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Covered Anaerobic Lagoon Simulation Using Computational Fluid Dynamics Jason G. Fleming and Richard R. Johnson Objectives This research project proposes to do the following: (1) adapt a conventional anaerobic process model for use in a computational fluid dynamic simulation; (2) create a CFD simulation of a covered lagoon; (3) investigate the effect of fluid dynamics on the performance of a covered lagoon digester; and (4) provide recommendations concerning the design and operation of covered lagoons. Figure 1. Currently, waste material from large scale swine operations in North Carolina is typically handled using open lagoons. Open lagoons provide some waste treatment (depending on temperature and loading rate), but they also release methane and ammonia to the atmosphere. Large land areas are required for the application of lagoon effluent. Figure 2. Anaerobic digestion is an attractive alternative treatment process because it typically removes 90% of the Chemical Oxygen Demand (COD) from the waste, it prevents ammonia from escaping, it captures the valuable biogas, and it uses naturally occurring microorganisms. The disadvantages of anaerobic digestion are expense and instability. For example, this European anaerobic digestion plant is too expensive to be practical in North Carolina. Figure 5. The overall behavior of the covered anaerobic lagoon is governed by several interdependent processes simultaneously. The fluid velocities were calculated using the Semi-Implicit Method for Pressure Linked Equations (Patankar, 1980). SIMPLE is a conservative finite volume method that iterates line-by-line to find velocity in incompressible flow. Once the velocities were known, the effect of bulk fluid motion on species transport was modeled with an advection method for incompressible flow (LeVeque, 1996). Gravity creates a downward settling flux for non-dissolved species such as biomass and raw substrate. This flux was set to 5% per day. Figure 11. The transient response of the conventional model shows no benefit from a gradual start, and actually predicts that steady state is reached more quickly with an abrupt start. The transient response of the multidimensional simulation is much more realistic, with more gradual starts providing higher steady state performance. Figure 6. The multidimensional anaerobic digestion simulation starts by solving for the fluid velocity field. The concentration gradients are formed by iterating through the models that describe the important processes inside the covered lagoon: advection, settling, chemical reaction and biological reaction. Post processing consists of comparison with experimental data as well as 3D visualization. The overall model results will be validated with experimental biogas production data from the Barham Farm covered lagoon. Changes to Reaction Model Figure 3. Covered lagoons represent a “middle way,” combining the low cost of an open lagoon with the controlled environment of an anaerobic tank reactor. However, conventional anaerobic digestion models are insufficient for covered lagoon process design. The Barham Farm covered lagoon (in Zebulon, NC) is pictured here. Figure 10. In order to test the dynamic response during startup, three cases of unsteady inlet boundary conditions were used. In the “slow startup” case, the inlet concentration is ramped up from zero to full load over a period of 150 days. In the “fast startup” case, the ramp time is 60 days, and in the “abrupt startup” case, the ramp time is zero. The microbial model from Hill et al. (1983) was modified by removing the terms involving hydraulic retention time (HRT). The HRT—used to account for the concentration difference between mass entering and leaving the reactor—becomes redundant when fluid velocity and species transport are explicitly simulated. The kinetic parameters from the model were used directly, without adjustment or calibration. Figure 12. Contours of steady state methane production are shown for the gradual and abrupt startup cases. Waste is flowing in from the right and out to the left, while solids settle to the lagoon floor. A large unproductive area is evident in the abrupt loading case. Conclusions and Recommendations The conventional model showed no real difference resulting from different startup procedures. On the other hand, the multidimensional model predicts better performance from a gradual startup, validating the recommendations found in the literature. Background Furthermore, the 3D visualization revealed the mechanism behind the performance enhancement: gradual startup avoids localized inhibition near the lagoon inlet. Based on these results, it is recommended that covered lagoons similar to the Barham lagoon ramp up the inlet concentrations over a period of at least 120 days during initial startup. Covered lagoon system design requires an accurate model of the biochemical and physical phenomena underlying the process. The literature on the biochemistry of anaerobic digestion is mature and well developed. However, physical modeling of anaerobic tank reactors is crude; perfect mixing and constant temperature are always assumed. These assumptions result in a process model consisting of ordinary differential equations (ODEs) which are easy to solve but do not match well with empirical observations in real systems (Heinzle, et al., 1993). The assumptions of perfect mixing and constant temperature are particularly inappropriate for covered lagoons because covered lagoons are not mixed and have seasonally varying temperatures. dS dt dA dt S 0 S M HRT Y A0 A M 1 Y c M c HRT Y Yc dM k d 1 M dt HRT d M c c k dc 1 Mc dt HRT Figure 4. A conventional model from Hill, et al (1983) represents conservation of mass for four species: The S and A terms represent waste concentrations and the M and Mc terms represent microbial concentrations. HRT is hydraulic retention time. References Heinzle, E., Dunn, I.J., and Ryhiner, G.B. 1993. Modeling and control for anaerobic wastewater treatment. In Advances in Biochemical Engineering/Biotechnology No. 48. Berlin: Springer-Verlag. Figure 7. The fluid velocity in the lagoon is visualized with white stream tubes (fluid enters on the right and progresses toward the left). Landscape graphics add scale and context. Figure 8. Biodegradable volatile solids (BVS) are the primary component of raw waste. The effect of the settling model on BVS concentration is clearly visible here (the contours of concentration slope downward toward the exit). Figure 9. Methane generation is highest near the center of this sludge blanket because the inlet side is overloaded while the outlet side is underloaded. Application to Startup Procedures In order to measure the performance of this model against that of a conventional model, the transient startup responses were compared. The physical characteristics of the models were selected to match that of the Barham Farm covered lagoon. The startup transient was chosen as a “standard problem” because anaerobic digesters require extra attention during startup; the natural initial concentration of methanogenic bacteria in the environment is small and these bacteria tend to become inhibited before developing sufficient biomass. One recommended startup procedure is to dilute the raw waste until the microbial community can establish itself (Dalla Torre and Stephanopoulos, 1986). This recommendation was tested with three sets of inlet boundary conditions (see Figure 10). Hill, D.T., Tollner, E.W., and Holmberg, R.D. 1983. The kinetics of inhibition in methane fermentation of swine manure. Agricultural Wastes 5, pp. 105-123. LeVeque, R.J. 1996. High resolution conservative algorithms for advection in incompressible flow. SIAM J. Numer. Anal. Vol. 33, No. 2, pp. 627—665. Patankar, S.V. 1980. Numerical heat transfer and fluid flow. Washington, DC: Taylor & Francis. Torre, A.D., and Stephanopoulos, G. 1986. Mixed culture model of anaerobic digestion: application to the evaluation of startup procedures. Biotechnology and Bioengineering. Vol. 28, pp. 1106—1118. Acknowledgements We would like to acknowledge financial support (fellowship, internship, and equipment grant) from SGI, Cray Research, and the North Carolina Supercomputer Center. We would also like to thank Dr. Jay Cheng and Mr. Julian Barham for their collaborative effort.