WEFTEC 2011
Moving Forward in Process Modeling: Integrating Anaerobic Digester Into Liquid Stream Models Derya Dursun1*, Jose Jimenez1, John Bratby2 1
Brown and Caldwell, 850 Trafalgar Court, Suite 300 Maitland, FL 32814 Brown and Caldwell, 1697 Cole Blvd, Suite 200 Golden, CO 80401 Corresponding Author,
[email protected] 2
ABSTRACT Modeling of Anaerobic Digestion (AD) can provide significant benefits in the design, operation, and optimization of the digestion performance. However, AD model inputs are relatively complex and may require a wide array of components which are, most of time, not measured in wastewater treatment facilities. The result of this study highlights whole-plant simulation and the components that have significant impact on AD modeling. Influent wastewater characteristics and operational parameters have shown to be the most important elements of AD modeling. Sensitivity analysis also indicated that the hydrolysis rate has a major effect on the model predictions and may need to be modified to predict plant performance. Biogas production and volatile solids reduction (VSR) predictions are also highly affected by the quality of the input data. An integrated AD model can be utilized as a tool to simulate various scenarios to design a new digestion system and/or upgrade an existing one. KEYWORDS Anaerobic digestion, whole-plant simulation, model validation, biogas production, volatile solids reduction INTRODUCTION Anaerobic digestion (AD) is a complex process that converts organic material into a gaseous mixture, mainly composed of methane and carbon dioxide, through biological and physical processes. AD has been widely used for the degradation and destruction of organic matter from excess waste and primary and secondary sludge from treatment facilities, with consequent sludge stabilization and pathogen reduction. Although AD has been traditionally used for sludge processing, increasing awareness of global warming and rising energy prices have led to a growing interest for biogas production and increased the significance and interest in AD during recent years. Biogas, the end product of AD, is an energy source with many advantages and can be used in treatment facilities to offset energy costs and achieve GHG emission credits in life cycle analyses of sustainability.
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Although beneficial, anaerobic digesters are complex process units that require special attention; inadequate knowledge of the principles governing the process often prevents satisfactory digester performance and AD systems often suffer from instability. AD has traditionally been treated as a black box system, and optimization has been based on experience or trial and error methods. As experiments of AD processes are expensive and time consuming, investigations with properly calibrated models can provide a useful tool for process understanding and optimization. In addition, when low effluent nutrient limits are required at the treatment facility, the inclusion of the anaerobic digestion process in the whole-plant model can influence design and operation strategies of the liquid portion of the plant. AD models have potentials for revealing nonintuitive behaviors of the system and to quantify the performance of alternative operational setups. Mechanistic and semi-theoretical models can serve as useful tools to deepen the understanding of complex AD systems, and to facilitate operation and design of the process. If the behavior of a system can be predicted, the VSR and biogas production can be optimized and process failure can be prevented. Despite these motivations, modeling has rarely been applied on AD (Batstone, et al., 2002). The obstacles for introducing modeling to the industry include that the models of AD are complex and require extensive input data. Furthermore, the performance of the models on full scale processes has not yet been tested broadly (Batstone, 2006). It is, therefore, necessary to perform validations and uncertainty/sensitivity analysis of these models to gain knowledge that will facilitate their application. To facilitate design, system analysis, operational analysis, and control, a mathematical model describing the processes is required (Batstone, 2006). Therefore, selection of an appropriate model becomes crucial in integrating anaerobic digesters into liquid stream simulations. The application of the existing models has primarily focused on the individual liquid unit operations that make up the activated sludge (AS) system. Years of research has supported AS system models with the result that there are relatively reliable steady state design. Complex dynamic simulation models (e.g. Dold et al., 1980, 1991; Henze et al., 1995) have been developed, including biological N and/or P removal. A turning point was reached with the publication of the International Water Association Task Group Anaerobic Digester Model No.1 (ADM-1) (Batstone et al., 2002), and with it there is a growing drive to develop plant-wide WWTP simulation models which include the full solids processing flow sheet. The idea of whole-plant modeling is complicated by the fact that the models used for each unit process have been for the most part developed in isolation, typically without consideration of the whole treatment process or other unit process models. Hence, each unit process model often uses a different set of state variables. This is especially true if the models were developed to describe fundamentally different biological processes like aerobic and anaerobic treatment. To overcome these differences and facilitate plant-wide modeling, model interfaces are used as a means to convert state variables from one model into state variables of a different model. However, in some instances, plant simulators provide seamless interaction between liquid and solids treatment allowing for integration of all of the process within a treatment plant.
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There are several publications presenting results in calibration and validation of different process models for liquid treatment; however, very limited information has been presented on the calibration and validation of the anaerobic digesters’ model. This study presents a comparison between the structure and benefits of the ADM-1and the General Activated Sludge -Digestion Model (ASDM) included in the BioWin™ simulator. The use of an integrated AD model in liquid stream process model is assessed and the major components of the model that have significant impact on model predictions are identified. Sensitivity analysis is also conducted to determine the most important kinetic parameters that have impact on model predictions. Validation of the selected whole-plant model is done by using the data from several facilities. Finally, a validated model is utilized to simulate the impact of digester operation on process performance. METHODOLOGY Two widely used AD models -ADM-1 and ASDM were evaluated and compared at the beginning of this study. The BioWin™ 3.1 model platform integrates the ASDM within the activated sludge model is used in model evaluations in this investigation. BioWin™ is a widely used commercially available dynamic wastewater treatment process model and simulation package that was developed by Envirosim Associates of Canada. Historical plant data collected from different facilities were used in model validation. Digester performance evaluations were carried out with the validated model. RESULTS AND DISCUSSIONS Model Selection Although AD modeling can provide significant benefits in the design, operation and optimization of the digestion performance, AD model inputs are relatively complex and may require a wide array of components which are usually not measured in wastewater treatment facilities, especially on a dynamic basis. A precursor to ASDM is the International Water Association Task Group’s ADM-1. This model is described as a structured model with disintegration and hydrolysis, acidogenesis, acetogenesis and methanogenesis steps. An overview of the structure is shown in Figure 1.
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Figure 1. The structure of the ADM-1 anaerobic model. One of the significant issues with ADM-1 is that the inputs to the model are relatively complex. The ADM-1 requires characterizing the sludge into carbohydrates, proteins, and lipids (Batstone, et al., 2002). This is not an advantage because the approach of characterizing sludge into carbohydrates, lipids, and proteins requires measurements that are not routinely available on wastewater sludges and not easy to analyze (Kleerebezem and Van Loosdrecht, 2006). Other input parameters are organic acids, bicarbonate, and ammonia. In practice, this broad array of components is difficult to measure, particularly on a dynamic basis. ADM-1 has also been developed as a stand-alone platform, which makes it difficult to incorporate with existing liquid-stream wastewater models, requiring intermediate calculations. There are other generally recognized limitations of ADM-1, including: • Nitrogen and phosphorus are handled as fractions associated with organics and are, therefore, difficult to track. • ADM-1 contains a set of state variables that are not consistent with activated sludge models (i.e. Biowin™). In contrast, the ASDM model integrates the anaerobic digester model with the activated sludge model. Therefore, the models are based on the same set of state variables such that one can model the ‘whole plant’ on a consistent basis. For example, if the SRT of the activated sludge system is changed or the influent unbiodegradable particulate fraction of the influent wastewater changes, the composition of the stream going to the digester changes, as does the composition of the digester return stream back to the activated sludge system. BioWin™ allows a direct assessment of these interactions. For ADM-1 to achieve the same interaction, another integrative model would be required to translate one set of state variables into the other. • ADM-1 does not include P as a state variable. The release of P in digesters is particularly important when simulating nutrient removal plants. It is also an essential component
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•
when predicting pH within the digester due to the phosphate buffer. Furthermore, P is also an essential component of predicting struvite reactions within the digester. ADM-1 does not include N release and does not maintain a N mass balance. Ammonia release in the digester and return of digester supernatant, centrate, or filtrate to liquid stream processes is very important as tighter nutrient limits are required at facilities.
The BioWin™ simulator includes an anaerobic digestion module that allows an integrated simulation of the whole wastewater treatment plant. In BioWin™, both the liquid stream and AD model are based on the same set of state variables and stoichiometry and process rate equations, which allows the use of a comprehensive model for the biochemical systems in the treatment plant (Richard and Takács, 2004). In this respect, ASDM enables modeling the treatment plant using a whole-plant approach on both consistent and dynamic bases. Output from simulation modeling of the liquid stream processes, in the form of primary sludge and waste activated sludge, can be input directly into the anaerobic digestion elements within the same simulation model. This feature of the Biowin™ simulator makes it very practical and has tremendous value in modeling not only anaerobic digestion performance, but also the impacts of digester liquor sidestream returns on the liquid stream process. A conceptual schematic of the anaerobic degradation process in the BioWin™ simulator is shown on Figure 2. As the figure indicates, the end product of the digestion process, methane and carbon dioxide, is simulated by a set of complex reactions in the model. Methane production occurs as a result of the growth of two different groups of organisms, acetoclastic methanogens and hydrogenotropic methanogens. Acetoclastic methanogens consume acetic acid, whereas the substrate of hydrogenotrophic methanogens is dissolved hydrogen and dissolved carbon dioxide. The growth of the heterotrophs, acetogens, and methanogens is strictly dependent on environmental conditions. Hence, the behavior of the anaerobic digester and the dominant reactions in the digestion process are dependent on environmental conditions, such as hydraulic retention time (HRT), temperature, and pH.
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Figure 2. Conceptual Schematic of the Biowin™ Anaerobic Degradation Model (from Envirosim Associates, 2009). ASDM is also being progressively upgraded and improved to keep pace with developments in the various AD technologies. One example stems from recent work on Cambi-Digestion, where the unusually high digestion ammonia concentrations have given rise to new approaches to understanding inhibition (Wett el al., 2009). Recent adjustments to ASDM include an alternative biochemical pathway for the conversion of acetate to methane. The normal pathway involves the cleavage of acetate to form methane and carbon dioxide, known as aceticlastic methanogenesis. However, under high ammonia concentrations, a second pathway is favored whereby acetate is first oxidized to H2 and CO2, and then subsequently converted to methane. Based on the above brief discussion it is concluded that in practical applications of whole treatment plant simulations, currently the Biowin™ ASDM model is most useful to practitioners. The fact that the model can be calibrated to readily measurable parameters rather than assumed sludge constituents makes the model more useful..
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Sensitivity Analysis Sensitivity analysis can be conducted to find the input parameters that are most important to measure and to minimize the prediction uncertainty. It can also be used to test the model stability and to find the model parameters that are most suitable for model calibration. If a parameter has a negligible impact on the output, then the efforts to determine that parameter might be reconsidered. For some applications, like control, it is desirable to simplify the model as much as possible and, if the result is insensitive to a parameter, it can be excluded from the model. Another obvious purpose of sensitivity analysis is to improve the understanding of the system. The sensitivity analysis can help to quantify the contribution of different parts of the system to the output result and give hints on what parts of the process need special consideration. A specific goal of the sensitivity analysis in this study was to find key parameters that could be used to validate the model. The importance of individual input and model parameters was quantified by measuring the effect of individual parameter variations on the total variance of outputs. The kinetic constants for methanogens and acetogens were altered by no more than 10 percent from the default values described in the BioWin™ process simulator. Hydrolysis rate and hydrolysis half-saturation constants were also manipulated. It is important to note that, regardless of the general sensitivity analysis; to be useful for dimensioning of the process and realistic evaluation of its performance, the model must be calibrated and validated for a particular application. The results of the sensitivity analysis indicated the significance of the acetolastic maximum rate coefficient, acetolastic half saturation coefficient, and acetoclastic decay rate for methanogens. These three parameters alter the volatile fatty acids (VFAs) in the digesters. The kinetic parameters for acetogens did not change the VFAs notably. None of the kinetic parameters for acetogens or methanogens had any notable effect on volatile solids reduction (VSR) or biogas production rates; however, hydrolysis rate and hydrolysis half-saturation constants significantly changed the VSR and biogas production rates. As digesters are extremely sensitive to pH, alkalinity was added artificially in the form of lime (3 Molar) in the model. The sensitivity analyses were then repeated with the addition of various amounts of lime. The results indicated that the biogas production and VSR are not sensitive to alkalinity. The VFAs, on the other hand, are very much affected by the carbonate alkalinity. To investigate the influence of influent wastewater characteristics on sludge quality and the performance of the digesters, sensitivity analyses were carried out by systematically varying influent wastewater characteristics. The BioWin configuration used for sensitivity analysis is given in Figure 3.
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Influent
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Figure 3. A simple whole-plant BioWin™ configuration used for sensitivity analysis. The results from the sensitivity analysis showed that biogas production and VSR are most sensitive to: • • • • •
Influent carbonaceous oxygen demand (COD) Fxsp - the non colloidal, slowly biodegradable fraction of the COD Fup - the unbiodegradable particulate fraction of COD Ratio of primary sludge and secondary sludge volatile solid content Solid removal efficiency of the primary treatment
It is important to note that it would be generally unacceptable to simply change selected influent wastewater characteristics since it is important to maintain a balance between all the wastewater parameters and be consistent with activated sludge calibration. However, the objective herein was to develop a basis for sensitivity analysis. A full wastewater characterization would determine the wastewater fractions, such as Fxsp and Fup, and would provide more accurate prediction of biogas production and VSR. Model Validation To investigate the biogas production of the anaerobic digesters, several data sets from municipal wastewater treatment plants were examined (San Jose WWTP, CA and Marlay Taylor WWTP, MD). In order to simplify the modeling of the San Jose WWTP, liquid stream modeling was conducted separately with BioWin™ and the output of the liquid stream model was input to the digestion model. The liquid and solid streams were modeled together for the Marlay Taylor WWTP. Model calibration and validation studies also showed that the hydrolysis rate and hydrolysis halfsaturation constant have to be adjusted based on actual plant data. The model was validated using the available 2009 historical data for the San Jose WWTP by using available influent characteristics and manipulating hydrolysis rate. The resulting validated model output for biogas production is shown on Figure 4. Dynamic simulation results were compared to reported values during the first 10 months of 2009 in the San Jose WWTP. Although there are some differences between the reported measurements and the simulation results, the values are reasonably close for the purpose of validation. Copyright ©2011 Water Environment Federation. All Rights Reserved. 735
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Figure 4. Validation results for biogas production at the San Jose WWTP. Figure 5 presents the BioWin™ configuration for the Marlay Taylor WWTP. A stress-testing of the anaerobic digesters was conducted by increasing the influent mass rate to the units so the performance of the units would be affected. This information was used to calibrate the ASDM model in the BioWin™ simulator. It should be noted that the liquid process was also calibrated, since this defines the characteristics of the influent stream to the anaerobic digesters.
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Figure 5. An example whole-plant BioWin™ configuration used for model validation. One of the advantages of conducting a whole-plant model is the prediction of the sidestream returns. This is very important, especially in cases where anaerobic digesters are employed in treatment facilities with low effluent nutrient requirements. Figures 6 and 7 depict the observed (squares) and predicted (solid lines) values for the anaerobic digesters’ biogas production rates and VSR (respectively) at the Marlay Taylor WWTP.
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Figure 6. Validation results for biogas production at the Marlay Taylor WWTP.
Figure 7. Validation results for VSR at the Marlay Taylor WWTP. The following are important observations based on the validation of the BioWin™ simulator for the two facilities: • • •
The ASDM model built in the BioWin™ simulator predicts the biogas production and VSR in anaerobic digestion process reasonably well. As indicated in the sensitivity analysis, the most important influent fractions for the modeling of the anaerobic digester are the non colloidal, slowly biodegradable fraction and the unbiodegradable particulate fraction of COD. No major changes were necessary to the model’s kinetic default values to calibrate the model.
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• • •
The hydrolysis rate was modified for both cases to match the AD performance. BioWin™ model predictions match most of the plant data. BioWin™ under predicts the pH levels in the digesters; however, it appears that the model predictions are not very sensitive to pH.
Impact of Digester Operation on Process Performance The main advantage of a validated model would be that it allows predicting the performance of digesters and optimizing their operation. The calibrated and validated model can be used to determine the impact of process changes on digester performance. This is found to be useful in predicting performance values to use in establishing the design basis when different HRTs and temperatures are being assumed for different alternatives and loading conditions. Model predictions can be used to determine the minimum HRT or temperature to reach a target biogas production and/or VSR rates. This also allows simulating future conditions with many different scenarios (such as increased flows and loads, various operational modes). The validated case study for San Jose WWTP was used as an example in a series of steady state simulations of the anaerobic digesters to check the dependence of predicted biogas production and VSR performance on digester HRT and temperature. As Figure 4 demonstrates, model predictions for the San Jose WWTP were very close to the actual values that indicates the reliability of model for the assessment of process performance. Predicted biogas production rates (ft3/d) are plotted in Figure 8. The impact of temperature can be examined: in the typical operating region of 20 to 30 days of HRT, the biogas production was approximately 1,800-1,900 ft3/d at 105°F (15.7 ft3/lb VS destroyed) and around 1,700-1,800 ft3/d at 95°C (15.4 ft3/lb VS destroyed). These values are within the expected range of typical performance. The biogas production is predicted to increase with increasing HRT and temperature. The specific biogas production rate (ft3 gas produced per lb VS destroyed) also increased with temperature, however variation of HRT did not alter the specific biogas production rate significantly.
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Figure 8. Model-predicted gas production rates as a Function of HRT and temperature.
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Model predictions of VSR are presented on Figure 9 as a function of HRT and temperature. In this case, it was possible to reach approximately 56% VSR by operating the digesters at 105°F and 30 days of HRT. Decreasing the HRT and temperature would lower the reduction rate.
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Figure 9. Model-predicted VSR as a function of HRT and temperature. The model can also be used to predict the concentration of volatile fatty acids in the digested sludge at various HRTs. This would be beneficial to determine at which point digesters would become unstable. An example of the results for that analysis is shown in Figure 10. As anticipated, VFAs increase as HRT is shortened. For the case evaluated, at HRTs below 10 days, VFAs rise very rapidly, approaching conditions that could represent impending process instability below 7 days.
Figure 10. An example of model prediction of volatile fatty acid concentrations at different HRTs (T=37°C).
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CONCLUSIONS Anaerobic digestion is a complex system of biochemical and physical processes that convert organic matter into biogas via bacterial activity. Biogas is a major energy source and can be used in treatment facilities to offset the energy costs. Thus, anaerobic digestion is increasingly used in treatment facilities for energy recovery purposes. Due to the complexity of the process, anaerobic digestion has traditionally been treated as a black box system, and optimization has been based on experience, trial-and-error methods, and/or experimental studies. The following conclusions can be drawn based on this study: • The BioWin™ ASDM model was compared to the ADM-1 model and was found to be the most useful to practioners and provide important and useful information. The fact that the model can be readily integrated with liquid stream unit process into a whole plant simulator makes ASDM particularly useful. • Sensitivity analysis indicated that the biogas production and VSR were strongly related with hydrolysis rate in addition to the influent COD concentration and fractions (Fup, Fxsp). The model-predicted biogas production was determined to be relatively insensitive to the kinetic parameters. • Operational parameters, such as HRT, temperature, influent volatile solid load, and ratio between primary and secondary sludge loads, strongly influence the biogas production of the digesters. • A whole-plant simulation provides interaction between liquid and solids treatment and allows estimating the impact of sidestream returns. • The BioWin™ ASDM model is shown to be a useful tool to evaluate the digester performance under different operational conditions and to optimize the design and operation of the digestion process for biogas production. ACKNOWLEDGMENTS The authors would like to gratefully acknowledge San Jose/Santa Clara WPCP staff, Dr. Alex Ekster, Dr. Stephanie Vermande, and Mr. Rong Liu, for their insightful idea of modeling their digester process linked to their liquid stream process model to provide important process performance parameters, for their diligent work in developing the initial liquid stream process model, for providing important plant data, and for their collaboration in the modeling efforts. The authors are also thankful to staff at the Marlay Taylor WWTP for providing valuable ideas and information from their facility for this work. REFERENCES Batstone, D. J. (2006). Mathematical modelling of anaerobic reactors treating domestic. Reviews in Environmental Science and Biotechnology , 5, 57. Batstone, D.J; Keller, J.; Angelidaki, I.; Kalyuzhnyi, S.V.; Pavlostathis, S.G.; Rozzi, A.; Sanders, W.T.M.; Siegrist, H.; Vavilin, V.A. (2002) The IWA Anaerobic Digestion Model No 1 (ADM1) Wat. Sci. and Tech Vol 45(10) 65. Dold, P.L.; Ekama, G.A.; Marais, G.V.R. (1980) A general model for the activate sludge process. Prog. Water Technol. 12 (Tor) 47-77.
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Dold, P.L.; Wentzel, M.C.; Billing, A.E.; Ekama, G.A.; Marais, G.V.R. (1991) Activated Sludge Simulation Programs. WRC Report No TT 52/91, Water Research Commission, Private Bag X03, Gezina, 0031, South Africa. Envirosim Asociates Ltd (2009) BioWin 3 Manual, Flamborough, Ontario, Canada. Henze, M.; Gujer, W.; Mino, T.; Matsuo, T.; Wentzel, M.C.; Marais, G.V.R. (1995) Activated sludge model No 2. IWA Scientific and Technical Report No 3, IWA London. ISBN 1900222-00-0, 32 pp. Jones, R.; Takács, I. (2004) Modeling the Impact of Anaerobic Digestion on the Overall Performance of Biological Nutrient Removal Wastewater Treatment Plants. Proceedings of the Water Environment Federation, WEFTEC, October 2-6, 2004, pg 244-257, New Orleans, LA. Kleerebezem, R.; Van Loosdrecht, M.C.M (2006) Waste characterization for implementation in ADM1 Wat. Pol. Research Vol 54(4) 167. Wett, B.; Murthy, S.N.; Takács, I.; Wilson, C.A.; Novak, J.T.; Panter, K.;Bailey, W. (2009) Simulation of Thermal Hydrolysis at the Blue Plains AWT: A New Toolkit Developed for Full-Plant Process Design. Proceedings of the Water Environment Federation, WEFTEC, October 10-14, 2009, pg 2688-2698, Orlando, FL.
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