MODELING OF AN IFAS PROCESS WITH FUNGAL BIOMASS TREATING PHARMACEUTICAL WASTEWATER Main Street West Hamilton, ON, Canada L8S 1G5

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1 MODELING OF AN IFAS PROCESS WITH FUNGAL BIOMASS TREATING PHARMACEUTICAL WASTEWATER Oliver Schraa 1, Paul Robinson 2, and Anette Selegran 2 1 Hydromantis, Inc Main Street West Hamilton, ON, Canada L8S 1G5 2 AstraZeneca Pharmaceuticals ABSTRACT The project involved the dynamic modeling of a wastewater treatment plant (WWTP) treating pharmaceutical wastewater. The WWTP uses an integrated fixed-film activated sludge (IFAS) process with suspended carriers for biological treatment. The plant is unique in that it uses both fungal and bacterial IFAS reactors. The fungal biomass has been shown in laboratory testing to degrade a portion of the waste not degraded by bacterial biomass alone. The GPS-X simulator was used to model the plant. The mantis model, which is a modified version of ASM1, was used for the biological processes. Modifications were made to mantis to model the adsorption of inert soluble COD onto particulate matter and the uptake of phosphorus for biomass growth. The GPS-X model was calibrated to the first 9 days of data from 24 and verified using data from the remainder of 24. Calibration involved characterizing the influent waste streams, specifying operational variables, and adjusting certain key model parameters to minimize the error between the measured and predicted data. Minimal adjustments to the model parameters were required to calibrate the model. The model was found to be applicable to simulation of plant performance over the entire year. This suggests that the International Water Association (IWA) activated sludge models (ASM) with some modifications are applicable to the modeling of fungal biomass. For demonstration purposes, a number of simulations were conducted to study the sensitivity of the plant performance. It was found that taking a train out of service increased the effluent soluble COD while lowering the temperature or using less air had little impact on the effluent soluble COD. KEYWORDS Modeling, simulation, pharmaceutical wastewater, IFAS, fungal biomass, calibration. INTRODUCTION The Gärtuna wastewater treatment plant (WWTP) treats liquid waste from a pharmaceutical production facility. The plant has a capacity of 1,8 m 3 /d (.48 Mgal/d) and on average treats 1,4 m 3 /d (.37 Mgal/d) of waste with a chemical oxygen demand (COD) of 1,38 mg/l. The hydraulic residence time of the plant is approximately 24 hours. Nitrogen and phosphorus are 1723

2 added to satisfy nutrient requirements. Laboratory testing has shown that due to the complex nature of the wastewater, a hybrid activated sludge process involving bio-film carriers (i.e. an integrated fixed-film activated sludge or IFAS process) shows performance advantages over the conventional activated sludge process. In the Gärtuna plant, two distinct biological reactor stages are used: a fungal stage and a bacterial stage. Using this combination of fungal and bacterial reactors in series allows for the degradation of complex organic molecules not degraded by the bacterial biomass alone. The liquid temperature entering the fungal reactors is 29 o C on average and increases by 3 to 4 o C in the fungal reactors. Other unit operations and processes in the plant include dissolved air flotation (DAF), chemical phosphorus removal, powdered activated carbon (PAC), tertiary filtration, and dewatering. A diagram of the plant is shown in Figure 1. This paper describes the application of a modified version of the ASM1 model (mantis model in GPS-X with revisions) to the modeling and simulation of the Gärtuna WWTP. The calibrated model will be used by AstraZeneca to improve the understanding of the plant and to assess the impact of operational changes, process changes, and increased organic loadings. Figure 1 Gärtuna WWTP as Represented in GPS-X METHODOLOGY 1724

3 The modeling study was conducted using GPS-X, which is a steady-state and dynamic wastewater treatment plant simulator. GPS-X can model a wide variety of wastewater treatment unit operations and processes including suspended growth and attached growth biological reactors, solids separation processes, and sludge treatment processes. Due to the unique nature of the plant, the project was completed in three stages to minimize risk: data analysis, preliminary calibration, and final calibration. The data analysis stage was used to analyze the historical data, to determine if additional data was required for calibration, and to plan the modeling work. After the initial data analysis, the Gärtuna WWTP staff collected additional sampling data to assist in the characterization of the influent wastewater and to quantify the performance of the biological reactors. Because the modeling of fungal biomass treating pharmaceutical wastewater has not been well documented, a preliminary calibration was performed to determine whether the mantis model could adequately describe the behavior of the fungal reactors in the plant. The preliminary calibration indicated that dynamic modeling of the Gärtuna plant was feasible. The final stage of the project reported here involves the final calibration of the plant model. The plant data used for calibration of the model consisted of values generated using daily and weekly total organic carbon (TOC) measurements taken at various locations across the plant. The plant routinely measures total organic carbon (TOC) but not COD which is used to characterize carbonaceous material in the model. Results from the sampling study were used to establish ratios such as COD:TOC and TSS:COD ratios which were then used to generate calibration data at various locations throughout the plant. The main goal of the model calibration work was to develop a model that accurately predicted plant performance. The plant model was constructed in GPS-X based on the plant schematics and operating data and included all of the important unit operations and processes in the plant. Simplifications were made where possible to reduce the complexity of the model. These included combining the three IFAS trains into one train and using a simple filter to model the sand filters. The plant model was calibrated to the dynamic data from the first three months of 24 (calibration period) by adjusting the influent characterization and by adjusting certain key model parameters. The calibrated model was then verified using additional dynamic data from 24. The calibrated GPS-X simulation model was used as a basis for a number of what-if scenarios to demonstrate possible applications of the model. The scenarios studied included the following: Taking process trains out of service Reducing the reactor temperature Reducing the amount of air supplied to the IFAS reactors for biological treatment RESULTS 1725

4 Layout Description During calibration, a simplified GPS-X model was used that did not include the buffer tanks or the recycle of centrate from the centrifuges back to the buffer tanks. This was required as the influent measurements were taken after the buffer tanks. After calibration, another layout was developed and delivered to the client that did not include these simplifications. The GPS-X layout used for calibration includes the following unit operations and processes: Effluent stream from buffer tanks Nutrient addition streams after buffer tanks Fungal IFAS reactors Dissolved air flotation units after fungal reactors (DAF 1) Bacterial IFAS reactors Powdered activated carbon process (PAC) DAF 2 after the activated carbon process Ferric sulphate addition before DAF 2 Sand filter after DAF 2 Sludge storage tanks Centrifuges The plant primarily performs COD and solids removal with nutrient removal being mainly due to biomass growth and chemical precipitation. The carbon-nitrogen-phosphorus model library (cnplib) was selected as AstraZeneca wished to model phosphorus removal in the plant. A COD fraction-based influent model is used to describe the buffer tank effluent stream (influent stream in the calibration model) because it uses COD, TKN, and ammonia as inputs which are the most convenient wastewater quality variables in this case. The biological reactors (both fungal and bacterial) were modeled using the hybrid reactor object with the mantis biological model. The mantis model is a version of ASM1 which is the most widely recognized model for carbon and nitrogen removal. The mantis model includes the following modifications to ASM1: Temperature dependence of kinetic parameters Two additional growth processes are introduced to account for the uptake of nitrate nitrogen as a nutrient source under conditions of low ammonia. The existing growth processes are modified to use ammonia nitrogen switching functions so that growth is limited during conditions of low ammonia. Simultaneous nitrification/denitrification Updated kinetic parameter values (based on project experience) The modeling effort showed that the influent soluble COD had a fraction that was not readily degraded in the fungal reactors but was removed in the bacterial reactors. A steady-state solids mass balance indicated that unless the bacterial yield was much higher than typically encountered, conversion of the soluble COD to particulate matter was likely occurring in the bacterial reactors. A possible mechanism for this behavior is adsorption of the soluble COD onto particulate COD so that it becomes associated with the volatile suspended solids (VSS) and 1726

5 total suspended solids (TSS). In the mantis model, the adsorption of the soluble COD was modeled by converting soluble inert COD (S i ) to particulate COD in the form of the internal cell storage product (X sto ). X sto is a state variable in the carbon-nitrogen-phosphorus library. It is not part of the biological reactions in the mantis model but is tracked using mass balances and is included in the COD and TSS calculations. X sto was used instead of biomass or particulate inert COD, as they contain nitrogen and phosphorus and their creation would cause nitrogen and phosphorus mass balance errors. To account for the biological conversion of the S i to X sto the following reaction was added to the mantis model: S i X sto The rate expression used for this reaction is r si = k si S i S i S i + K si where: rsi rate of reaction for S i due to adsorption (mg COD/L d) k maximum specific S i adsorption rate (1/d) si K half-saturation coefficient for S i adsorption (mg COD/L) si The equation describes the adsorption of S i onto X sto making use of an ASM1-type switching function to limit the rate as S i approaches a concentration of zero. The mass balance for this conversion is maintained by ensuring that one unit of X sto is produced for each unit of S i that disappears. The use of this new reaction was found to improve the prediction of the soluble COD in the fungal reactors and the VSS and TSS in the bacterial reactors. Phosphorus uptake for the growth of biomass was also added to the mantis model as it allows the model to predict phosphorus limitations. Phosphorus uptake was modeled as in the Barker and Dold model (1997) with the exception that the autotrophic bacteria also use phosphorus for growth as in ASM2d (Henze et al., 2). The DAF processes were modeled using a mechanistic flotation model based on the Takács et al. (1991) settling model with enhancements to account for coagulant and air addition. The powdered activated carbon (PAC) object and the modified mantis model were used to model the activated carbon process. The model combines biological degradation (if applicable) with adsorption of soluble COD onto the powdered activated carbon. Because little operating and physical data were available for the sand filters, they were modeled as membrane filters. The in-line chemical dosing object uses a chemical equilibrium model to predict phosphorus removal given the dosage of the metal ion used. The centrifuges were modeled using the 1727

6 dewatering object and an empirical model where the user specifies the desired performance. Model Calibration Calibration of biological wastewater treatment models involves characterizing the influent waste streams, specifying operational variables, and adjusting certain key model parameters to minimize the error between the measured and predicted data. Two alternative calibration approaches are commonly used for adjusting the model variables: non-linear regression analysis employing a suitable objective function and an optimization algorithm, or a manual regression analysis based on engineering judgment combined with visual inspection of the graphical simulation results. Manual regression analysis was used in this work as calibration often requires only minor adjustment of model kinetic parameters. Typically, influent characterization and the correct specification of physical and operating parameters are the most important steps in activated sludge model calibration. Important plant variables to consider when calibrating activated sludge models are the mixed liquor volatile suspended solids (MLVSS), oxygen or air usage in the reactors, dissolved oxygen concentrations in the reactors, soluble COD in the reactor effluent, and the nutrient concentrations in the reactors and effluent. By matching these variables we ensure that we have the correctly modeled the following: Influent biodegradable COD Influent inert soluble COD Amounts of inert and biodegradable particulate COD Aeration system setup Biological and chemical removal of nutrients The steps involved in the calibration and verification of the Gärtuna model were as follows: Selection of calibration period: First 9 days of 24 Steady-state calibration (using historical averages): Reactors and DAF 1 only Dynamic calibration (initially for reactors and DAF 1 and then entire plant) Verification of model over entire 24 period The steady-state calibration provided a starting point for the dynamic calibration but had to be refined so that the dynamic data could be accurately predicted. The model was calibrated to the first 9 days of data from 24. The influent characterization used is summarized in Table 1 and the important parameter changes required in the IFAS reactors are summarized in Table 2. Table 1 Buffer Tank Effluent Stream Characterization Stoichiometric Coefficient Calibrated Value Reason for Adjustment particulate COD:VSS ratio 2.2 left at GPS-X default 1728

7 soluble fraction of total COD.88 used average fraction from sampling data inert fraction of soluble COD varies between.4 estimated using soluble COD and.18 (Average = in fungal reactors and influent.9) soluble COD substrate fraction of particulate.94 adjusted to match volatile COD suspended solids (VSS) in fungal reactors VSS/TSS.67 used average fraction from ammonium fraction of soluble TKN sampling data.46 adjusted based on sampling data Table 2 Calibrated Parameter Values in IFAS Reactors Parameter Calibrated Value Reason for Adjustment Nitrogen content of active.45 gn/gcod adjusted to match VSS and biomass and endogenous/inert mass (all reactors) ammonia nitrogen in fungal reactors (supported by nitrogen sampling data) Phosphorus content of active biomass and endogenous/inert mass (all reactors) Autotrophic maximum specific growth rate (fungal reactors) Maximum specific soluble inert COD adsorption rate (all reactors) Soluble inert COD adsorption half-saturation coefficient Maximum biofilm thickness.17 gp/gcod adjusted to match orthophosphate phosphorus in fungal reactors (supported by phosphorus sampling data).15 d -1 fine-tuned to match measured ammonia. d -1 (Fungal reactors) 8 d -1 (1 st Bacterial reactor).5 d -1 (2 nd Bacterial reactor) nitrogen in fungal reactors adjusted to match soluble COD in reactors 5 gcod/m 3 adjusted to match soluble COD in reactors.5 mm (Fungal) adjusted to match to match.1 mm (Bacterial) TSS in reactors The calibration simulations were started at steady-state. GPS-X calculates the steady-state solution using the averaged input data from the first 9 days of 24. After the steady-state solution is calculated, GPS-X solves the model over time. The calibration results for the soluble COD, volatile suspended solids (VSS), and ammonia 1729

8 nitrogen in the last fungal reactor are presented in Figures 2 through 4. Plots of the soluble COD and VSS in the last bacterial reactor are shown in Figures 5 and 6. Each plot shows the simulated results (solid lines) and the plant data (squares) over time. As shown in the calibration plots, the model is able to predict the plant data reasonably well in most cases considering the variability typically associated with wastewater measurements and the fact that the measurements were generated using ratios measured during a 5-day sampling program. A plot of the predicted soluble COD versus the measured soluble COD in the last fungal reactor (see Figure 7) suggests that the fit is quite good as the majority of data points are close to 45 o line (perfect fit line) and have much less than 2 % relative error as shown by the dashed red lines indicating constant relative error. Most importantly, the model predicts the general trends in the water quality variables in the final tanks of each biological reactor section indicating that the model is able to track overall plant performance. It is concluded that the modified mantis model is suitable for modeling fungal biomass. Matching the VSS and TSS concentrations in the reactors was challenging as the model underpredicted the solids concentrations in the fungal and bacterial reactors. It was determined that changes in the influent stoichiometry were not sufficient to correct the discrepancy. Because the model predicted quite low ammonia concentrations in the fungal reactors it was postulated that a nutrient limitation might be limiting the biomass growth in the model. The nitrogen fractions were reduced in the reactors and it was found that the model could reasonably predict the VSS and TSS in the fungal reactors. Using lower nutrient fractions is often required when modeling industrial wastewater as nutrient limitations can occur and the biomass can adapt and exist with lower than typical nutrient fractions. The discrepancy between the predicted and measured VSS in the bacterial reactors was corrected when the adsorption of soluble inert COD was added to the model and the new kinetic parameters were appropriately calibrated. The soluble inert COD is converted to particulate COD after adsorption which raises the VSS in the reactors. Addition of the adsorption process also allowed the model to match the soluble COD in the bacterial reactors. In order to match the soluble COD in the fungal reactors, it was necessary to vary the inert fraction of soluble COD in the buffer tank effluent over time. It was also necessary to reduce the maximum bio-film thickness in the bacterial reactors to further increase the TSS and VSS concentrations. In the model, the average airflow rates provided by AstraZeneca were used and it was found that the dissolved oxygen (DO) concentrations in the reactors varied within the ranges measured during the sampling period. This coupled with the VSS predictions provided evidence that the influent characterization was reasonable. Figure 2 Calibration Results for Soluble COD in Last Fungal Reactor 173

9 2 15 Soluble COD (mg/l) Simulated Measured Figure 3 Calibration Results for VSS in Last Fungal Reactor 4 3 VSS (mg/l) Simulated Measured Figure 4 Calibration Results for Ammonia Nitrogen in Last Fungal Reactor 1731

10 1.8 Ammonia Nitrogen (mg/l) Simulated Measured Figure 5 Calibration Results for Soluble COD in Last Bacterial Reactor 5 4 Soluble COD (mg/l) Simulated Measured Figure 6 Calibration Results for VSS in Last Bacterial Reactor 1732

11 1 8 VSS (mg/l) Simulated Measured Figure 7 Predicted Versus Measured Soluble COD in Last Fungal Reactor (Calibration) 2 Predicted Soluble COD (mg/l) Perfect Fit Line Boundary of 2% Relative Error Measured Soluble COD (mg/l) Model Verification 1733

12 Model verification involves ensuring that the calibrated model can reasonably predict the trends and levels in an independent data set not used for calibration. The model was verified by running an entire 365 day simulation using 24 data (including the 9 day calibration period). Plots of the verification results for the soluble COD and VSS in the last fungal reactor are shown in Figures 8 and 9. As shown, the simulated results are reasonable for both the soluble COD and the VSS. There are certain clusters of data that the model does not predict (such as near the end of the simulation). Possible explanations for this behavior include changes in influent characterization, inhibitory or other events not captured by the sampled plant data, or that the measurements are outliers. Based on these results the project team concluded that the model could adequately predict plant performance and was suitable for analyzing plant operation and performing what-if simulations. Figure 8 Verification Results for Soluble COD in Last Fungal Reactor 3 25 Soluble COD (mg/l) Simulated Measured Figure 9 Verification Results for VSS in Last Fungal Reactor 1734

13 6 5 4 VSS (mg/l) Simulated Measured Plant Analysis The calibrated Gärtuna WWTP layout was used to run a number of what-if simulations to demonstrate the use of the model. Scenarios investigated included taking a process train out of service, lowering the reactor liquid temperature, and using lower air flow rates in the reactors. In all three cases the simulations were conducted using the calibration input data. For the simulation with one process train out of service, it was found that both the TSS in the last fungal reactor (see Figure 1) and the effluent soluble COD (see Figure 11) were higher than in the calibration simulation. In the simulation with a lower reactor temperature (25 o C instead of 33 o C), the TSS was also higher in the last fungal reactor but the effluent soluble COD was relatively unaffected. Another scenario where the total airflow supplied to the reactors was decreased by 1/3 was also conducted. It was found that the overall performance results were very similar to those for the existing operation, but that the DO concentration in the first fungal reactor dropped to approximately 4 mg/l which may not be enough to ensure adequate mixing of the carriers. The model suggests that less air could be used without sacrificing biological performance. The lower limit on airflow will depend on the amount of air required to suspend the carriers. Figure 1 Impact of Taking One Train Out of Service on TSS in Last Fungal Reactor 1735

14 TSS (mg/l) Existing One Train Out of Service Figure 11 Impact of Taking One Train Out of Service on the Effluent Soluble COD Soluble COD (mg/l) Existing One Train Out of Service DISCUSSION 1736

15 The use of process modeling as an aid in the design, analysis, and optimization of industrial wastewater treatment plants is a relatively new area of application. The work done in this study and by others (e.g. Bury et al., 22; Goodfellow et al., 25; Schraa et al., 24; Stricker and Racault, 25; Takács et al., 1998) has demonstrated that it is possible to model industrial wastewater treatment plants in a wide variety of industries such as organic chemical, pharmaceutical, pulp and paper, food and beverage, and plastics production. The published International Water Association (IWA) models such as ASM1 can be used to model industrial WWTPs although it may be necessary to customize the models. Industrial WWTP modeling has the potential to provide substantial cost benefits. Models are cost-effective tools for trouble-shooting process problems or training operations staff without the risk of upsetting the plant. In addition, plant staff can study their operating and control procedures and find ways to reduce energy and chemical costs. Modeling is also an effective tool for assessing alternative plant designs which can lead to large capital cost savings. The work done in this study has demonstrated that it is possible to model an IFAS process with fungal biomass treating pharmaceutical wastewater. A modified version of the mantis model (which is an extension of ASM1) found GPS-X was found to reasonably predict plant performance after characterization of the influent wastewater and calibration of model parameters. The calibrated model will be used in the future to investigate operational changes such as: Taking trains out of service Increased hydraulic and organic loading Changes in airflow Changes in temperature Increased nutrient addition CONCLUSIONS A GPS-X model of the Gärtuna WWTP was configured and calibrated using measurements generated from TOC measurements taken at the plant in 24. The calibration results were reasonable given the variability typically encountered with WWTP measurements. The model was verified by running a simulation for the entire 24 year. Overall the model calibration appeared to be valid over the entire year except for a few periods where the influent characterization may have changed. The model is applicable for plant analysis, trouble-shooting, and optimization. It was demonstrated that the mantis model found in GPS-X, with some minor modifications, can be used to model an IFAS process with fungal biomass treating pharmaceutical wastewater. Minimal adjustments to the model parameters were required to calibrate the model. This is significant as it suggests that the IWA ASM models are applicable to fungal biomass. For demonstration purposes, a number of simulations were conducted to study the sensitivity of the plant. It was found that taking a train out of service increased the effluent soluble COD 1737

16 while lowering the temperature or using less air had little impact on the effluent soluble COD. ACKNOWLEDGMENTS The authors would like to thank the staff at the Gärtuna wastewater treatment plant for collecting the sampling data required for model calibration. REFERENCES Barker, P. S.; Dold, P. L. (1997) General Model for Biological Nutrient Removal Activatesludge Systems: Model Presentation. Water Environ. Res., 69 (5). Bury, S.J.; Groot, C.K.; Huth, C.; Hardt, N. (22) Dynamic simulation of chemical industry wastewater treatment plants. Wat. Sci. Technol., 45 (4-5), 355. Goodfellow, J., Malyk, B., Hahn, D., Johnson, D. (24) Evaluation of Biological Phosphorous Removal Versus Chemical Phosphorous Removal at a Potato Processing Facility. Proceedings of the WEF/A&WMA 1th Annual Industrial Wastes Technical and Regulatory Conference, August 22-25, Philadelphia, Pennsylvania. Henze, M.; Gujer, W.; Mino, T.; van Loossdrecht, M. (2) Activated Sludge Models ASM1, ASM2, ASM2d and ASM3; Scientific and Technical Report No 9; IWA Publishing, London, England. Schraa, O.; Belia, E.; Churn, C.C. (24) The Use of Process Modeling to Investigate the Performance of a Large Industrial Wastewater Treatment Plant. Proceedings of the Water Environment Federation s 77th Annual Technical Exhibition and Conference, October 2-6, New Orleans, Louisiana. Stricker, A.E.; Racault, Y. (25) Application of Activated Sludge Model No. 1 to biological treatment of pure winery effluents: case studies. Wat. Sci. Technol, 51 (1), 121. Takács, I.; Lockwood, S.; Caplis, J.R. (1998) Simulation model helps manufacturing facility maintain regulatory compliance, optimize treatment processes, and train operators. Industrial Wastewater, May/June Takács, I.; Patry, G. G.; Nolasco, D. (1991) A Dynamic Model of the Clarification-Thickening Process. Water Research, 25 (1),