Examining the phenomenon of self regulation of biofilm density under dynamic conditions using a biofilm model

Rajeev Goel1*, Amit Kaldate2, Sudhir Murthy3, Oliver Schraa1, Beverley Stinson4

1Hydromantis Environmental Software Solutions, Inc., Suite 1601, James Street S, Hamilton, Canada 2Degremont Technologies (Suez Environnement), 8007 Discovery Drive, Richmond, VA, 23229, USA 3Blue Plains AWTF, DCWASA, Washington, DC, USA 4AECOM Water, 14504 Greenview Drive, Suite 400, Laurel, MD, 20708, USA * To whom correspondence should be addressed. Email: goel@hydromantis-software.com ABSTRACT The moving bed biofilm reactor (MBBR) is a well accepted technology in wastewater treatment. The process modeling and simulation is often used to design and optimize the full scale MBBR facilities. In this study, a process model calibrated to experimental data from a post-denitrification MBBR pilot plant is used to elucidate the effect of temperature and NOX-N loading on the biomass areal density. During the 122 day long experimental period, the biofilm density was observed to change under variable NOX-N loading and temperature condition. The visual inspections of the trends in temperature and measured biofilm density implied that the temperature may affect the biofilm density significantly. The simulation results using the calibrated model however suggest that the observed variation in the biofilm density is mainly due to the variation in NOX-N loading to the plant. The model also indicates that the temperature variation did not affect the biofilm density suggesting that the temperature dependent growth and decay processes did not affect total solid production at different temperatures in the methanol fed denitrification system. This observation seems to be the artifact of the model structure in which the methylotroph decay byproduct XS accumulates in the system due to limited hydrolysis. Further simulations with modified model with hypothetical temperature dependent detachment rate indicated that the temperature dependent biofilm density change could improve the fit between simulated and measured density. The detachment rate depends on the physical and biological characteristics of the system and is influenced by hydrodynamic shear and more importantly the self immobilization strength of bacteria. It is likely that these physical and biological factors are influenced by temperature, but more research is needed to quantify these effects. KEYWORDS: denitrification, MBBR, process model, nutrient removal, simulation, external Carbon INTRODUCTION The moving bed biofilm reactor has become a popular technology for plant capacity upgrades and green field applications. The technology is suitable for various treatment purposes of BOD/COD removal, nitrification and denitrification for both municipal and industrial wastewater (Ødegaard et al. 2000). The MBBR utilizes the principle of biomass immobilization on small plastic carriers. The carriers with microbial biofilm are kept in suspension through mechanical or air mixing. The design criteria for MBBR have evolved through pilot experimental studies over a period of time. Although, the general principals of designing MBBR are fairly well understood, the understanding on the mechanisms affecting biofilm structure and density are still evolving.

One of the key parameters for MBBR is the total biomass immobilized on the carrier material. Therefore, an understanding of the parameters that influence the biomass accumulation in the MBBR reactor is important for its proper design and operation. In biofilm systems, the biofilm growth and microbial ecology within the biofilm is determined by the balance of growth and detachment processes (van Loosdrecht et al. 1995). The biological growth is the main process which produces the biological solids in the system. The biological growth is influenced by the substrate, nutrient supply and other environmental factors like temperature, pH etc.. The biofilm systems lose solids mainly due to the detachment of solids from the biofilm. Thus detachment rate has big influence in controlling the biomass quantities in the reactor. Three mechanisms have been identified for biofilm detachment i.e. erosion, abrasion and sloughing (Rochex et al. 2009). The erosion is attributed to the shear force due to gas or liquid flow around the biofilm. The abrasion is attributed to particle and particle collision. The sloughing may be attributed to loss of large chunk of biomass and is probably due to loss of cohesiveness due to substrate limitation. In general, the phenomenon of sloughing is not well understood. In Tchobanoglous et al. 2003, it is hypnotized that as the biofilm increases in thickness, the substrate penetration into the internal biofilm layer becomes limited. This leads microorganisms in the internal layers to enter into endogenous respiration and lose their ability to self immobilize, leading to sloughing. These hypotheses are strengthened by experiments in which anaerobic condition are suggested to cause sloughing (Ahimou et al. 2007). Other factors like the growth of higher organism like metazoan and protozoa in the biofilm and their effect of biofilm stability and sloughing have also been discussed in many investigations (Zahid and Ganczarczyk 1994, Gschioβl et al. 1997, Salvetti et al. 2006). It is clear that the underlying mechanisms governing detachment in biofilm systems are quite complex and depend on the physical and biological characteristics in the system. The present day biofilm models provide a better platform to analyze and understand the interactions in a complex multi-process system. A number of mathematical models have been developed for biofilm systems which consider the biological growth on multiple substrates and addresses the physical process of detachment empirically. A review on the characteristics of different biofilm models and their applications is presented in Wanner et al. 2006 and Boltz et al. 2009. The present day 1-D biofilm models account for the biomass detachment/attachment substrate diffusion and biomass growth and are used in different engineering applications. The purpose of this study is to use an existing biofilm model and apply it to the data from a post denitrification pilot plant to evaluate the affect of different operational parameters like NOX loading and temperature on predicted biomass areal density. The post denitrification MBBR pilot plant data used in this study was collected at District of Columbia Water and Sewer Authority’s (DC WASA) Blue Plains Advanced Wastewater Treatment Facility (AWTF) from January 2008 to July 2008 (Stinson et al. 2009, Peric et. al. 2009). The data from this study was used to develop a calibrated model for the MBBR pilot plant (Kaldate et al. 2010). The plant was operated under four different phases of variable loading and temperature. During all the phases of the pilot plant study, the effluent was able to meet the set effluent criteria but a significant variation in the biofilm areal density was observed.

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Dzombak (1992). The model handles soluble material diffusion, biofilm growth, and particulate attachment, detachment and solid exchange between the biofilm layers. The standard implementation of 5 layer biofilm model in GPS-X was used. The detachment rate in the model is considered to be a linear function of the biofilm thickness up to the maximum biofilm thickness specified by the user.

The biofilm model uses the same biological reactions found in the suspended-growth models and can predict the extent of carbon and nitrogen removal (by uptake or oxidation) and denitrification, and phosphorus uptake and release. This model incorporates the growth kinetics and transport processes for the corresponding state variables. The profiles of the various components through the biofilm are modeled so that different environments (aerobic, anoxic and anaerobic) can exist within the biofilm. A new comprehensive plant wide model (MANTIS2) was implemented in GPS-X and used in the simulation study. The new model includes the processes of two-step nitrification/denitrification thus making it possible to simulate the behavior of both the NO2-N and NO3-N concentrations. A new methylotroph biomass was incorporated in the model to account for the growth kinetics and stoichiometry of methanol degraders. The “states” model in the influent object was used for inputting the data to the model. To estimate each influent state in the model, following assumptions were made:

i. The biomass concentrations in the effluent of secondary treatment plant were considered to

be zero. Considering the very small solid concentration in the effluent of secondary treatment this assumption seems appropriate. Nevertheless, it may be important to check the effect of this assumption in future studies. In some situations seeding or presence of the active biomass has been suggested to influence the biological reactions.

ii. The 80% of the volatile suspended solids were considered to be inert particulate while 20% were considered to be slowly biodegradable substrate. As the VSS concentration was very small and did not serve as a major source for biodegradable substrate for denitrification, this assumption is considered to not affect the simulation results significantly.

iii. The nitrogen and phosphorous fractions of slowly biodegradable substrate were fixed at 5% and 1% respectively.

On the days when the measured data showed discrepancy (e.g. estimated methanol COD is higher than the measured soluble COD or TKN is less than soluble TKN), a correction to the data points was made to achieve appropriate influent characterization. It shall be noted that biomass areal density (g/m2) is defined as the mass of TSS per unit surface area of the media and is different than the biofilm volumetric density (kg/m3 biofilm). The biofilm volumetric density used in the model is a constant number and is used to estimate the model biofilm thickness. RESULTS AND DISCUSSION

Influent Characteristics The characteristics of the feed to pilot plant are shown in Figure 2 to Figure 4. The Figure 2 shows the variation of flow rate, NO3-N and soluble COD into the reactor. The flow rate to the reactor was fairly constant during the experimental period except during the end of phase 2 (day

74-78) and phase 4 (day113-122). The NO3-N and soluble COD concentration were observed to change throughout the experimental period. The Figure 3 shows the variation of ortho-P and NH3-N in the feed. As compared to the variation in NH3-N concentration the variation in the ortho-phosphate concentration was less pronounced. The estimated influent TSS, TKN and COD are as shown in Figure 4.

Figure 2 Influent characteristics influent flow, soluble COD, NO3-N

Figure 3 Influent characteristics of ortho-P and NH3-N

Figure 4 Influent characteristics of TSS, TKN, COD during the experiment The average concentrations for parameters in different phases are as shown in Table 1. The phase-2 and Phase-4 corresponded to low temperature conditions while the phase-3 characterized high temperature conditions. In phase 4, the average flow rate to the plant was reduced while the NOX-N and COD concentration was increased. The data from all the phases was combined together for conducting a contiguous simulation for all the four phases. All the essential measured operational data was fed to the model using file input during simulation. Table 1. Average characteristics of influent during different operational phases

Phase

Influent Flow Temp COD

Methanol COD TKN NH3-N NO2-N NO3-N TP PO4-P

gpm deg C mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L Phase 1 (day 1- 29) 6.70 16.63 53.40 38.49 9.23 5.76 0.14 4.99 0.74 0.58 Phase 2 (day 30-81) 6.37 13.46 61.33 52.80 4.48 2.65 0.11 7.09 0.59 0.55 Phase 3 (day 81-108) 6.67 21.54 46.28 39.75 8.34 5.97 0.07 3.90 0.58 0.40 Phase 4 (day 109-122) 3.90 13.73 109.70 104.39 3.36 1.83 0.09 10.65 0.87 0.83

Model Calibration The MBBR model was calibrated by adjusting the half saturation coefficients (KSNOx-N, KSCOD) of methylotrophs and the detachment rate for the biofilm. A detailed description of the calibration method and parameter set is provided in Kaldate et al. 2010. The same set of parameters was used for conducting the simulation in this study. The calibration results for various parameters for reactor-1, reactor-2 and reactor-3 are shown in Figure 5 to Figure 13. In general, the measured dataset for all the reactors showed good agreement with the simulated results. A more detailed

analysis of calibration results is presented in (Kaldate et al. 2010).

Figure 5 Measured and simulated parameters of TSS and soluble COD in reactor-1

Figure 6 Measured and simulated parameters of ortho-P and NH3-N in reactor-1

Figure 7 Measured and simulated parameters of NO2-N and NO3-N in reactor-1

Figure 8 Measured and simulated parameters of TSS and soluble COD in reactor-2

Figure 9 Measured and simulated parameters of ortho-P and NH3-N in reactor-2

Figure 10 Measured and simulated parameters of NO2-N and NO3-N in reactor-2

Figure 11 Measured and simulated parameters of TSS and soluble COD in reactor-3

Figure 12 Measured and simulated parameters of ortho-P and NH3-N in reactor-3

Figure 13 Measured and simulated parameters of NO2-N and NO3-N in reactor-3 Biofilm Areal Densities under experimental conditions Figure 14 shows the measured and simulated biomass areal densities throughout the experimental study. Although some differences can be observed between the measured and the simulated biofilm areal density, the observed and simulated trends seems to show similar behavior. The simulated biofilm density on day 67 and 74 seem to be the far from the measured areal density.

Figure 14 Measured and simulated biofilm areal density

To compare the temperature and NOX-N loading variation to the variation in the biomass areal density, Figure 15 and Figure 16 were prepared. Figure 15 shows the variation of temperature along with the measured and simulated biofilm density. The visual inspection of the biofilm areal density and the temperature variation suggest that when the temperature reduced during the period of day 30 to day 50, the areal biofilm density increased. The biofilm density is also seen to decrease as the temperature started to rise during the period of day 85-day 100. Figure 16 shows the variation of NOX-N loading and the measured and simulated biofilm areal density. Visual inspection of Figure 16 suggests that the biofilm areal density is positively correlated to the NOX-N loading.

Figure 15 Measured and simulated biofilm density and variation in temperature

Figure 16 Measured and simulated biofilm density and variation in NOX-N loading

Apparently, both the NOX-N loading and the temperature seems to have effect on the biofilm density, however, the contribution from each of these variables is not clear. To establish the relative contribution of each factor, simulations were conducted under the following conditions.

1. Variable loading with constant temperature – In this scenario, it was assumed that all the influent feed characteristics are same as the experimental conditions except that the temperature in the reactor is constant at a constant value of 16.6oC.

2. Constant loading with variable temperature – In this scenario, it is assumed that all the feeding is done at a constant loading conditions with the variation in temperature similar to the experimental conditions.

The results from both the scenarios are discussed in following sections. Variable loading with constant temperature The simulation results for a variable loading feed with constant temperature are shown in Figure 17. It is interesting to note that the simulated biofilm areal density profile is very similar to the profile observed with variable temperature. Based on a comparison of simulation and measured biofilm densities, it appears that there is a direct relationship between the NOX-N loading and the biofilm density under non-limiting organic substrate for denitrification. The increase in NOX-N loading results in more production of biomass. The excess growth of biomass increases the thickness of the biofilm. As the detachment rate increases as the biofilm thickness increases, a new balance is achieved between the biomass accumulation and biomass loss leading to new higher biofilm areal density.

Figure 17 Measured and simulated biofilm density with variable NOx-N loading and

constant temperature

Constant loading with variable temperature The simulation results for a constant loading and variable temperature are as shown in Figure 18. It appears that the change in temperature did not have any significant effect on the simulated biofilm densities. The simulation results indicate that as the temperature increases the biomass density increases slightly and vice versa. These results are contrary to the suggestion in Stinson et al., 2009 that the reduced decay rates result in higher winter yield causing higher biofilm density at low temperature. To understand the contradiction, the model formulation for growth and decay of methylotrophic biomass was further explored. It appears that the methylotrophic biomass decay creates unbiodegradable biomass residue (Xu) and slowly biodegradable substrate (Xs) according to death and regeneration concept of modeling the decay process. In a methanol fed system in which the methanol utilizers are the major biomass type in the biofilm, the hydrolysis of slowly biodegradable compounds which depends on the concentration of heterotrophic biomass is very limited. Therefore although at higher temperatures the methylotrophic biomass yield goes down, the corresponding accumulation of decay products Xu and Xs, do not change the net solid yield in the system. Thus, as the temperature changes, the relative concentration of methylotrophic biomass, endogenous residue and slowly biodegradable solids changes but the net solid production at a given loading does not change much. Although, this explains the observed behavior of biofilm density in the model, but it also raises the following questions:

1) Do methylotrophs contribute to hydrolysis of slowly biodegradable substrate? 2) Is death regeneration concept for decay applicable to methylotrophs?

More experimental studies are required to answer these questions. Based on above discussion, it is clear that the NOX-N loading had much more significant effect on the biofilm areal density than temperature in the pilot experiments. The effect of temperature as indicated by the existing model structure seems to be minor. This conclusion however requires further experimental validation and is not applicable for the heterotrophic biofilms. In addition to growth and decay processes, the temperature may affect some of the underlying physical and biological mechanisms which change the detachment rates at different temperature. The original model considers the effect of temperature on the growth and decay rate of microorganism, however does not consider a temperature dependency of detachment rate. As the model for temperature dependent detachment rate do not exist yet, a simple Arrhenius type temperature dependency of detachment rate was considered to see how the simulation results will be affected by a temperature dependent detachment rate.

Figure 18 Measured and simulated biofilm density with constant NOx-N loading and variable temperature

Simulation with temperature dependent detachment rate To assess how the model results would change if the detachment rate were to be a function of temperature, a temperature dependent detachment rate was introduced in the model. The temperature dependent detachment rates were estimated using the following expression.

, , Where: kdetach,T detachment rate at temperature T kdetach,20 detachment rate at 20oC θdetach temperature constant for detachment rate, - T Temperature, oC For the simulation study a value of 1.05 was assumed for detachment rate temperature constant, θdetach. The simulation results with temperature dependent detachment rate are as shown in Figure 19. The simulation results seem to show some improvement over the results from the simulation with constant detachment rates. The simulated trend for the first thirty days seems to improve and match the measured values well. The simulation results also seem to improve for the days of 67 and 74.

Figure 19 Measured and simulated biofilm density with a temperature dependent detachment rate

To check how the biofilm density may change with the temperature at constant load after the temperature dependent detachment rates are considered, additional simulation at constant loading was conducted. The results of this simulation are shown in Figure 20. It is clear that the biofilm areal density may show significant variation if detachment rate were to be a function of temperature. The effect of temperature on detachment rate is not well studied experimentally. The factors like biofilm thickness, shear rate and self immobilization strength of bacteria may affect the detachment rates. The detachment rate used in the original model uses a first order relationship with biofilm thickness. The shear rate, self immobilization strength of bacteria and how these factors changes with temperature are not accounted in the model. The shear rate in a biological reactor depends on the power input, viscosity and volume of the reactor. The viscosity of the water increases as the temperature decreases, which may result in lower shear rate for the same power input. However, as the power input itself depends on the liquid viscosity, the net effect of temperature on shear rate may not be significant. Salvetti et al. 2006 studied the effect of temperature on tertiary nitrification in MBBR. The data presented in their study suggest that the biofilm density may depend on temperature. Further, the role of seasonal growth of metazoa and its effect on biofilm stability were also highlighted. As the research information on the role of temperature on biofilm growth is limited, no conclusive comments can be made at this stage regarding the interrelationship between temperature, biofilm structure and detachment rate. More studies elucidating the effect of temperature on the biological changes like the extra cellular polymers, self immobilization strength and microbial ecology are required to quantify and model these effects.

Figure 20 Measured and simulated biofilm density with a temperature dependent

detachment rate at constant loading CONCLUSIONS The following conclusions are made from this modeling study.

1) The variation in biofilm areal density during the experimental period was influenced mostly by the variation in the NOX-N loading to the pilot plant.

2) The temperature dependence of growth and decay processes and the corresponding changes in biomass production at different temperature was not significant to affect the biofilm areal density in methanol fed post-denitrification MBBR. These results are considered to be the artifact of the resulting microbial composition in model that was rich in methylotrophs but lacked heterotrophic microorganism.

3) The effect of temperature on biofilm density due to its effect on detachment rate is not yet clear. Additional research quantifying the effect of temperature on underlying mechanism affecting detachment rate like shear, microbial community and biofilm strength is required.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the kind permission granted us by the District of Columbia Water and Sewer Authority (DCWASA), DC and AECOM Water to share the information from the pilot study. They would also like to thank Marija Peric and Dilli Neupane of AECOM Water and Arbina Shrestha of George Washington University for their technical support.

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