CFD simulation of an expanded granular sludge bed (EGSB) reactor for biohydrogen production

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 4 ( 2 0 0 9 ) 9 6 8 6 – 9 6 9 5

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CFD simulation of an expanded granular sludgebed (EGSB) reactor for biohydrogen production

Xu Wang, Jie Ding*, Nan-Qi Ren, Bing-Feng Liu, Wan-Qian Guo

State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 202 Haihe Road, Nangang District,

Harbin, Heilongjiang 150090, China

a r t i c l e i n f o

Article history:

Received 5 August 2009

Received in revised form

22 September 2009

Accepted 9 October 2009

Available online 31 October 2009

Keywords:

Hydrogen production

Expanded Granular Sludge Bed (EGSB)

Hydraulic Retention Time (HRT)

Computational Fluid Dynamics (CFD)

Hydrodynamics

Eulerian–Eulerian model

* Corresponding author. Tel./fax: þ86 0 451 8E-mail address: [email protected] (J.

0360-3199/$ – see front matter ª 2009 Profesdoi:10.1016/j.ijhydene.2009.10.027

a b s t r a c t

Understanding how a bioreactor functions is a necessary precursor for successful reactor

design and operation. This paper describes a two-dimensional computational fluid

dynamics simulation of three-phase gas–liquid–solid flow in an expanded granular sludge

bed (EGSB) reactor used for biohydrogen production. An Eulerian–Eulerian model was

formulated to simulate reaction zone hydrodynamics in an EGSB reactor with various

hydraulic retention times (HRT). The three-phase system displayed a very heterogeneous

flow pattern especially at long HRTs. The core-annulus structure developed may lead to

back-mixing and internal circulation behavior, which in turn gives poor velocity distribu-

tion. The force balance between the solid and gas phases is a particular illustration of the

importance of the interphase rules in determining the efficiency of biohydrogen produc-

tion. The nature of gas bubble formation influences velocity distribution and hence sludge

particle movement. The model demonstrates a qualitative relationship between hydro-

dynamics and biohydrogen production, implying that controlling hydraulic retention time

is a critical factor in biohydrogen-production.

ª 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.

1. Introduction [7–13]; however, the physical characteristics of biohydrogen

Fossil fuels are a finite resource that are being used at an

increasing rate, and there are well known environmental

problems associated with fossil fuel combustion [1]. Hydrogen

gas is noted as a valuable and clean energy source, and is

widely used in numerous industries [2,3]. Biological hydrogen

production through fermentation of organic waste has

attracted considerable attention as an effective production

route because of its high hydrogen production rate coupled

with the possibility for reducing environmental organic

pollution [4–6]. There have been many publications on both

biohydrogen-producing mechanisms and microorganisms

6282193.Ding).sor T. Nejat Veziroglu. Pu

production reactors are not so well described.

Understanding how a bioreactor functions is a necessary

precursor for successful reactor design and operation. For

a chosen bioprocess, the bioreactor should provide optimum

conditions of shear stress, mass transfer, mixing, control of pH,

temperature and substrate concentrations [14]. Various types

of biohydrogen production reactors have been designed and

developed [15–20], but most of these reactors were designed by

means of semi-empirical correlations, and quantitative

studies on the fluid dynamics of such reactors are scarcely

reported. Velocity fields, turbulent intensity and volume frac-

tion of multi-phases in the reactor, biomass activity, intrinsic

blished by Elsevier Ltd. All rights reserved.

mailto:[email protected]

http://www.elsevier.com/locate/he

Notation

CD drag coefficient, dimensionless

d diameter, m

k turbulent kinetic energy, m2 s�2

p model evaluated parameters or pressure,

dimensionless or Pa

Re Reynolds number, dimensionless

u superficial velocity, m h�1

l volume fraction, dimensionless

3 dissipation rate, m s�3

m viscosity accounting for turbulence, Pa s

mt turbulent viscosity, Pa s

r density, kg m�3

s standard deviation, mg L�1

sk turbulent Prandtl numbers for k, dimensionless

s3 turbulent Prandtl numbers for 3, dimensionless

Subscripts

ef effective value

L liquid

G gas

S solid

k physical quantity related to phase

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n e n e r g y 3 4 ( 2 0 0 9 ) 9 6 8 6 – 9 6 9 5 9687

kinetics and size distributions, compressibility and activated

sludge sedimentation, all of which influence microbial ecology,

have been neglected [21–25].

When analyzing bioreactor fluid dynamics, the use of

single experimental techniques is highly time-consuming and

restricted by the limitations of even high-end measuring

equipment. Additionally, such measurements often cannot

satisfy current research requirements understanding fluid

dynamics at the micro-level. Computational fluid dynamics

(CFD) is becoming more widely available to analyze the

characteristics of a bioreactor [26–30]. For example, Milewska

and Molga [31] applied CFD to simulate accidents in industrial

batch stirred tank reactors. In the study, they found that even

for a reactor operating at potentially safe conditions, a failure

of the stirring system can lead to serious thermal runaway.

Murthy et al. [32] used CFD simulation to predict the critical

impeller speed for solid suspension in a gas–liquid–solid stir-

red reactor.

In the light of the above, an attempt was made to use CFD

simulation to analyze hydrodynamics information in an

expanded granular sludge bed (EGSB) reactor used for bio-

hydrogen production. With the help of this model, the

hydrodynamics of the EGSB reaction zone at various condi-

tions of HRT were simulated. The flow fields, i.e., velocity

distribution and volume fraction, were simulated for different

HRTs and these results were used to improve the reactor

design and to optimize reaction conditions.

2. Materials and methods

2.1. Description of the laboratory-scale H2-producingEGSB

A sequence of H2-production experiments were carried out in

a Plexiglas EGSB reactor with an internal diameter of 6 cm and

a height of 120 cm (Fig. 1). The reactor has a working volume of

3.35 L and was operated at 35� 1 �C.

The EGSB reactor was seeded with a mixture of activated

sludge taken from bed mud from a domestic wastewater

discharge channel and sludge from an acidogenic reactor

working as part of the wastewater treatment facilities in

a local pharmaceutical factory (Harbin, China). The final ratio

of mixed liquor volatile suspended solids (MLVSS) to mixed

liquor suspended solids (MLSS) was 0.68 in the inoculating

sludge, based on a MLVSS of 8.49 g/L. Detailed information

about the start-up and steady-state performance of this

H2-producing EGSB reactor are described elsewhere [18].

2.2. Computational fluid dynamics model

2.2.1. Eulerian–Eulerian modelIn this paper, a two-dimensional Eulerian–Eulerian three-

phase fluid model has been employed to describe the flow

behavior of each phase, so the H2 gas, wastewater and sludge

granules are all treated as different continua, with wastewater

as the primary phase, and the gas & sludge granules as the

secondary phase. This model was chosen because of the large

number of gas bubbles and granular particulates [33–35]. Each

phase is presumed to be incompressible in this study. It is

possible to make this assumption about the gas phase, as this

phase is present as very small bubbles which, as a result of

their size do exhibit incompressible behavior. The wastewater

was regarded as pure water, with a density of 1050 kg/m3 at

37 �C. The sludge granules took up about 35% of the volume in

the bed region and were considered to be 1 mm-diameter

spherical solid granules with a density of 1460 kg/m3. The

hydrogen gas was assumed to have a density of 1.225 kg/m3.

The gas phase volume fraction was related to gas production

and the gas bubbles were assumed to have a diameter of

0.1 mm.

2.2.2. Governing equationsThe mass and momentum conservation equations are solved

in a computational two-dimensional mesh (Fig. 2). The pres-

sure field was assumed to be shared by the three phases, in

proportion to their respective volume fractions. The motion of

each phase is governed by respective mass and momentum

conservation equations.

The continuity equation for each phase is

vðrklkÞvt

þ VðrklkukÞ ¼ 0 (1)

where rk is the density of phase k, lk is the volume fraction of

phase k, and uk is the velocity vector of phase k.

If it is assumed that each phase is incompressible, equation

(1) can be simplified to:

VðrklkukÞ ¼ 0 (2)

Fig. 1 – Schematic diagram of the EGSB reactor.


The momentum balance equation for each phase is

vðrLlLuLÞvt

þ VðrLlLuLuLÞ ¼ �lLVpþ V�lLmef ;L

�VuL þ

�VuL

�T��þ rLlLg�MI;LG (3)

vðrSlSuSÞvt

þ VðrSlSuSuSÞ ¼ �lSVpþ V�lSmef ;S

�VuS þ

�VuS

�T��þ rSlSg�MI;LS (4)

vðrGlGuGÞvt

þ VðrGlGuGuGÞ ¼ �lGVpþ V�lGmef ;G

�VuG þ

�VuG

�T��þ rGlGg�MI;LG

(5)

where p is the pressure, mef is the effective viscosity, g is the

gravitational acceleration, and MI is the interphase transfer

force.

The volume fractions satisfy the compatibility conditions

Xn

k¼1

lk ¼ lL þ lS þ lG ¼ 1: (6)

2.2.3. Interphase momentum transferIn this paper, the drag forces exerted by the solid phase and

gas phase on the liquid phase are calculated as:

MD;LG¼34

CD;LG

dGrLlGjuG � uLjðuG � uLÞ (7)

MD;LG¼34

CD;LG

dGrLlGjuG � uLjðuG � uLÞ (8)

where CD is the drag coefficient and d is the diameter of a gas

bubble (dG) or a solid particle (dS).

The drag coefficient CD, LG exerted by the gas phase on the

liquid phase is obtained by the Schiller and Naumann drag

model [36], as follows:

CD;LG ¼(

24ð1þ0:15ð1�lGÞRe0:687Þð1�lGÞRe ð1� lGÞ�2:65

0:44

ð1� lGÞRe � 1000ð1� lGÞRe > 1000

(9)

where Re is the relative Reynolds number, which is obtained

from:

Re ¼ rLdGjuG � uLjmL

(10)

The drag coefficient exerted by the solid phase on the liquid

phase, CD, LS, is calculated from the Wen and Yu drag model

[36], which is as follows:

CD;LS ¼24

lSRe

h1þ 0:15ðlSReÞ0:687

il�0:265

S (11)

The relative Reynolds number for the liquid phase and the

solid phase is estimated as follows:

Fig. 2 – Two-dimensional computational domain.


Re ¼ rLdSjuS � uLjmL

(12)

The lift force acting perpendicular to the direction of the

relative motion of the solid and gas phase is given by:

Fig. 3 – Experimental and simulated values of the water velocity

2 h; C. HRT 4 h; D.HRT 6 h.

ML;LG ¼ CLrLlGðuG � uLÞ � ðV� uLÞ (13)

ML;LS ¼ CLrLlSðuS � uLÞ � ðV� uLÞ (14)

where CL is the lift coefficient and has a value of 0.5.

2.2.4. Turbulence closureWe assume that a standard k–3 model for single phase flows

(with extra terms that include interphase turbulent

momentum transfer [37]) can take into account the effects of

turbulence. The modeling of multiphase turbulent flows is

much more complex and computationally expensive for three

phase flows mainly because of the influence of the secondary

phases on the turbulence of the primary phase [32]. Therefore,

we have assumed for our three-phase CFD turbulence model,

that turbulence in a multiphase fluidized bed is restricted to

the primary phase.

The turbulence viscosity of the continuous phase is

obtained by the k-3 turbulence model:

mt;L ¼ CmrL

�kL

3L

�(15)

The equations of change for the turbulent kinetic energy (k)

and the energy dissipation rate for the primary phase are

given by

DlLrLkL

Dt¼ V

�lL

�mþ

mt;L

skL

�VkL

�þ lLrL

�pkL � 3L

�þ lLrL

YkL

(16)

DlLrL3L

Dt¼V

�lL

�mþ

mt;L

s3L

�V3L

�þlLrL

�C31pkL�C323L

�þlLrL

Y3L

(17)

in the reaction zone at different HRTs: A. HRT 1 h; B. HRT

Fig. 4 – Water-velocity vectors of H2-production reaction zone: A. HRT 1 h; B. HRT 1.5 h; C. HRT 2 h; D.HRT 3 h; E. HRT 4 h;

F. HRT 6 h.


Here,Q

kL represents the influence of the secondary phase on

the primary phase and the predictions for turbulence quan-

tities for the dispersed, andQ

3L represents the predictions for

turbulence quantities for the secondary phases, which are

both obtained using the Tchen theory of the dispersion of

discrete particles by homogeneous turbulence [35]. Standard

values were used for the turbulence parameters: C31¼ 1.44,

C32¼ 1.92, Cm¼ 0.09, sk¼ 1.0, s3¼ 1.3.

2.2.5. Numerical solutionIn this paper, we have analyzed the reaction zone of our EGSB

reactor. The simulated reaction zone was 120 cm tall and 6 cm

diameter. The meshes were created in the Ansys Fluent

GAMBIT preprocessor program and exported into the Ansys

Fig. 5 – Local enlargement – water-velocity vectors: M.

Fluent 6.3 CFD Flow Modeling Software package to solve the

continuity and momentum equations.

The simulation results vary little with grid density so

truncation errors in the numerical simulation can be

neglected. An analysis independent of grid was performed

to eliminate errors in simulation accuracy, numerical

stability, convergence and computational step related to

grid coarseness. The grid independent analysis was done

with a maximum cell density of 12382, through the selected

grid number of 5544 cells, to a minimum of 968 cells. When

the optimum cell number was used, the difference in pres-

sure drop was below 3%. Therefore, a two-dimensional

computational domain of the reaction zone of the EGSB

reactor was devised with 5544 cells, 11314 faces, and 5771

enlargement E of Fig. 4; N. enlargement F of Fig. 4.

Fig. 6 – Contours of water velocity magnitude of H2-produciton reaction zone: A. HRT 1 h; B. HRT 1.5 h; C. HRT 2 h; D. HRT 3 h;

E. HRT 4 h; F. HRT 6 h.


nodes (see Fig. 1). The initial sludge bed was packed with

granular solids with a volume fraction of 0.55. The initial

hydrogen gas phase was assumed to be accumulated into

the sludge blanket, and it was further assumed that the gas

would be released when force balance between gas phase

and solid phase broke up. The reactor wastewater inlet was

modeled with a velocity-inlet boundary condition. The

outlet was set as a pressure outlet boundary condition. All

other solid surfaces were defined by wall boundary condi-

tions with free slip for the biogas and no slip for the sludge

or wastewater. In this study, the simulation was operated in

steady state conditions. The convergent solution was

defined as the solution for which the normalized residual for

all variables was less than 1� 10�3 and the calculated

outflow rate had reached a constant value. Convergence was

typically reached after 350 iterations. All the simulations

were carried on a computer with a 32 bit processor (Intel�

Core� 2 Duo CPU T9300) with 2GB of random access

memory, run at a clock speed of 2.50 GHz.

Fig. 7 – Contours of sludge volume fraction: A. HRT 1 h; B. H

2.3. Tracer experiment

Velocity patterns at four different HRTs were obtained from

tracer experiments using Particle Image Velocity measure-

ments. For the each test, a pulse micro glass beads was

injected as a tracer into the reactor input stream. A planar

cross section of the flow was illuminated with a sheet light

source, and an image was formed of the tracer particles. The

measurement of velocity was based on the displacement of

the tracer particles during a given time interval.

3. Results and discussion

3.1. Model validation and error analysis

As far as the CFD model validation is concerned, Fig. 3 pres-

ents a comparison between the experimentally measured and

the simulated values of the water velocity in the reaction zone

RT 1.5 h; C. HRT 2 h; D. HRT 3 h; E. HRT 4 h; F. HRT 6 h.

Fig. 8 – Contours of H2-gas volume fraction: A. liquid up-flow velocity of 6 m/h; B. liquid up-flow velocity of 4 m/h; C. liquid

up-flow velocity of 3 m/h; D. liquid up-flow velocity of 2 m/h; E. liquid up-flow velocity of 1.5 m/h; F. liquid up-flow velocity

of 1 m/h.


at different HRTs showing good agreement between measured

and predicted values. The relative error between measured

and simulated data was within 10% indicating that the model

provides a good overall description of reaction zone behavior.

The errors incurred in calculation derived from several

sources, such as geometry meshing, numerical treatment of

initial and boundary conditions, and incomplete iteration.

With regard to geometry meshing, the grid selected was

considered to be grid independent, thus related influences

such as truncation errors can be ignored. Adopting an iterative

approach, discrete equations cannot satisfy absolute conver-

gence, and the iterative process will only stop when a certain

condition occurs, for example, the normalized residual for all

variables. This is why we see incomplete iterations. However,

it should be noted that in the study, the inaccuracy due to

incomplete iteration was less than 1� 10�3. The definition of

initial values and boundary treatments are the most signifi-

cant (and complex) steps in the setup of the numerical

calculation. To obtain a high accuracy simulation, the virtual

conditions should be suitably simplified in addition to

selecting the right parameters. Errors incurred at this stage are

difficult to analyze and quantify, and require further exami-

nation, to be described elsewhere.

Table 1 – Operation strategy of the EGSB reactor.

Operating time (d) OLR (kg COD/m3 d) HRT (h) COD (mg/L)

1–6 8 6 2000

7–13 12 4 2000

14–20 24 4 4000

21–27 36 4 6000

28–35 48 4 8000

36–42 96 2 8000

43–51 192 1 8000

52–59 96 2 8000

60–150 120 2 10,000

3.2. Hydrodynamics analysis

In a biohydrogen production reactor, liquid flow pattern,

sludge activity, and mass balance analysis are interdepen-

dent. Highly reactive sludge and long hydraulic retention

times (HRT) are required to obtain a good mass balance.

A good mass balance results in more organics being trans-

ferred to biogas, and a good flow regime is required for effi-

cient contact between microbes and wastewater, which in

turn supplies a good growing environment for the culture. To

obtain hydrodynamic information from the reaction zone of

the EGSB reactor, six steady state simulations, at HRTs of 1,

1.5, 2, 3, 4, 6 hours (assuming uniform recycle ratio, and HRT

transformed to inlet up-flow velocity) were conducted.

Figs. 4–6 present the water velocity in the reaction zone of

an H2-production EGSB reactor with various HRTs. When the

influent runs up through the bottom of the sludge bed at

a fixed up-flow speed, and bed expansion is observed due to

the low-density of the sludge granules. Due to variation with

time of the path tortuosity and consequent length of passage

through the particle interstices of the sludge blanket, and the

random geometry of the granules and impurities, the flow

regime is not homogeneous in the direction of flow. In addi-

tion, it is assumed that gas bubbles produced by biodegrada-

tion or biofermentation, such as methane, carbon dioxide and

hydrogen, will flow upwards with the stream swirl. This

movement will make the bulk up-flow velocity greater than

the up-flow velocity of liquid alone, and the cross-sectional

velocity will be non-homogeneous.

When the HRT is long, and liquid up-flow velocity is low,

lateral pulsating movement will take place as the fluid

elements flowing through sludge blanket take the paths of

least resistance around individual sludge granules. As can

been seen (Fig. 5), internal circulation behavior occurs, and the

wastewater is not distributed very evenly. The core-annulus

structure occurs because the water velocities in the core

region are much lower than those in the annulus region,

whereas because water velocities near the wall are increasing

Fig. 9 – Hydrogen production rate, hydrogen yield and OLR variation in the EGSB reactor.


and upward, this may lead to back-mixing and internal

circulation behavior. Similar results have been obtained by

other researchers [38–40]. When the liquid flows upward with

a moderate velocity to obtain an appropriate HRT (see

Fig. 4B–D), axial pulsation and disturbances between granules

of sludge are small, but compared with the velocity of lower

fluid levels, the turbulent motion and velocity distribution are

greater. When the HRT is short (see Fig. 4A), an increasing

distance between individual sludge granules results due to

the high liquid up-flow velocity, which in turn decreases the

lateral pulsating of the liquid. Additionally because of the

lifting action of the biogas, the flow pattern will be in a highly

turbulent state.

Another important reactor hydrodynamic characteristic,

influencing the process of biohydrogen production is the

relative volume fraction of the sludge and the gas phases.

Sludge bed expansion and the existence of sludge floccs,

greatly change the nature of the interaction between the

liquid and gas phases. This interaction plays a significant role

in the expanded sludge bed reactor [38]. When the mean

sludge concentration is significantly higher than local sludge

concentrations; this indicates that the particles are aggre-

gating into clusters. Fig. 7 shows the sludge volume fraction at

six different simulation states. The values of the averaged bed

Fig. 10 – Hydrogen production rate, MFR and HRT variation

in the EGSB reactor.

heights were estimated as ranging between 0.41 and 0.44 m,

indicating that the bed expansion was about 8%w10%,

reasonably agreeing with experimental results [18]. The

simulation indicated that the gas–liquid–solid system exhibits

a more heterogeneous structure, with particle clusters form-

ing and dissolving dynamically. In the numerical simulation,

clusters could be seen to fall along the wall, stacking together

into agglomerated clusters which ultimately become large

enough to escape from the wall. Particles are dynamically

squeezed out of these clusters and pushed upward by the up-

flowing biogas, and then these particles are further aggregated

into strands in an upper section of the bed. This cluster

formation, which may involve gas–sludge, sludge–sludge, and

sludge–wall interactions, is currently too complex to be well

understood. To discover the rules of interphase interaction,

such as force balance between sludge particles and gas

bubbles, is crucial to the understanding of biohydrogen

production, because gas release from the sludge blanket will

occur only when the force balance between particles and

bubbles is disrupted. It should be noted that work described in

this paper on solid phase simulation, only describes stable

conditions. Certain details about the movement of sludge

particles during changes in process conditions, such as wash-

out, have not been addressed; however, these problems could

no doubt be solved by further research. Fig. 8 presents the

biogas volume fraction predicted by CFD simulation; the

simulation results demonstrate that gas bubbles are an

important phenomenon. The motion of the biogas bubbles is

both upward and transverse. Sludge particle clusters were

dragged transversely, following the biogas bubbles. This

behavior could lead to wash-out, and a lower biomass with

a consequent reduction in biohydrogen yield. Bubble move-

ment, bursting, and size change are key factors affecting

particle circulation and cluster formation in the reactor. We

intend to further extend and validate our work by comparing

our modeling results with experimentation.

3.3. Hydrogen production analysis

The strategy of operation for hydrogen production using the

laboratory-scale H2-producing EGSB reactor (more completely

described by Guo et al. [18]) is shown in Table 1. Fig. 9 shows

hydrogen production rate, hydrogen yield and Organic Loading


Rate (OLR) variation in the EGSB reactor. Fig. 10 shows a rela-

tionship between HRT and biohydrogen production rate in an

experiment where inlet chemical oxygen demand was kept

constant and HRT was varied, and a simulated relationship

between HRT and the outlet hydrogen mass flow rate (MFR).

From our experimental data we see that HRT, being related

to water up-flow velocity, affects biohydrogen production.

When the HRT exceeds 2 hours the hydrodynamic behavior

occurring is suitable for biohydrogen production. This is

because (see section 3.2), this behavior gives an appropriate

velocity distribution to maximize interphase interaction. By

integrating this information with the previous simulation

results, a qualitative relationship between hydrodynamics

and biohydrogen production can be obtained. Although

uniformity of velocity distribution in the reactor is improved

when the HRT is reduced, the hydrogen yield does not

continually increase. Short HRTs need a high liquid up-flow

velocity, although this generates better velocity distribution,

the consequent strong turbulence causes damage to the

microorganisms and sludge-flocs, reducing biohydrogen

production. Despite being essentially a hydrodynamic study,

our work clearly indicates that choosing an appropriate HRT is

extremely important to optimized biohydrogen production.

4. Conclusions

We have performed a two-dimensional CFD simulation of

a gas–liquid–solid three-phase flow in the reaction zone of

a laboratory-scale EGSB reactor producing biohydrogen. To

evaluate the role of hydrodynamics in the design of a reactor,

CFD simulations have been carried out over a range of HRT.

The results showed that the gas–liquid–solid system in an

EGSB reactor displayed significant variation in heterogeneous

structure particularly when the HRT was extended. The

typical core-annulus structure developed has lower water

velocities in the core region than those in the annulus. Water

velocities near the wall are increasing and upward; this may

lead to the back mixing and internal circulation behavior

which is detrimental to velocity distribution. The rules of

interphase, such as force balance between solid and gas, are

known to have a direct bearing on biohydrogen-gas release,

and there is a need for better understanding to optimize bio-

hydrogen production. Bubble movement, bursting, and size

change are key factors affecting velocity distribution and

sludge particle movement in the reactor. Therefore, recording

and analyzing bubble information in transient condition

models are likely to be a fruitful area for further research.

Experimental data from biohydrogen production integrated

with our simulation indicate that a qualitative relationship

between hydrodynamics and biohydrogen production can be

obtained. These results indicate that an appropriate HRT is

crucial to optimized biohydrogen production.

Acknowledgments

The authors would like to thank the National Natural Science

Fund of China (No. 30870037), the National Natural Science

Key Fund of China (No. 50638020), the Provincial Youthful

Science Foundation of Heilongjiang (Grant No. QC06C036), and

State Key Laboratory of Urban Water Resource and Environ-

ment, Harbin Institute of Technology (2008QN02) for their

supports for this study.

r e f e r e n c e s

[1] Liu XM, Ren NQ, Song FN, Yang CP, Wang AJ. Recentadvances in fermentative biohydrogen production. Progressin Natural Science 2008;18(3):253–8.

[2] Kapdan IK, Kargi F. Bio-hydrogen production from wastematerials. Enzyme and Microbial Technology 2006;38(5):569–82.

[3] Demirbas A. Progress and recent trends in biofuels. Progressin Energy and Combustion Science 2007;33(1):1–18.

[4] Han SK, Shin HS. Biohydrogen production by anaerobicfermentation of food waste. International Journal ofHydrogen Energy 2004;29(6):569–77.

[5] Hawkes FR, Dinsdale R, Hawkes DL, Hussy I. Sustainablefermentative hydrogen production: challenges for processoptimisation. International Journal of Hydrogen Energy 2002;27(11–12):1339–47.

[6] Guo WQ, Ren NQ, Chen ZB, Liu BF, Wang XJ, Xiang WS, et al.Simultaneous biohydrogen production and starchwastewater treatment in an acidogenic expanded granularsludge bed reactor by mixed culture for long-term operation.International Journal of Hydrogen Energy 2008;33(24):7397–404.

[7] Ren N, Wang X, Xiang W, Lin M, Li J, Guo W. Hydrogenproduction with high evolution rate and high yield byimmobilized cells of hydrogen-producing bacteria strain B49in a column reactor. High Technology Letters (EnglishLanguage Edition) 2002;8(4):21–5.

[8] Khanal SK, Chen WH, Li L, Sung SW. Biohydrogen productionin continuous-flow reactor using mixed microbial culture.Water Environment Research 2006;78(2):110–7.

[9] Wang AJ, Ren NQ, Shi YG, Lee DJ. Bioaugmented hydrogenproduction from microcrystalline cellulose using co-culture –Clostridium acetobutylicum X-9 and Etilanoigenens harbinenseB-49. International Journal of Hydrogen Energy 2008;33(2):912–7.

[10] Ren NQ, Lin HL, Zhang K, Zheng GX, Duan ZJ, Lin M. Cloning,expression, and characterization of an acetate kinase froma high rate of biohydrogen bacterial strain Ethanoligenens sphit B49. Current Microbiology 2007;55(2):167–72.

[11] Ren NQ, Liu BF, Ding J, Xie GJ. Hydrogen production with R.faecalis RLD-53 isolated from freshwater pond sludge.Bioresource Technology 2009;100(1):484–7.

[12] Ren NQ, Xing DF, Rittmann BE, Zhao LH, Xie TH, Zhao X.Microbial community structure of ethanol type fermentationin bio-hydrogen production. Environmental Microbiology2007;9(5):1112–25.

[13] Xing DF, Ren NQ, Gong ML, Li JZ, Li QB. Monitoring ofmicrobial community structure and succession in thebiohydrogen production reactor by denaturing gradient gelelectrophoresis (DGGE). Science in China Series C-LifeSciences 2005;48(2):155–62.

[14] Xia JY, Wang SJ, Zhang SL, Zhong JJ. Computationalinvestigation of fluid dynamics in a recently developedcentrifugal impeller bioreactor. Biochemical EngineeringJournal 2008;38(3):406–13.

[15] Lee KS, Wu JF, Lo YS, Lo YC, Lin PJ, Chang JS. Anaerobichydrogen production with an efficient carrier-inducedgranular sludge bed bioreactor. Biotechnology andBioengineering 2004;87(5):648–57.


[16] Chang JS, Lee KS, Lin PJ. Biohydrogen production with fixed-bed bioreactors. International Journal of Hydrogen Energy2002;27(11–12). PII S0360-3199(02) 00130-1.

[17] Chang FY, Lin CY. Biohydrogen production using an up-flowanaerobic sludge blanket reactor. International Journal ofHydrogen Energy 2004;29(1):33–9.

[18] Guo WQ, Ren NQ, Wang XJ, Xiang WS, Meng ZH, Ding J,et al. Biohydrogen production from ethanol-typefermentation of molasses in an expanded granular sludgebed (EGSB) reactor. International Journal of HydrogenEnergy 2008;33(19):4981–8.

[19] Mohan SV, Babu VL, Sarma PN. Anaerobic biohydrogenproduction from dairy wastewater treatment in sequencingbatch reactor (AnSBR): effect of organic loading rate. Enzymeand Microbial Technology 2007;41(4):506–15.

[20] Zhang ZP, Tay JH, Show KY, Yan R, Liang DT, Lee DJ, et al.Biohydrogen production in a granular activated carbonanaerobic fluidized bed reactor. International Journal ofHydrogen Energy 2007;32(2):185–91.

[21] Chisti MY, Mooyoung M. Hydrodynamics and oxygen-transfer in pneumatic bioreactor devices. Biotechnology andBioengineering 1988;31(5):487–94.

[22] Tardieu E, Grasmick A, Geaugey V, Manem J. Hydrodynamiccontrol of bioparticle deposition in a MBR applied towastewater treatment. Journal of Membrane Science 1998;147(1):1–12.

[23] Allen DG, Robinson CW. Hydrodynamics and mass-transferin Aspergillus-niger fermentations in bubble column andloop bioreactors. Biotechnology and Bioengineering 1989;34(6):731–40.

[24] Jin B, Lant P. Flow regime, hydrodynamics, floc sizedistribution and sludge properties in activated sludge bubblecolumn, air-lift and aerated stirred reactors. ChemicalEngineering Science 2004;59(12):2379–88.

[25] Krishna R, van Baten JM, Urseanu MI. Scale effects on thehydrodynamics of bubble columns operating in thehomogeneous flow regime. Chemical Engineering &Technology 2001;24(5):451–8.

[26] Lee KS, Lin PJ, Chang JS. Temperature effects on biohydrogenproduction in a granular sludge bed induced by activatedcarbon carriers. International Journal of Hydrogen Energy2006;31(4):465–72.

[27] Ferng YM, Su A. A three-dimensional full-cell CFD modelused to investigate the effects of different flow channeldesigns on PEMFC performance. International Journal ofHydrogen Energy 2007;32(17):4466–76.

[28] Hawkes G, O’Brien J, Stoots C, Hawkes B. 3D CFD model ofa multi-cell high-temperature electrolysis stack.

International Journal of Hydrogen Energy 2009;34(9):4189–97.

[29] Nagarajan V, Ponyavin V, Chen Y, Vernon ME, Pickard P,Hechanova AE. CFD modeling and experimental validation ofsulfur trioxide decomposition in bayonet type heatexchanger and chemical decomposer for different packedbed designs. International Journal of Hydrogen Energy 2009;34(6):2543–57.

[30] Rismanchi B, Akbari MH. Performance prediction of protonexchange membrane fuel cells using a three-dimensionalmodel. International Journal of Hydrogen Energy 2008;33(1):439–48.

[31] Milewska A, Molga EJ. CFD simulation of accidents inindustrial batch stirred tank reactors. Chemical EngineeringScience 2007;62(18–20):4920–5.

[32] Murthy BN, Ghadge RS, Joshi JB. CFD simulations of gas-liquid-solid stirred reactor: prediction of critical impellerspeed for solid suspension. Chemical Engineering Science2007;62(24):7184–95.

[33] Bin Y, Zhang MC, Dou BL, Song YB, Wu J. Discrete particlesimulation and visualized research of the gas–solid flow inan internally circulating fluidized bed. Industrial &Engineering Chemistry Research 2003;42(1):214–21.

[34] He YR, Lu HL, Sun QQ, Yang LD, Zhao YH, Gidaspow D, et al.Hydrodynamics of gas–solid flow around immersed tubes inbubbling fluidized beds. Powder Technology 2004;145(2):88–105.

[35] Ren TT, Mu Y, Ni BJ, Yu HQ. Hydrodynamics of upflowanaerobic sludge blanket reactors. Aiche Journal 2009;55(2):516–28.

[36] Diez L, Zima BE, Kowalczyk W, Delgado A. Investigation ofmultiphase flow in sequencing batch reactor (SBR) by meansof hybrid methods. Chemical Engineering Science 2007;62(6):1803–13.

[37] Elghobashi SE, Abouarab TW. A 2-equation turbulencemodel for 2-phase flows. Physics of Fluids 1983;26(4):931–8.

[38] Dou BL, Dupont V, Williams PT. Computational fluiddynamics simulation of gas–solid flow during steamreforming of glycerol in a fluidized bed reactor. Energy &Fuels 2008;22(6):4102–8.

[39] Sun J, Battaglia F. Hydrodynamic modeling of particlerotation for segregation in bubbling gas-fluidized beds.Chemical Engineering Science 2006;61(5):1470–9.

[40] Huilin L, Gidaspow D, Bouillard J, Wentie L. Hydrodynamicsimulation of gas–solid flow in a riser using kinetic theory ofgranular flow. Chemical Engineering Journal 2003;95(1–3):1–13.

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CFD simulation of an expanded granular sludge bed (EGSB) reactor for biohydrogen production