L. Similarly, for the 2 L-3G case, the TIS model predicted 1.5 mg/L in the aerated region but Figure 7(b) shows that DO concentration range between 0 and 2.1 mg/L. Hence, the TIS model values are not a good representative of the average tank behaviour as seen in Figure 8 and, therefore, mostly need calibration of the half saturation indices to fit the measurements.
In addition to the comparison between the concentrations, the autotrophic growth rates are calculated for both models under different operational conditions (L-G and 2 L-3G). The results are shown in Appendix 7 (available online). It should be noted that the process rates for the CFD model are volume-weighted average process rates calculated from the local concentrations. The process rates predicted by the TIS model are less than the CFD based process rates. Moreover, Appendix 4 shows that the growth rate is not uniform and has a distribution similar to the concentration distributions. For example, in the aerated tank (TS), the TIS predicted 0.013 (×10−4kg/m3·s) to be the process rate, whereas Appendix 6a shows that it varies between 0.018 and 0.07 (×10−4kg/m3·s). The difference between the TIS and the volume-averaged process rates can help in providing an insight about how much calibration will be needed for the TIS model. It can also provide a basis to devise a protocol for calibration procedures taking spatial variations and flow dynamics into consideration.
Sensor placement/reading and calibration
The comparison between the TIS and CFD displayed the imminent need of calibration for the TIS model to correct for the errors induced due to the assumption of complete mixing.
In the TIS modelling techniques, the KO,A (half saturation index of oxygen for aerobic growth of autotrophs) values are calibrated based on a measured dataset, collected at a specific (easily accessible) location in a reactor. The model should be able to take the sensor location into account for a robust calibration effort. The TIS model can take into account the location of sensor to a certain extent but is unable to distinguish between lateral locations of the sensors. However, Figure 6 shows that the measured dataset would be highly dependent on the sensor location (due to spatial variations across the cross section). Therefore, resulting calibrated KO,A values would potentially be different for different physical sensor locations in the bioreactor.
Moreover, the hydrodynamic results showed a change in flow patterns with the change in operational conditions (Figure 3(c) and 3(d)) and their impact on the concentrations (Figure 6). This can potentially impact the sensor measurements and hence the calibration. One can investigate this by assuming a fixed sensor location to be close to the inner wall (Figure 6) in the aerated section. For the L-G case, the measured DO concentration would be 0.22 mg/L (Figure 6), whereas the TIS model predicts 0.4 mg/L for the aerated tank (TS) (Figure 8). Therefore, for the L-G case, measured DO is lower than DO predicted by the TIS model. However, for the same sensor location in the 2 L-3G case, measured DO (1.82 mg/L) is higher than the DO (1.5 mg/L) predicted by the TIS model (Figure 8). Therefore, in order to calibrate TIS model, for the L-G case, the KO,A value will have to be reduced, whereas for the 2 L-3G case, it needs to be increased to fit dynamically measured data (meaning that no single calibrated K-value can be found). Similarly, calibration of other half saturation indices will also depend on local mixing conditions.
In addition, Figure 7(b) provides the information about the sensor measurement/placement with respect to the CDPs under the different operational conditions. As the CDPs were drawn for a range of concentrations (step change), therefore, it is important to indicate the step change where the measured value belongs. The sensor measurement for the L-G case is 0.22 mg/L, thus it corresponds to the 0.2–0.3 range. Hence, it can be said that the measured value represents at maximum 23% of the aerated region. Similarly, the sensor measurement for the 2 L-3G case at maximum represents only 4% of the aerated region. Therefore, relatively, the sensor measurements for the L-G case would be more reliable (in terms of representativeness of tank behaviour) as compared to the 2 L-3G case. This information can certainly be useful while deciding the sensor location, accounting for this in control actions and for potential calibration efforts. The impact of the sensor location and the need of recalibration while extrapolating model results to different operational conditions, such as from dry to wet weather conditions, are often ignored. Here, we clearly illustrate what this leads to and show why dynamic calibration with a fixed mixing model does not work, i.e. never results in a single parameter set describing the system under both dry and wet weather conditions.
One major reason behind the calibration of K-values is the inability of TIS based ASM models to account for local hydrodynamics. Thus, it is quite probable that these limitations are lumped into the K-values and, hence, the default K-values being used are overestimated. However, as it is shown, the CFD takes local mixing limitations into account, thus true K-values would likely be lower than the currently employed default values in the integrated model. Therefore, Appendix 8 (available online) shows the impact of reducing the half-saturation index of dissolved oxygen (KO,A) for autotrophic organisms by 15% on both local dissolved oxygen concentration and autotrophic growth rate. This can be regarded as a reduction in ‘resistance’ that a molecule senses when moving from the bulk to the cell internal (Arnaldos et al. 2015) as the advective portion is inherently accounted for by the CFD model. The reduction in KO,A value increases the growth rate (Appendix 8b) and thus in turn increases the dissolved oxygen uptake resulting in lower dissolved oxygen concentrations. These results are a mere illustration of how the reduced KO,A values will locally impact the process rate and concentrations. Proper determination will need detailed data collection at different locations in the reactor.
TIS models after calibration are usually used to evaluate control strategies under different process conditions or with different design configurations. However, it is shown that the a calibrated TIS model would not hold true for another set of operational conditions and would thus need recalibration. Moreover, it is most likely that a different aerator or propeller design would result in different mixing conditions which TIS models are not able to account for and hence again recalibration will be needed. Here one can argue about having a dynamic K-value TIS model i.e. different K-values under different conditions. However, this would lead to major uncertainties while troubleshooting or optimizing a plant operation. One would not be able to distinguish whether problems in the plant operation arise due to biological limitations or mixing limitations and hence will be vulnerable towards taking wrong decisions.
The impact of these findings on the way WRRFs are currently modelled could be significant depending on the final objective of the modelling study.
It is shown that operational conditions influence the process performance in terms of ammonium removal and dissolved oxygen concentrations. Local concentration gradients exist in the reactor and current modelling methods (i.e. TIS modelling) are unable to account for them (as seen in Figure 8). This inadequacy of TIS models can lead to erroneous outputs and, hence, wrong decisions. However, detailed modelling such CFD-ASM modelling can be useful for detailed process design, evaluation and optimization. For instance, aerator design and placement for optimal process performance would benefit from the CFD-ASM model developed in this study; conventional process engineering does not address the heterogeneities in the dissolved oxygen and contaminant concentrations caused by different aerator configurations and operational regimes. Similarly, knowledge of local substrate conditions is required in order to make appropriate decisions in terms of sensor location for both process monitoring and control. Aerated zones are commonly perceived as homogeneously mixed; sensor location is, thus, normally decided upon driven by maintenance and operation convenience. Even though this should still be considered when deciding sensor location, it has been clearly shown that different locations along the width and depth of the reactor will bring about significantly different concentration measurements. Therefore, the information shown will be necessary to carry out decisions regarding appropriate sensor placement and its impact on controller performance by accounting for this in the control algorithm (e.g. use of a different setpoint). Additionally, the results presented have far-reaching consequences in terms of process evaluation. For instance, phosphorus removal has been widely documented in systems with no anaerobic sections, such as aerobic membrane bioreactors (Rosenberger et al. 2002; Verrecht et al. 2010; Barnard et al. 2012). The modelling results presented provide evidence that this could be due to the existence of anaerobic zones in the reactor, even in places supposedly aerated and well mixed. In the specific case of MBR, sludge rheology becomes more important which can be accounted for in a CFD setting. Another example where heterogeneous mixing could be of importance is in simultaneous nitrification-denitrification (SND) processes (and the rest of the nitrogen conversion processes taking place at low dissolved oxygen concentrations and leading to N2O production). Even though SND has been largely attributed to diffusion limitation in flocs (Münch et al. 1996), from the results presented here, it is evident that non-uniform mixing will definitely also play a significant (and maybe even a leading) role.
In general, the previous discussion underlines the fact that an integrated ASM-CFD model approach provides extremely useful and detailed information about system behaviour, which can be adopted in process understanding, improved design, optimisation and process evaluation. It is therefore recommended to use this information to revise certain conclusions taken with models that ignore mixing heterogeneity or start using models that better describe the reactor's mixing behaviour by means of compartmental models which we will address in future work.
In the present study, an integrated hydrodynamic-biokinetic model has been developed to describe the aerated compartment of a full-scale wastewater treatment plant. The model incorporates the detailed oxygen mass transfer using constant bubble size and local gas holdup. The model also takes into account local density variations as a function of local suspended solids concentrations. It was found that density has a significant impact (10–15% improvement in velocity predictions) on the hydrodynamics of the bioreactor. Furthermore:
Regions of bad mixing resulting in non-uniform substrate (e.g. ammonium) and electron acceptor (e.g. dissolved oxygen) concentrations were shown to exist in areas commonly assumed to be well mixed.
The effects of changing influent and air flows on the substrate and electron acceptor distributions have been investigated. It was observed that a single TIS mixing model would not suffice for dynamic operational conditions and there would be a potential need of recalibration of half-saturation values for TIS-based biokinetic models when moving from dry to wet weather conditions.
The impact of sensor location on the corresponding measurements was evaluated and quantified; it was observed that the reliability of sensor measurement changed with the variation in the operational conditions.
The presented findings can have far-reaching consequences in the terms of optimal sensor location, control system design and process evaluation.
The authors would like to thank Dr Dave Kinnear for providing the ADCP device for velocity measurements. The research leading to these results has received funding from the People Program (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/2007–2013, under REA agreement 289193 – Project SANITAS. This publication reflects only the authors' views and the European Union is not liable for any use that may be made of the information contained therein.