Chemical Engineering Journal 250 (2014) 230–239

Contents lists available at ScienceDirect

Chemical Engineering Journal

journal homepage: www.elsevier .com/locate /ce j

Experimental and numerical investigation of hydrodynamics in racewayreactors used for algaculture

http://dx.doi.org/10.1016/j.cej.2014.03.0271385-8947/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding authors at: INSA, LISBP (Laboratoire d’Ingénierie des SystèmesBiologiques et des Procédés), 135 Avenue de Rangueil, F-31077 Toulouse, France.Tel.: +33 (0)561559774.

E-mail addresses: hreiz.rainier@gmail.com (R. Hreiz), jerome.morchain@insa-toulouse.fr (J. Morchain).

Rainier Hreiz a,b,c,⇑, Bruno Sialve d, Jérôme Morchain a,b,c,⇑, Renaud Escudié d, Jean-Philippe Steyer d,Pascal Guiraud a,b,c

a Université de Toulouse; INSA, UPS, INP; LISBP, 135 Avenue de Rangueil, F-31077 Toulouse, Franceb INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés, F-31400 Toulouse, Francec CNRS, UMR5504, F-31400 Toulouse, Franced INRA, UR0050, LBE, Avenue des Etangs, F-11100 Narbonne, France

h i g h l i g h t s

� The hydrodynamics in a raceway reactor is studied experimentally and numerically.� Effects of the paddlewheel speed and geometry are investigated.� Simulations successfully predict the flow rate and the impeller power consumption.� Mixing rates are related to the flow low-frequency unsteadiness.� Even at moderate intensities, wind has a crucial impact on the hydrodynamics.

a r t i c l e i n f o

Article history:Received 28 October 2013Received in revised form 24 February 2014Accepted 9 March 2014Available online 19 March 2014

Keywords:RacewayMixingTracer pulse injectionCFDSliding-meshPulsed Ultrasonic Doppler Velocimetry

a b s t r a c t

Raceways are nowadays the most used large-scale reactors for microalgae culture. This paper focuses onthe hydrodynamics in such a reactor, and emphasizes on the effects of the paddlewheel geometry (twoimpeller configurations are tested). The global hydrodynamics behavior of the raceway (flow velocity,mixing time) is characterized experimentally by the tracer pulse injection method, and local velocitymeasurements are acquired by Pulsed Ultrasonic Doppler Velocimetry. Finally, the flow is modeled byusing the sliding-mesh CFD technique, a method overcoming many limitations of the simulationapproaches used in the literature. CFD simulations successfully estimate the flow rate in the reactorand the power consumption of the paddlewheel. It is shown that the mixing efficiency in the racewayreactor is closely related to the low-frequency flow unsteadiness that arises from the periodic motionof the blades. Concerning local velocities, CFD and experimental data are in good agreement at some posi-tions in the reactor, but a significant disagreement is observed at some other locations. Taking intoaccount the wind presence in the simulations reduces the discrepancy between the experimental andnumerical results, showing that, even with a moderate intensity, wind has an important effect on thehydrodynamics in the reactor.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

Nowadays, growing concerns about global warming and energyshortage motivate the society to seek renewable and carbon neu-tral fuels as alternatives to fossil fuels. Eukaryotic microalgae andcyanobacteria (to which we will refer collectively by microalgae)

are a potential source of various biofuels [1,2] by virtue of their fastgrowth rate, and their oil yields, substantially higher then terres-trial oil crops, even exceeding 80% of the dry biomass weight undercertain stress conditions [3]. Cultivation of microalgae in wastewa-ter treatment reactors is even interesting from economical andsynergic perspectives [4,5]. Today cultured microalgae are usedfor pharmaceuticals, nutraceuticals, cosmetics, aquaculture andproduction of high value molecules (pigments, stable isotopebiochemicals. . .) [6,7].

Microalgae culture systems can be broadly classified into closedsystems where the culture is enclosed into a translucent vessel,

R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239 231

and open-air systems where the culture medium is directlyexposed to the environment. Open-air cultures are grown outdoorsand utilize sunlight. This is also the case for some closed systems,while others – (referred as photobioreactors) – are operatedindoors with artificial illumination. Photobioreactors allow higherproductivities [1] since the physicochemical environment of theculture is well controlled, light intensity and cycles optimized,and risks of invasion by competing microorganisms minimized.On the other hand, open-air systems are susceptible to location-related variables as weather conditions, and the daily and seasonalvariations in light levels and temperature. However, most commer-cial systems used today are outdoor open-air reactors [7,8]. In fact,photobioreactors are expensive to purchase, maintain and operate,difficult to scale up, and suffer from several drawbacks as over-heating, bio-fouling and toxic oxygen accumulation, while open-air systems are simpler to design, cheap and easy to build andoperate. Among open culture bioreactors, raceway ponds are byfar the most widely used nowadays [9,10].

A raceway is a pond consisting of long channels arranged in aracetrack closed loop, in which the algae growth medium is cycledcontinuously around the central dividing walls by means of a slow-moving paddlewheel (Figs. 1 and 2). The pond is kept shallow, typ-ically 15–40 cm, to ensure an adequate penetration of sunlight inthe liquid.

Fig. 1. Main dimensions of the raceway reactor used in this study. (a) Top view and ponumerical domain.

Liquid in the raceway is mechanically circulated at about0.15–0.4 m/s, which ensures mixing of the culture medium thanksto the turbulence of the flow. Although various devices have beenproposed to circulate and mix the culture medium [9,11,12], pad-dlewheels remain by far the mostly used. In fact, they match wellthe pumping requirements of the ponds as they are high flow rate,low head devices [13]. Moreover, they are mechanically simple andrequire little maintenance, and their gentle mixing minimizesdamage to flocculated or fragile algae. To minimize backflow, thepaddlewheel may sit in an invagination in the pond floor, keepinga small clearance between the blades tip and the bottom of thechannel: the paddlewheel will then behave more as a positivedisplacement pump [13].

Mixing the culture medium is necessary to avoid gradients innutrient concentration, temperature and pH. It also prevents mic-roalgae sedimentation, increases liquid-to-cell mass transfer rates,and enhances algal photosynthetic efficiency. In fact, mixing con-tinuously moves the algal cells between the well-lit zone (closeto the free interface) and the dark zone (bottom of the pond), whatreduces photolimitation and photoinhibition [1]. In the racewaystraight sections, there are no features that disturb the flow, andtherefore, despite turbulent dispersion, mixing is relatively pooras shown experimentally by Mendoza et al. [10]. However, nearthe paddlewheel and in the bends, dispersion rates are important

sitions of the PUDV measurements planes, (b) side view, and (c) side view of the

Fig. 2. Geometry of the paddlewheel: schematic representation and CFD modeling.

232 R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239

[10,11]. Indeed, secondary flows – of Prandtl’s first kind – developin the bends, what enhances macro-mixing.

Thanks to mixing, raceways productivity can be up to ten-foldhigher than that of unmixed ponds [6,14]. However, mixing is ofgreat significance in terms of input energy costs. Chisti [15] esti-mated that 28% of the global energy input to the algal oil produc-tion process is for microalgae cultivation (so mainly for liquidcirculation). According to Neenan et al. [16], mixing accounts formore than 69% of the utilities cost. Hence, reducing head lossesin the raceway is of primary importance in order to make theindustry competitive, particularly if the culture is dedicated to bio-fuel production. Thus, one of the greatest challenges in algacultureis to adequately mix the culture medium with minimum powerconsumption.

The raceway design must address in particular the problem of‘‘dead zones’’ (separated flow) that develop near the middle walldownstream of bends, since they increase energy dissipation andreduce the pond capacity. Moreover, the resulting non-uniformvelocity field leads to uneven cells residence time in the reactor,which is harmful to the pond productivity, especially if the race-way operates in continuous mode. Different strategies have beenproposed to minimize the dead zones extent, among which install-ing flow deflectors, rectifiers or guide vanes, and modifying theisland or the bends design [13,17,18]. Reducing the areas of stagna-tion straightens the flow, what lowers head losses in the reactor[10].

In the context of improving the hydrodynamics in bioreactors,CFD (Computational Fluid Dynamics) is a powerful low-cost toolthat has already proven its efficiency with respect to photobioreac-tors design (see [19] for a review on the topic). In fact, CFD modelsthe spatio-temporal hydrodynamics variations, and hence, itallows characterizing/estimating various key variables that arepractically inaccessible to experimental measurements, as localshear stresses (that above a certain limit lead to shear-inducedinjury of algal cells) or cells individual history. For example, Pern-er–Nochta and Posten [20] simulated microalgae trajectories in atubular photobioreactor to obtain light intensity fluctuations asexperienced by individual cells. Pruvost et al. [21] designed aninnovative reactor with CFD support. Despite these successes, itis only recently that CFD has been applied to raceway ponds, withthe works of Hadiyanto et al. [17] and Liffman et al. [18]. Theseworks focused on the evaluation of different raceway geometriesin terms of flow uniformity and head losses in the reactor (how-ever, none of the numerical results was confronted to experimentaldata). For simplification purposes, in both papers, the free surfacewas considered flat (with a slip boundary condition), and the

paddlewheel was not included in the numerical domain. Liffmanet al. [18] modeled the propulsive thrust effect of the paddlewheelby adding a body force term in the Reynolds equations. Thisapproach had already been used to simulate the flow in aquacul-ture ponds [22,23]. A different modeling strategy was used byHadiyanto et al. [17]. The section of the channel surrounding thepaddlewheel was removed from the numerical domain, and aninlet (downstream of the wheel position) and an outlet (upstreamof the wheel position) boundary conditions were used to generatethe flow. These two modeling approaches are interesting, since theypermit low time-consuming calculations, allowing therefore a rapidevaluation of the performance of various raceway designs. However,both approaches have some major drawbacks, as they cannot captureseveral aspects of the hydrodynamics in the raceway:

� They cannot take into account the paddlewheel geometry, aparameter that however has a crucial effect on the mixingperformances in the reactor. Thus, both methods only considerthe effects of the pond geometry and the liquid flow rate, andcannot be used to study the impact of the impeller or toimprove its design.� They require experimental measurements to be able to repro-

duce given experimental conditions: the flow rate for theLiffman et al. [18] approach, and the paddlewheel mechanicalpower for the Hadiyanto et al. [17] method (so the additionalterm in the Reynolds equations can be calculated).� The Liffman et al. [18] approach fails to reproduce streamlines

straightening immediately downstream of the paddlewheel,while the Hadiyanto et al. [17] method imposes a uniform flowat the inlet plane located downstream of the paddlewheel.� In these two studies, the steady-state equations were solved,

and thus, the flow unsteadiness related to the periodic motionof the blades could not be accounted for. As it will be shownlater, these low-frequency waves have a crucial impact on themixing rate in the reactor.� The Liffman et al. [18] approach does not model reliably the

paddlewheel effect. According to their method, the flow ratein the pond will be determined by the viscous dissipation (thatmust balance the paddlewheel power), while in reality, as thepaddlewheel behaves as a positive displacement pump, the flowrate is only slightly affected by the viscous dissipation (viscousdissipation however determines the energy demand. The flowrate depends mainly on the geometry of the pond and thepaddlewheel). In other words, this method imposes thepaddlewheel power consumption, while in reality, it is a resultof the paddlewheel speed set by the operator.

R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239 233

This paper tries to overcome these limitations through the useof a more general modeling approach. First, the hydrodynamicsin a semi-industrial sized raceway pond is investigated experimen-tally. Two paddlewheels geometries are tested, which is novel tothe authors’ knowledge. The tracer pulse injection method is usedto determine the global hydrodynamics characteristics in the pond(i.e. the residence time and the degree of mixing) for differentoperating conditions. Results demonstrate that the low-frequencyunsteadiness arising from the periodic blades movement has amajor impact on the mixing process in the reactor. Local velocitymeasurements are then acquired at different locations in the pond,using the pulsed Doppler ultrasonic technique. Detailed localvelocity measurements in a raceway have never been reported sofar in the literature (although Chiaramonti et al. [11] conductedmicroacoustic Doppler velocimeter measurements, they onlyreported qualitative results in their paper). Then, the flow in theraceway is predicted through a numerical simulation. The paddle-wheel rotation, the moving free surface as well as wind effects areexplicitly taken into account. This is achieved through the combi-nation of sliding-mesh and VOF (Volume of Fluid) techniques.Numerical results under various operating conditions are then con-fronted to experimental measurements. Power consumption, cir-culation time and mean velocities are accurately predictedthrough the simulation; local velocity profiles are more difficultto reproduce numerically. Taking into account the wind effectreduces this discrepancy between the experimental and numericalresults at some measurements planes.

Fig. 3. Typical system response to the tracer pulse injection (case of 10 rpmpaddlewheel speed, non-aligned blades configuration). The rounded symbolscorrespond to the experimental data, and the continuous line to the Vonckenet al. [24] model.

2. Materials and methods

2.1. Experimental set-up

2.1.1. The raceway pondThe raceway investigated in this study is a semi-industrial scale

reactor operated outdoors, and located at Narbonne, in the south ofFrance. The region climate is characterized by high sunlight inten-sities and powerful winds. The raceway main dimensions areshown in Fig. 1. The algal culture depth was set to 40 cm duringall the experiments. The pond is plastic lined to reduce head lossesand eliminate water loss by percolation. The flow is circulated by a1.75 m wide metallic paddlewheel (Fig. 2), with an inner and outerdiameter of 0.33 m and 0.65 m respectively. The impeller is com-posed of three sections including 5 blades each: this arrangementallows an angular offset between the paddles of the different sec-tions. Two paddlewheel configurations were investigated in thisstudy, with 22.5� (case illustrated in Fig. 2) and without any angu-lar offset between the different sections. These two arrangementsare referred as ‘‘non-aligned blades’’ and ‘‘aligned blades’’ configu-rations respectively. The paddlewheel is connected to a three-phase motor through a variable-speed drive (which controls therotational speed of the impeller). Its shaft is located 0.84 m abovethe pond floor; therefore, the minimum gap between the bladestip and the pond bottom is 19 cm, which is quite important.

2.1.2. Global hydrodynamics characterizationThe tracer experiments are used to determine the global hydro-

dynamic behavior of the reactor, and more precisely, the impact ofboth the blades configuration and the paddlewheel speed on thecirculation rate and mixing time. A similar method is used in therecently published paper of Mendoza et al. [10]. A pulse input of5 L of salt water (5.5 mol L�1 of NaCl) is added upstream of the pad-dlewheel and used as a tracer. The response to the pulse is mea-sured by a conductivity probe (WTW Multi 3410). Mixing time isdefined as the time required to reach about 95% of the final stablevalue of conductivity. Stimulus–response experiments were

performed for both paddlewheel configurations, and for three rota-tional speeds: 3.5, 8 and 12.5 rpm.

Tracer experiments are analyzed using the model of Vonckenet al. [24] (see Fig. 3). The model is suitable for loop reactor anddescribes the variation of the tracer concentration at the injectionpoint assuming a plug flow model with dispersion. The equationfor the scalar transport in a non-dimensional form is:

@C@hþ @C@s� 1

Pe@2C@s2 ¼ 0

where C is the tracer concentration, s the curvilinear abscissa, t thetime, h = t/Tc the reduced time, with Tc the circulation time definedas the ratio between the average length of one turn, L, and the aver-age velocity U, D the axial dispersion coefficient and Pe the PecletNumber defined as Pe = U.L/D.

The normalized concentration at the injection point E(h) = C(h)/C1 is given by:

EðhÞ ¼ 12

ffiffiffiffiffiffiPeph

r Xp

j¼0

exp � Pe4hðj� hÞ2

� �

Pe and Tc were identified through a least square method for variousexperimental conditions.

2.1.3. Local velocity measurementsThe Pulsed Ultrasonic Doppler Velocimetry (PUDV) technique is

used to conduct velocity measurements in the raceway pond.These experimental measurements will serve to validate our CFDmodeling approach. To the authors’ knowledge, no detailed localvelocity measurements in raceway reactors have been reportedso far in the literature.

PUDV is a slightly intrusive technique that can perform velocitymeasurements in opaque liquids, what makes it particularly suit-able for algal reactors. A transducer emits a short ultrasonic burstperiodically, then receives the echoes scattered by seed particlescontained in the flowing liquid (e.g. microalgae in our experi-ments). PUDV only measures the velocity component in the direc-tion of the ultrasonic beam axis. For given operating parameters ofthe instrument, there is a maximum measurable velocity, and amaximum distance from the transducer at which measurementscan be performed. These maximum depth and velocity are inver-sely proportional, what is the main drawback of the technique.Details on the PUDV working principles can be found in [25].

Fig. 4. The flow bulk velocity versus the paddlewheel speed.

234 R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239

The PUDV instrument DOP2000 (Signal Processing�) was usedin the present investigation. The piezoelectric element of the trans-ducer is 10 mm diameter, and emits 1 MHz ultrasonic waves.Experiments were realized during a day when the wind speedwas relatively low. Only the experimental case with the ‘‘alignedblades’’ paddlewheel configuration at a rotational speed of12.5 rpm has been investigated. The PUDV probe was inclinedabout 45� from the horizontal (as shown in Fig. 1b) to preventultrasonic reflections from the bottom wall from disturbing themeasurements. The probe was oriented in the opposite directionthan the mean flow to minimize the disturbance of the flow inthe measurements zone. To ensure that the transducer remainswithin the liquid phase (40 cm height), the probe was placed35 cm above the pond bottom, apart in sections F, H, L and P whereit was pushed down by additional 5 cm (in these planes, the liquidheight is low near the middle wall because of centrifugal effects).In fact, due to its very low acoustic impedance, an air layerbetween the transducer’s face and the liquid would virtually reflectall the ultrasonic waves, preventing its penetration into the culturemedium. Measurements were acquired in sections A, B, C, D, F, H, I,J, L and P (Fig. 1a), at several transverse positions d (0.9 m, 1.3 m,1.7 m and 2.1 m).

2.2. CFD modeling

Numerical modeling of the flow in the raceway pond is carriedout using the commercial CFD package ANSYS 13. The computa-tional domain geometry is constructed using Design Modeler(Fig. 2). The sliding-mesh technique is used to account for the pad-dlewheel rotation. Unlike the approaches used in the literature,this method actually represents the movement of the entire pad-dlewheel. Since the paddlewheel is not entirely submerged, thecomputational domain was extended up to 2 m from the groundlevel in order to fully encompass the impeller. Therefore, it consistsin the raceway itself and a volume of air above the water such thatthe entire paddlewheel is included inside (Fig. 1c). The sliding-mesh technique requires dividing the domain in two zones: acylindrical zone encompassing the paddlewheel (rotating-meshzone), and the rest of the computational domain (immobile zone).The two zones slide past each other at a cylindrical interface. Thesliding-mesh method does not require a prior knowledge of anyof the flow characteristics: the reactor geometry and the impeller’srotation speed are the only needed inputs. Regarding the modelingof the paddlewheel itself, neither the paddlewheel shaft nor theplates connecting the paddles were included in the numericalgeometry. This simplification results in the geometry presentedin (Fig. 2): only the blades, modeled as zero-thickness walls (giventhat their thickness is about 2 mm only) are considered. Althoughthey seem to float in the domain, the movement of the blades isprescribed by the rotation of the cylindrical zone. As the cylindricalzone is rotating, the blades actually turn around a virtual axis (notrepresented) corresponding to the shaft of the real paddlewheel.

The main drawback of the sliding-mesh method is that it iscomputationally expensive, since the blades position is updatedat each time step (thus, transient calculations are performed).Therefore, to estimate the mesh size achieving grid-independentresults, preliminary tests were conducted using the Hadiyantoet al. [17] method (i.e. the paddlewheel section is removed fromthe computational domain, and the flow is circulated using inletand outlet boundary conditions). Based on these results, thecomputational domain was finally discretized using a multi-blocstructured mesh of 310,000 cells (mainly hexahedral non-uniformelements). Grid refining is adopted close to the pond walls to allowthe y + values to be in the correct range (30–300, since wallfunctions are used). A fine mesh is used in high gradients zones(e.g. bends’ intrados, zone around the paddlewheel), while larger

cells are used in the gas phase domain and in the straight areasof the pond.

The Fluent code is used to resolve the Reynolds AveragedNavier–Stokes equations in transient mode (also called Unsteadyor U-RANS simulation). Turbulence is modeled through the high-Reynolds realizable k–e turbulence model. The free interface istracked using the Volume of Fluid (VOF) technique (via the implicitcompressive scheme). The objective is to be able to reflect changesin liquid height near the paddlewheel as well as wind effect ratherthan predicting the free surface shape and wave motion accurately.The convective terms are discretized using the QUICK scheme, andthe diffusion terms are central-differenced. Simulations were per-formed for both aligned and non-aligned blades configurations, fora paddlewheel speed of 12.5 rpm. Advancement in time is donethrough a second-order implicit scheme with a 0.009 s time step,until the permanent flow regime is reached. From that pointonward, instantaneous values are stored and further averaged toproduce mean values to compare with experimental data.

3. Results and discussion

3.1. Tracer experiments

Fig. 3 shows a comparison between experimental data (non-aligned blades configuration, paddlewheel speed of 10 rpm) andthe Voncken et al. [24] model prediction. A good agreement wasobserved between model and tracer concentration for all theexperimental conditions tested. By using the calibration parame-ters of the model, it is possible to extract the Peclet number (Pe)and the circulation time (Tc) for each conditions. The liquid bulkvelocity and the dispersion coefficient versus the paddlewheelvelocity are plotted in Figs. 4 and 5 respectively, for both paddle-wheel configurations. Note that 2 experiments were run for eachexperimental condition (Table 1): discrepancy between theirresults certainly includes wind effects.

Fig. 4 shows a linear relation between the flow rate and the pad-dlewheel speed, indicating that the paddlewheel behaves like apositive displacement pump. It is however possible that this linearbehavior slightly changes for higher liquid flow rates, as the liquidheight at the intake of the paddlewheel decreases with increasing

Fig. 5. The dispersion coefficient versus the paddlewheel speed.

R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239 235

viscous dissipation. As they sweep the same liquid volume (grayarea in Fig. 1b), for the same speed, both paddlewheel configura-tions generate similar liquid flow rates.

Fig. 5 and Table 2 show that the mixing rate is enhanced whenthe liquid flow rate increases, as expected. However, a veryinteresting point is revealed by these data: the aligned bladesconfiguration is quite superior to the non-aligned blades arrange-ment in terms of mixing performances (this superiority gets morepronounced as the liquid flow rate increases), and this for a slightlyhigher power demand only (Table 2). The reason behind thisobservation has to be elucidated thanks to CFD simulations. A more

Table 1Experimental conditions and identified parameters for tracer experiments.

Experimental conditions I

Paddlewheel geometry Paddlewheel speed (rpm) T

Aligned blades 3.5 33.5 3

Non-aligned blades 3.5 33.5 3

Aligned blades 8 18 1

Non-aligned blades 8 18 1

Aligned blades 12.5 912.5 8

Non-aligned blades 12.5 812.5 8

Table 2Experimental conditions and the corresponding mixing time and electrical power consum

Experimental conditions

Paddlewheel geometry Paddlewheel speed (rpm)

Aligned blades 3.5Non-aligned blades 3.5Aligned blades 8Non-aligned blades 3.5Aligned blades 12.5Non-aligned blades 12.5

efficient paddlewheel allows a great reduction in operating costs asmixing can be performed with a lower energy input. Note that thenon-aligned blades configuration is recommended in the literature,in order to reduce pulsations on the drive train and in the waterflow [13].

3.2. CFD results and comparison with experimental data

3.2.1. Aligned-blades configuration: experimental versus numericalresults

Fig. 6 shows CFD predictions of the mean velocity magnitude at0.3 m above the ground level, for the case where the blades arealigned and the paddlewheel speed is 12.5 rpm. The flow acceler-ates near the intrados of the bends, and decelerates near the extra-back. In accordance with preliminary visualizations, dead zonesdevelop downstream of the bends what results in a non-uniformflow in a cross-section. Flow streamlines are greatly straightenedafter the flow passage through the paddlewheel, and the velocitiesget more uniform. This effect cannot be simulated by any of themodeling approaches used in the literature. Fig. 7 shows the profileof |VZ|avg along the raceway as calculated by CFD. s represents thecurvilinear abscissa (nil at measurements section A), |VZ|avg theabsolute value of the mean vertical velocity, averaged over a verti-cal section. |VZ|avg is strongly correlated with the vertical mixing inthe reactor, which ensures that the microalgae are intermittentlyexposed to light. According to CFD results, the vertical velocitycomponent is one or two order of magnitude smaller than the bulkliquid velocity. Vertical mixing is more pronounced near the pad-dlewheel due to the movement generated by the blades passage,and in the bends, due to secondary flows. These results are inagreement with the experimental measurements of Mendozaet al. [10].

Figs. 8 and 9 show a comparison between experimental andnumerical results, along the raceway at 0.18 m above the groundlevel, and at different measurements sections, respectively. The

dentified parameters

c (s) D (m2 s�1) U (m s�1) Pe

24 0.072 0.091 3721 0.043 0.092 63

49 0.047 0.084 5354 0.045 0.083 55

43 0.121 0.207 5144 0.143 0.205 42

40 0.076 0.210 8240 0.095 0.211 65

3 0.180 0.319 529 0.178 0.333 55

8 0.121 0.337 828 0.117 0.334 84

ption.

Experimental results

Mixing time (s) Electrical power consumption (W)

�2000 95>2300 94�800 137�1500 128�500 216�1000 201

Fig. 6. Colored contour plot of the mean velocity magnitude at 0.3 m above the ground level (numerical results): aligned-blades configuration, paddlewheel speed at12.5 rpm.

Fig. 7. Profile of |VZ|avg along the raceway (numerical results).

236 R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239

coordinate u represents the position along the ultrasonic beam axis(which is inclined 45� from the horizontal. The reference value,u = 0, corresponds to the ground level.), Vu the mean velocity com-ponent along the ultrasonic beam axis (positive velocities repre-sent velocities pointing in the same direction as the mean flow,negative ones represent reverse flow). Each graph or profile is ref-erenced by a value d representing the transverse distance from themiddle of the central divider to the measurements location (seeFig. 1). This point to point comparison against experimental datais a severe test for the simulation and it must be outlined here thatthe experimental positioning of the probe can never be as stableand accurate as the numerical one. Despite the mismatch betweenexperimental and numerical results at some measurements planes,one observes that owing to the U-RANS simulation, the general fea-tures of the flow (Fig. 8), as well as local profiles are predicted witha fairly acceptable accuracy (Fig. 9). The simulation underestimatesthe width of the dead zones (see for example the results at sectionH in Fig. 9), and as the raceway is an endless loop, this error prop-agates and alters the numerical results in the whole domain. Thediscrepancy between numerical and experimental results can arisefrom an inaccurate calculation of the wall shear stresses (by theclassical law of the wall) or from the used turbulence model, butit certainly includes the wind effects as it will be discussed later.

CFD predicts a flow rate of 360 kg/s, i.e. a liquid bulk velocity ofabout 0.47 m/s. Surprisingly, CFD results are in good agreementwith local velocity measurements, but the calculated flow meanvelocity is quite different from that measured by the tracer exper-iments, i.e. 0.32 m/s. This indicates an important differencebetween the tracer and the PUDV results. To better understandthe reason behind this disagreement between the experimentaldata, a new CFD simulation was run, this time using a paddlewheelexternal radius of 0.585 m instead of 0.65 m (10% lower). The goalis to study the sensibility of the liquid flow rate relative to

uncertainties on the paddlewheel dimensions or on the liquidheight in the raceway. The flow rate generated by the smallerpaddlewheel is about 263 kg/s, i.e. a circulation velocity of0.34 m/s. This great variation in the circulation velocity is due tothe low submersion of the blades, which make the flow rate verysensitive to the liquid height. In fact, the liquid volume swept bythe blades is equal to the paddlewheel width, times the gray areashown in Fig. 1b. The blade-sweeping area is given by:

Area ¼ R2 cos�1 R� hR

� �� ðR� hÞ

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2Rh� h2

q

with R the paddlewheel radius, and h the maximum height thatblades submerge in the liquid.

If the paddlewheel radius is reduced from 0.65 m to 0.585 m,the blade-sweeping area decreases from 0.139 m2 to about0.076 m2, what explains this important variation in the generatedflow rate. Thus, we think that the liquid height was actually lowerthan 0.4 m during the tracer experiments. Another possibility isthat the shaft vertical position has changed between the two seriesof experiments, as the paddlewheel has been taken off then reas-sembled after the tracer experiments.

CFD shows a non-linear relationship between the blade-sweep-ing area and the flow rate in the raceway. This effect is due to thelow submersion of the paddles which leaves an important gapbetween the blades tip and the pond bottom. In fact, the globalpaddlewheel action deviates from the positive displacement pumpbehavior when this gap increases, since a greater part of the liquidflow rate flows – due to its inertia – from below the impeller (i.e.without being directly pushed by the paddlewheel).

3.2.2. Aligned versus non-aligned-blades configuration: numericalresults

The numerical simulation with the non-aligned blades configu-ration predicts a mass flow rate of 350 kg/s, which is substantiallysimilar to the 360 kg/s generated by the aligned blades configura-tion. This result is in agreement with experiments, which showedthat the flow rate is independent of the paddlewheel configuration(Fig. 4). Concerning energy demand, the calculated power con-sumption (torque exerted on the paddlewheel times the impellerrotational speed) for the aligned and non-aligned configurationsare respectively 229 W and 198 W, what is in an excellent agree-ment with experimental measurements (Table 2): simulations sat-isfyingly predict a slightly higher power demand for the alignedblades configuration. Thus, the sliding mesh technique is a power-ful tool that successfully takes into account the effect of the pad-dlewheel geometry on the flow rate and the power consumption.

Fig. 8. Numerical versus experimental velocities at 0.18 m above the ground level. The star symbols represent experimental measurements and the rounded symbols with acontinuous line represent numerical results.

Fig. 9. Numerical versus experimental velocities at different measurements planes. Black rounded symbols, red stars, green crosses and blue diamonds representexperimental measurements at d = 0.9 m, d = 1.3 m, d = 1.7 m and d = 2.1 m respectively. The continuous black line, the dashed red line, the green dashed dotted line and thedotted blue line represent CFD results at d = 0.9 m, d = 1.3 m, d = 1.7 m and d = 2.1 m respectively. (For interpretation of the references to colour in this figure legend, thereader is referred to the web version of this article.)

R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239 237

Fig. 10. Temporal variation of the torque exerted on the paddlewheel (permanentregime), for both blades arrangements (numerical results).

238 R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239

Concerning the flow field, according to CFD results, the paddle-wheel configuration has only a slight influence on the mean flowhydrodynamics: the mean velocity field is nearly the same for bothpaddlewheel configurations (comparison not shown here). How-ever, this is not at all the case of the low-frequency unsteadyeffects. As can be seen from Fig. 10, the torque exerted on the pad-dlewheel exhibit much higher amplitude variations when thealigned blades arrangement is used (the dominant frequency isthe blades passage frequency: 1.04 Hz). The same effect isobserved concerning the flow rate (however, the oscillationsamplitude decreases while going downstream of the paddlewheel).In fact, the aligned blades configuration results in a non-smooth

Fig. 11. Numerical versus experimental velocities at different measurements planes: cacrosses and blue diamonds represent experimental measurements at d = 0.9 m, d = 1.3 mthe green dashed dotted line and the dotted blue line represent CFD results at d = 0.9 m, dcolour in this figure legend, the reader is referred to the web version of this article.)

flow generation conditions: the paddles of the three sections ofthe paddlewheel advance simultaneously, pushing forward a largeamount of water all at once. It is this effect that enhances themixing rate in the reactor, by increasing both macro (convection)and micro mixing (turbulence).

3.2.3. Wind effectWind effect can be a potential cause of the discrepancy between

numerical and experimental results in the raceway reactor. Theexperimental facility operates outdoors, thus experimental veloc-ity measurements are influenced by both wind direction and inten-sity. Thereby, an additional simulation accounting for the windpresence was run. Wind enters the computational domain throughan inlet boundary condition. Its intensity was taken as 20 km/h,and its direction from left to right on Fig. 1a. These are only roughestimations of the wind characteristics when PUDV measurementswere conducted as no precise measurements are available.

Compared to the previous simulation (Fig. 9), taking intoaccount the wind presence considerably improved the numericalresults at some measurement sections (Fig. 11). CFD results werehowever altered at some other measurement planes. It must bekept in mind that wind intensity and direction continuously varywith time, which is very difficult to reproduce numerically. Thus,the goal of this last simulation is not to completely reconcile exper-imental and numerical results, but to demonstrate that even at amoderate intensity, wind has a considerable effect on the hydrody-namics in the reactor. It can be expected that its effect would bemore pronounced if the raceway reactor is longer, if the circulationvelocity is decreased, or obviously, if the wind intensity is higher.This last case is very difficult to simulate with the sliding meshand VOF techniques. In fact, for high wind intensities, the free inter-face gets wavy, and the air–liquid momentum transfer gets closelyrelated to the interfacial roughness (ripples and low-amplitude

se when the wind effect is accounted for. Black rounded symbols, red stars, green, d = 1.7 m and d = 2.1 m respectively. The continuous black line, the dashed red line,= 1.3 m, d = 1.7 m and d = 2.1 m respectively. (For interpretation of the references to

R. Hreiz et al. / Chemical Engineering Journal 250 (2014) 230–239 239

waves). Simulating such small deformations requires a prohibitivecalculation time.

The results of this last simulation emphasize the need for futureexperiments on a pilot scale raceway operating indoors, where the‘‘wind’’ characteristics can be controlled. Understanding the windimpact on the hydrodynamics in the raceway is crucial to choosean adequate orientation of the pond with regard to the prevailingwinds, so as to take advantage of the wind to improve the mixingefficiency in the reactor (and so decrease the energy consumption).

4. Conclusion

The hydrodynamics in the raceway is critical to obtain highmicroalgae productivity, and CFD is a powerful tool of assistancein the reactor design. This paper focused on a numerical and exper-imental investigation of the hydrodynamics in such a pond. Twopaddlewheel configurations have been tested in the present study.

Results demonstrate that the blades arrangement do not influ-ence the generated flow rate, but however, has a crucial impacton the mixing rate. Contrary to the CFD approaches previously pre-sented in the literature, the sliding mesh technique used here cap-tures the low-frequency motion induced by the paddlewheel. It isfound that the mixing process is strongly correlated to the flowunsteadiness resulting from the periodic passage of the blades.The numerical simulations calculated accurately the flow rate andthe pumping force in the pond, for both paddlewheel configura-tions. Numerical results were in good agreement with velocitymeasurements at some locations in the reactor, but a significantdiscrepancy existed at some other locations. Taking into accountthe wind effect in the simulations improved the concordancebetween experimental and numerical results at some measurementplanes. The result demonstrated that even at a moderate intensity,wind has an important effect on the hydrodynamics of a raceway.

Acknowledgment

The authors are grateful to OSEO FUI Salinalgue for its financalsupport.

References

[1] Y. Chisti, Biodiesel from microalgae, Biotechnol. Adv. 25 (2007) 294–306.[2] R.J. Craggs, S. Heubeck, T.J. Lundquist, J.R. Benemann, Algae biofuel from

wastewater treatment high rate algal ponds, Water Sci. Technol. 63 (2011)660–665.

[3] F.B. Metting, Biodiversity and application of microalgae, J. Ind. Microbiol. 17(1996) 477–489.

[4] J.B.K. Park, R.J. Craggs, A.N. Shilton, Wastewater treatment high rate algalponds for biofuel production, Bioresour. Technol. 102 (2011) 35–42.

[5] L. Christenson, R. Sims, Production and harvesting of microalgae forwastewater treatment, biofuels, and bioproducts, Biotechnol. Adv. 29 (2011)686–702.

[6] J.R. Benemann, W.J. Oswald, Systems and economic analysis of microalgaeponds for conversion of CO2 to biomass, Final Report, Subcontract XK 4-04136-06, Pittsburgh Energy Technology Center Grant No. DE-FG22-93PC93204,1996.

[7] P. Spolaore, C. Joannis-Cassan, E. Duran, A. Isambert, Commercial applicationsof microalgae, J. Biosci. Bioeng. 101 (2006) 87–96.

[8] M.A. Borowitzka, Commercial production of microalgae: ponds, tanks, tubesand fermenters, J. Biotechnol. 70 (1999) 313–321.

[9] M.A. Borowitzka, N.R. Moheimani, Algae for Biofuels and Energy, Springer,2013.

[10] J.L. Mendoza, M.R. Granados, I. De Godos, F.G. Acién, E. Molina, C. Banks, S.Heaven, Fluid-dynamic characterization of real-scale raceway reactors formicroalgae production, Biomass Bioenergy 54 (2013) 267–275.

[11] D. Chiaramonti, M. Prussi, D. Casini, M.R. Tredici, L. Rodolfi, N. Bassi, G.C.Zittelli, P. Bondioli, Review of energy balance in raceway ponds for microalgaecultivation: Re-thinking a traditional system is possible, Appl. Energy 102(2013) 101–111.

[12] B. Ketheesan, N. Nirmalakhandan, Development of a new airlift-drivenraceway reactor for algal cultivation, Appl. Energy 88 (2011) 3370–3376.

[13] J.C. Weissman, J.P. Goebel, Design and analysis of microalgal open pondsystems for the purpose of producing fuels, Subcontract report for the U.S.department of energy, 1987.

[14] A. Darzins, P.T. Pienkos, L. Edye, Current status and potential for algal biofuelsproduction, Report for IEA Bioenergy Task 39, 2010.

[15] Y. Chisti, Response to Reijnders: do biofuels from microalgae beat biofuelsfrom terrestrial plants? (letters response), Trends Biotechnol. (2008) 351–352.

[16] B. Neenan, D. Feinberg, A. Hill, R. McIntosh, K. Terry, Fuels from Micro-algae:Technology Status, Potential and Research Requirements, SERI/SP-231-2550,Solar Energy Research Institute, Golden, Colo., 1986.

[17] H. Hadiyanto, S. Elmore, T. Van Gerven, A. Stankiewicz, Hydrodynamicevaluations in high rate algae pond (HRAP) design, Chem. Eng. J. 217 (2013)231–239.

[18] K. Liffman, D.A. Paterson, P. Liovic, P. Bandopadhayay, Comparing the energyefficiency of different high rate algal raceway pond designs usingcomputational fluid dynamics, Chem. Eng. Res. Des. 91 (2013) 221–226.

[19] J.P. Bitog, I.-B. Lee, C.-G. Lee, K.-S. Kim, H.-S. Hwang, S.-W. Hong, I.-H. Seo, K.-S.Kwon, E. Mostafa, Application of computational fluid dynamics for modelingand designing photobioreactors for microalgae production: a review, Comput.Electron. Agricult. 76 (2011) 131–147.

[20] I. Perner-Nochta, C. Posten, Simulations of light intensity variation in photo-bioreactors, J. Biotechnol. 131 (2007) 276–285.

[21] J. Pruvost, L. Pottier, J. Legrand, Numerical investigation of hydrodynamic andmixing conditions in a torus photobioreactor, Chem. Eng. Sci. 61 (2006) 4476–4489.

[22] Y.H. Kang, M.O. Lee, S.D. Choi, Y.-S. Sin, 2-D hydrodynamic model simulatingpaddlewheel-driven circulation in rectangular shrimp culture ponds,Aquaculture 231 (2004) 163–179.

[23] E.L. Peterson, J.A. Harris, L.C. Wadhwa, CFD modelling pond dynamicprocesses, Aquacultural Eng. 23 (2000) 61–93.

[24] R.M. Voncken, G.D. Holmes, H.W. Den Hartog, Fluid flow in turbine-stirred,Baffled Tanks-II, Chem. Eng. Sci. 19 (1964) 209–213.

[25] M. Messer, Pulsed ultrasonic Doppler velocimetry for measurement of velocityprofiles in small channels and capillaries, Master’s thesis, The Georgia Instituteof Technology, 2005.