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Effect of the kinetics of ammonium and nitrite oxidation on nitritation success or failure for different biofilm reactor geometries
Biochemical Engineering Journal 69 (2012) 123–129
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Biochemical Engineering Journal journal homepage: www.elsevier.com/locate/bej
Regular article
Effect of the kinetics of ammonium and nitrite oxidation on nitritation success or failure for different biofilm reactor geometries Susanne Lackner a,b,∗ , Barth F. Smets b a b
Karlsruhe Institute of Technology, Engler-Bunte-Institut, Water Chemistry and Water Technology, Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, 2800 Lyngby, Denmark
a r t i c l e
i n f o
Article history: Received 6 June 2012 Received in revised form 30 July 2012 Accepted 5 September 2012 Available online 13 September 2012 Keywords: Nitritation Biofilm modeling Biofilm geometry MABR Oxygen affinity Specific growth rate
a b s t r a c t The effect of biokinetics on nitritation was investigated in two biofilm geometries, the Membrane Aerated Biofilm Reactor (MABR) and a conventional biofilm system. A 1D biofilm model was used and evaluated by global sensitivity analysis using the variance based Sobol method. The main focus was on the influence of key biokinetic parameters (maximum specific growth rates, oxygen and nitrogen affinity constants of AOB (ammonium oxidizing bacteria) and NOB (nitrite oxidizing bacteria)) and their ratios on nitritation efficiency in these geometries. This exhaustive simulation study revealed that nitritation strongly depends on the chosen kinetic parameters of AOB and NOB. The maximum specific growth rates (max,AOB and max,NOB ) had the strongest impact on nitritation efficiency (NE). In comparison, the counter-diffusion geometry yielded more parameter combinations (27.5%) that resulted in high NE than the co-diffusion geometry (7.9%). The oxygen concentrations at the relevant biofilm interfaces (membrane/biofilm for counter-diffusion or bulk/biofilm for co-diffusion) were not predictive of NE. However, the maximum allowable oxygen concentration to maintain higher NE was higher for the counter-diffusion geometry. © 2012 Elsevier B.V. All rights reserved.
1. Introduction In contrast to conventional biofilm systems with an impermeable substratum, the membrane aerated biofilm reactor (MABR) accommodates microorganisms on a gas permeable membrane through which oxygen is supplied, thereby decoupling the oxygen and substrate sources, leading to a high degree of controllability [1]. By adjusting the oxygen flux through the membrane, separate aerobic and anaerobic zones can develop inside the biofilm. Nitrogen (N) removal from wastewater is a process that requires both aerobic and anoxic conditions, thereby making the MABR approach advantageous by providing such conditions within one reactor biofilm. Many researchers have already shown successful application of the MABR technology for advanced N removal [1–6]. The “short-cut” process of N removal via nitrite (NO2 − ) coupled with either denitrification or anaerobic ammonium oxidation (anammox) has garnered significant interest due to its potential energy and cost savings. This process requires less oxygen, less or no additional carbon source, and consumes less alkalinity compared to the traditional nitrification/denitrification route. Adjusting reac-
∗ Corresponding author at: Karlsruhe Institute of Technology, Engler-BunteInstitut, Water Chemistry and Water Technology, Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany. Tel.: +49 721 60843849. E-mail addresses: [email protected] (S. Lackner), [email protected] (B.F. Smets). 1369-703X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bej.2012.09.006
tor operation to facilitate only conversion of ammonium (NH4 + ) is accomplished by favoring growth of ammonium oxidizing bacteria (AOB) that convert NH4 + to NO2 − , while also inhibiting or outcompeting nitrite oxidizing bacteria (NOB), which further convert NO2 − to nitrate (NO3 − ). Strategies implemented to obtain partial nitrification (i.e. NO2 − accumulation) include: washout of NOB in systems with low or no biomass retention, taking advantage of the higher growth rate of AOB compared to NOB [7]; control of the pH and subsequently the amount of free ammonia (FA), with inhibition of NOB at concentrations of 1–5 mg NH3 l−1 [8]; and the limitation of NO2 − formation via oxygen limitation. Operating reactors under oxygen limitation has proven most successful to achieve NO2 − accumulation while still maintaining NH4 + conversion [9–13], consistent with the notion that NOB have higher oxygen affinity constants (lower affinities) than AOB. In the MABR, with its established and defined oxygen fluxes, partial nitritation has also been demonstrated [2]. The steep gradients of nitrogenous compounds and oxygen inside a MABR-grown biofilm may, however, provide niches for many different AOB and NOB types. It is well known that the microbial community composition changes dynamically and that reactor performance [14] or certain operation regimes [15] will influence these microbial dynamics. The variety and complexity of the microbial community in biofilms is expected to be larger compared to suspended culture processes due to the spatial heterogeneity in a biofilm. To control reactor performance and achieve the desired outcome (i.e. nitritation), it is important to know which group of
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organisms will likely be present or survive under certain conditions and how these different groups interact. The specific growth kinetics of these groups, along with the environmental conditions, will determine and impact reactor performance. There is no consensus on nitrification kinetics, and reported values for kinetic parameters for both AOB and NOB groups (including affinity and inhibition constants for nitrogenous compounds and oxygen) vary greatly [2,8,10,16–25]. Conventionally, kinetic parameters for two-step nitrification are retrieved from studies where AOB have higher maximum specific growth rates than NOB [17,22]. However, this is not necessarily true and several researchers also report the opposite, e.g. cases where the NOB growth rate was higher than its AOB counterpart [26], even sometimes by a factor of two [23]. This discrepancy in growth kinetics might especially influence estimates of reactor performance and microbial composition in a biofilm under specific conditions, such as a scenario where nitritation is the desired pathway. We, therefore, speculate that, due to the complexity of the process, i.e., the competition and interaction between AOB and NOB [14,21,27], but also competition between different species within one of these groups [11,28], generally applicable AOB and NOB biokinetic parameter sets do not exist or are only transferable between sets of similar reactor environments. Therefore, the objectives of this study were twofold: (i) what physiological attributes, represented by the kinetic parameters of AOB and NOB govern successful nitritation comparing different biofilm geometries, i.e. the MABR vs. conventional biofilms (ii) and how do these predictions match observations with operational conditions tested in experiments or with other simulation studies?
2. Materials and methods 2.1. Biofilm model A 1D biofilm model was constructed in Aquasim 2.1 g [29]. The model included two bacterial groups, AOB and NOB. Substrate consumption was simulated with standard Monod kinetics with rates depending on the amount of biomass (X), the specific yields (Y), and two substrates (nitrogen and oxygen). Decay was expressed with first order terms as described previously [30]. All relevant parameters and the stoichiometric matrix of the model are included in the Supplementary Information (Tables SI-1 to SI-3). Two biofilm geometries were compared as shown in Fig. SI-1. The counterdiffusion biofilm (simulating a MABR) was composed of a biofilm compartment without bulk aeration and a completely mixed reactor compartment that represented the membrane lumen; these two compartments were connected by a diffusive link. To simulate mass transfer limitations through the membrane and achieve almost constant oxygen concentrations at the biofilm base independent of biofilm activity, a very high total oxygen mass transfer coefficient (ktot = 106 m d−1 ) was chosen. The co-diffusion biofilm model consisted of a biofilm compartment with an additional bulk aeration term. Table 1 gives an overview of the studied parameters and the considered scenarios. Parameters were chosen based on sensitivity analysis (data not shown) with the most important parameters for this study being the following kinetic parameters: the specific growth rates, and the affinity constants for the nitrogenous compounds and oxygen; growth rates and affinity constants were chosen as the most representative parameters of AOB and NOB kinetics which are potentially influenced by the different biofilm geometries. From the input conditions ammonium and oxygen concentrations and, as operation condition, the biofilm thickness were included. In scenario 1 the parameter ranges were chosen in the range of commonly reported literature values, and the entire
Table 1 Model input: assumed parameter uncertainties for considered kinetic values of AOB and NOB, input conditions and biofilm thickness. Uniform distributions were applied for all parameter ranges. Scenario 1 uses this distribution for all parameters. Scenario 2 examines three discrete ratios of specific growth rates, r = max,AOB /max,NOB = 0.75, 1, 1.25. Parameter max,AOB [d−1 ] max,NOB [d−1 ] KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SNH4 ,in [g-N m−3 ] SO2 ,in [g-O2 m−3 ] Lf,max [mm]
Scenario 1 0.1–2.5 0.1–2.5 0.1–2.5 0.1–2.5 0.2–6 0.2–6 20–800 0.5–100 0.1–1
Scenario 2 1.5/2/2 2/2/1.6 0.1–2.5 0.1–2.5 0.2–6 0.2–6 200 0.5–100 0.8
parameter range was included in the simulation. Scenario 2 used more defined conditions for certain parameters (see Section 2.2)
2.2. Experimental setup (for comparison) The experimental setup used to define the reactor geometry, the model input and the operating conditions (scenario 2) is described in [31] and [32]. The lab scale reactor system had a volume of 0.5 L with a specific surface area of 16 m2 m−3 , and a hydraulic retention time (HRT) of 0.8 d. The initial biofilm thickness was 20 m with an equal AOB/NOB ratio. Scenario 2 used a influent concentration SNH4 ,in = 200 g-N m−3 and, based on experimental observations, a maximum biofilm thickness of 800 m was imposed. All simulations were run for 1000 days to ensure steady state conditions. Furthermore, the specific growth rates were fixed and three different combinations were applied (see Table 1).
2.3. Global sensitivity analysis The main objective was to identify the kinetic parameters with the highest impact on the model output (system performance). Hence, specific growth rates, affinity constants, and input parameters were varied over the given range. Table 1 summarizes the parameter distributions used in this study. To assess bias due to specific input conditions, the influent ammonium concentration, the oxygen concentration in the membrane lumen (bulk saturation concentration for the co-diffusion biofilm), and the biofilm thickness were also varied within the given range. Global sensitivity analysis was performed by the variance based Sobol sensitivity analysis [33]. This method provides a first order effect sensitivity index (Si ) and a total order sensitivity index STi for each studied model parameter Xi . These indices give an indication of the influence of Xi on the model output and the interaction between parameters. The first order index Si measures the possible reduction in the variance (on average) if the Xi is fixed. STi is greater than Si , or equal to Si in cases where Xi has no interactions with other parameters. The difference between STi and Si measures the interaction between Xi and other model parameters. If STi is 0, Xi has no influence on the model output. Input files to the Aquasim model were created and the data analyzed with Simlab 2.2.1 [34] with a sample size of 10,240 for scenario 1 and 6144 for scenario 2. The Sobol method was applied to the output of the Aquasim model. The model result was evaluated by parameters that reflect the reactor performance best: the nitritation efficiency (NE) and the ammonium removal efficiency (NH4r ).
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NE is defined as NE [%] =
SNO2 ,eff SNH4 ,in − SNH4 ,eff
× 100
(1)
where SNO2 ,eff is the effluent nitrite concentration [mg l−1 ], and SNO2 ,in and SNO2 ,eff are the ammonium influent and effluent concentrations [mg l−1 ], respectively. NH4r is calculated by
NH4r [%] =
1−
SNN4 ,eff
× 100
SNH4 ,in
(2)
Additionally, the ammonium (JNH4 ) and oxygen (JO2 ) fluxes are defined as
JO2 [g-O2 m−2 d−1 ] = ktot SO2 ,in − SO2 ,interface JNH4 [g-N m−2 d−1 ] =
DNH4 LL
SNH4 ,eff − SNH4 ,bt
(3) (4)
with SO2 ,in being the oxygen concentration in the membrane lumen or bulk liquid [mg l−1 ], SNH4 ,eff the bulk effluent ammonium concentration [mg l−1 ], SNH4 ,bt the ammonium concentration at the top of the biofilm [mg l−1 ], and ktot the total mass transfer coefficient [m d−1 ]. DNH4 is the diffusion coefficient for ammonium [m2 d−1 ], and LL the liquid boundary layer thickness [m]. The ammonium loading (LNH4 ) to the reactor is calculated by LNH4 [g-N m−2 d−1 ] =
Qin S A NH4 ,in
(5)
with Qin being the influent flow rate [m3 /d], and A the surface area [m2 ]. 3. Results and discussion
Fig. 1. Cumulative probability distributions of predicted nitritation efficiency and ammonium removal for the counter-diffusion and the co-diffusion biofilm (data from scenario 1).
3.1. General performance (scenario 1) To compare the general performance of the counter-diffusion and the co-diffusion biofilm geometry, all simulations (scenario 1) were evaluated for their NE and NH4r and plotted in Fig. 1 as cumulative probability distributions. Generally, the counter-diffusion geometry showed better NE whereas the co-diffusion geometry offered better NH4r . A NE of more than 75% was achieved with the counter-diffusion biofilm in 27.5% of all cases, whereas the co-diffusion biofilm only resulted 7.9% combinations that led to NE > 75%. On the contrary the counter-diffusion model showed a 59.5% probability of reaching NH4r > 50%, whereas in the codiffusion geometry 75.2% of all parameter combinations resulted in NH4r > 50%. These results suggest that the difference in biofilm geometry has a strong effect on reactor performance. The opposing supply of oxygen and ammonium in the counter-diffusion biofilm provides conditions that are more suitable for high NEs, due to the fact that high ammonium concentrations occur simultaneously with low oxygen concentrations in the outer part of the biofilm thereby limiting nitrate production more effectively. In the co-diffusion biofilm substrate supply from the bulk liquid only results in simultaneously high ammonium and oxygen concentrations leading higher NH4r but also less efficient NE due to more nitrate production which is reflected in the probability distributions in Fig. 1. 3.2. Global sensitivity analysis (scenario 1) Table 2 gives an excerpt of the first order (Si ) and total effect (STi ) sensitivity indices (as introduced in Section 2.3) for NE as the representative output parameter for all considered input parameters for both counter-diffusion and co-diffusion biofilm models.
The complete results of the global sensitivity analysis with all relevant model output variables are summarized in Tables SI-4 to SI-7. They also include the sums of the indices for all considered model input parameters for each of the output state variables. If the sum of all Si across all i equals 1, the model displays additive behavior (with respect to the model parameters) and the respective output variable (output variance) is only influenced by first order effects. An example for such behavior is observed for SO2 ,bb (Table SI-4) which is only influenced by SO2 ,in . The total order effect (STi ) provides an indication of the variance caused by higher order interactions involving variable i. The higher the total order effect, the higher the interaction of variable i with other parameters. A large difference between STi and Si thus indicates high influence (interactions) of higher order on the output variance. This was reflected, e.g., in the effect of max,AOB on NE:
Table 2 Sensitivity indices (Si – first order, STi – total order) on nitritation efficiency (NE) in scenario 1 for counter-diffusion and co-diffusion biofilm. Parameter
max,AOB [d−1 ] max,NOB [d−1 ] KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SNH4 ,in [g-N m−3 ] SO2 ,in [g-O2 m−3 ] Lf,max [mm]
Counter-diffusion
Co-diffusion
Si
STi
Si
STi
0.154 0.270 −0.003 −0.017 0.002 0.001 0.111 0.031 0.002
0.468 0.562 0.047 0.028 0.004 0.003 0.366 0.204 0.039
0.096 0.216 0.021 0.007 0.002 0.003 0.011 0.211 0.031
0.384 0.535 0.211 0.182 0.046 0.012 0.134 0.560 0.042
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the difference between STi and Si is very high and indicates that NE is influenced by higher order effects. To decrease the models variance in the output variables the parameters with the highest indices should therefore be known and fixed [34]. In the counter-diffusion geometry the most influential kinetic parameters are the maximum specific growth rates, max,AOB and max,NOB . The significant differences between Si and STi indicated high interaction of these growth rates with other model parameters. A similar finding is made for the co-diffusion biofilm model. The range of reported AOB and NOB specific growth rates is wide, and values vary from around 0.4–2 d−1 for both groups of organisms [15,19,22] and references therein). However, most studies exploring nitrification or nitritation use or measure specific growth rates where AOB /NOB = r > 1.4 or even higher [2,13,21,35]. There is no reason to a priori assume this type of growth rate balance. Our model outputs show that a critical r (>0.5) has to be reached to allow NE of 100% (data not shown) and that with higher r the chance of high NE also increases significantly but is not guaranteed. Experimental observations from MABR and co-diffusion biofilms with different inoculum compositions and problems with achieving high NE support this observation [32]. A different picture emerges for the affinity constants. Whereas the counter-diffusion performance displays almost no sensitivity to any affinity constants, for the co-diffusion biofilm a high indirect impact of especially the oxygen affinity constants on nitrite concentration is noted (seen in SNO2 and NE). Ammonium levels (seen in SNH4 and NH4r ) are impacted more by the specific AOB growth rate in the co-diffusion model than in the counter-diffusion model. The counter-diffusion model is more influenced by the input conditions in this case. The influent conditions (SNH4 ,in and SO2 ,in ) also have a differing impacts on each biofilm geometry. SNH4 ,in has a higher impact on the model variance in the counter-diffusion biofilm compared to the co-diffusion model (represented in STi ), whereas SO2 ,in has a much bigger indirect impact on the co-diffusion model (Table 2). Interestingly, the maximum biofilm thickness Lf,max has little impact on any output parameter for both geometries. Our results show that for a counter-diffusion biofilm system, nitritation success (evaluated as NE) is mainly governed by the maximum specific growth rates and to some extent by the influent conditions, but not responsive to the affinity constants. In the co-diffusion geometry, the main influence also derives from the maximum specific growth rates with more impact of the influent conditions and the oxygen affinity constants. Other parameters (decay coefficients, inhibition terms, and temperature dependencies) were not considered for this comparison as they are assumed to have similar effects on both biofilm geometries. The Sobol indices with respect to SO2 ,in indicate, that setting the oxygen concentration at the membrane/biofilm or bulk/biofilm interface as main control parameter for nitritation [2] may, however, be difficult, as the influence of SO2 ,in on the NE was not immediate. The difference of the total order effect of SO2 ,in on NE and the respective first order index was significant and governed by higher order impacts (interactions with other parameters). Fig. 2 (top) indeed illustrates that the oxygen concentration at the biofilm base, SO2 ,membrane/biofilm (the equivalent to SO2 ,in in case of the counter-diffusion biofilm) does not predict the NE. Conclusions regarding nitritation success based only on SO2 ,membrane/biofilm or JO2 (data not shown) were not possible. Even correlating JO2 with the ammonium loading LNH4 to the reactor (Fig. 2, middle), does not provide a clear relationship with NE. Of course a change in SO2 ,membrane/biofilm (and subsequently in JO2 ) will influence the ammonium removal rate, but our results clearly show that by solely adjusting the membrane oxygen concentration nitritation success or failure cannot be guaranteed and the correlation between JO2 and the loading rate LNH4 was not ultimately conclusive. However,
Fig. 2. Relation between the predicted nitritation efficiency (NE) in a counterdiffusion biofilm and (a) the oxygen concentration at the membrane/biofilm interface, (b) the JO2 /LNH4 ratio, and (c) the JO2 /JNH4 ratio.
the ratio JO2 /JNH4 (JNH4 from Eq. (4)) clearly related to the NE (Fig. 2, bottom). Since JO2 and JNH4 (fluxes of oxygen and ammonium) are governed by the microbial activity in the biofilm, these results simply confirm that microbial kinetics played a very important role. The ratio of oxygen versus ammonium flux into/out of the biofilm is a very clear indicator of NE independent of SO2 ,membrane/biofilm . JNH4 , in turn, strongly depends on the ammonium conversion inside the biofilm, and for nitritation to occur in a counter-diffusion biofilm we, therefore, contend that it is essential that the microbial community is properly adapted to the treatment goal. In the co-diffusion geometry (Fig. SI-2) similar correlations cannot be drawn. There is, again, no clear dependency between the SO2 ,bulk/biofilm (oxygen concentration at the biofilm top and equivalent to SO2 ,in in the co-diffusion model) and NE. But even the flux ratio JO2 /JNH4 does not correlate as well with NE. We speculate that the varying locations of the different bacterial groups (AOB and NOB) between geometries and the co- versus counter-diffusion substrate supply determine these results. The microbial conversion
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Table 3 Sensitivity indices (Si – first order, STi – total order) on nitritation efficiency in scenario 2 for the counter-diffusion biofilm. Parameter
Specific growth rate ratio max,AOB /max,NOB 1.5/2
KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SO2 ,in [g-O2 m−3 ]
2/2
2/1.6
Si
STi
Si
STi
Si
STi
0.006 0.003 0.000 0.006 0.325
0.518 0.383 0.019 0.034 0.992
0.009 0.006 0.000 0.003 0.574
0.380 0.246 0.013 0.037 0.991
0.007 0.021 0.002 0.002 0.745
0.210 0.243 0.000 0.015 1.006
will shape different gradients and fluxes inside the biofilm thereby resulting in an altered flux regime. 3.3. Impact of oxygen affinity constants on nitritation success (scenario 2) Because global sensitivity analysis revealed high sensitivity with respect to the influent conditions (SNH4 ,in and SO2 ,in ) and because these results differ for both geometries, scenario 2 looked at more defined operational conditions to evaluate the model sensitivity with respect to the kinetic parameters only. From scenario 1 we also learned that the specific growth rates greatly impacted the model output and therefore those were fixed (see Table 1) and 3 different ratios (r = max,AOB /max,NOB ) were applied. To minimize the impact of the input parameters, SNH4 ,in was set to 200 mg l−1 and the biofilm thickness to 800 m (to be comparable to the experimental studies. SO2 ,in is an often considered control parameter for nitritation and was, therefore, allowed to vary. Reference values for the specific growth rates for AOB and NOB (max,AOB = 2.05 d−1 ; max,NOB = 1.45 d−1 ) were in accordance with literature on nitrification modeling in biofilms [22,30,36]. Hence, with max,AOB and max,NOB values constrained, sensitivity with respect to the substrate affinity constants could be isolated. The Sobol indices with respect to the NE in scenario 2 are shown in Table 3 for the counter diffusion biofilm and in Table SI-8 for the co-diffusion biofilm geometry. These results clearly indicate the high indirect impact of the oxygen affinity constants on the NE. Whereas in the co-diffusion biofilm the indices (STi ) for the oxygen affinities do not vary for different growth rate ratios, the highest impact of KO,AOB on NE is noted at the lowest r in the counterdiffusion biofilm, and this impact decreases at higher r . A similar trend was observed for SO2 ,in : in the counter-diffusion biofilm the first order sensitivity index increases with increases in r , while the sensitivity index for SO2 ,in does not vary with r for the co-diffusion biofilm. Fig. 3 displays the ratio of the oxygen affinities KO,r = KO,AOB /KO,NOB for both geometries at r = 1 (complete results see Fig. SI-3). NEs of 100% only become possible for KO,r below 0.85 in this case (r = 1) in the counter-diffusion biofilm. The co-diffusion geometry requires even a lower KO,r of less than 0.6 for the possibility to achieve successful NE. The required maximum KO,r values favoring NEs of 100% increase with increasing r for both geometries with the allowable values being higher for the counterdiffusion biofilm. However, KO,r alone cannot predict NE of 100%: even if KO,r is below the critical value (where NEs of 100% are possible) certain parameter combinations can still yield low NEs. However, nitritation failure can be firmly inferred for KO,r above the critical values (Fig. 3). These results are in accordance with the Sobol analysis indicating a high indirect impact of the oxygen affinities on the model output: the influence of these affinities highly depends on the specific growth rates (or their ratio r ).
Fig. 3. Impact of the of KO,AOB /KO,NOB ratio on the predicted nitritation efficiency (NE) for the counter-diffusion (top) and the co-diffusion (bottom) biofilms in scenario 2 for a growth rate ratio of max,AOB /max,NOB of 2/2. The gray lines indicate the maximum KO,r where a NE = 100% was possible.
These datasets (Fig. SI-3) clearly show the high influence of KO,r on NE, and KO,r can serve as a strong indicator and predictor for NE in both geometries, especially under unfavorable growth rate conditions (r < 1). In this case KO,r has to be < 0.6–0.8 to allow for NE of 100% such as in the work by Ahn et al. [23] where high nitrite production was sustained at higher NOB growth rates compared to the ones of the AOB, in combination with a KO,r = 0.42. Similar trends were observed by [26], who found that a lower KO,AOB was the key parameter for partial nitrification. Reported values for AOB and NOB oxygen affinity constants vary significantly between species and also depend on reactor type and conditions. [22] report a range of 0.4–2 g-O2 m−3 for KO,AOB and KO,NOB . Even though KO,r values vary significantly between different studies, our conclusion regarding the influence of KO,r on NE is consistent with literature observations. Studies where complete nitrification was demonstrated tended to show higher KO,r values (e.g., KO,r = 1.5 in a MBR and KO,r = 2.82 for an activated sludge system [24]; KO,r = 1.36 in a MBR and KO,r = 1.68 in an activated sludge system [20]). On the other hand, studies on partial nitrification had associated KO,r values < 1 (see Table SI-9), which is in good agreement with our modeling results. Fig. 4 shows a comparison of the counter-diffusion and codiffusion biofilm geometry for r = 1 with regards to the oxygen concentrations at the relevant interfaces, i.e. the membrane/biofilm
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Fig. 4. Relationship between the oxygen concentration at the respective interface on predicted nitritation efficiency (NE) for counter-diffusion (top – membrane/biofilm interface) and co-diffusion (bottom – bulk biofilm interface) biofilm in scenario 2 for a growth rate ratio max,AOB /max,NOB of 2/2.
interface for the counter diffusion biofilm, and the bulk/biofilm interface for the co-diffusion geometry (complete results see Fig. SI4). Both systems can tolerate relatively high oxygen concentrations, the critical concentration for high NE increasing with increasing r . The results also underscore that the counter-diffusion biofilm has a wider range of permissible oxygen concentration at the membrane/biofilm interface that support high NE compared to the co-diffusion biofilm. In the counter-diffusion biofilm concentrations of up to 7.5 g-O2 m−3 still support high NE, whereas in the co-diffusion biofilm bulk dissolved oxygen concentrations may not exceed 1.0 g-O2 m−3 for a similar NE. Although only two bacterial groups were considered, these sensitivity analyses demonstrate the high influence and complexity of microbial growth kinetics on bioreactor performance. Downing et al. (2008) concluded in their study that the oxygen membrane concentration is a controlling factor for nitritation success in MABRs. Our study, however, conclusively shows that NE depends more on the microbial kinetics than solely on the oxygen concentration at the membrane, and that such a conclusion can only be drawn for one specific situation. The fact that we observed high NE independent of the oxygen concentrations at the membrane/biofilm interface (Fig. 2, top) and at high oxygen concentrations (Fig. SI-4) strengthens the conclusion that SO2 ,in is not the only key parameter in MABRs.
Nevertheless, it remains important to consider SO2 ,in , as this will determine the ammonium removal efficiency of the reactor. For better comparison between the different oxygen concentrations we neglected an explicit discussion of ammonium removal here, but it was of course affected by the change in SO2 ,in . Table SI-9 summarizes several previous nitritation/nitrification studies along with their reported kinetic parameters. We used each of these parameter sets in the counter-diffusion model. The predicted NEs for SO2 ,in = 2 g-O m−3 and 9 g-O m−3 are reported together with the ratios of the growth rates and affinity constants. The three modeling studies [30,36,37] used the same set of parameters to simulate partial nitrification coupled with anaerobic ammonium oxidation (Anammox). Both r and KO,r are within the optimal range for successful nitritation, with predicted NE of 100%. Within these modeling studies, the additional competition of Anammox bacteria with NOB for nitrite may have contributed to successful suppression of NOB growth. Parameters estimated from experimental systems aiming at partial nitrification, intended for coupling with Anammox or denitrification, also exhibited kinetic parameters favorable for nitritation, mainly reflected in the high r value. However, some experimental studies on partial nitrification yielded parameter combinations that did not predict high NE in our model scenario (due to the low r ). Also, the parameters suggested by Kaelin et al. [17] did not yield 100% NE at the lower oxygen concentration; these parameters, however, represent an extension of the activated sludge models (ASM3) by two-step nitrification and may not be representative for the conditions required to obtain high NE. The summary in Table SI-9 emphasizes that a wide range of kinetic parameters is reported for different systems and different objectives, underlining the complexity and specificity of two-step nitrification, i.e. the variations in the biokinetic parameter sets of AOB and NOB that are then able to represent a specific system. Future research should focus on better or standardized methods to individually define biokinetic parameters of specific operation systems with, e.g. experimental estimates rather than guessing or using literature values. In gradient systems such as biofilm reactors one set of parameters will most likely be not sufficient to describe one group of organisms (AOB and NOB here). Hence, an extension of the modeling efforts should include more than one species of each bacterial group involved for better representation of system performance. Experimental trails to verify such differences and their extent would be highly desirable. With oxygen concentration not being a reliable parameter to control NE with high enough certainty, control strategies incorporating other or a combination of measured values should be sought.
4. Conclusions This study presented an exhaustive computational investigation on the sensitivity of nitritation performance in counter-diffusion biofilms in comparison with conventional co-diffusion biofilms with respect to the kinetic parameters of the involved microorganisms. The main outcomes are:
(i) 27.5% of all parameter combinations, as reveal by the cumulative probability distribution, predicted a NE above 75% in the counter-diffusion biofilm (vs. only 7.9% in the co-diffusion biofilm) and the maximum specific growth rates (max,AOB and max,NOB ) are the most determining parameters. (ii) NE in MABRs does not solely depend on the oxygen concentration at the membrane/biofilm interface, but on the biokinetics of the AOB and NOB. The latter is reflected in the correlation of JO2 /JNH4 , the ratio of the substrate fluxes, with NE. Similar correlations are not found for the co-diffusion biofilms.
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(iii) Oxygen affinity constants determine NE to a higher extent in the co-diffusion biofilms; A critical value of KO,r below 0.6–0.8 is necessary, but not sufficient to attain a NE of 100%. (iv) The maximum oxygen concentrations at the respective biofilm interfaces (with membrane or with bulk for counter and codiffusion biofilms respectively) permissible for high NE were higher for the counter-diffusion biofilms. NE failure could be predicted with larger confidence than NE success. Acknowledgment The authors would like to acknowledge financial support from the Technical University of Denmark and the Danish Agency for Science Technology and Innovation (FTP – ReSCoBiR). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bej.2012.09.006. References [1] E. Syron, E. Casey, Membrane-aerated biofilms for high rate biotreatment: performance appraisal, engineering principles, scale-up, and development requirements, Environ. Sci. Technol. 42 (2008) 1833–1844. [2] L.S. Downing, R. Nerenberg, Effect of oxygen gradients on the activity and microbial community structure of a nitrifying, membrane-aerated biofilm, Biotechnol. Bioeng. 101 (2008) 1193–1204. [3] A. Schramm, D. de Beer, A. Gieseke, R. Amann, Microenvironments and distribution of nitrifying bacteria in a membrane-bound biofilm, Environ. Microbiol. 2 (2000) 680–686. [4] A. Terada, K. Hibiya, J. Nagai, S. Tsuneda, A. Hirata, Nitrogen removal characteristics and biofilm analysis of a membrane-aerated biofilm reactor applicable to high-strength nitrogenous wastewater treatment, J. Biosci. Bioeng. 95 (2003) 170–178. [5] A. Terada, T. Yamamoto, R. Igarashi, S. Tsuneda, A. Hirata, Feasibility of a membrane-aerated biofilm reactor to achieve controllable nitrification, Biochem. Eng. J. 28 (2006) 123–130. [6] D.L. Timberlake, S.E. Strand, K.J. Williamson, Combined aerobic heterotrophic oxidation, nitrification and denitrification in a permeable-support biofilm, Water Res. 22 (1988) 1513–1517. [7] C. Hellinga, A.A.J.C. Schellen, J.W. Mulder, M.C.M. van Loosdrecht, J.J. Heijnen, The SHARON process: an innovative method for nitrogen removal from ammonium-rich waste water, Water Sci. Technol. 37 (1998) 135–142. [8] A.C. Anthonisen, R.C. Loehr, T.B. Prakasam, E.G. Srinath, Inhibition of nitrification by ammonia and nitrous acid, J. Water Pollut. Control Fed. 48 (1976) 835–852. [9] R. Blackburne, Z. Yuan, J. Keller, Partial nitrification to nitrite using low dissolved oxygen concentration as the main selection factor, Biodegradation 19 (2008) 303–312. [10] R. Blackburne, Z. Yuan, J. Keller, Demonstration of nitrogen removal via nitrite in a sequencing batch reactor treating domestic wastewater, Water Res. 42 (2008) 2166–2176. [11] H.-D. Park, D.R. Noguera, Evaluating the effect of dissolved oxygen on ammoniaoxidizing bacterial communities in activated sludge, Water Res. 38 (2004) 3275–3286. [12] K. Pynaert, B.F. Smets, D. Beheydt, W. Verstraete, Start-up of autotrophic nitrogen removal reactors via sequential biocatalyst addition, Environ. Sci. Technol. 38 (2004) 1228–1235. [13] S. Wyffels, S.W.H. Van Hulle, P. Boeckx, E.I.P. Volcke, O. Van Cleemput, P.A. Vanrolleghem, W. Verstraete, Modeling and simulation of oxygen-limited partial nitritation in a membrane-assisted bioreactor (MBR), Biotechnol. Bioeng. 86 (2004) 531–542.
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Contents lists available at SciVerse ScienceDirect
Biochemical Engineering Journal journal homepage: www.elsevier.com/locate/bej
Regular article
Effect of the kinetics of ammonium and nitrite oxidation on nitritation success or failure for different biofilm reactor geometries Susanne Lackner a,b,∗ , Barth F. Smets b a b
Karlsruhe Institute of Technology, Engler-Bunte-Institut, Water Chemistry and Water Technology, Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany Department of Environmental Engineering, Technical University of Denmark, Miljoevej, Building 113, 2800 Lyngby, Denmark
a r t i c l e
i n f o
Article history: Received 6 June 2012 Received in revised form 30 July 2012 Accepted 5 September 2012 Available online 13 September 2012 Keywords: Nitritation Biofilm modeling Biofilm geometry MABR Oxygen affinity Specific growth rate
a b s t r a c t The effect of biokinetics on nitritation was investigated in two biofilm geometries, the Membrane Aerated Biofilm Reactor (MABR) and a conventional biofilm system. A 1D biofilm model was used and evaluated by global sensitivity analysis using the variance based Sobol method. The main focus was on the influence of key biokinetic parameters (maximum specific growth rates, oxygen and nitrogen affinity constants of AOB (ammonium oxidizing bacteria) and NOB (nitrite oxidizing bacteria)) and their ratios on nitritation efficiency in these geometries. This exhaustive simulation study revealed that nitritation strongly depends on the chosen kinetic parameters of AOB and NOB. The maximum specific growth rates (max,AOB and max,NOB ) had the strongest impact on nitritation efficiency (NE). In comparison, the counter-diffusion geometry yielded more parameter combinations (27.5%) that resulted in high NE than the co-diffusion geometry (7.9%). The oxygen concentrations at the relevant biofilm interfaces (membrane/biofilm for counter-diffusion or bulk/biofilm for co-diffusion) were not predictive of NE. However, the maximum allowable oxygen concentration to maintain higher NE was higher for the counter-diffusion geometry. © 2012 Elsevier B.V. All rights reserved.
1. Introduction In contrast to conventional biofilm systems with an impermeable substratum, the membrane aerated biofilm reactor (MABR) accommodates microorganisms on a gas permeable membrane through which oxygen is supplied, thereby decoupling the oxygen and substrate sources, leading to a high degree of controllability [1]. By adjusting the oxygen flux through the membrane, separate aerobic and anaerobic zones can develop inside the biofilm. Nitrogen (N) removal from wastewater is a process that requires both aerobic and anoxic conditions, thereby making the MABR approach advantageous by providing such conditions within one reactor biofilm. Many researchers have already shown successful application of the MABR technology for advanced N removal [1–6]. The “short-cut” process of N removal via nitrite (NO2 − ) coupled with either denitrification or anaerobic ammonium oxidation (anammox) has garnered significant interest due to its potential energy and cost savings. This process requires less oxygen, less or no additional carbon source, and consumes less alkalinity compared to the traditional nitrification/denitrification route. Adjusting reac-
∗ Corresponding author at: Karlsruhe Institute of Technology, Engler-BunteInstitut, Water Chemistry and Water Technology, Engler-Bunte-Ring 1, 76131 Karlsruhe, Germany. Tel.: +49 721 60843849. E-mail addresses: [email protected] (S. Lackner), [email protected] (B.F. Smets). 1369-703X/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.bej.2012.09.006
tor operation to facilitate only conversion of ammonium (NH4 + ) is accomplished by favoring growth of ammonium oxidizing bacteria (AOB) that convert NH4 + to NO2 − , while also inhibiting or outcompeting nitrite oxidizing bacteria (NOB), which further convert NO2 − to nitrate (NO3 − ). Strategies implemented to obtain partial nitrification (i.e. NO2 − accumulation) include: washout of NOB in systems with low or no biomass retention, taking advantage of the higher growth rate of AOB compared to NOB [7]; control of the pH and subsequently the amount of free ammonia (FA), with inhibition of NOB at concentrations of 1–5 mg NH3 l−1 [8]; and the limitation of NO2 − formation via oxygen limitation. Operating reactors under oxygen limitation has proven most successful to achieve NO2 − accumulation while still maintaining NH4 + conversion [9–13], consistent with the notion that NOB have higher oxygen affinity constants (lower affinities) than AOB. In the MABR, with its established and defined oxygen fluxes, partial nitritation has also been demonstrated [2]. The steep gradients of nitrogenous compounds and oxygen inside a MABR-grown biofilm may, however, provide niches for many different AOB and NOB types. It is well known that the microbial community composition changes dynamically and that reactor performance [14] or certain operation regimes [15] will influence these microbial dynamics. The variety and complexity of the microbial community in biofilms is expected to be larger compared to suspended culture processes due to the spatial heterogeneity in a biofilm. To control reactor performance and achieve the desired outcome (i.e. nitritation), it is important to know which group of
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organisms will likely be present or survive under certain conditions and how these different groups interact. The specific growth kinetics of these groups, along with the environmental conditions, will determine and impact reactor performance. There is no consensus on nitrification kinetics, and reported values for kinetic parameters for both AOB and NOB groups (including affinity and inhibition constants for nitrogenous compounds and oxygen) vary greatly [2,8,10,16–25]. Conventionally, kinetic parameters for two-step nitrification are retrieved from studies where AOB have higher maximum specific growth rates than NOB [17,22]. However, this is not necessarily true and several researchers also report the opposite, e.g. cases where the NOB growth rate was higher than its AOB counterpart [26], even sometimes by a factor of two [23]. This discrepancy in growth kinetics might especially influence estimates of reactor performance and microbial composition in a biofilm under specific conditions, such as a scenario where nitritation is the desired pathway. We, therefore, speculate that, due to the complexity of the process, i.e., the competition and interaction between AOB and NOB [14,21,27], but also competition between different species within one of these groups [11,28], generally applicable AOB and NOB biokinetic parameter sets do not exist or are only transferable between sets of similar reactor environments. Therefore, the objectives of this study were twofold: (i) what physiological attributes, represented by the kinetic parameters of AOB and NOB govern successful nitritation comparing different biofilm geometries, i.e. the MABR vs. conventional biofilms (ii) and how do these predictions match observations with operational conditions tested in experiments or with other simulation studies?
2. Materials and methods 2.1. Biofilm model A 1D biofilm model was constructed in Aquasim 2.1 g [29]. The model included two bacterial groups, AOB and NOB. Substrate consumption was simulated with standard Monod kinetics with rates depending on the amount of biomass (X), the specific yields (Y), and two substrates (nitrogen and oxygen). Decay was expressed with first order terms as described previously [30]. All relevant parameters and the stoichiometric matrix of the model are included in the Supplementary Information (Tables SI-1 to SI-3). Two biofilm geometries were compared as shown in Fig. SI-1. The counterdiffusion biofilm (simulating a MABR) was composed of a biofilm compartment without bulk aeration and a completely mixed reactor compartment that represented the membrane lumen; these two compartments were connected by a diffusive link. To simulate mass transfer limitations through the membrane and achieve almost constant oxygen concentrations at the biofilm base independent of biofilm activity, a very high total oxygen mass transfer coefficient (ktot = 106 m d−1 ) was chosen. The co-diffusion biofilm model consisted of a biofilm compartment with an additional bulk aeration term. Table 1 gives an overview of the studied parameters and the considered scenarios. Parameters were chosen based on sensitivity analysis (data not shown) with the most important parameters for this study being the following kinetic parameters: the specific growth rates, and the affinity constants for the nitrogenous compounds and oxygen; growth rates and affinity constants were chosen as the most representative parameters of AOB and NOB kinetics which are potentially influenced by the different biofilm geometries. From the input conditions ammonium and oxygen concentrations and, as operation condition, the biofilm thickness were included. In scenario 1 the parameter ranges were chosen in the range of commonly reported literature values, and the entire
Table 1 Model input: assumed parameter uncertainties for considered kinetic values of AOB and NOB, input conditions and biofilm thickness. Uniform distributions were applied for all parameter ranges. Scenario 1 uses this distribution for all parameters. Scenario 2 examines three discrete ratios of specific growth rates, r = max,AOB /max,NOB = 0.75, 1, 1.25. Parameter max,AOB [d−1 ] max,NOB [d−1 ] KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SNH4 ,in [g-N m−3 ] SO2 ,in [g-O2 m−3 ] Lf,max [mm]
Scenario 1 0.1–2.5 0.1–2.5 0.1–2.5 0.1–2.5 0.2–6 0.2–6 20–800 0.5–100 0.1–1
Scenario 2 1.5/2/2 2/2/1.6 0.1–2.5 0.1–2.5 0.2–6 0.2–6 200 0.5–100 0.8
parameter range was included in the simulation. Scenario 2 used more defined conditions for certain parameters (see Section 2.2)
2.2. Experimental setup (for comparison) The experimental setup used to define the reactor geometry, the model input and the operating conditions (scenario 2) is described in [31] and [32]. The lab scale reactor system had a volume of 0.5 L with a specific surface area of 16 m2 m−3 , and a hydraulic retention time (HRT) of 0.8 d. The initial biofilm thickness was 20 m with an equal AOB/NOB ratio. Scenario 2 used a influent concentration SNH4 ,in = 200 g-N m−3 and, based on experimental observations, a maximum biofilm thickness of 800 m was imposed. All simulations were run for 1000 days to ensure steady state conditions. Furthermore, the specific growth rates were fixed and three different combinations were applied (see Table 1).
2.3. Global sensitivity analysis The main objective was to identify the kinetic parameters with the highest impact on the model output (system performance). Hence, specific growth rates, affinity constants, and input parameters were varied over the given range. Table 1 summarizes the parameter distributions used in this study. To assess bias due to specific input conditions, the influent ammonium concentration, the oxygen concentration in the membrane lumen (bulk saturation concentration for the co-diffusion biofilm), and the biofilm thickness were also varied within the given range. Global sensitivity analysis was performed by the variance based Sobol sensitivity analysis [33]. This method provides a first order effect sensitivity index (Si ) and a total order sensitivity index STi for each studied model parameter Xi . These indices give an indication of the influence of Xi on the model output and the interaction between parameters. The first order index Si measures the possible reduction in the variance (on average) if the Xi is fixed. STi is greater than Si , or equal to Si in cases where Xi has no interactions with other parameters. The difference between STi and Si measures the interaction between Xi and other model parameters. If STi is 0, Xi has no influence on the model output. Input files to the Aquasim model were created and the data analyzed with Simlab 2.2.1 [34] with a sample size of 10,240 for scenario 1 and 6144 for scenario 2. The Sobol method was applied to the output of the Aquasim model. The model result was evaluated by parameters that reflect the reactor performance best: the nitritation efficiency (NE) and the ammonium removal efficiency (NH4r ).
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NE is defined as NE [%] =
SNO2 ,eff SNH4 ,in − SNH4 ,eff
× 100
(1)
where SNO2 ,eff is the effluent nitrite concentration [mg l−1 ], and SNO2 ,in and SNO2 ,eff are the ammonium influent and effluent concentrations [mg l−1 ], respectively. NH4r is calculated by
NH4r [%] =
1−
SNN4 ,eff
× 100
SNH4 ,in
(2)
Additionally, the ammonium (JNH4 ) and oxygen (JO2 ) fluxes are defined as
JO2 [g-O2 m−2 d−1 ] = ktot SO2 ,in − SO2 ,interface JNH4 [g-N m−2 d−1 ] =
DNH4 LL
SNH4 ,eff − SNH4 ,bt
(3) (4)
with SO2 ,in being the oxygen concentration in the membrane lumen or bulk liquid [mg l−1 ], SNH4 ,eff the bulk effluent ammonium concentration [mg l−1 ], SNH4 ,bt the ammonium concentration at the top of the biofilm [mg l−1 ], and ktot the total mass transfer coefficient [m d−1 ]. DNH4 is the diffusion coefficient for ammonium [m2 d−1 ], and LL the liquid boundary layer thickness [m]. The ammonium loading (LNH4 ) to the reactor is calculated by LNH4 [g-N m−2 d−1 ] =
Qin S A NH4 ,in
(5)
with Qin being the influent flow rate [m3 /d], and A the surface area [m2 ]. 3. Results and discussion
Fig. 1. Cumulative probability distributions of predicted nitritation efficiency and ammonium removal for the counter-diffusion and the co-diffusion biofilm (data from scenario 1).
3.1. General performance (scenario 1) To compare the general performance of the counter-diffusion and the co-diffusion biofilm geometry, all simulations (scenario 1) were evaluated for their NE and NH4r and plotted in Fig. 1 as cumulative probability distributions. Generally, the counter-diffusion geometry showed better NE whereas the co-diffusion geometry offered better NH4r . A NE of more than 75% was achieved with the counter-diffusion biofilm in 27.5% of all cases, whereas the co-diffusion biofilm only resulted 7.9% combinations that led to NE > 75%. On the contrary the counter-diffusion model showed a 59.5% probability of reaching NH4r > 50%, whereas in the codiffusion geometry 75.2% of all parameter combinations resulted in NH4r > 50%. These results suggest that the difference in biofilm geometry has a strong effect on reactor performance. The opposing supply of oxygen and ammonium in the counter-diffusion biofilm provides conditions that are more suitable for high NEs, due to the fact that high ammonium concentrations occur simultaneously with low oxygen concentrations in the outer part of the biofilm thereby limiting nitrate production more effectively. In the co-diffusion biofilm substrate supply from the bulk liquid only results in simultaneously high ammonium and oxygen concentrations leading higher NH4r but also less efficient NE due to more nitrate production which is reflected in the probability distributions in Fig. 1. 3.2. Global sensitivity analysis (scenario 1) Table 2 gives an excerpt of the first order (Si ) and total effect (STi ) sensitivity indices (as introduced in Section 2.3) for NE as the representative output parameter for all considered input parameters for both counter-diffusion and co-diffusion biofilm models.
The complete results of the global sensitivity analysis with all relevant model output variables are summarized in Tables SI-4 to SI-7. They also include the sums of the indices for all considered model input parameters for each of the output state variables. If the sum of all Si across all i equals 1, the model displays additive behavior (with respect to the model parameters) and the respective output variable (output variance) is only influenced by first order effects. An example for such behavior is observed for SO2 ,bb (Table SI-4) which is only influenced by SO2 ,in . The total order effect (STi ) provides an indication of the variance caused by higher order interactions involving variable i. The higher the total order effect, the higher the interaction of variable i with other parameters. A large difference between STi and Si thus indicates high influence (interactions) of higher order on the output variance. This was reflected, e.g., in the effect of max,AOB on NE:
Table 2 Sensitivity indices (Si – first order, STi – total order) on nitritation efficiency (NE) in scenario 1 for counter-diffusion and co-diffusion biofilm. Parameter
max,AOB [d−1 ] max,NOB [d−1 ] KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SNH4 ,in [g-N m−3 ] SO2 ,in [g-O2 m−3 ] Lf,max [mm]
Counter-diffusion
Co-diffusion
Si
STi
Si
STi
0.154 0.270 −0.003 −0.017 0.002 0.001 0.111 0.031 0.002
0.468 0.562 0.047 0.028 0.004 0.003 0.366 0.204 0.039
0.096 0.216 0.021 0.007 0.002 0.003 0.011 0.211 0.031
0.384 0.535 0.211 0.182 0.046 0.012 0.134 0.560 0.042
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the difference between STi and Si is very high and indicates that NE is influenced by higher order effects. To decrease the models variance in the output variables the parameters with the highest indices should therefore be known and fixed [34]. In the counter-diffusion geometry the most influential kinetic parameters are the maximum specific growth rates, max,AOB and max,NOB . The significant differences between Si and STi indicated high interaction of these growth rates with other model parameters. A similar finding is made for the co-diffusion biofilm model. The range of reported AOB and NOB specific growth rates is wide, and values vary from around 0.4–2 d−1 for both groups of organisms [15,19,22] and references therein). However, most studies exploring nitrification or nitritation use or measure specific growth rates where AOB /NOB = r > 1.4 or even higher [2,13,21,35]. There is no reason to a priori assume this type of growth rate balance. Our model outputs show that a critical r (>0.5) has to be reached to allow NE of 100% (data not shown) and that with higher r the chance of high NE also increases significantly but is not guaranteed. Experimental observations from MABR and co-diffusion biofilms with different inoculum compositions and problems with achieving high NE support this observation [32]. A different picture emerges for the affinity constants. Whereas the counter-diffusion performance displays almost no sensitivity to any affinity constants, for the co-diffusion biofilm a high indirect impact of especially the oxygen affinity constants on nitrite concentration is noted (seen in SNO2 and NE). Ammonium levels (seen in SNH4 and NH4r ) are impacted more by the specific AOB growth rate in the co-diffusion model than in the counter-diffusion model. The counter-diffusion model is more influenced by the input conditions in this case. The influent conditions (SNH4 ,in and SO2 ,in ) also have a differing impacts on each biofilm geometry. SNH4 ,in has a higher impact on the model variance in the counter-diffusion biofilm compared to the co-diffusion model (represented in STi ), whereas SO2 ,in has a much bigger indirect impact on the co-diffusion model (Table 2). Interestingly, the maximum biofilm thickness Lf,max has little impact on any output parameter for both geometries. Our results show that for a counter-diffusion biofilm system, nitritation success (evaluated as NE) is mainly governed by the maximum specific growth rates and to some extent by the influent conditions, but not responsive to the affinity constants. In the co-diffusion geometry, the main influence also derives from the maximum specific growth rates with more impact of the influent conditions and the oxygen affinity constants. Other parameters (decay coefficients, inhibition terms, and temperature dependencies) were not considered for this comparison as they are assumed to have similar effects on both biofilm geometries. The Sobol indices with respect to SO2 ,in indicate, that setting the oxygen concentration at the membrane/biofilm or bulk/biofilm interface as main control parameter for nitritation [2] may, however, be difficult, as the influence of SO2 ,in on the NE was not immediate. The difference of the total order effect of SO2 ,in on NE and the respective first order index was significant and governed by higher order impacts (interactions with other parameters). Fig. 2 (top) indeed illustrates that the oxygen concentration at the biofilm base, SO2 ,membrane/biofilm (the equivalent to SO2 ,in in case of the counter-diffusion biofilm) does not predict the NE. Conclusions regarding nitritation success based only on SO2 ,membrane/biofilm or JO2 (data not shown) were not possible. Even correlating JO2 with the ammonium loading LNH4 to the reactor (Fig. 2, middle), does not provide a clear relationship with NE. Of course a change in SO2 ,membrane/biofilm (and subsequently in JO2 ) will influence the ammonium removal rate, but our results clearly show that by solely adjusting the membrane oxygen concentration nitritation success or failure cannot be guaranteed and the correlation between JO2 and the loading rate LNH4 was not ultimately conclusive. However,
Fig. 2. Relation between the predicted nitritation efficiency (NE) in a counterdiffusion biofilm and (a) the oxygen concentration at the membrane/biofilm interface, (b) the JO2 /LNH4 ratio, and (c) the JO2 /JNH4 ratio.
the ratio JO2 /JNH4 (JNH4 from Eq. (4)) clearly related to the NE (Fig. 2, bottom). Since JO2 and JNH4 (fluxes of oxygen and ammonium) are governed by the microbial activity in the biofilm, these results simply confirm that microbial kinetics played a very important role. The ratio of oxygen versus ammonium flux into/out of the biofilm is a very clear indicator of NE independent of SO2 ,membrane/biofilm . JNH4 , in turn, strongly depends on the ammonium conversion inside the biofilm, and for nitritation to occur in a counter-diffusion biofilm we, therefore, contend that it is essential that the microbial community is properly adapted to the treatment goal. In the co-diffusion geometry (Fig. SI-2) similar correlations cannot be drawn. There is, again, no clear dependency between the SO2 ,bulk/biofilm (oxygen concentration at the biofilm top and equivalent to SO2 ,in in the co-diffusion model) and NE. But even the flux ratio JO2 /JNH4 does not correlate as well with NE. We speculate that the varying locations of the different bacterial groups (AOB and NOB) between geometries and the co- versus counter-diffusion substrate supply determine these results. The microbial conversion
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Table 3 Sensitivity indices (Si – first order, STi – total order) on nitritation efficiency in scenario 2 for the counter-diffusion biofilm. Parameter
Specific growth rate ratio max,AOB /max,NOB 1.5/2
KO,AOB [g-O2 m−3 ] KO,NOB [g-O2 m−3 ] KN,AOB [g-N m−3 ] KN,NOB [g-N m−3 ] SO2 ,in [g-O2 m−3 ]
2/2
2/1.6
Si
STi
Si
STi
Si
STi
0.006 0.003 0.000 0.006 0.325
0.518 0.383 0.019 0.034 0.992
0.009 0.006 0.000 0.003 0.574
0.380 0.246 0.013 0.037 0.991
0.007 0.021 0.002 0.002 0.745
0.210 0.243 0.000 0.015 1.006
will shape different gradients and fluxes inside the biofilm thereby resulting in an altered flux regime. 3.3. Impact of oxygen affinity constants on nitritation success (scenario 2) Because global sensitivity analysis revealed high sensitivity with respect to the influent conditions (SNH4 ,in and SO2 ,in ) and because these results differ for both geometries, scenario 2 looked at more defined operational conditions to evaluate the model sensitivity with respect to the kinetic parameters only. From scenario 1 we also learned that the specific growth rates greatly impacted the model output and therefore those were fixed (see Table 1) and 3 different ratios (r = max,AOB /max,NOB ) were applied. To minimize the impact of the input parameters, SNH4 ,in was set to 200 mg l−1 and the biofilm thickness to 800 m (to be comparable to the experimental studies. SO2 ,in is an often considered control parameter for nitritation and was, therefore, allowed to vary. Reference values for the specific growth rates for AOB and NOB (max,AOB = 2.05 d−1 ; max,NOB = 1.45 d−1 ) were in accordance with literature on nitrification modeling in biofilms [22,30,36]. Hence, with max,AOB and max,NOB values constrained, sensitivity with respect to the substrate affinity constants could be isolated. The Sobol indices with respect to the NE in scenario 2 are shown in Table 3 for the counter diffusion biofilm and in Table SI-8 for the co-diffusion biofilm geometry. These results clearly indicate the high indirect impact of the oxygen affinity constants on the NE. Whereas in the co-diffusion biofilm the indices (STi ) for the oxygen affinities do not vary for different growth rate ratios, the highest impact of KO,AOB on NE is noted at the lowest r in the counterdiffusion biofilm, and this impact decreases at higher r . A similar trend was observed for SO2 ,in : in the counter-diffusion biofilm the first order sensitivity index increases with increases in r , while the sensitivity index for SO2 ,in does not vary with r for the co-diffusion biofilm. Fig. 3 displays the ratio of the oxygen affinities KO,r = KO,AOB /KO,NOB for both geometries at r = 1 (complete results see Fig. SI-3). NEs of 100% only become possible for KO,r below 0.85 in this case (r = 1) in the counter-diffusion biofilm. The co-diffusion geometry requires even a lower KO,r of less than 0.6 for the possibility to achieve successful NE. The required maximum KO,r values favoring NEs of 100% increase with increasing r for both geometries with the allowable values being higher for the counterdiffusion biofilm. However, KO,r alone cannot predict NE of 100%: even if KO,r is below the critical value (where NEs of 100% are possible) certain parameter combinations can still yield low NEs. However, nitritation failure can be firmly inferred for KO,r above the critical values (Fig. 3). These results are in accordance with the Sobol analysis indicating a high indirect impact of the oxygen affinities on the model output: the influence of these affinities highly depends on the specific growth rates (or their ratio r ).
Fig. 3. Impact of the of KO,AOB /KO,NOB ratio on the predicted nitritation efficiency (NE) for the counter-diffusion (top) and the co-diffusion (bottom) biofilms in scenario 2 for a growth rate ratio of max,AOB /max,NOB of 2/2. The gray lines indicate the maximum KO,r where a NE = 100% was possible.
These datasets (Fig. SI-3) clearly show the high influence of KO,r on NE, and KO,r can serve as a strong indicator and predictor for NE in both geometries, especially under unfavorable growth rate conditions (r < 1). In this case KO,r has to be < 0.6–0.8 to allow for NE of 100% such as in the work by Ahn et al. [23] where high nitrite production was sustained at higher NOB growth rates compared to the ones of the AOB, in combination with a KO,r = 0.42. Similar trends were observed by [26], who found that a lower KO,AOB was the key parameter for partial nitrification. Reported values for AOB and NOB oxygen affinity constants vary significantly between species and also depend on reactor type and conditions. [22] report a range of 0.4–2 g-O2 m−3 for KO,AOB and KO,NOB . Even though KO,r values vary significantly between different studies, our conclusion regarding the influence of KO,r on NE is consistent with literature observations. Studies where complete nitrification was demonstrated tended to show higher KO,r values (e.g., KO,r = 1.5 in a MBR and KO,r = 2.82 for an activated sludge system [24]; KO,r = 1.36 in a MBR and KO,r = 1.68 in an activated sludge system [20]). On the other hand, studies on partial nitrification had associated KO,r values < 1 (see Table SI-9), which is in good agreement with our modeling results. Fig. 4 shows a comparison of the counter-diffusion and codiffusion biofilm geometry for r = 1 with regards to the oxygen concentrations at the relevant interfaces, i.e. the membrane/biofilm
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Fig. 4. Relationship between the oxygen concentration at the respective interface on predicted nitritation efficiency (NE) for counter-diffusion (top – membrane/biofilm interface) and co-diffusion (bottom – bulk biofilm interface) biofilm in scenario 2 for a growth rate ratio max,AOB /max,NOB of 2/2.
interface for the counter diffusion biofilm, and the bulk/biofilm interface for the co-diffusion geometry (complete results see Fig. SI4). Both systems can tolerate relatively high oxygen concentrations, the critical concentration for high NE increasing with increasing r . The results also underscore that the counter-diffusion biofilm has a wider range of permissible oxygen concentration at the membrane/biofilm interface that support high NE compared to the co-diffusion biofilm. In the counter-diffusion biofilm concentrations of up to 7.5 g-O2 m−3 still support high NE, whereas in the co-diffusion biofilm bulk dissolved oxygen concentrations may not exceed 1.0 g-O2 m−3 for a similar NE. Although only two bacterial groups were considered, these sensitivity analyses demonstrate the high influence and complexity of microbial growth kinetics on bioreactor performance. Downing et al. (2008) concluded in their study that the oxygen membrane concentration is a controlling factor for nitritation success in MABRs. Our study, however, conclusively shows that NE depends more on the microbial kinetics than solely on the oxygen concentration at the membrane, and that such a conclusion can only be drawn for one specific situation. The fact that we observed high NE independent of the oxygen concentrations at the membrane/biofilm interface (Fig. 2, top) and at high oxygen concentrations (Fig. SI-4) strengthens the conclusion that SO2 ,in is not the only key parameter in MABRs.
Nevertheless, it remains important to consider SO2 ,in , as this will determine the ammonium removal efficiency of the reactor. For better comparison between the different oxygen concentrations we neglected an explicit discussion of ammonium removal here, but it was of course affected by the change in SO2 ,in . Table SI-9 summarizes several previous nitritation/nitrification studies along with their reported kinetic parameters. We used each of these parameter sets in the counter-diffusion model. The predicted NEs for SO2 ,in = 2 g-O m−3 and 9 g-O m−3 are reported together with the ratios of the growth rates and affinity constants. The three modeling studies [30,36,37] used the same set of parameters to simulate partial nitrification coupled with anaerobic ammonium oxidation (Anammox). Both r and KO,r are within the optimal range for successful nitritation, with predicted NE of 100%. Within these modeling studies, the additional competition of Anammox bacteria with NOB for nitrite may have contributed to successful suppression of NOB growth. Parameters estimated from experimental systems aiming at partial nitrification, intended for coupling with Anammox or denitrification, also exhibited kinetic parameters favorable for nitritation, mainly reflected in the high r value. However, some experimental studies on partial nitrification yielded parameter combinations that did not predict high NE in our model scenario (due to the low r ). Also, the parameters suggested by Kaelin et al. [17] did not yield 100% NE at the lower oxygen concentration; these parameters, however, represent an extension of the activated sludge models (ASM3) by two-step nitrification and may not be representative for the conditions required to obtain high NE. The summary in Table SI-9 emphasizes that a wide range of kinetic parameters is reported for different systems and different objectives, underlining the complexity and specificity of two-step nitrification, i.e. the variations in the biokinetic parameter sets of AOB and NOB that are then able to represent a specific system. Future research should focus on better or standardized methods to individually define biokinetic parameters of specific operation systems with, e.g. experimental estimates rather than guessing or using literature values. In gradient systems such as biofilm reactors one set of parameters will most likely be not sufficient to describe one group of organisms (AOB and NOB here). Hence, an extension of the modeling efforts should include more than one species of each bacterial group involved for better representation of system performance. Experimental trails to verify such differences and their extent would be highly desirable. With oxygen concentration not being a reliable parameter to control NE with high enough certainty, control strategies incorporating other or a combination of measured values should be sought.
4. Conclusions This study presented an exhaustive computational investigation on the sensitivity of nitritation performance in counter-diffusion biofilms in comparison with conventional co-diffusion biofilms with respect to the kinetic parameters of the involved microorganisms. The main outcomes are:
(i) 27.5% of all parameter combinations, as reveal by the cumulative probability distribution, predicted a NE above 75% in the counter-diffusion biofilm (vs. only 7.9% in the co-diffusion biofilm) and the maximum specific growth rates (max,AOB and max,NOB ) are the most determining parameters. (ii) NE in MABRs does not solely depend on the oxygen concentration at the membrane/biofilm interface, but on the biokinetics of the AOB and NOB. The latter is reflected in the correlation of JO2 /JNH4 , the ratio of the substrate fluxes, with NE. Similar correlations are not found for the co-diffusion biofilms.
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(iii) Oxygen affinity constants determine NE to a higher extent in the co-diffusion biofilms; A critical value of KO,r below 0.6–0.8 is necessary, but not sufficient to attain a NE of 100%. (iv) The maximum oxygen concentrations at the respective biofilm interfaces (with membrane or with bulk for counter and codiffusion biofilms respectively) permissible for high NE were higher for the counter-diffusion biofilms. NE failure could be predicted with larger confidence than NE success. Acknowledgment The authors would like to acknowledge financial support from the Technical University of Denmark and the Danish Agency for Science Technology and Innovation (FTP – ReSCoBiR). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.bej.2012.09.006. References [1] E. Syron, E. Casey, Membrane-aerated biofilms for high rate biotreatment: performance appraisal, engineering principles, scale-up, and development requirements, Environ. Sci. Technol. 42 (2008) 1833–1844. [2] L.S. Downing, R. Nerenberg, Effect of oxygen gradients on the activity and microbial community structure of a nitrifying, membrane-aerated biofilm, Biotechnol. Bioeng. 101 (2008) 1193–1204. [3] A. Schramm, D. de Beer, A. Gieseke, R. Amann, Microenvironments and distribution of nitrifying bacteria in a membrane-bound biofilm, Environ. Microbiol. 2 (2000) 680–686. [4] A. Terada, K. Hibiya, J. Nagai, S. Tsuneda, A. Hirata, Nitrogen removal characteristics and biofilm analysis of a membrane-aerated biofilm reactor applicable to high-strength nitrogenous wastewater treatment, J. Biosci. Bioeng. 95 (2003) 170–178. [5] A. Terada, T. Yamamoto, R. Igarashi, S. Tsuneda, A. Hirata, Feasibility of a membrane-aerated biofilm reactor to achieve controllable nitrification, Biochem. Eng. J. 28 (2006) 123–130. [6] D.L. Timberlake, S.E. Strand, K.J. Williamson, Combined aerobic heterotrophic oxidation, nitrification and denitrification in a permeable-support biofilm, Water Res. 22 (1988) 1513–1517. [7] C. Hellinga, A.A.J.C. Schellen, J.W. Mulder, M.C.M. van Loosdrecht, J.J. Heijnen, The SHARON process: an innovative method for nitrogen removal from ammonium-rich waste water, Water Sci. Technol. 37 (1998) 135–142. [8] A.C. Anthonisen, R.C. Loehr, T.B. Prakasam, E.G. Srinath, Inhibition of nitrification by ammonia and nitrous acid, J. Water Pollut. Control Fed. 48 (1976) 835–852. [9] R. Blackburne, Z. Yuan, J. Keller, Partial nitrification to nitrite using low dissolved oxygen concentration as the main selection factor, Biodegradation 19 (2008) 303–312. [10] R. Blackburne, Z. Yuan, J. Keller, Demonstration of nitrogen removal via nitrite in a sequencing batch reactor treating domestic wastewater, Water Res. 42 (2008) 2166–2176. [11] H.-D. Park, D.R. Noguera, Evaluating the effect of dissolved oxygen on ammoniaoxidizing bacterial communities in activated sludge, Water Res. 38 (2004) 3275–3286. [12] K. Pynaert, B.F. Smets, D. Beheydt, W. Verstraete, Start-up of autotrophic nitrogen removal reactors via sequential biocatalyst addition, Environ. Sci. Technol. 38 (2004) 1228–1235. [13] S. Wyffels, S.W.H. Van Hulle, P. Boeckx, E.I.P. Volcke, O. Van Cleemput, P.A. Vanrolleghem, W. Verstraete, Modeling and simulation of oxygen-limited partial nitritation in a membrane-assisted bioreactor (MBR), Biotechnol. Bioeng. 86 (2004) 531–542.
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