Bioresource Technology 101 (2010) 7005–7013

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Bioresource Technology

journal homepage: www.elsevier .com/locate /bior tech

CFD optimization of continuous stirred-tank (CSTR) reactorfor biohydrogen production

Jie Ding a,*, Xu Wang a, Xue-Fei Zhou b, Nan-Qi Ren a,**, Wan-Qian Guo a

a State Key Laboratory of Urban Water Resource and Environment (HIT), Harbin Institute of Technology, Harbin 150090, Chinab State Key Laboratory of Pollution Control and Resource Reuse Research, Tongji University, Shanghai 200092, China

a r t i c l e i n f o

Article history:Received 4 February 2010Received in revised form 23 March 2010Accepted 30 March 2010Available online 27 April 2010

Keywords:Biohydrogen productionContinuous stirred-tank reactors (CSTR)Computational fluid dynamics (CFD)HydrodynamicsReactor design

0960-8524/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.biortech.2010.03.146

* Corresponding author. Tel./fax: +86 0 451 862821** Corresponding author. Tel./fax: +86 0 451 862821

E-mail addresses: dingjie123@hit.edu.cn (J. Ding), rn

a b s t r a c t

There has been little work on the optimal configuration of biohydrogen production reactors. This paperdescribes three-dimensional computational fluid dynamics (CFD) simulations of gas–liquid flow in a lab-oratory-scale continuous stirred-tank reactor used for biohydrogen production. To evaluate the role ofhydrodynamics in reactor design and optimize the reactor configuration, an optimized impeller designhas been constructed and validated with CFD simulations of the normal and optimized impeller over arange of speeds and the numerical results were also validated by examination of residence time distribu-tion. By integrating the CFD simulation with an ethanol-type fermentation process experiment, it wasshown that impellers with different type and speed generated different flow patterns, and hence offereddifferent efficiencies for biohydrogen production. The hydrodynamic behavior of the optimized impellerat speeds between 50 and 70 rev/min is most suited for economical biohydrogen production.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Hydrogen energy is an obvious candidate as a sustainablereplacement for fossil fuel, as it produces lower emissions in useand is potentially environmentally benign and cleaner than fossilfuels (Ren et al., 1997; Kapdan and Kargi, 2006; Li et al., 2008). Bio-hydrogen production through anaerobic fermentation of organicsubstrates has been extensively researched, as the process canuse materials regarded as pollution to generate hydrogen (Renet al., 2006; Cai et al., 2004; Bhaskar et al., 2008). Although biohy-drogen production is a complex, multiphase chemical, biologicaland physical process with numerous internal interactions betweengas, liquids and solids, present research on biohydrogen produc-tion has focused primarily on the biological and chemical charac-teristics. A number of chemical and biological factors whichaffect the efficiency of hydrogen production, such as fermentationtype, anaerobic fermentation terminal products and the effect ofdifferent substrates, have been investigated (Chang et al., 2002;Lee et al., 2004; Nath and Das, 2004; Guo et al., 2009). Through pre-vious research several fundamental breakthroughs have beenachieved in the understanding of biohydrogen production, includ-ing the isolation of microbial strains with high hydrogen produc-tion capability, identification of high-efficiency and low-cost

ll rights reserved.

93.93.q@hit.edu.cn (N.-Q. Ren).

carbon sources and optimization of the microbial fermentationprocess (Guo et al., 2008a,b; Cao et al., 2010).

In contrast, physical characteristics affecting the efficiency ofbiohydrogen production have received very little attention.Although a variety of laboratory-scale and pilot-scale reactors witheither continuous flow or microbial growth carrier media madefrom foam or plastic have been developed (Kraemer and Bagley,2005; Logan et al., 2002; Gavala et al., 2006), most of these reactorswere designed by semi-empirical correlation. The effect of velocityfields, distributions of shear stresses, turbulent intensity and vol-ume fraction of multiphases that significantly affect the composi-tion of the microbial community, biomass activity and settlingrate of the activated sludge, have largely been ignored (Jin andLant, 2004; Hamzehei and Rahimzadeh, 2009; Meroney andColorado, 2009). Understanding the hydrodynamic phenomena in-volved in biohydrogen production is a necessary precursor toindustrial scale application. To optimize the reactor configurationand therefore improve the performance of a biohydrogen produc-tion reactor, it is essential to develop and apply new methods toenhance our understanding of reactor hydrodynamics.

Modern computational fluid dynamics (CFD) software can pre-dict fluid flow, heat and mass transfer, chemical reactions andother related phenomena by solving a set of appropriate mathe-matical equations, describing these processes as mass, momentum,energy and species balances, methods, which have been widelyand successfully employed in water and wastewater treatmentsystems (Wang et al., 2009; Terashima et al., 2009; Pougatch

Nomenclature

Ci tracer concentration in the effluent at ti (g l�1)DI impeller diameter (m)g gravitational constant (m s�2)h depth of mixture in the reactor (m)k turbulent kinetic energy (m2 s�2)m tracer mass injected (g)n impeller speed (rev)N impeller speed (rev m�1)p pressure(N m�2)Q tracer flux (l s�1)pini initial pressure (N m�2)P power (W)r radial distance (m)Re Reynolds numbert time (s)ti sampling time (s)U vector of velocity (m s�1)

Vtip impeller tip velocity (m s�1)V velocity in reference frame (m s�1)VFa volume fraction of phase aVFb volume fraction of phase bz axial distance (m)e turbulent energy dissipation rate per unit mass (m2 s�3)l molecular viscosity (Pa s)q density of mixture (kg m�3)h dimensionless time, defined in Eq. (14)s the mean residence time (s)ra volume fraction of phase aCD dimensionless drag coefficientsSMSa specified mass sourcesCab mass flow rate per unit volume from phase a to phase bMa interfacial forces acting on phase a due to the presence

of phases b

7006 J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013

et al., 2007) eliminating the requirement for expensive post-con-struction field tests. Furthermore, if performed before constructionthis approach eliminates the painful realization that a system isinefficient after installation.

This paper establishes a computational model of a continuousstirred-tank reactor (CSTR) for biohydrogen production and thesimulation results are validated by examination of residence timedistribution (RTD). The CFD simulation is used to portray hydrody-namics behavior in the reactor, including the velocity field, biogasvolume fraction, turbulence kinetic energy and shear strain rate.Configuration optimization of the reactor is achieved by optimizingthe impeller design. Comparisons were then made between normaland optimized impeller performance and the influence of hydrody-namic behavior on biohydrogen production was predicted andvalidated.

2. Methods

2.1. Reactor configuration and operating conditions

A continuous stirred-tank reactor (CSTR) with a total capacity of17 L was operated in a continuous flow mode for biohydrogen pro-duction (Fig. 1A). Normal molasses, containing about 53% sugars,was diluted by water to a chemical oxygen demand (COD) concen-tration of 3000 mg/L and used as a substrate. The influent substratewas pumped into the reactor continuously from the feed tank andthen mixed with anaerobic-activated sludge by impeller. The shaftof the top-driven impeller was concentric with the axis of the reac-tor. The normal impeller design, with a blade angle of 45� and anexternal diameter of 100 mm (Fig. 1B), was run at varying speedsto get different flow patterns. The optimized impeller has a bladeangle of 45� and an external diameter of 120 mm (Fig. 1C). Fourbaffles were equally placed around the inner tank, each with awidth of 20 mm.

The temperature was maintained at the level of 35 ± 1 �C.NaHCO3 was added to the feed solution to maintain pH at 6.5–7.5 in the influent and hence to keep a pH level of 5.0 in the reactor.The hydraulic retention time (HRT) was 8 h. COD Nitrogen: Phos-phorus ratio in the influent was maintained at an average of250:5:1 by adding synthetic fertilizer in order to supply the micro-organisms with adequate nitrogen and phosphorus. The mixed li-quor volatile suspended solid (MLVSS) in the reactor wasapproximately 13.5 g/L. Different impeller geometries were usedto determine the hydrodynamic effect impeller design on biohy-

drogen production, running each design at impeller speeds of 50,70, 90, 110 and 130 rev/min.

2.2. Analytical methods

The hydrogen fermentation performance was evaluated usingkey parameters, such as pH, oxidation reduction potential (ORP),alkalinity, ethanol concentration, volatile fatty acid (VFAs) distri-bution and biogas production (Lin and Hung, 2008). In this work,temperature, pH, alkalinity, biogas yield and composition weremeasured or monitored daily. Analyses of viscosity, density, totalsuspended- and volatile-suspended solids of mixture were carriedout once a week. These analyses were performed according toappropriate standard methods (APHA, 1998). Biogas yield wasmeasured at room temperature by a wet gas meter (LMF-2, FarAsia Co. Ltd.), while its constituents were analyzed using an on-lineindustrial gas chromatography (RQD-102, Chongqing SichuanInstrument Co. Ltd.) or an outline gas chromatography (SC-7,Shandong Lunan Instrument Factory). The outline gas chromatog-raphy was equipped with a thermal conductivity detector and astainless steel column (2 m � 5 mm) filled with Porapak Q(50–80 meshes). Nitrogen was used as the carrier gas at a flow rateof 40 ml/min. Dose of injected sample was 0.5 ml each time. Basedon the percentage of hydrogen in biogas, the hydrogen yield couldbe calculated. The hydrogen yield could be then calculated basedon the percentage of hydrogen in the biogas.

2.3. Methodology for computational fluid dynamics model generation

2.3.1. Computational fluid dynamics modelThe equations which describe the processes of momentum,

heat and mass transfer are discretized and solved iteratively foreach control volume. As a result, an approximation of the valueof each variable at specific points throughout the domain can beobtained. The turbulence equations were also solved in conjunc-tion with the continuity equation, the Navier–Stokes equation,and the energy equation. According to Meroney and Colorado(2009), the standard k-e model is the most adopted turbulenceclosure because of its simplicity, low computational requirementand good convergence for complex turbulent flows. We assumethat the mixture of substrate and activated sludge in the biohy-drogen production reactor is homogeneous and incompressible,and that the various components of the mixture shared the samemean velocity, pressure and temperature fields. The biogas which

Fig. 1. (A) Schematic diagram of the CSTR biohydrogen production system; schematic diagram of the impeller (B) normal impeller; (C) optimized impeller; (D) mesh layoutfor reactor geometry; and (E) location of planes and lines for evaluation.

J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013 7007

was produced in process of fermentation was introduced into thereactor as additional fluid-mass source from twelve points thatwere distributed over the reacting area of the reactor. It was alsoassumed that biogas bubbles were distributed in the mixture asspherical particles with a mean diameter of 1 mm. From the aboveassumptions, there is one gas phase (biogas) and one liquid phase(mixture) in the reactor, so the simulation can adopt multiphaseflow models. Two distinct multiphase flow models are availablein ANSYS CFX: an Eulerian–Eulerian multiphase model and aLagrangian Particle Tracking multiphase model. In the first ap-proach, the particles are modeled with a certain mass to imposethe momentum exchange at the point locations of the particles.In the second approach, particles are tracked through the flow ina Lagrangian way. In this case, an Eulerian–Eulerian multiphasemodel has been used to describe the flow behavior of each phase,and the gas and liquid phases are treated as different continua,interpenetrating and interacting everywhere in the computationaldomain. The mixture acts on each biogas particle influencing itspath. The biogas particles in turn affect the turbulence quantitiesof the mixture. For each phase, the full set of conservation equa-tions was solved and each phase had a different velocity field.The mechanisms of the interaction of the phases were the flowresistance modeled by momentum transfer, the phase changesmodeled by mass transfer and the heat conduction modeled byenergy transfer (Murthy et al., 2007). Two different sub-models,the homogeneous model and the inhomogeneous model are avail-

able for Eulerian–Eulerian multiphase flow. The inhomogeneousparticle model used in this simulation assumes that the mixtureis continuous (phase a) and the biogas is dispersed (phase b).The momentum equation of the multiphase system is givenbelow:

@

@tðraqaUaÞ þ r � ðraðqaUa � UaÞÞ

¼ �rarpa þr � ðralaðrUa þ ðrUaÞTÞÞ

þXNp

b¼1

ðCþabUb � CþbaUaÞ þMa ð1Þ

where Ma describes the interfacial forces acting on phase a due tothe presence of phase b.

The following general form is used to model interphase dragforces

Ma ¼ cðdÞab ðUb � UaÞ ð2Þ

where coefficient cðdÞab was computed applying the dimensionlessdrag coefficient

CD ¼D

12 qaðUa � UbÞ2A

ð3Þ

cðdÞab ¼CD

8AabqajUb � Uaj ð4Þ

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Continuity equations and conservation equations were de-scribed as follows:

@

@tðraqaÞ þ r � ðraqaUaÞ ¼ SMSa þ

XNp

b¼1

Cab ð5Þ

Xa

1qa

@qa

@tþr � ðraqaUaÞ

� �¼X

a

1qa

SMSa þXNP

b¼1

Cab

!ð6Þ

2.3.2. Computational domain and boundary conditionsThe multiple reference frame (MRF) approach is one of the most

commonly used numerical methods to model stirred reactors. Thismethod is adequate for simulating the flow and mixing generatedby the impellers and benefits from a low total computational time(Dakshinamoorthy et al., 2006; Pramparo et al., 2008). The reactorwas divided into a rotating domain (rotating reference frame) anda stationary domain (stationary reference frame). The boundary ofthe rotating domain comprising the impeller and part of the impel-ler shaft positioned from z = 60 mm to z = 80 mm with r = 70 mm.A MRF with Frozen Rotor interaction scheme was used in the sim-ulations to bond the stationary and the rotating domains. The mix-ture and biogas were used as the domain fluid, and the fluidproperties and other key parameters such as density and dynamicviscosity, were specified according to measurement results. Themultiphase flow was regarded as buoyant and was implementedby a density difference model. The additional buoyancy force wascalculated by considering the difference in the densities betweenphases. The buoyancy reference density was set as that of the lessdense fluid (the biogas).The gravity vector is aligned with the axisof rotation.

Before leaving the tank by the outlet pipe, the mixture over-flows continuously through the baffle at the top of the reactorwhere a free surface was formed. The height of this surface wasused to calculate initial value of volume fraction and the staticpressure

h ¼ 0:26½m� ð7ÞVFb ¼ stepððz� hÞ=1Þ ð8ÞVFa ¼ 1� VFb ð9Þpini ¼ q � g � ðh� zÞ � VFa ð10Þ

The inlet where the influent substrate was pumped into thereactor was modeled with a mass flow rate inlet boundary condi-tion. The turbulence boundary conditions at the inlet were giventhrough the low turbulence intensity (1%). The outlet of the mix-ture was set as a static pressure outlet boundary condition wherethe atmospheric pressure was specified, whereas the outlet ofthe biogas at the top of the reactor was set as the opening bound-ary condition. All other solid surfaces including impeller blades,shaft, baffle and reactor walls were defined by wall boundary con-ditions with free slip for the biogas and no slip for the mixture.Uniform temperature distributions were assumed.

2.3.3. Numerical solutionIn this work, full computational geometry was used for CFD cal-

culations to capture more accurate results from transient charac-teristics. The geometry and the unstructured grid of the reactorused in the experiments were generated by ANSYS ICEM with aset of user-specified mesh characteristics, which enables the finestand coarsest grid to be set up in each coordinate direction, with thegradient of the mesh being refined near solid boundaries (Fig. 1D).A tetra meshing algorithm was used to fill the volume with tetra-hedral elements and to generate a surface mesh on the object sur-faces. The predictions of turbulent quantities are usually quitesensitive to the number of grid nodes used in the solution domain,

so it is very important to use an adequate number of computa-tional cells while the governing equations over the solution do-main are solved numerically. The pressure was selected toconduct the mesh test. The simulation results vary little with griddensity so truncation errors in the numerical simulation can be ne-glected. An analysis independent of grid was performed to elimi-nate errors in simulation accuracy, numerical stability,convergence and computational step related to grid coarseness.The grid independent analysis was done with three different cellnumbers. When the optimum cell number was used, the differencein pressure drop was below 5%, which means the most positiveoutcome. The optimized mesh for the reactor is of 9,433,203 vol-ume elements and 1,618,028 nodes.

To minimize calculation time, the simulations were divided intotwo parts. First, a steady state simulation of the complete turbulentflow field for multiphase in the reactor was carried out. The secondstep was the transient simulation of the RTD with the first step re-sults as initial value input file. For convenient comparison with theRTD experiment results, a tracer transport method was adoptedwhich is generated by a step boundary condition at the inlet, attime zero of the transient run (Moullec et al., 2008). The responseat the outlet boundary condition is monitored.

A commercial computational fluid dynamics (CFD) code,namely ANSYS CFX, was employed to explain the reactor hydrody-namic behavior by numerical methods. The simulations were car-ried out on a PC equipped with an Intel Xeon 2.4 GHz processorand 8 GB RAM. Although the upwind scheme is very robust, it doesintroduce diffusive discretization errors. A high resolution schemewhich is both accurate and bounded was used along with auto-matic timescale control to achieve steady state conditions. In thesteady state simulations stage, the convergence criterion of1.E�4 for the root mean square (RMS) residual target was reachedwithin 400 iterations. The total simulation time for each case wasaround 50 h. For a transient simulation with time steps of 5 min, aconvergence criterion of 1.E�4 for RMS residual target was reachedwithin 600 iterations.

2.4. Trace experiment

Experimental measurements of the flow field are necessary forcalibrating and validating the simulation models. Further refine-ments to the model would be required to improve further theagreement of the simulation results with experimental values. Con-sidering the complex and small scale nature of the structure underexamination, intrusive measurement techniques, such as imped-ance probes, optical fiber probes, ultrasound probes and hot filmanemometry cannot be applied to investigate flow behavior in alaboratory-scale biohydrogen production reactor. The presence ofthe probe will inevitably affect the flow within the boundary layer,which is in turn likely to disturb the flow pattern under consider-ation. Particle image velocimetry (PIV) (Krepper et al., 2008;Darmana et al., 2005), laser doppler anemometry (LDA) (Kulkarniet al., 2007) and tomography (Vesselinov et al., 2008) are widelyused non-intrusive techniques to measure flow fields in transparentfluids. However, measuring opaque multiphase flows in a biohydro-gen production reactor is challenging, since there is no straightfor-ward application of the type widely used for transparentsingle-phase measurement techniques. In this work, RTD was car-ried out to compare experimental with simulation results. Althoughexperimental validation by RTD only is not sufficient for a deepinvestigation of the flow field, RTD is a fundamental parameter inreactor design which can give information on how long thesubstrate has been in the reactor for anaerobic fermentation. Atwo-point detection method was used, allowing the measurementof the concentration evolution at both the inlet and outlet of thereactor. A small quantity of lithium chloride (LiCl) was used as

J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013 7009

tracer and injected by syringe into the inlet tube, simulating a pulsewith minimal disturbance to the flow inside the reactor.

3. Results and discussion

3.1. Model validation

In the RTD experiment, comparison of the obtained inlet andoutlet curves allows an estimation of the mean residence time. Pre-vious results from steady state simulations have been taken as ini-tial conditions for transient RTD simulations at the same impellerspeeds. The results of the experiment and simulation are inter-preted in dimensionless and normalized form. h is the dimension-less time and s is the mean residence time. EðhÞ is normalized RTDfunction

h ¼ ts

ð11Þ

s ¼P

tiCiPCi

ð12Þ

EðhÞ ¼ Qm

CðhÞ ð13Þ

As far as the CFD model verification is concerned, Fig. 2A and Billustrates a comparison between the experimentally measuredand the simulated data of the RTD showing a good level of agree-ment between the measured and predicted values. The varianceof the RTD curve with different impeller at N = 90 rev/min. Thesimulation and experimental results are compared showing thatthe relative error between the measured and simulated data waswithin 20% indicating that the model provides a good overalldescription of reactor behavior.

3.2. Hydrodynamics evaluation

Five steady state simulations, at stirrer speeds from 50 rev/minto 130 rev/min at intervals of 20 rev/min, were conducted for two

Fig. 2. RTD curves with different impeller at N = 90 rev/min (A) normal impeller; (B) optimpeller).

impeller designs. The biogas volume fraction, velocity field, kineticenergy from turbulence and shear strain rates were evaluated incertain lines and planes passing through the reactor. The locationof these lines and planes are shown in Fig. 1E.

Fig. 3 A–D shows the mixture velocity in Reference Frame gen-erated by different impellers at various rotation rates. Using thisvariable instead of mixture velocity results in the velocity vectorsappears to be continuous at the interface between the rotatingand stationary domains. Velocity variables that do not include aframe specification always use the local reference frame. Fig. 3A–D shows the velocity vector over a cross-sectional plane (plane 1)which was placed at the lengthways mid section of the reactor.The mixture flow agitated by the impeller travels upward in the ra-dial direction and then splits into two streams near the wall of thetank. One stream above the impeller creates an upward vortex areanear the surface of mixture; another below the impeller creates alarge downward vortex area towards the bottom of the reactor.The mixture flows up in the inner part of the reactor tank and thenreturns downwards at the periphery, diverted by the separatingbaffle (see Fig. 1A for a diagram of the inner tank and separatingbaffle). The normal impeller (Fig. 3A and B) generates a more pow-erful vortex area near the bottom which suspends more sedimen-tary activated sludge than the optimized impeller, which (Fig. 3Cand D) brings a stronger vortex higher up the reactor, which is di-rectly available for mixing of fermentation substrates and anaero-bic-activated sludge in the top area of the reactor. Vortices are alsoindicating areas of locally higher residence times.

The predicted distributions of turbulent kinetic energy (k) in thebottom region of the reactor (Fig. 3E and F) show that the values ofk in the impeller region were higher than those in the bulk flow re-gion due to large spatial velocity gradients. The energy associatedwith the normal impeller flow is higher than that of the optimizedimpeller flow in the bottom region of the reactor, and so the turbu-lence intensity for the normal impeller is also relatively high in thisregion. Moreover, the increased speed of optimized impeller doesnot have much influence on the turbulence kinetic energy in thebottom of reactor. These simulation results show reasonable

imized impeller); velocity profile of the line (C) normal impeller; and (D) optimized

Fig. 3. Velocity vectors of plane 1 (A) normal impeller with 50 rev/min; (B) normal impeller with 130 rev/min; (C) optimized impeller with 50 rev/min; (D) optimizedimpeller with 130 rev/min; turbulence kinetic energy contour of reactor bottom region (E) normal impeller; and (F) optimized impeller.

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correlation with experimental observation of the sedimentarysludge in the bottom of the reactor. Different flow patterns in the

top area of the reactor (Fig. 4A and B) show the velocity contoursof plane 3 (a cross section in the top of the inner tank) with respect

Fig. 4. Velocity contour of plane 3 (A) normal impeller with different speed; (B) optimized impeller with different speed; shear strain rate contour of plane 2 (C) normalimpeller; and (D) optimized impeller.

J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013 7011

to impeller speed. It can be seen that with an increase in the impel-ler speed, the velocity of the mixture at the top of the reactor hasincreased. In the case of the normal impeller, the stagnation regionis obvious when impeller speed is under 70 rev/min. Uniformity ofvelocity distribution is achieved as the impeller speed increases. Incontrast, optimized impellers can generate better velocity distribu-tions in the top area of the reactor at lower impeller speeds.

Fig. 2C and D shows the velocity profile of line A (see Fig. 1E forLine A). The samples which compose line A are plotted along theinner tank wall of the reactor, with a non-dimensional distancefrom the wall of the samples of 0.3. Velocity distributions are rep-resented by five speeds with a normal impeller (Fig. 2C) and fivespeeds with the optimized impeller (Fig. 2D). The velocities arenormalized using the impeller tip velocity (Vtip). The axial distanceis the non-dimensional distance from the bottom of the inner tank.As can be seen, the normal velocity profile gives a maximum valuein the bottom of the inner tank and declines to minimum valuenear the top, whereas optimized velocity profile gives a maximumvalue above the impeller and not much decrease in the top area.The inner tank is the primary area for the process of anaerobic fer-

mentation, and a well-proportioned velocity value means effectiveuse of the volume of the reactor, therefore, an optimized impelleroffers best efficiency gains.

The magnitude of the fluid hydraulic forces, often expressed asshear stress or shear rate, is another important parameter for bio-reactor design and operation. Areas of high shear strain rate orshear stress are also typically areas where the highest mixing oc-curs, but the associated high shear stress can cause damage tomicroorganism cells and sludge flocs, and should be avoided (Caoand Alaerts, 1995; Luo and Muthanna, 2008). Proper understand-ing of the hydrodynamic shear stress is therefore crucial for thesuccessful design of a biohydrogen production reactor. Fig. 4Cand D shows the shear strain rate contour of plane 2, which is across section taken just above the impeller (see Fig. 1E for plane2). Sudden changes with the optimized impeller speed in the shearstrain rate can be clearly observed in Fig. 4D. The shear strain ratechanges change smoothly with variation in the normal impellerspeed (Fig. 4C).

Another important hydrodynamic characteristic which influ-ences the process of biohydrogen production is the biogas volume

Fig. 6. Average biogas yield and hydrogen yield by normal impeller and optimizedimpeller with different speed.

7012 J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013

fraction in the reactor. The biogas is mainly composed of CO2 andH2 with the percentage of H2 ranging from 35% to 45% and resultsfrom anaerobic fermentation. As it is a product of fermentationmetabolism, any biogas holdup in the mixture will restrain thereaction. Previous study indicates that a great amount of CO2 andVFAs (such as acetic acid, propionic acid, buritic acid, etc.) inhibitthe metabolic activities of anaerobic bacteria. Since H2 is a byprod-uct of fermentation, it is reasonable that H2 production is reducedwhen the metabolic pathways for VFAs and CO2 are inhibited. Con-tours of the biogas volume fraction in the reactor are shown inFig. 5. This picture indicates that the biogas volume fraction in-creased in proportion to the rotational speed of the normal or opti-mized impeller. The optimized impeller generates a higher biogasvolume fraction than the normal impeller at the same speed. Thebuoyancy of the biogas drives the gas flow upwards. With bothimpellers, the biogas volume fraction was reached a higher valuenear the surface and the separating baffle where biogas is sepa-rated from mixture.

3.3. Effect on biohydrogen production

Fig. 6 shows the average biogas yield and hydrogen yield bynormal impeller and optimized impeller with different speedsfrom 50 to 130 rev/min. In a reactor configured with the normalimpeller, the average biogas yield increased rapidly from 11.8 L/dto 26.1 L/d when the impeller speed increased from 50 to 70 rev/min, and reached a peak value 29.2 L/d when the impeller speedwas 90 rev/min. A maximum hydrogen production rate was ob-tained when impeller speed was 110 rev/min. Both average biogasand hydrogen yield decreased when the impeller speed increasedto 130 rev/min. Comparatively, the reactor configured with anoptimized impeller obtained a high biogas yield (24.3 L/d) at a low-er impeller speed and needed less startup time to reach a steadystate. The peak of biogas yield was reached at an impeller speedof 70 rev/min, from then onward the biogas yield decreasedacutely along with the increase in impeller speed. From these re-

Fig. 5. Biogas volume fraction contour of plane 1 (A

sults it is clear that the impeller type and speed affect the processof biohydrogen production. The hydrodynamic behavior of a nor-mal impeller in the speed range between 90 rev/min and110 rev/min is more suitable for biohydrogen production. By inte-grating with the previous results of simulation, a qualitative rela-tionship between hydrodynamics and biohydrogen productioncan be obtained. Although the uniformity of velocity distributionin the reactor is improved, along with the increase of impellerspeed, the average yield of hydrogen does not continually increase.The optimized impeller can better generate a velocity distributionin the reactor at a lower impeller speed, so higher average hydro-gen yield and less startup time are needed compared to a normalimpeller at an impeller speed of 50 rev/min. Shear strain stressand biogas volume fraction also increase with the impeller speed

) normal impeller; and (B) optimized impeller.

J. Ding et al. / Bioresource Technology 101 (2010) 7005–7013 7013

counteracting biohydrogen production, so the average yield ofhydrogen decreases at higher impeller speed. Shear strain stressbrought by optimized impeller increases intensively along withthe increasing stirring speed leading to low biohydrogen produc-tion results. The optimized impeller is not as cost-effective as anormal impeller for hydrogen production at higher speeds. Our re-sults clearly indicate that the hydrodynamics behavior of a biohy-drogen production reactor can affect the chemical–biologicalreactions occurred within and confirm CFD-based software as apowerful tool for the prediction of flow patterns in the reactor.

4. Conclusions

In this paper, three-dimensional CFD simulations of a gas–liquidtwo-phase flow in a laboratory-scale CSTR for biohydrogen produc-tion have been performed. It has been shown that impeller typeand speed significantly affects flow patterns, and thus offer differ-ent optimal efficiencies for biohydrogen production. By integratingthe results of simulations with process experimental observations,it is clearly shown that an optimized impeller can generate bettervelocity distribution in the reactor with lower impeller speed, sohigher average hydrogen yield and less startup time are neededthan a normal impeller when the impeller speed is in the rangefrom 50 to 70 rev/min.

Acknowledgements

The authors would like to thank the National Natural ScienceFund of China (No. 30870037), the National Natural Science KeyFund of China (No. 50638020), the Project 50821002 (NationalCreative Research Groups) supported by National Nature ScienceFoundation of China, the State Key Laboratory of Urban WaterResource and Environment, the Harbin Institute of Technology(2008QN02), the Fundamental Research Funds for the CentralUniversities (Grant No. HIT.NSRIF.2009115), and the DevelopmentProgram for Outstanding Young Teachers in Harbin Institute ofTechnology (HITQNJS.2009.046) for their support of this study.

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