Modeling flow inside an anaerobic digester by CFD techniques

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Volume 2, Issue 6, 2011 pp.963-974

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ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.
Modeling flow inside an anaerobic digester by CFD
techniques


Alexandra Martínez Mendoza
1
, Tatiana Montoya Martínez
2
, Vicente Fajardo
Montañana
2
, P. Amparo López Jiménez
1


1
Departamento de Ingeniería Hidráulica y Medio Ambiente, Universitat Politècnica de València,
Camino de Vera S/N 46022, Spain.
2
Grupo Aguas de Valencia. Avenida Marqués del Turia 19 46005 Valencia, Spain.


Abstract
Anaerobic processes are used to treat high strength organic wastewater as well as for the treatment of
primary and secondary sludge from conventional wastewater treatment plants. In these processes,
heterotrophic microorganisms convert biodegradable organic matter to methane and carbon dioxide in
the absence of dissolved oxygen and nitrate.
Some of the most important aspects of the design of anaerobic digesters are related to hydraulic
considerations. In spite of its important role in performance, hydraulics of flow inside digesters has not
been quantified or adequately characterized. In this contribution a three-dimensional steady-state
computational fluid dynamics (CFD) simulation has been performed for a particular anaerobic digester,
in order to visualize the flow patterns. Flow and velocities profiles have been represented inside the
digester to identify possible dead zones or stratifications. The geometry of a real digester installed in
Valencia Waste Water Treatment Plant (located in Quart-Benager, Valencia, Spain) has been used in
order to consider the proposed methodology.
Copyright © 2011 International Energy and Environment Foundation - All rights reserved.

Keywords: Anaerobic digester, Computational fluid dynamics, Hydrodynamic model, Velocities
profiles.



1. Introduction
Anaerobic digestion is the degradation of organic matter by microorganisms that do not require oxygen
for degradation of such material. Recently anaerobic digestion processes have taken much force, for the
excellent results obtained by producing a very stable sludge, but it is still an ongoing study looking for
improvements because it is a sensitive system that can be seen disturbed and not properly working. One
of the important reasons for failure is improper mixing provided in the digester. Proper mixing is needed
for optimal performance and operation since mixing or agitation is required to homogenize the contents
of digesters, to ensure uniform distribution of substrate and microorganisms culture, to avoid settling of
the heavy solid particles to the bottom, to avoid flotation of biomass at the surface of the sludge, and to
maintain the desired pH and temperature of the slurry for the microbial processes [1].
An understanding of the hydrodynamic behavior of anaerobic reactors is considered to be of paramount
importance for optimization of the process, enabling the detection and solution of problems that often
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

964
occur due to reactor design and geometrical aspects. The need for efficient mixing inside anaerobic
reactors has been recognized and stressed in the technical literature [2].
Nowadays, models become a more powerful tool for representing these digesters behavior. Recently,
references can be found related to digestion procedures optimization by analyzing the adequate mixing in
environment of anaerobic bacteria. [3]. Even some modeling studies can be found by using dynamic
models to investigate anaerobic digestion performance [2], in these studies, the importance of mixing
indicators such as retention time and the degree of mixing have been pointed out.
A 3-D flow model was developed by Fleming [4], considering a wide range of aspects such as bulk fluid
motion, sedimentation, bubble mixing, bubble entrainment, and buoyant mixing to simulate the flow
patterns of a covered lagoon digester.
The importance of defining performance indicators has been documented by Keshtkar et al. in [5]. They
proposed two characteristic mixing parameters: the relative volume of the flow-through region, and the
ratio of the internal exchange flow rate to the feed flow rate for evaluating the digestion performance. In
this sense, Pena et al. [6] studied modified pilot-scale anaerobic ponds receiving domestic sewage with
different configurations and flow rates.
First 3-D steady-state computational fluid dynamics (CFD) simulations were presented in Vesvikar and
Al-Dahhan [7] to visualize the flow patterns and to obtain hydrodynamic parameters. Also, in Vesvikar
and Al-Dahhan and Vesvikar et al. [8] computer automated radioactive particle tracking and computed
tomography were used to visualize the flow patterns and obtain hydrodynamic parameters in mimic
anaerobic digesters. Karim et al. [9-12] experimentally studied the effect of mixing (biogas recirculation,
impeller, and slurry recirculation) on the anaerobic digestion of animal waste for lab-scale digesters.
Binxin Wu [13] implemented a CFD model to predict the fields of flow in a mixing of an anaerobic
digester, considering non Newtonian Fluid, and obtaining completely different results.
Therefore, the present work is focused on modeling fluid dynamics within the digester and defining
parameters to identify the mixing inside it. The main objective of this work is the modeling of the
digester hydrodynamics in order to study and identify the mixing zones and zones of nor or little mixing
mixture that we call dead zones. CFD represents the possibility of visualizing the flow inside the
digester, and analyze in detail the mixture and parameters that represent this dynamics.

2. Model development
2.1 CFD model for representing dynamics inside the digester
Computational fluid dynamics (CFD) proves to be a valuable and efficient tool to understand and
evaluate the fluid dynamics of flow systems. This model has been development using a CFD commercial
package: Star CCM+. This model solves numerically the laws governing fluid dynamics, solving
equations by a geometric domain. The basic flow equations used are the Navier-Stokes, Viscous Fluxes
in laminar flow and turbulent, and momentum equation in discrete form. The magnitudes of velocity,
pressure and temperature are calculated in a discrete manner at the nodes of a mesh or network,
describing the flow geometry modeling [14].

2.2 Geometry
The model developed is based on a primary digester Quart-Benager WWTPs. The digester dimensions
are 30,50m in diameter and 8,80m high cylindrical, with a total volume of 6.504 m
3
. It is structurally
built of reinforced concrete with walls 0,50m thick. (See Figure 1). The removal of sludge is done by the
fund, through a set of valves and there is also a shot at half height for sludge extraction led to the
secondary digester.
The agitation system of the sludge is carried out using the DINOMIX system; this is a mixing device by
means of a jet, causing a stream into the digester with a drag due to the forces resulting from the
viscosity and flow jet velocity. The forces thus come into play, and it creating a speed zone of instability
that results in a turbulent area.
With the real dimensions of the digester a replica of the geometry was created using AutoCAD 3D
software, and then converted the file type lithography in order to be implemented in STAR CCM+
Software.

International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

965
Outletpipe

(a)


(b)

Figure 1. (a) Dimensions of the geometry of anaerobic digester; (b) Inlet and outlet of anaerobic digester
2.3 Mesh
a b
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

966
Mathematical model is created from the geometric model, called meshing or discretization of the
continuum, generating a system of equations. Several simplifications were made to develop the model to
better connection with CFD: The affluent is performed by 2 nozzles DINOMIX, with same input
velocity, and two pipes to feed a digester with mix mud and hot mud. The output of the mud is made
by emptying from a central pipe and other lateral out by to extract hot mud. The output for overflow is
omitted. Variations of temperature are not considered, only flows with fixed conditions of entry.
Geometry of the digester was maintained in all the models, only changing the angles of the nozzles. See
Figure 2.
Conditions of meshing are defined: surface remesher, volume polyhedral mesher, and prism layer
mesher. The surface mesh has generated 177.412 faces and volume mesh has generated 848.729 cells,.
The obtained mesh is very precise with cell size of 0.22 m it was appropriate for the dimensions of
model.



Figure 2. Details on volume mesh

2.3 Boundary conditions
A classical velocity inlet boundary condition has been used. The outlet was first defined as a pressure
outlet, the rest of geometry is represented by boundaries conditions of wall. Figure 3 shows the boundary
conditions used in the volume of the problem being solved. Physical conditions in the model are namely:
3D model. steady state.
The physical properties of a continuous fluid with density (999.66 kg/m
3
) and dynamic viscosity (0.065
Pa-s), sludge coming from Quart-Benager WWTPs. Segregated flow. Constant density. Turbulent flow
using the standard k-ε model. This standard model k-ε is a semi-empirical model based on transport
equations for kinetic energy turbulent (k) and the range of dissipation (ε) [15].
After model conditions have been defined, several iterations were performed, which results in velocity.
Turbulent conditions proposed the better convergence solutions as shown in Figure 4, with a smaller
value for final residuals.

3. Results
Once the system has been designed, it has confirmed the efficiency of the mixture to be looking. The
final results means values about the hydrodynamic of the sludge inside the digester, as velocity fields,
percentage of volume with dead zones and mixing zones. Further the flow pattern is also studied by
means of parameters as the geometry influence and turbulence variations.


International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

967




Figure 3. Boundary conditions



Figure 4. Values of residuals in the model


3.1 Velocity fields on flow pattern
Nowadays, modern Computational Fluid Dynamics (CFD) software helps modelers to define mixing
efficiencies for different digester configurations before construction; or to determine possible virtual
changes in construction parameters. CFD visualization and analysis also provide an opportunity to
examine inlet configurations. Velocity is then considered the more representative parameter of behavior
of flow. For this reason the velocity is detailed carried on by defining virtual planes inside the digester
(0m and 0.7 m of height) to study the maximum and minimums values, flow direction and turbulent
zones. In Figure 5, is visible the details of velocity fields in scalar and vector form in the areas of the
nozzles and the central pipe. The blue color represents lower velocities and red color the highest values.
Areas where more mixing is present lay on the height of nozzles, which are responsible for giving the
turn to flow, and areas near to the outlet in the center pipe, which dimensions the output is generated
turbulence in this area. On the planes at 0m and 0.7m of height, the velocities were mostly of 0.31 m/s
and 0.0749 m/s respectively. There has been a maximum speed of 1.4 m/s. Planes of bottom in the
digester and 0.7 m. have been considered in all simulations, as depicted in Figures 6 and 7.




International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

968

(a) (b)


(c) (d)

Figure 5. Detail on velocity fields in significant points of the digester. (a) y (b) nozzles and (c) y (d)
centre pipe



Figure 6. Planes in the simulations considered for analyzing velocities and streamlines



b
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

969

(a) (b)

Figure 7. Detail on velocity fields inside digester on bottom (a) height h=0m and on the nozzle (b) height
h=0.7m

3.2 Changing the flow direction
One of the more important parameter determining the inside velocities is the geometry at the entry angle
of flow direction on the nozzles; that is defined like α if viewed from the top view of the digester, and β
if viewed from the side right face. Four simulations with change of direction flow have been performed:
Simulation 1, with angles α=30º and β=11º. Simulation 2, with angles α=0º and β=0º. Simulation 3, with
angles α=30º and β=22º. Simulation 2, with angles α=15º and β=0º. On Figure 8, are detailed angles of
the nozzles from both perspectives.
All simulations providing a good mixing inside the digester, but some differences were found between
the simulations. In cases 1, 3 and 4, the flow follows the course of the shape tank and down a continuous
circular movement within the digester. In simulation 2, the flow presented a different behavior, as the
shock front is a sweeping flow from the middle of the digester which begins in the nozzle exit, crossing a
distance of roughly the distance from the radio and returns.



Figure 8. (a) Right side view of simulations. (b) top view of simulations

The stream lines predict the trajectory of starting flow in a determinate moment, which confirms the
movement inside the digester. Figure 9 present streamlines in all simulation in the plane of the entrance
nozzles (h=0.7m).
a
b
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

970

(a) (b)


(c) (d)

Figure 9. Stream lines inside digester. (a) Simulation Nº1. (b) Simulation Nº2. (c) Simulation Nº3. (d)
Simulation Nº4

3.3 Mixing zones definition
For defining dead zones Stoke´s law for settling has been considered. Velocity flow less the Stoke´s
settling velocity of a biomass particle of 245 µm diameter and 1050 kg/m3 bulk density, 0.001 m/s [9,
16] is implemented. Therefore defining velocity limits: Dead zones (velocity <= 0.001m/s) and mixing
zones (velocity >= 0.001m/s).
On simulation Nº1, at 0m and 0.7m of height, the 85% of zones are mixing and at 4m and 7.8m the 94%.
It shows that the more nixing zone is near to the walls of digester that is stimulated for the angle of
nozzles.
The dead zone is in the middle in the bottom of digester. For the simulation Nº2, the values of mixing
zones are the 96% at 0m, 98% at 0.7m of height and at 4m and 7.8m the 95%; this simulation has less
dead zone on the middle and the wall in the bottom of digester. In the simulation Nº3 the 94% of mixing
zones are at 0m and 0.7m of height, and 95% for 4m and 7.8m of height; and the dead zone is located at
the same part of the previous simulations.
In simulation Nº4, mixing zones are the 66% at 0m, 83% at 0,7m and to 4m and 7.8m the 100%. In this
case the amounts injected flow and does not interact with the sludge that is below nozzles, forming dead
zones. The flow when is going up by the walls, begins to mix from the walls, since a total mixture in the
upper parts of the digester is finally achieved.
In Figure 10 velocity is represented, mixing zones by visible colour areas and dead zones in blanks
spaces, from both perspectives front view and side view. The colour range is the same as is followed in
all graphics text.

International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

971

(a) (b)


(c) (d)


(e) (f)


(g) (h)

Figure 10. Simulation Nº1, at 0m top view (a) and side view (b). Simulation Nº2, at 0m top view (c) and
side view (d). Simulation Nº3, at 0m top view (e) and side view (f). Simulation Nº4, at 0m top view (g)
and side view (h)
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

972
3.4 Evaluation criteria of good mixing
With the results of the simulations, the best mixing flow geometry was identified; by means of
percentages of mixing zones analyzed, whereas if all values in the different sections studied (0, 0.7, 4 and
7.8 meters of height) made in the digester are above 85% has a good quality mix or below this
percentage has a inappropriate mix. Once the percentages of mixing is analyzed, that matters is the
located of dead zone and thus is sensitive to the change in the direction of flow. Simulations number 2
and 3 showed better mixing zones. See Figure 11.

0,0%
10,0%
20,0%
30,0%
40,0%
50,0%
60,0%
70,0%
80,0%
90,0%
100,0%
0 
meter
0.7 
meter
4 
meter
7.8 
meter
0 
meter
0.7 
meter
4 
meter
7.8 
meter
0 
meter
0.7 
meter
4 
meter
7.8 
meter
0 
meter
0.7 
meter
4 
meter
7.8 
meter
Sim 1Sim 2Sim 3Sim 4
Mixing zone Dead zone


Figure 11. Mixing percentages in each of simulation

4. Conclusion
CFD simulations on an anaerobic digester system have been performed. The applied closures, boundary
conditions and models predicted the observed flow pattern in the digester.
Through building a CFD to the flow inside this anaerobic digester, hydraulic behavior of the inside flow
has been represented and an interpretation of internal movement of the sludge has been provided.
By means of different simulations, the variability of the data flow changes has been studied, the
distribution of velocities and streamlines in the geometry, playing a decisive role as the inflow nozzles,
and which may determine the occurrence of dead zones along the flow.
The knowledge of the velocity within the digester as a whole is of paramount importance to identify
areas below the threshold velocity of sedimentation of the solids of 0.001 m/s. This supposition has
allowed the determination of when the solid particles can be considered as sedimentable.
Thus, most of cells have presented simulated velocities above this value, only some of them are below
this velocity.
Different entrance angles of the nozzles directing the flow have been analyzed, determining an optimum
direction, when considering mixing in different heights of the digester. Particular geometry of nozzles
enhances the digester’s mixing and thus the overall performance. A methodology of quantifying the
presence of dead zones has been presented in this paper.
Anaerobic digester is an element of treatment plant completely opaque for managers. Thus, to have a
computational model that allows the internal analysis at all points of the geometry is of vital importance
for the analysis of the behavior and detection of possible deficiencies. Design optimization based on
calibrated models becomes an ideal tool to determine best solutions in engineering proposals for
anaerobic digesters.
There is a need for further research and improvement of this model in order to validate the numerical
results presented in this paper. Work is ongoing towards this goal.
The results obtained emphasize the importance of considering mixing when simulating anaerobic
digestion and, consequently, during reactor design. Decisions must be taken in the design phases of the
International Journal of Energy and Environment (IJEE), Volume 2, Issue 6, 2011, pp.963-974
ISSN 2076-2895 (Print), ISSN 2076-2909 (Online) ©2011 International Energy & Environment Foundation. All rights reserved.

973
digester as they affect very much the performance of the whole system. In this sense, CFD techniques
postulate as a very promising tool for designers to optimize hydraulic behavior of digesters.

Acknowledgements
Authors want to acknowledge to Entidad Pública de Saneamiento de Aguas Residuales. This work has
been partially supported by Instituto de la Mediana y Pequeña Industria Valenciana (IMPIVA, Project
IMIDTF/20102/152) and Fondo Europeo de Desarrollo Regional (FEDER), and the Project “Aplicación
de técnicas de la mecánica de Fluidos Computacional al Movimiento de Flujos Ambientales” Universitat
Politècnica de València research incentives; which are gratefully acknowledged

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