Algal Research 5 (2014) 249–257

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Algal Research

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Aeration energy requirements for lipid production by Scenedesmus sp. inairlift bioreactors

Sarah M.J. Jones, Susan T.L. Harrison ⁎Centre for Bioprocess Engineering Research (CeBER), Department of Chemical Engineering, University of Cape Town, Private Bag X3, Rondebosch 7701, Cape Town, South Africa

Abbreviations: NER, Net Energy Ratio; PBR, PhotobiorCSR, CO2 Supply Rate.⁎ Corresponding author at. Centre for Bioprocess E

Department of Chemical Engineering, University ofRondebosch 7701, Cape Town, South Africa. Tel.: +27 21

E-mail address: sue.harrison@uct.ac.za (S.T.L. Harrison

http://dx.doi.org/10.1016/j.algal.2014.03.0032211-9264/© 2014 Published by Elsevier B.V.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 8 November 2013Received in revised form 3 February 2014Accepted 23 March 2014Available online 1 May 2014

Keywords:MicroalgaeBioenergyLipidNet energy ratioAirlift photobioreactorMass transfer

Microalgae have potential to yield various bioenergy products, including algal biodiesel. For algal energy pro-duction, the process energy input must be substantially lower than the product energy. Airlift photobioreactorsprovide controlled environments with good mixing and mass transfer; however, previous work reports a netenergy ratio (NER; energy produced divided by energy consumed) less than 1. Here, the energy consumptionin these reactors was improved by combined optimisation of superficial gas velocity and its CO2 concentration.Increasing CO2 concentration resulted in increased tolerance to lower superficial gas velocities, down to a criticalminimum value. A 75% reduction in aeration power input was obtained by reducing superficial gas velocity from0.0210 to 0.0052m s−1 at 5 400 ppmCO2, without substantial reduction in biomass concentration (2.27 to 1.93 gL−1, respectively) or productivity (0.189 to 0.173 g L−1 d−1, respectively). The NER under these conditions was5.47 for biomass plus lipid and 1.01 for lipid only. The CO2 supply rate, product of superficial gas velocity and CO2

concentration, correlated with the CO2 transfer rate which influenced algal productivity. The range of NERsmeasured across the superficial gas velocities studied indicates the ability to optimise algal cultivation inphotobioreactors for the improved feasibility of algal bioenergy.

© 2014 Published by Elsevier B.V.

1. Introduction

Global interest in the commercial production of algae has increasedin recent years, driven by the potential of bioproducts as sustainablealternatives to their chemical and petrochemical based counterparts,and the growing demand for biofuels and bioenergy from renewablesources [1,27,38].

Many species of microalgae show potential as useful bioresourcesand have been used commercially in food or nutritional supplements,pharmaceuticals, animal feed, aquaculture, wastewater treatment andbioenergy; including biodiesel, algal oil, methane, hydrogen and ethanol[4,6,13,15,33]. The ability of algae to produce a variety of products haspotential from a biorefinery point of view. Numerous microalgae havehigh lipid content, making them good candidates for the production ofbiodiesel, biosurfactants and high value oil products such as poly-unsaturated and omega-3 fatty acids [20,26]. Research has shown thatthe lipid content of microalgae can be further increased under nitrogenlimitation [18,35,49,54,55].

However, the use of algae or other microbial sources for theproduction of commodity products is not without challenges, and

eactor; CTR, CO2 Transfer Rate;

ngineering Research (CeBER),Cape Town, Private Bag X3,650 4021.).

much research is being carried out into the improvement of thesesystems [28,42,57,60]. Currently co-production is typically necessaryto maintain economic feasibility [3,9].

The cultivation stage of an algal bioprocess has the highest energyburden, and a significant portion of this energy consumption is formixing and aeration [36,46]. For the production of bioenergy frommicroalgae, it is essential to reduce energy requirements for the processto be sufficiently energy positive and thus feasible [45,46,54,55].

Open ponds, typically raceway ponds, are the oldest and mostcommonly used microalgae cultivation systems. However, there is on-going debate over the benefits and economic feasibility of open systemsand closed photobioreactors (PBR) [9,29,43,47]. Closed PBRs have thebenefit of providing controlled environments for algal cultivation,with limited contamination, temperature changes and evaporation[26,53]. Although the PBR tends to have higher capital costs and energyrequirements than open ponds, some studies indicate that its highproductivity leads to lower projected running cost per unit of product[9,45,47], while other studies show the contrary [29,45].

Carvalho [5], Chisti [9], Hadiyanto et al. [24], Harrison et al. [25], andSingh and Sharma [53] identified gas transfer, nutrient distribution andlight requirements as key factors in algal cultivation and review currentcultivation systems designed for improved productivities. The mostpopular PBR configurations are tubular and flat panel, due to theirenhanced light availability [5,9]. The airlift concept has been usedsuccessfully for microalgal cultivation in both flat plate and tubularreactors [9,22,34,51,52,59]. In airlift reactors, gas is sparged from the

250 S.M.J. Jones, S.T.L. Harrison / Algal Research 5 (2014) 249–257

base, rising up in a discrete region of the reactor (the riser) and down ina separate region (the downcomer) [5,53].

Gas transfer, nutrient distribution and light requirements aredependent on mixing and aeration, and hence energy consumption[36]. In reactors driven by the airlift concept, mixing and gas–liquidmass transfer both occur by sparging gas (aeration). It is known thatairlift reactors have good gas–liquid mass transfer capabilities [5,10,17]. Studies have reported factors influencing improved mixing andmass transfer in airlifts, including geometric design, presence of stirrer,sparger type and bubble size [11,32,39,61]. However, optimisation ofmass transfer and mixing has not been reported explicitly as a functionof energy input. Net energy ratios (NER), defined as the ratio of theenergy that can be obtained from a product to the energy consumedto obtain that product [49], are useful for assessing the feasibility ofalgal energy products. For bioenergy production, maximising NER iscritical.

The aim of this study was to determine the minimum aerationenergy input required to maintain algal biomass and lipid productionin an internal loop airlift reactor. Biomass and lipid concentrationsweremeasured at decreasing superficial gas velocities, and the resultingNER calculated. Carbon dioxide mass transfer is dependent on bothsuperficial gas velocity and CO2 content. By including the CO2 partialpressure in the sparge gas as a variable, both energy and mass transferinfluences were considered.

2. Materials and methods

2.1. Algae strain, bioreactor and cultivation conditions

Starter cultures of Scenedesmus sp. (isolated from Upington, SouthAfrica) were grown in glass bottles at a volume of 500 mL, with lightand CO2 enriched air provided. A 3 N BBM growth medium [2] wasused, adapted to contain 150 mg L−1 NO3 for increased lipid contentin airlift photobioreactor experiments.

For batch cultures of Scenedesmus sp. in airlift PBRs, the reactorsconsisted of a glass cylinder (600 mm height; 100 mm external diame-ter), with aworking volumeof 3.2 L, containing an inner glass columnordraft tube (475 mm height; 50 mm external diameter) to separate theriser (inner tube) and downcomer regions (outer annulus) [34]. After7 to 10 days of growth, the starter culture was inoculated into the airliftreactors to an optical density (OD) of 0.1 at 750 nm. The cultures weregrown for 2 weeks under constant light (300 μmol m−2 s−1) providedusing 18 W cool white fluorescent bulbs (Osram). For ‘standard condi-tions’ cultures were sparged with CO2 enriched air at 10,400 ppm CO2

and a superficial gas velocity of 0.0210 m s−1, equating to an air flowrate of 2 L min−1 (0.0625 vvm). The temperature, measured regularly,remained at 25 ± 2 °C.

2.2. Effect of superficial gas velocity and CO2 concentration

Following establishment of the performance of Scendesmus sp. understandard conditions, its performance was studied under a range ofsuperficial gas velocities (0.0021, 0.0052, 0.0105 and 0.0210 m s−1,equivalent to air flow rates of 0.2 to 2 L min−1). At each superficialgas velocity Scenedesmus sp. was cultivated at four CO2 concentrations(400, 1400, 5400 and 10,400 ppm) such that the effect of CO2 masstransfer and energy supply could be considered separately. For eachset of experiments, a positive control was run under the ‘standardcondition’ to check consistency and provide data on reproducibility.

2.3. Biomass and lipid quantification

Biomass of the starter cultures was measured by optical density at750 nm [19]. Biomass in the airlift reactors was quantified by bothoptical density at 750 nm and measuring the dry weight. Samples(10–30 mL) were filtered through pre-weighed 0.45 μm cellulose

nitrate filters (Sartorius Stedim) and dried at 80 °C for 48 h beforebeing weighed. The total lipid content (measured as the total fattyacid content) and the fatty acid profiles were measured by the directtransesterification method followed by gas chromatography [21].Under the ‘standard condition’, duplicate samples were taken to calcu-late the standarddeviation for both thebiomass and lipid quantification.For all other experiments the standard deviation of lipid and biomasswere calculated from the multiple runs at the ‘standard condition’repeated with each experiment.

2.4. Mass transfer coefficient

The gassing-in method was used to measure the O2 mass transfercoefficient (kLa). Dissolved oxygen was displaced by bubbling nitrogeninto the reactor. Air was then sparged into the reactor at the desiredsuperficial gas velocity (0.0021–0.0210 m s−1). A dissolved oxygenmetre and probe (Mettler Toledo, O2 4100; response time 10–20 s),the latter placed at the top and centre of an airlift reactor, was used tomeasure the increase in dissolved oxygen at 5 s time intervals. The kLafor oxygen was calculated as the slope of the linearised two film theoryequation (Eq. (1)):

lnC�−CL0

C�−CL

� �¼ kLa � t ð1Þ

where C⁎ is the saturation concentration of dissolved oxygen, CLO is theinitial dissolved oxygen concentration at time t0 and CL is the oxygenconcentration at time t [10].

The kLa measured for oxygen was converted to the kLa for carbondioxide (kLa;CO2 ) using the relative diffusivities of these gases and(Eq. (2)):

kLa;CO2¼ kLa;O2

DCO2

DO2

" #0:5

ð2Þ

where the diffusivity for oxygen (DO2) and carbon dioxide (DCO2

) (Talbotet al., 1991) were taken to be 2.278 [58] and 1.94 cm2 s−1 [56],respectively, at 25 °C in dilute solutions.

2.5. Mixing time

Mixing time was determined using a 6 M NaOH tracer and a singleconductivity probe (AZ Instruments) placed at the top and centre ofthe reactor as described by Chisti et al. [7]. A phenolphthalein pHindicator was also used to monitor mixing patterns in the airlift reactorvisually [16].

2.6. Net energy ratio

The Net Energy Ratio (NER) at each superficial gas velocity and CO2

concentration was calculated according to (Eq. (3)):

NER ¼ EOUTEIN

ð3Þ

where EOUT is the energy that could be obtained from resulting biomassor lipid (kJ), and EIN is the energy required to aerate the reactors (kJ).Values for EOUT were calculated based on the calorific values of theresulting biomass or lipid (Eq. (4)):

EOUT ¼ CX � Vr � Cal ð4Þ

where CX is the biomass or lipid concentration (g L−1), Vr is the reactorvolume (L) and Cal is the calorific value of biomass or lipid (kJ g−1). Thecalorific values of high-lipid Scenedesmus cultures grown at high(10,400 ppm) and low (3900 ppm) CO2 were measured using bomb

Fig. 1. Power input required for aeration (Wm−3) at increasing superficial gas velocities(ms−1)with respect tomixing time (s) andkLa;CO2 (s

−1) at these superficial gas velocities.Error bars represent the standard deviation of n = 3 replicates.

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calorimetry (Department of Forest and Wood Science, University ofStellenbosch, South Africa), and the calorific value of lipid was takenfrom estimates by Lardon et al. [35].

Values for EIN were calculated according to the power used and thecultivation time (Eqs. (5) and (6)):

EIN ¼ PG � t ð5Þ

PG

VL¼ ρL � g � UG

1þ Ad

Ar

ð6Þ

where PG is the power for aeration (W), t is time (s), VL is the liquidvolume (m3), ρL is the liquid density (kg m−3), g is the gravitationalacceleration (m s−2), UG is the superficial gas velocity (m s−1), Ad isthe cross-sectional area of the downcomer (m2), and Ar is the cross-sectional area of the riser (m2) [8].

2.7. CO2 transfer rate

The carbon dioxide transfer rate (CTR) can be described by (Eq. (7)):

CTR ¼ dCCO2

dt¼ kLa;CO2

� CCO2 ;sat−CCO2 ;l

� �ð7Þ

whereCCO2;lis the residual dissolved CO2 in the bulk liquid andCCO2 ;sat

isthe saturation solubility of CO2 at a given partial pressure, temperatureand ionic strength of the growth media. CCO2 ;sat

was calculated for eachof the CO2 partial pressures used in these experiments, by thermo-dynamic modelling using Visual MINTEQ software, version 3.0developed by Gustafsson [23]. The CTR was then calculated using the kLa;CO2

obtained at each superficial gas velocity used (Section 2.4). In thiswork we calculated the maximum CTR obtainable when the CO2 iscompletely depleted from solution and so the CCO2 ;l

term was excluded.

2.8. Carbon uptake

The amount of carbon fixed into lipid was calculated using (Eq. (8)):

CUlipid ¼ Y � nlipid ð8Þ

where CUlipid is the number of moles of carbon used in lipid productionin a given experiment, Y is the number of moles of lipid produced, andnlipid is the average number of moles of carbon in a mole of lipid.These values were estimated based on the fatty acid profile ofScenedesmus sp. cultivated under nitrogen limitation, yielding 46.2%oleic acid (C18:1), 24.3% palmitic acid (C16:0) and 15.9% linoleic acid(C18:2) [22].

2.9. CO2 supply rate

The CO2 supply rate (CSR) was calculated according to (Eq. (9)):

CSR ¼ UG � CCO2ð9Þ

where CSR is the rate atwhich CO2 is supplied to the reactor given as thesuperficial CO2 velocity in m s−1, UG is the superficial gas velocity of thesparge gas, and CCO2

is the percentage of this sparge gas that is CO2.

3. Results and discussion

3.1. Mixing and mass transfer

The mixing time and CO2 mass transfer coefficient (kLa;CO2 ) weremeasured at increasing superficial gas velocity (UG) in an airlift reactorcontaining cell-free 3N BBMmedia (Fig. 1). These parameters improved

(i.e. mixing time decreased and kLa;CO2 increased) with increasingaeration, as expected. The power input was calculated at each super-ficial gas velocity according to Eq. (6). Fig. 1 shows that the increasedmass transfer and improved mixing were at the expense of increasedpower input.

Similar data are found in the literature, however kLa andmixing timevary according to the type of sparger used, the liquid viscosity and theairlift dimensions [32,37,41]. According to previous studies, mixingtimes in airlift reactors range from 20 to 150 s at a UG of 0.0210 m s−1

[11,39,40,50]. The reactor dimensions and sparger described in thiswork resulted in a mixing time of 32.9 s at 0.0210 m s−1 (Fig. 1), i.e.at the lower end of the range found in the literature, indicating goodmixing.

Carbon dioxide transfer rates (CTR) were calculated with respect tothe saturation solubility of CO2 at various partial pressures (400 to 10400 ppm) and the kLa;CO2 at superficial gas velocities of 0.0021,0.0052, 0.0105 and 0.0210m s−1 (Eq. (7)). These indicated the amountof carbon available to algal cells. Fig. 2 shows CTRs at increasing powerinput for aeration at the given velocities (Fig. 2a) and at increasingCO2 supply rates (CSR; a function of superficial gas velocity and CO2

concentration, Fig. 2b). This figure demonstrates the importance ofhigh CO2 concentrations for good CTR. The CTR increasedwith increasedpower for aeration at each CO2 concentration, and dropped substantial-ly with reduced CO2 concentration. The CTR correlated well with CO2

supply rate (superficial CO2 velocity) across varying both superficialgas velocity and CO2 concentration.

Moo-Young and Blanch [41] describe the increase in kLa withincreased power input in different reactor types. In accordance withthis work, they show the same relationship between power input andmass transfer, highlighting the dependence of carbon availability onthe energy provided to the reactor for algal cultivation.

3.2. Biomass and lipid production

Fig. 3 shows the algal growth and lipid production with respect totime under ‘standard cultivation conditions’ (10 400 ppm CO2; 0.0210m s−1 superficial gas velocity). Concentrations of 2.27 g L−1 biomassand 0.635 g L−1 lipid were measured after 12 days cultivation. Amaximum lipid content of 32.1% biomass, and maximum biomass andlipid productivities of 0.306 and 0.081 g L−1 d−1, respectively, werealso obtained under ‘standard conditions’. The subsequent experimentsassessed the effect of reduced input for aeration on maintaining theseconcentrations.

The maximum biomass and lipid concentrations in Fig. 3 correlatewith previous results from the growth of Scenedesmus sp. under

Fig. 2. CO2 transfer rates (a function of CO2 saturation solubilities andkLa;CO2) with respectto a) power input, and b) CO2 supply rate at various CO2 partial pressures and superficialaeration velocities. Note: the deeper the shade of the point, the higher the UG; where theblack points represent data at a superficial gas velocity of 0.0210 m s−1.

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nitrogen limited conditions [22]. In a comparison of several microalgalspecies it was seen that Scenedesmus sp. had high lipid productivity,motivating its use in the current work [22]. Griffiths and Harrison [20]reviewed the maximum lipid content, biomass and lipid productivityof a wide variety of algal species. Biomass productivities were reportedto range from 0.03 to 0.59 g L−1 d−1 with biomass productivityobtained in this work well within this range (0.306 g L−1 d−1). Griffithsand Harrison [20] reported lipid content to range from 5 to 64% biomassunder nitrogen deficient conditions, and lipid productivity from0.017 to0.164 g L−1 d−1. Scenedesmus sp. cultivated in this work reportedtowards the upper end of these ranges, demonstrating its ability as alipid producer. Scenedesmus sp. also had higher biomass and lipidproductivities under standard conditions compared to a number offreshwater algal species reviewed by Rodolfi et al. [49].

Fig. 3. Growth curve of Scendesmus sp. showing biomass and lipid production. Error barsindicate the standard deviation of n = 2 dry weight and lipid measurements.

Fig. 4. a) Maximum biomass concentration; b) overall biomass productivity, calculatedaccording to the number of days required to reach maximum biomass concentration;c) instantaneous biomass productivity obtained in 2 week cultivation in relation to thesuperficial CO2 velocity or CO2 supply rate to the reactor. Note: the deeper the shade ofthe point, the higher the UG; where the black points represent data at a superficial gasvelocity of 0.0210 m s−1. The dotted line represents the CSR threshold. Error bars showthe standard deviation calculated from n = 5 repeats under ‘standard conditions’ (0.144g L−1 biomass).

Growth experiments at superficial gas velocity and CO2 con-centration lower than the ‘standard conditions’ were conducted, andbiomass growth and lipid production were monitored. The superficialCO2 velocity or CO2 supply rate (CSR) was calculated as the product ofthe percentage CO2 sparged into the reactor (400, 1400, 5400 and10,400 ppm) at the four superficial gas velocities (0.0021, 0.0052,0.0105 and 0.0210 m s−1). Fig. 4a shows the increased biomass con-centration with increased CSR to a maximum of 2.27 g L−1. Above thecritical CSR of 2.7 × 10−5 m s−1 (equivalent to a superficial gas velocity

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of 0.0052 m s−1 and CO2 concentration of 5400 ppm), further increasein either CO2 concentration or superficial gas velocity did not increasebiomass production significantly. This is a significant consideration forenergy input. While differences in the maximum biomass concentra-tions obtained may also be influenced by limitation of other nutrients,these concentrations were not altered in this study. The rate of growthduring the linear phase, representing biomass productivity, is a betterindication of the influence of CO2 limitation independently of othernutrients. This productivity, given as overall and instantaneous pro-ductivity in Fig. 4b and c respectively, follows similar trends to themaximum biomass concentration given in Fig. 4a, where a critical CO2

supply rate can be reached at low superficial gas velocity (0.0052 m s−1) and high CO2 concentration (10,400 ppm).

Fig. 5a illustrates themaximum biomass concentration attained as afunction of CO2 transfer rate. The same trend is seen as in Fig. 4, with acritical CTR (0.00185 mol L−1 h−1) above which no further increase inbiomass concentration is observed. Figs. 4 and 5 demonstrate that at400 and 1 400 ppm CO2, the standard superficial gas velocity of0.0210 m s−1; (shown as black points on the graphs) is required tomaintain maximum biomass concentrations of ≥2 g L−1; whereas at5400 and 10,400 ppm CO2, the superficial gas velocities could bereduced to 0.0052 m s−1 without sacrificing biomass concentration orproductivity. According to Fig. 1, this equates to a 75% reduction in thepower input required for aeration.

Fig. 5a shows that, at lower superficial gas velocities, the maximumbiomass concentration is not achieved despite the critical CTR beingmet. This suggests that superficial gas velocity influences a second factor(in addition to carbon limitation) required for growth. This highlights

Fig. 5. a) Maximumbiomass concentration with respect to CO2 transfer rate (a function ofCO2 concentration andkLa;CO2). Note: the deeper the shade of the point, the higher theUG;the horizontal line indicates the drop from the maximum biomass, where mixing or lightcould be limiting. Error bars show the standard deviation calculated from n = 5 repeatsunder ‘standard conditions’ (0.144 g L−1 biomass). b) Maximum biomass obtained withrespect to CO2 concentration in the sparge gas at each of the superficial gas velocitiesindependently.

the complexity of algal cultivation systems, with interdependentparameters. In an airlift reactor, gas sparging is responsible for CO2

provision and for mixing. Mixing is important for distribution ofnutrients (carbon, nitrogen, phosphorous, and other nutrients) andaccess of algal cells to sufficient light for photosynthesis. Therefore,the reduction in growth at low superficial gas velocity under non-limiting CTR suggests mixing or light limitation. The latter is shown byGani and Harrison (in prep). Fig. 5b shows the decrease in biomasswith decreased superficial gas velocity,which affects both CO2 provisionand mixing. The rate at which biomass increases with increased CO2

concentration is higher at the lower gas velocities, indicating the degreeof carbon limitation. Also, at higher CO2 there is a smaller difference inbiomass across superficial gas velocities.

Biomass productivity depends on both the rate of biomass formationand its concentration (Q = μX); its relationship with time thusinfluences the energy input requirement. Fig. 6 shows that at 10,400and 5400 ppm CO2, the superficial gas velocity could be reduced from0.021 to 0.0052 m s−1 without a significant decrease in biomassproductivity. Energy consumption is discussed further in Section 3.3.

Typical CO2 concentrations used in previous algal cultivation studiesrange from 10,400 to 150,400 ppm [31,49,52]. In support of this study,results from Sasi [52] showed that air enriched with CO2 above50,000 ppm does not lead to further increase in growth rate of Chlorellavulgaris; and similarly Langley et al. [34] show a minimum threshold of1200 ppm CO2 to maintain algal biomass productivity of C. vulgaris.Kaewpintong et al. [31] investigated the effect of superficial gas velocityon the growth ofHaematococcus pluvialis and demonstrated an increasein growth with increased velocity up to 0.04 m s−1, above which nofurther increase occurred. These results support the claim that a criticalCO2 availability or sparging rate exists. However, the reports studyeither CO2 concentration or superficial gas velocity independently, anddata represented by varying only one of these factors is system-specific and of limited value to generalised application. This work isthe first to report the combined effect of both CO2 concentration andsuperficial gas velocity, represented as a critical CO2 supply rate or CO2

transfer rate.Lipid production data also showed a critical CSR, but at 1.4 × 10−5 m

s−1 (0.0105ms−1 superficial gas velocity and 1400ppmCO2)with lipidcontent as a percentage of the biomass reaching 38.3% (Fig. 7a). Inter-estingly, the lipid content peaked between 1.4 × 10−5 and 2.1 × 10−5

m s−1 and then dropped with further increase in CSR before risingagain to 36.3%. This perhaps indicates that at lower CSR (around 1.4 ×10−5 m s−1) the algae favour lipid storage over cellular replication,and above this CSR the algae return to favouring cellular replicationand use up the lipid stores for growth, thus leading to a reduction inthe cellular lipid content. The lipid content increased again when

Fig. 6. Superficial gas velocity and CO2 concentration with respect to biomass productivity(Qx), calculated according to the number of days required to reach maximum biomassconcentration. Error bars indicate standard deviation calculated from n = 5 repeatsunder ‘standard conditions’.

Fig. 7. The CO2 supply rate is also shown in relation to a) themaximum lipid content (as %of biomass) and b) themaximum lipid concentration obtained during 2 weeks cultivation,and c) the lipid productivity (Qp) calculated according to the time taken to reachmaximum lipid concentration. Note: the deeper the shade of the point, the higher theUG. Error bars indicate the standard deviation calculated from n = 5 repeats under‘standard conditions’.

Fig. 8. Carbon uptake, or the total number of moles of carbon fixed into lipids (calculatedbased on themoles of lipid produced, and the averagenumber ofmoles of carbon in amoleof lipid), with respect to the CO2 supplied to the reactor. Note: the deeper the shade of thepoint, the higher the UG. Error bars indicate the standard deviation calculated from n= 5repeats under ‘standard conditions’.

254 S.M.J. Jones, S.T.L. Harrison / Algal Research 5 (2014) 249–257

therewas excess carbon available for growth and lipid storage. A similartrend was seen in Fig. 7b and c, for volumetric lipid production (g L−1)and lipid productivity (g L−1 d−1), respectively. However, these graphsindicate that the drop in lipid production and productivity coincideswith lower superficial gas velocity. This suggests a second factor, otherthan carbon limitation, such as mixing rates (affecting light regimes,and distribution of media components, as discussed earlier), influencesthe lipid production and productivity despite the CSRs. This trend canalso be seen in Fig. 8, where the moles of carbon fixed into lipidincreased to a peak at a lower CSR (0.01 mol at 2.8 × 10−5 m s−1),

then decreased, and finally reached a maximum at high CSR (0.13 molat 1.1 × 10−4 m s−1), when superficial gas velocity is high.

Previous studies have highlighted the link between nutrientavailability and lipid production, but most of these studies have focusedon nitrogen, phosphorous and silicon [13–15,20]. The findings in thiswork are important to begin understanding the relationship betweencarbon availability and lipid production by algae. A study by Chiu et al.[12] reported similar results to thiswork,whereNannochloropsis oculatahad biomass and lipid productivities that were poor at 400 ppm CO2

(air),reached maximum at 20,400 ppm (0.145 g L−1 d−1 lipid), anddecreased again between 50,400 and 150,400 ppm CO2.

3.3. Net energy ratios

Net energy ratios (NER)were calculated according to Eqs. (3) to (6).For a NER greater than 1,more energy can be obtained from the biomassor lipid than is used for aeration. Fig. 9 shows that at the ‘standard’superficial gas velocity (0.0210m s−1,) the energy required for aerationoutweighed the energy that could be obtained from the cultivatedbiomass (NER b 1). For 0.0021, 0.0052 and 0.0105 m s−1, the NER wasabove 1 provided that the CO2 concentration was above 400 ppm. At400 ppm, low biomass concentrations resulted in NERs below 1 at allsuperficial gas velocities. A maximum NER of 10.87 was obtained at0.0021 m s−1 and 5 400 ppm CO2, however, under these conditions thebiomass concentration was only 1.49 g L−1 (compared to 2.27 g L−1

under ‘standard conditions’, Fig. 3). A lower biomass concentrationcould lead to increased energy input required for harvesting and down-stream processing, as well as a greater reactor volume required to yieldthe same amount of product. At 0.0052 m s−1 and 5 400 ppm, onthe other hand, the NER was 5.47 and the biomass concentration was2.08 g L−1, indicating an improved energy ratio and minor reductionin biomass compared to the standard conditions (0.0210 m s−1 and10,400 ppm).

The instantaneous biomass productivity and NER increased simulta-neously (Fig. 9b) due to the dependence of NER on the maximum bio-mass concentration obtained and the time taken to reach this. Fig. 9bshows that this increase reaches a maximum at 0.223 g L−1 d−1 andan NER of 2.65 (at 0.01 m s−1 and 10,400 ppm), and that an NER of4.99 can be reached with only a slight decrease in productivity (0.199g L−1 d−1; at 0.005 m s−1 and 10,400 ppm).

In amicroalgae bio-production plant, the energy for aeration,mixingand harvesting have major impacts on the NER [29,49]. In this study,values of NER were calculated based on mixing and aeration by gassparging, and did not include light provision, pumping, harvesting or

Fig. 9. a)Maximumnet energy ratios obtained during growth at various superficial gas ve-locities (UG) and CO2 concentrations (ppm). b) Instantaneous biomass productivity (g L−1

d−1)with respect to NER;where the dotted line is at NER=1, and the solid line illustratesthe small decrease in productivity (0.223 to 0.199 g L−1 d−1), and large increase in NER(2.65 to 4.99, respectively) between these points. Error bars indicate the standard devia-tion calculated from n = 5 repeats under ‘standard conditions’.

Fig. 10. a) Maximum net energy ratios at various superficial gas velocities (UG) and CO2

concentrations (ppm). b) Instantaneous lipid productivity (g L−1 d−1) with respect toNER. The dotted line indicates NER= 1. Error bars indicate the standard deviation calcu-lated from n = 5 repeats under ‘standard conditions’.

255S.M.J. Jones, S.T.L. Harrison / Algal Research 5 (2014) 249–257

lipid extraction, owing to these having smaller impacts on NER com-pared to the substantial impact of the reactor energy requirements [46].

Previous studies have shown NER based on mixing, aeration andharvesting (or dewatering) to be between b1 (for a flat panel reactorwith bubble column concept for mixing; and external loop airlift) and6 (for an algal biofilm reactor) [30,44,49]. This indicates the potentialfor improvement of NER values based on reactor choice and design, aswell as aeration, mixing and harvesting approaches. The optimisationof aeration and CO2 supply in this work resulted in a maximum NERwell above the range found in literature (10.87), and a NER of 5.47 atbiomass concentrations comparable to literature values, indicatingthe importance of this work in progressing towards feasible algalproduction processes in PBRs.

NER based on lipid production only aremuch lower than for biomassplus lipid. Fig. 10 shows that NER values were below 1 for all reactorconditions, except 0.0021 m s−1 and 0.005 m s−1 at 5400 and10,400 ppm CO2. However, under these conditions the volumetriclipid concentrations were 0.376, 0.375, 0.403 and 0.343 g L−1,respectively, which are substantially lower than lipid concentrationunder ‘standard conditions’ (0.635 g L−1). Hence, both biomass andlipid must be used for feasible bioenergy production.

Several comprehensive techno-economic evaluations have beenpublished with respect to the feasibility of algal bioenergy. They allhighlight the considerable impact of power formixing and gas provisionon the overall energy consumption and cost. Jonker and Faaij [29] andChisti [9] suggest that using airlift devices in place ofmechanical stirringcould reduce the high energy consumption and cultivation costsassociated with bioenergy production from microalgae. Optimisation

of aeration and CO2 supply strategies, as shown in the current work(Figs. 4 to 10), can further improve the energetic feasibility of algallipid production and thus reduce cost. Evaluation of CO2 uptake efficien-cies is also important for further improvements [29,48].

Jonker and Faaij [29] and Zhang et al. [60] illustrate the energetic andeconomic improvements incurred by using CO2 from flue gas as well aswastewater for algal cultivation. There is a considerable energy cost as-sociatedwith CO2 supply in the formof compressed gas, but in thisworkwe assume that due to the availability of numerous high CO2 contentwaste streams, this requirement can be avoided. In addition to reducedreactor energy, use of waste CO2 streams is important for CO2 cycling, toenable on-going value generation from the same CO2, rather than liber-ating new CO2.

4. Conclusions

For the effective production of algal bioenergy, NER is a major con-sideration in selecting reactor operating conditions. This study soughtto improve the NER of algal biomass and lipid production in an airliftPBR by optimising superficial gas velocity and CO2 concentration. Athigh CO2 concentration in the gas phase (5400–10,400 ppm), the super-ficial gas velocity could be reduced fourfold over that previously report-ed (0.02 m s−1) without substantial decrease in biomass concentrationor productivity. On further reduction of superficial gas velocity below0.005 m s−1, it was proposed that the decreased biomass formationobserved was attributed to compromised mixing. On sparging withgases of lower CO2 concentration (400–1400 ppm), some loss ofproductivity was observed with decreasing superficial gas velocity.These factors were considered using the combined term, carbon supply

256 S.M.J. Jones, S.T.L. Harrison / Algal Research 5 (2014) 249–257

rate (CSR, also termed CO2 superficial velocity), to develop a generalisedrelationship to describe CTR (carbon transfer rate). A critical value forthe CSR was demonstrated which, if exceeded, had no further benefitto productivity; this has not been reported previously. By consideringthis approach, the NER could be increased to values of 5.5 at appropriatebiomass productivities and 10.9 at reduced biomass productivity. Theserepresent a substantial increase over those below 1, reported pre-viously. The NER based on lipid only was also increased above 1, butonly at lower lipid productivity, indicating the importance of dual prod-uct systems where lipid is the desired product. The results of this workare testament to the importance of this approach toward the feasibleproduction of algal bioenergy.

Acknowledgments

The assistance of Dr Griffiths (University of Cape Town) and DrMenkin (Stellenbosch University) are gratefully acknowledged, as isthe assistance of Murray Fraser through the provisions of mixing timedata for the reactor system. The financial support of the South AfricanResearch Chairs Initiative (SARChI) (UID 64778) of the Department ofScience and Technology and the National Research Foundation (NRF)of South Africa (80049) is also acknowledged. The Grantholder ac-knowledges that opinions, findings and conclusions or recommenda-tions expressed in any publication generated by the NRF supportedresearch are that of the authors, and that the NRF accepts no liabilitywhatsoever in this regard.

References

[1] M. Alvarado-Morales, J. Terra, K.V. Gernaey, J.M. Woodley, R. Gani, Biorefining: com-puter aided tools for sustainable design and analysis of bioethanol production, Spec.Issue Biorefinery Integr. - Biorefinery Integr. SI, 87, 2009, pp. 1171–1183.

[2] H.C. Bold, The morphology of Chlamydomonas chlamydogama, Sp. Nov. Bull. TorreyBot. Club, 76, 1949, pp. 101–108.

[3] H.L. Bryant, I. Gogichaishvili, D. Anderson, J.W. Richardson, J. Sawyer, T.Wickersham,M.L. Drewery, The value of post-extracted algae residue, Algal Res. 1 (2012)185–193.

[4] K.H.M. Cardozo, T. Guaratini, M.P. Barros, V.R. Falcão, A.P. Tonon, N.P. Lopes, S.Campos, M.A. Torres, A.O. Souza, P. Colepicolo, E. Pinto, Metabolites from algaewith economical impact, Fourth Spec. Issue CBP Dedic. Face Lat. Am. Comp.Biochem. Physiol. Organ. Marcelo Hermes-Lima Braz. Co-Ed. Carlos Navas Braz.Rene Beleboni Braz. Rodrigo Stabeli Braz. Tania Zenteno-Savín Mex. Ed. CBP - ThisIssue Is Dedic. Mem. Two Except. Men Peter Lutz One Pioneers Comp. Integr. Phys-iol. Cicero Lima Journal. Sci. Lover Hermes-Limas Dad, 146, 2007, pp. 60–78.

[5] A. Carvalho, L.A. Meireles, F.X. Malcata, Microalgal reactors: a review of enclosedsystem designs and performances, Biotechnol. Prog. 22 (2006) 1490.

[6] S.A. Castine, N.A. Paul, M. Magnusson, M.I. Bird, R. de Nys, Algal bioproducts derivedfrom suspended solids in intensive land-based aquaculture, Bioresour. Technol. 131(2013) 113–120.

[7] M.Y. Chisti, B. Halard,M.Moo-Young, Liquid circulation in airlift reactors, Chem. Eng.Sci. 43 (1988) 451–457.

[8] Y. Chisti, Airlift Bioreactors, Elsevier Science Publishers Ltd, England, 1989.[9] Y. Chisti, Biodiesel from microalgae, Biotechnol. Adv. 25 (2007) 294–306.

[10] Y. Chisti, Mass Transfer, in: Kirk–Othmer Encyclopedia of Chemical Technology, JohnWiley & Sons, Inc., 2007

[11] Y. Chisti, U.J. Jauregui-Haza, Oxygen transfer and mixing in mechanically agitatedairlift bioreactors, Biochem. Eng. J. 10 (2002) 143–153.

[12] S.-Y. Chiu, C.-Y. Kao, M.-T. Tsai, S.-C. Ong, C.-H. Chen, C.-S. Lin, Lipid accumulationand CO2 utilization of Nannochloropsis oculata in response to CO2 aeration,Bioresour. Technol. 100 (2009) 833–838.

[13] L. Christenson, R. Sims, Production and harvesting of microalgae for waste watertreatment, biofuels, and bioproducts, Biotechnol. Adv. 29 (2011) 686–702.

[14] A.F. Clarens, E.P. Resurreccion, M.A. White, L.M. Colosi, Environmental life cyclecomparison of algae to other bioenergy feedstocks, Environ. Sci. Technol. 44(2010) 1813–1819.

[15] A. Demirbas, Use of algae as biofuel sources, Energy Convers. Manag. 51 (2010)2738–2749.

[16] E.A. Fox, V.E. Gex, Single-phase blending of liquids, AIChE J 2 (1956) 539–544.[17] M. Gavrilescu, R.Z. Tudose, Mixing studies in external-loop airlift reactors, Chem.

Eng. J. 66 (1997) 97–104.[18] M.J. Griffiths, Optimising Microalgal Lipid Productivity for Biodesel Production, Uni-

versity of Cape Town, South Africa, 2011.[19] M.J. Griffiths, C. Garcin, R.P. van Hille, S.T.L. Harrison, Interference by pigment in the

estimation of microalgal biomass concentration by optical density, J. Microbiol.Methods 85 (2011) 119–123.

[20] M.J. Griffiths, S.T.L. Harrison, Lipid productivity as a key characteristic for choosingalgal species for biodiesel production, J. Appl. Phycol. 21 (2009) 493.

[21] M.J. Griffiths, R.P. van Hille, S.T.L. Harrison, Selection of direct transesterification asthe preferred method for assay of fatty acid vontent of microalgae, Lipids 45(2010) 1053–1060.

[22] M.J. Griffiths, R.P. van Hille, S.T.L. Harrison, Lipid productivity, settling potential andfatty acid profile of 11 microalgal species grown under nitrogen replete and limitedconditions, J. Appl. Phycol. 24 (2012) 989–1001.

[23] J.P. Gustafsson, Visual MINTEQ, 2012.[24] H. Hadiyanto, S. Elmore, T. Van Gerven, A. Stankiewicz, Hydrodynamic evaluations

in high rate algae pond (HRAP) design, Chem. Eng. J. 217 (2013) 231–239.[25] S.T.L. Harrison, C. Richardson, M.J. Griffiths, Analysis of microalgal biorefineries for

bioenergy from an environmental and economic perspective: focus on algal biodie-sel, Biotechnological Applications of Microalgae: Biodiesel and Value-addedProducts, Taylor and Francis Group, CRC Press, 2013, pp. 113–136.

[26] R. Harun, M. Singh, G.M. Forde, M.K. Danquah, Bioprocess engineering of microalgaeto produce a variety of consumer products, Renew. Sustain. Energy Rev. 14 (2010)1037–1047.

[27] B.G. Hermann, M. Patel, Today's and tomorrow's bio-based bulk chemicals fromwhite biotechnology: a techno-economic analysis, Appl. Biochem. Biotechnol. 136(2007) 361–388.

[28] C. Jiménez-González, J.M. Woodley, Bioprocesses: modeling needs for process eval-uation and sustainability assessment, Process Model. Control Drug Dev. Manuf., 34,2010, pp. 1009–1017.

[29] J.G.G. Jonker, A.P.C. Faaij, Techno-economic assessment of micro-algae as feedstockfor renewable bio-energy production, Spec. Issue Adv. Sustain. Biofuel Prod. Use -XIX Int. Symp. Alcohol Fuels - ISAF, 102, 2013, pp. 461–475.

[30] O. Jorquera, A. Kiperstok, E.A. Sales, M. Embiruçu, M.L. Ghirardi, Comparative energylife-cycle analyses of microalgal biomass production in open ponds andphotobioreactors, Bioresour. Technol. 101 (2010) 1406–1413.

[31] K. Kaewpintong, A. Shotipruk, S. Powtongsook, P. Pavasant, Photoautotrophic high-density cultivation of vegetative cells of Haematococcus pluvialis in airlift bioreac-tor, Bioresour. Technol. 98 (2007) 288–295.

[32] P.M. Kilonzo, A. Margaritis, M.A. Bergougnou, J. Yu, Q. Ye, Effects of geometrical de-sign on hydrodynamic and mass transfer characteristics of a rectangular-columnairlift bioreactor, Biochem. Eng. J. 34 (2007) 279–288.

[33] T. Kuda, M. Tsunekawa, H. Goto, Y. Araki, Antioxidant properties of four edible algaeharvested in the Noto Peninsula, Japan, J. Food Compos. Anal. 18 (2005) 625–633.

[34] N.M. Langley, S.T.L. Harrison, R.P. van Hille, A critical evaluation of CO2 supplemen-tation to algal systems by direct injection, Biochem. Eng. J. 68 (2012) 70–75.

[35] L. Lardon, A. Helias, B. Sialve, J.-P. Steyer, O. Bernard, Life-cycle assessment of biodie-sel production from microalgae, Environ. Sci. Technol. 43 (2009) 6475–6481.

[36] F. Lehr, C. Posten, Closed photo-bioreactors as tools for biofuel production, EnergyBiotechnol. Environ. Biotechnol. 20 (2009) 280–285.

[37] L. Luo, F. Liu, Y. Xu, J. Yuan, Hydrodynamics and mass transfer characteristics in aninternal loop airlift reactor with different spargers, Chem. Eng. J. 175 (2011)494–504.

[38] L.R. Lynd, M.Q.Wang, A product-nonspecific framework for evaluating the potentialof biomass-based products to displace fossil fuels, J. Ind. Ecol. 7 (2003) 17–32.

[39] J.C. Merchuk, A. Contreras, F. García, E. Molina, Studies of mixing in a concentric tubeairlift bioreactor with different spargers, Chem. Eng. Sci. 53 (1998) 709–719.

[40] E. Molina Grima, F.G.A. Fernández, F. García Camacho, Y. Chisti, Photobioreactors:light regime, mass transfer, and scaleup, Biotechnol. Asp. Mar. Sponges, 70, 1999,pp. 231–247.

[41] M. Moo-Young, H.W. Blanch, Design of biochemical reactors: mass transfer criteriafor simple and complex systems, Adv. Biochem. Eng. 19 (1981) 1–69.

[42] A. Moser, Ecotechnology in industrial practice: implementation using sustainabilityindices and case studies, Ecol. Eng. 7 (1996) 117–138.

[43] S. Nagarajan, S.K. Chou, S. Cao, C. Wu, Z. Zhou, An updated comprehensive techno-economic analysis of algae biodiesel, Bioresour. Technol. 145 (2013) 150–156.

[44] Ozkan, A., Kinney, K., Katz, L., Berberoglu, H., n.d. Reduction of water and energy re-quirement of algae cultivation using an algae biofilm photobioreactor. Bioresour.Technol.

[45] E.P. Resurreccion, L.M. Colosi, M.A. White, A.F. Clarens, Comparison of algae cultiva-tion methods for bioenergy production using a combined life cycle assessment andlife cycle costing approach, Adv. Biol. Waste Treat. Bioconversion Technol. 126(2012) 298–306.

[46] C. Richardson, Investigating the Role of Reactor Design to Maximise the Environ-mental Benefit of Algal Oil for Biodiesel, University of Cape Town, 2011.

[47] J.W. Richardson, M.D. Johnson, J.L. Outlaw, Economic comparison of open pond race-ways to photo bio-reactors for profitable production of algae for transportation fuelsin the Southwest, Algal Res. 1 (2012) 93–100.

[48] M. Rickman, J. Pellegrino, J. Hock, S. Shaw, B. Freeman, Life-cycle and techno-economic analysis of utility-connected algae systems, Algal Res. 2 (2013) 59–65.

[49] L. Rodolfi, G.C. Zittelli, N. Bassi, G. Padovani, N. Biondo, G. Bonini, M.R. Tredici,Microalgae for oil: strain selection, induction of lipid synthesis and outdoor masscultivation in a low-cost photobioreactor, Biotechnol. Bioeng. 102 (2009) 100.

[50] A. Sánchez Mirón, M.-C. Cerón García, F. García Camacho, E. Molina Grima, Y. Chisti,Mixing in bubble column and airlift reactors, Chem. Eng. Res. Des. 82 (2004)1367–1374.

[51] A. Sánchez Mirón, M.-C. Cerón Garcı́a, F. Garcı́a Camacho, E. Molina Grima, Y. Chisti,Growth and biochemical characterization of microalgal biomass produced in bubblecolumn and airlift photobioreactors: studies in fed-batch culture, Enzyme Microb.Technol. 31 (2002) 1015–1023.

[52] D. Sasi, Growth kinetics and lipid production using Chlorella vulgaris in a circulatingloop photobioreactor, J. Chem. Technol. Biotechnol. 86 (2011) 875–880.

[53] R.N. Singh, S. Sharma, Development of suitable photobioreactor for algae production— a review, Renew. Sustain. Energy Rev. 16 (2012) 2347–2353.

257S.M.J. Jones, S.T.L. Harrison / Algal Research 5 (2014) 249–257

[54] A.L. Stephenson, J.S. Dennis, C.J. Howe, S.A. Scott, A.G. Smith, Influence of nitrogen-limitation regime on the production by Chlorella vulgaris of lipids for biodiesel feed-stocks, Biofuels 1 (2010) 47–58.

[55] Anna L. Stephenson, E. Kazamia, J.S. Dennis, C.J. Howe, S.A. Scott, A.G. Smith, Life-cycle assessment of potential algal biodiesel production in the United Kingdom: acomparison of raceways and air-lift tubular bioreactors, Energy Fuels 24 (2010)4062–4077.

[56] A. Tamimi, E.B. Rinker, O.C. Sandall, Diffusion coefficients for hydrogen sulfide, car-bon dioxide, and nitrous oxide in water over the temperature range 293–368 K, J.Chem. Eng. Data 39 (1994) 330–332.

[57] YongWang, J. Chu, Y. Zhuang, YonghongWang, J. Xia, S. Zhang, Industrial bioprocesscontrol and optimization in the context of systems biotechnology, Biotechnol. Sus-tain. Hum. Soc. - Invit. Pap. IBS, 27, 2009, pp. 989–995.

[58] C.R. Wilke, P. Chang, Correlation of diffusion coefficients in dilute solutions, AIChE J 1(1955) 264–270.

[59] X. Yuan, A. Kumar, A.K. Sahu, S.J. Ergas, Impact of ammonia concentration on Spiru-lina platensis growth in an airlift photobioreactor, Bioresour. Technol. 102 (2011)3234–3239.

[60] Y. Zhang, M.A. White, L.M. Colosi, Environmental and economic assessment of inte-grated systems for dairy manure treatment coupled with algae bioenergy produc-tion, Bioresour. Technol. 130 (2013) 486–494.

[61] W.B. Zimmerman, M. Zandi, H.C. Hemaka Bandulasena, V. Tesař, D. James Gilmour,K. Ying, Design of an airlift loop bioreactor and pilot scales studies with fluidic oscil-lator induced microbubbles for growth of a microalgae Dunaliella salina. Spec. IssueEnergy Algae Curr, Status Future Trends 88 (2011) 3357–3369