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Harvesting of microalgae by flocculation with poly (c-glutamic acid)

Hongli Zheng, Zhen Gao, Jilong Yin, Xiaohong Tang, Xiaojun Ji, He Huang ⇑State Key Laboratory of Materials-Oriented Chemical Engineering, College of Biotechnology and Pharmaceutical Engineering, Nanjing University of Technology,No. 5, Xinmofan Road, Nanjing 210009, People’s Republic of China

a r t i c l e i n f o

Article history:Received 7 October 2011Received in revised form 7 February 2012Accepted 13 February 2012Available online 27 February 2012

Keywords:MicroalgaeMicrobial flocculantResponse surface methodologyBiomass harvest

a b s t r a c t

In an effort to search for an efficient and environmentally friendly harvesting method, a commerciallyavailable microbial flocculant poly (c-glutamic acid) (c-PGA) was used to harvest oleaginous microalgae.Conditions for flocculation of marine Chlorella vulgaris and freshwater Chlorella protothecoides were opti-mized by response surface methodology (RSM) and determined to be 22.03 mg L�1 c-PGA, 0.57 g L�1 bio-mass, and 11.56 g L�1 salinity, and 19.82 mg L�1 c-PGA and 0.60 g L�1 biomass, respectively. Applicationof the two optimized flocculation methods to Nannochloropsis oculata LICME 002, Phaeodactylum tricornu-tum, C. vulgaris LICME 001, and Botryococcus braunii LICME 003 gave no less than 90% flocculation effi-ciency and a concentration factor greater than 20. Micrographs of the harvested microalgal cellsshowed no damage to cell integrity, and hence no lipid loss during the process. The results show that floc-culation with c-PGA is feasible for harvesting microalgae for biodiesel production.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Two of the challenging global problems are the exhaustion offossil fuels and climate change. Microalgae are among the mostprimitive forms of plant life able to capture CO2. In addition, somemicroalgae can produce lipids suitable for biodiesel (Chiu et al.,2009; Sialve et al., 2009). Compared with other energy crops, theadvantages of deriving biodiesel from microalgae include rapidgrowth rates and a high per-acre yield. In addition, biodiesel haslow toxicity, is highly biodegradable and contains no sulfur (Hsiehand Wu, 2009; Fu et al., 2009). Considering all the steps involved inthe biodiesel production from microalgae, harvest is a particularlyimportant step. Harvesting of microalgae is challenging because oflow cell concentrations (<0.5 kg m�3 dry mass) in the medium andtheir small sizes (3–30 lm in diameter) (Grima et al., 2003), thesmall density differences between microalgae and growth media,the ability of microalgae to remain in suspension, and the high io-nic strength of seawater (Lee et al., 2009). Efficient and environ-mentally friendly harvesting would be important for commercialbiodiesel production from microalgae.

Flocculation is one of the most convenient methods for harvest-ing microalgae (Oh et al., 2001). Inorganic flocculants such as ferricchloride (FeCl3) and aluminum sulfate (Al2(SO4)3) and organic syn-thetic high-polymer flocculants such as polyacrylamide derivativesand polyethylene imine have been used for recovering microalgalbiomass (Papazi et al., 2009; Pushparaj et al., 1993). However,

these flocculants are required in high doses (the dosages of FeCl3

and Al2(SO4)3 are both 0.75 g L�1, and that of polyacrylamide is0.35 mmol L�1) (Papazi et al., 2009; Grima et al., 2003), and thusmay result in contamination of the microalgal lipids for biodieselproduction. Because the flocculants are not readily degradable, amajor environmental concern, water pollution, may result. More-over, the feedstock for biodiesel from microalgae is intracellularlipids. Cell integrity may be affected by some chemical flocculants,causing lipid loss during flocculation (Papazi et al., 2009), leadingto a decrease in the efficiency of lipid recovery in the downstreammicroalgal lipid extraction process.

In recent years, naturally occurring microbial flocculants havebeen applied to harvest microalgae for aquaculture and biodieselproduction because of their high harvesting efficiency, innocuityand biodegradability (Lee et al., 2009). Poly (c-glutamic acid)(c-PGA) has been produced as an extracellular product of Bacillussubtilis at large scales (Yokoi et al., 1996) and used commerciallyas a microbial flocculant in wastewater treatment (Taniguchiet al., 2005). The purpose of this study was to evaluate c-PGA asa flocculant for harvesting lipid-rich microalgae by response sur-face methodology (RSM) and to investigate the effect of c-PGAon harvested cell integrity using microscopy.

2. Methods

2.1. Materials

c-PGA produced by B. subtilis was purchased from RundoBiotech Japan Co. Ltd, Nanjing, China.

0960-8524/$ - see front matter � 2012 Elsevier Ltd. All rights reserved.doi:10.1016/j.biortech.2012.02.086

⇑ Corresponding author. Tel./fax: +86 25 83172094.E-mail address: biotech@njut.edu.cn (H. Huang).

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2.2. Microalgal strains and cultivation conditions

Species of microalgae were obtained from the Culture Collectionof Algae at the University of Texas at Austin (Chlorella prototheco-ides UTEX 255 and Phaeodactylum tricornutum UTEX 640), theChina Center for Type Culture Collection at Wuhan in China (mar-ine Chlorella vulgaris, strain CCTCC M 209256), and our laboratoryisolations (freshwater C. vulgaris LICME 001, Nannochloropsisoculata LICME 002, and Botryococcus braunii LICME 003). MarineC. vulgaris and N. oculata were grown in medium composed of (inmg L�1): KNO3, 100; KH2PO4, 10; Na2EDTA, 10; FeSO4�7H2O, 2.5;MnSO4, 0.25; Vitamin B1, 0.006; Vitamin B12, 0.00005; instantocean synthetic sea salt (Aquarium Systems, Inc., USA), 26,000.Freshwater C. vulgaris LICME 001, C. protothecoides, and B. brauniiLICME 003 were grown in BG-11 medium. P. tricornutum wasgrown in F/2 medium. The media were autoclaved at 121 �C for20 min without pH adjustment. A 10 L bubble column photobiore-actor (50.0 cm in height, 16.0 cm in diameter, a closed system) cul-ture system with a working volume of 8 L was used. The culturetemperature of 25 �C was regulated by water recycled in the outerlayer of the photobioreactor. Ten fluorescent lamps were arrangedaround the photobioreactor to supply continuous illumination of80 lmol photons m�2 s�1 with a 12/12 h light/dark cycle. At thebottom of the reactor, there was a gas sparger. CO2 of 3.0% wasprepared with a combination of room air and pure CO2 from a com-pressor and an aeration rate of 200 mL min�1 was carried out. Thecultivation cycle was 15 days.

2.3. Analytical methods

The biomass concentrations (dry mass) of microalgae (BC, g L�1)were calculated from measurements of the optical density (OD) ofcultures at 680 nm according to the following equations: marine orfreshwater C. vulgaris BC = 0.560 � OD680 (R2 = 0.986); N. oculataBC = 0.580 � OD680 (R2 = 0.995); P. tricornutum BC = 0.652 � OD680

(R2 = 0.988); C. protothecoides BC = 0.558 � OD680 (R2 = 0.994); B.braunii BC = 0.885 � OD680 (R2 = 0.991).

The microalgal suspension of 150 ml was placed into each of the250 mL glass beakers, and the salinity of the media was adjustedby addition of instant ocean synthetic sea salt or distilled waterto 10, 20, 30, 40, and 50 g L�1 for the salinity effect experiment(Figs. 1C and 2C) or according to Table 1. pH values were adjustedto 6.5, 7.0, 7.5, 8.0, and 8.5 with 0.5 M HCl or 0.5 M NaOH for thepH effect experiment, otherwise the pH was kept at 7.5. The initialoptical density of the microalgal suspension in the beakers wasmeasured at 680 nm. The c-PGA powder was added under mag-netic stirring (HJ-3, Jiangsu Tianyou Co. Ltd, Jiangsu Province,China) at a stirring rate of 500 rpm for 5 min. The microalgal sus-pension was left to settle for 2 h without agitation. Subsequently,the optical density of the supernatant from half the height of theclarified layer and the sludge was measured. The flocculation effi-ciency was defined as the ratio of the mass of cells recovered to thetotal mass of cells and the concentration factor was the ratio of thefinal product concentration to the initial concentration (Bosmaet al., 2003). The flocculation efficiency and concentration factorwere calculated as:

Flocculation efficiency ð%Þ ¼ A0V0 � A1V1

A0V0� 100 ð1Þ

Concentration factor ¼ A2

A0ð2Þ

where A1 is OD680 of the supernatant from half the height of theclarified layer after flocculation, A2 is OD680 of the sludge after

flocculation, and A0 is OD680 of the microalgal suspension beforeflocculation. V0 is the volume of microalgal suspension before floc-culation, and V1 is the volume of microalgal supernatant afterflocculation.

2.4. Experiment design

2.4.1. Evaluation of flocculation parameters for C. vulgaris and C.protothecoides

In order to optimize the flocculation of microalgae with c-PGA,C. vulgaris and C. protothecoides were used as model systems formarine and freshwater microalgae, respectively. The effects ofthe flocculation parameters such as c-PGA dosage, biomassconcentration, pH and salinity on C. vulgaris and c-PGA dosage, bio-mass concentration and pH on C. protothecoides were individuallyinvestigated by analyzing flocculation efficiency and concentrationfactor.

The zeta potentials of the microalgal suspensions before floccu-lation (1.2 g L�1 biomass, pH 7.5 and 30 g L�1 salinity for C. vulgarisand 1.2 g L�1 biomass and pH 7.5 for C. protothecoides) and thoseof the flocculated suspensions (obtained from the above c-PGAdosage experiment containing 20 and 30 mg L�1 c-PGA, respec-tively) were measured with a Zeta Potential Analyzer utilizingphase analysis light scattering (Brookhaven Instruments Corpora-tion, USA).

2.4.2. Optimization of flocculation of C. vulgaris and C. protothecoideswith c-PGA

To improve flocculation efficiencies and concentration factors,the interaction between the three most significant factors (c-PGAdosage, biomass concentration and salinity) for C. vulgaris and thatbetween the two most significant factors (c-PGA dosage and bio-mass concentration) for C. protothecoides identified by preliminaryevaluation experiments were studied. Since it is known that RSMcan evaluate the interaction between the significant factors of anexperiment and optimize them (Ghosh and Hallenbeck, 2010; Jiet al., 2009), RSM using central composite design was applied todetermine the optimal levels of the three selected variables for C.vulgaris and the two selected variables for C. protothecoides, whichsignificantly affected the flocculation efficiency and concentrationfactor. The three independent factors with five different levels(�1.682, �1, 0,+1,+1.682) of C. vulgaris and the two independentfactors with five different levels (�1.414, �1, 0,+1,+1.414) of C.protothecoides were investigated and the experimental designsare shown in Tables 1 and 2. The factors were coded according tothe following equation:

xi ¼Xi � X0

DX; i ¼ 1;2;3; . . . ; k ð3Þ

where xi is the coded independent factor, Xi is the real independentfactor, X0 is the value of Xi at the center point and DX is the stepchange value.

The flocculation efficiencies and concentration factors of c-PGAwere fitted using a polynomial equation and four multiple regres-sions of the data were carried out to obtain four empirical modelsrelated to the three and two most significant factors in the case ofC. vulgaris and C. protothecoides, respectively. The general form ofthe polynomial equation is:

Y ¼ b0 þX

biXiþX

biiX2i þ

XbijXiXj; . . . i; j ¼ 1;2;3; . . . ; k ð4Þ

where Y is the predicted response, Xi and Xj are independent factors,b0 is the intercept, bi is the linear coefficient, bii is the quadraticcoefficient, and bij is the interaction coefficient.

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To maximize the two response variables flocculation efficiencyand concentration factor simultaneously (m = 2), an optimizationusing the global desirability function (D) was performed forC. vulgaris and C. protothecoides, respectively, which consisted inconverting each response into a single desirability function (di)ranging from 0 to 1 (0 6 di 6 1) (Derringer and Suich, 1980). Theindividual desirability’s were then combined using the geometricmean, which gives the overall desirability D:

D ¼Ymi¼1

di

!1=m

ð5Þ

Microalgal biomass samples harvested at optimal flocculationparameters and cells harvested before flocculation (control), wereexamined microscopically using a scanning fiber-optic microscope(Quanta 200, FEI Company, USA) and a Leica microscope (Leica DM1000, Leica Microsystems, Germany). The powder forms of micro-algae were sputter-coated with gold by the JFC-1600 auto finecoater (JEOL Ltd., Tokyo, Japan) before observation using the scan-ning fiber-optic microscope.

2.5. Data analysis and software

Statistical software Statistica 6.0 (StatSoft Inc., Oklahoma, USA)was applied to the experimental design and statistical analysis ofthe experimental data. The experiment was designed and carried

out at random. All the treatments were repeated three times anddata are reported as the mean ± SD values.

3. Results and discussion

3.1. Evaluation of flocculation parameters

3.1.1. Effect of c-PGA dosage on flocculation of C. vulgaris and C.protothecoides

Figs. 1A and 2A show the effect of c-PGA dosage on the floccula-tion efficiency and the concentration factor for C. vulgaris. The opti-mal c-PGA dosage was 20 mg L�1 with a flocculation efficiency of82% and a concentration factor of 15.1. Both the flocculation effi-ciency and concentration factor increased significantly (P < 0.05)with increasing c-PGA dosage up to a concentration of 20 mg L�1.However, both flocculation efficiency and concentration factor de-creased when the c-PGA dosage was increased above 20 mg L�1.Similar results were found for C. protothecoides flocculation(Fig. 3A and C) at an optimal c-PGA dosage of 20 mg L�1 with a floc-culation efficiency of 90% and a concentration factor of 23.7. Godoset al. (2011) reported similar results for above and below optimumdosages of five polymeric flocculants including chitosan. Our resultindicated that overdosing of c-PGA resulted in dispersion restabili-zation. Similar results were obtained by Vandamme et al. (2009).The zeta potentials of the microalgal suspensions before flocculationwere �19.08 and �13.62 mV for C. vulgaris and C. protothecoides,

0

20

40

60

80

100

Flo

ccul

atio

n ef

ficie

ncy

(%)

γ-PGA dosage (mg L-1)

aab

bc

d

A

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20

40

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100

Flo

ccul

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n ef

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(%)

Biomass concentration (g L-1)

ab

c

de

B

0

20

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60

80

100C

Flo

ccul

atio

n ef

ficie

ncy

(%)

CK2

Salinity (g L-1)

10 20 30 40 50 CK1

a ab

c

d

e e

Fig. 1. Effects of c-PGA dosage, biomass concentration and salinity on the flocculation efficiency of marine Chlorella vulgaris. The different letters in the graphs indicate asignificant difference at P < 0.05. (A biomass concentration: 1.2 g L�1, pH: 7.5 and salinity: 30 g L�1; B c-PGA: 20 mg L�1, pH: 7.5 and salinity: 30 g L�1; C c-PGA: 20 mg L�1,biomass concentration: 1.2 g L�1 and pH: 7.5; CK1 10 g L�1 sea salt with 1.2 g L�1 biomass concentration and pH of 7.5 without the addition of c-PGA; CK2 50 g L�1 sea saltwith 1.2 g L�1 biomass concentration and pH of 7.5 without the addition of c-PGA.).

214 H. Zheng et al. / Bioresource Technology 112 (2012) 212–220

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respectively, and those of the corresponding flocculated suspen-sions with optimal 20 and overdosing 30 mg L�1 c-PGA were+0.83 and +21.50 mV,+2.04 and +22.37 mV, respectively. These

results indicate that c-PGA could adsorb at the surface of the micro-algae and such adsorption caused a reduction of surface potential bycharge neutralization and a resulting destabilization of the

10 15 20 25 30

0

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cent

ratio

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A

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CK2

Salinity (g L-1)

10 20 30 40 50 CK1

C

Con

cent

ratio

n fa

ctor

a

bc

d

e

f f

Fig. 2. Effects of c-PGA dosage, biomass concentration and salinity on the concentration factor of marine Chlorella vulgaris. (Same legends as in Fig. 1).

Table 1The central composite design of RSM for optimization of the flocculation parameters of marine Chlorella vulgaris with c-PGA.

Run Factors Flocculation efficiency (%) Concentration factor

c-PGA dosage Biomass concentration Salinity

X1 P (mg L�1) X2 B (g L�1) X3 S (g L�1)

1 �1 15 �1 0.5 �1 10 86 ± 2 18.6 ± 0.62 �1 15 �1 0.5 1 30 79 ± 3 11.2 ± 0.43 �1 15 1 1.5 �1 10 82 ± 4 11.9 ± 0.54 �1 15 1 1.5 1 30 70 ± 2 5.4 ± 0.85 1 25 �1 0.5 �1 10 88 ± 1 18.8 ± 0.66 1 25 �1 0.5 1 30 85 ± 4 13.6 ± 0.77 1 25 1 1.5 �1 10 87 ± 3 8.8 ± 0.38 1 25 1 1.5 1 30 75 ± 2 8.3 ± 0.49 �1.682 11.59 0 1.0 0 20 74 ± 3 8.1 ± 0.510 1.682 28.41 0 1.0 0 20 86 ± 2 9.3 ± 0.311 0 20 �1.682 0.16 0 20 90 ± 2 20.4 ± 0.612 0 20 1.682 1.84 0 20 83 ± 4 11.8 ± 0.513 0 20 0 1.0 �1.682 3.18 90 ± 3 18.1 ± 0.414 0 20 0 1.0 1.682 36.82 78 ± 2 13.3 ± 0.315 0 20 0 1.0 0 20 87 ± 2 16.2 ± 0.416 0 20 0 1.0 0 20 87 ± 3 16.4 ± 0.4

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microalgae. Continuous adsorption beyond the point of charge neu-tralization by overdosing c-PGA caused charge reversal and restabi-lization occured.

3.1.2. Effect of biomass concentration on flocculation of C. vulgaris andC. protothecoides

Flocculation efficiency and concentration factor of c-PGA as afunction of biomass concentration are shown in Figs. 1B and 2B.Biomass concentration was strongly correlated with flocculationefficiency and concentration factor as both decreased significantly

(P < 0.05) with increasing biomass concentration. When the bio-mass concentration increased from 0.4 to 2.0 g L�1, the flocculationefficiency decreased from 89% to 65% and the concentration factordecreased from 17.1 to 9.8. Similar results were found forC. protothecoides flocculation (Fig. 3B and D). The flocculationmechanisms of microbial flocculants were not well established(Esser and Kues, 1983), but a series of flocculation mechanismsof microbial flocculants, like charge neutralization, bridging,sweep-out and precipitation enmeshment (Divakaran and Pillai,2002; Salehizadeh and Shojaosadati, 2001; Strand et al., 2002),

Table 2The central composite design of RSM for optimization of the flocculation parameters of freshwater Chlorella protothecoides with c-PGA.

Run Factors Flocculation efficiency (%) Concentration factor

c-PGA dosage Biomass concentration

X1 P (mg L�1) X2 B (g L�1)

1 �1 15 �1 0.5 92 ± 2 27.5 ± 0.82 �1 15 1 1.5 80 ± 2 8.9 ± 0.63 1 25 �1 0.5 93 ± 1 24.8 ± 0.94 1 25 1 1.5 87 ± 2 20.5 ± 0.55 �1.414 12.93 0 1.0 87 ± 3 10.7 ± 0.66 1.414 27.07 0 1.0 90 ± 1 19.6 ± 0.77 0 20 �1.414 0.29 96 ± 2 32.9 ± 0.88 0 20 1.414 1.71 82 ± 1 14.8 ± 0.49 0 20 0 1.0 94 ± 1 25.4 ± 0.510 0 20 0 1.0 94 ± 2 25.5 ± 0.6

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32D

Biomass concentration (g L-1)

Con

cent

ratio

n fa

ctor

aa

b

c

d

Fig. 3. Effects of c-PGA dosage and biomass concentration on the flocculation efficiency and concentration factor of freshwater Chlorella protothecoides. The different letters inthe graphs indicate a significant difference at P < 0.05. (A and C biomass concentration: 1.2 g L�1 and pH: 7.5; B and D c-PGA: 20 mg L�1 and pH: 7.5).

216 H. Zheng et al. / Bioresource Technology 112 (2012) 212–220

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have been proposed. Individual microalgal cells were visible in themicroalgal suspensions (Supplementary Fig. S1A and S1B) and thecells were interlaced with c-PGA in flocs, indicating inter-cellbridging between microalgal cells (Supplementary Fig. S1C andS1D). Based on the observations of zeta potentials and SEM imagesof the suspensions, the flocculation mechanisms were likelymainly cell aggregation by charge neutralization and bridging withc-PGA, but more detailed investigations are needed to further val-idate this hypothesis.

3.1.3. Effect of pH on flocculation of C. vulgaris and C. protothecoidesThe surface electric property of the particles for flocculation in

the suspension changed with pH, which influences flocculationwith microbial flocculants (Chaiwong and Nuntiya, 2008). c-PGAis a homopolymer of D- and L-glutamic acid units produced by B.subtilis (Shih and Van, 2001), and the dissolution of c-PGA inmicroalgal suspension may be influenced by pH. Microalgae wereharvested at the late logarithmic phase of growth and the pHvalues of the culture media for C. vulgaris and C. protothecoides wereapproximately 8.4 and 7.8, respectively. In order to investigate theeffect of pH on flocculation efficiency and concentration factorwith c-PGA, pHs of 6.5, 7.0, 7.5, 8.0 and 8.5 were evaluated. Floccu-lation efficiencies of C. vulgaris were approximately 81% andconcentration factors were approximately 15.2 with pH valuesranging from 6.5 to 8.5. Flocculation efficiencies (�89%) andconcentration factors (�23.6) of C. protothecoides varied little with-in the same pH range. This demonstrates that pH had little effecton flocculation efficiency and concentration factor. Yokoi et al.(1996) also reported high flocculation activity for a kaolin suspen-sion with c-PGA and only small changes were observed when thepH changed from 6.0 to 8.0.

3.1.4. Effect of salinity on flocculation of C. vulgarisHigh salinity is an important feature of culture media for mar-

ine microalgae. Sukenik et al. (1988) reported that microalgal floc-culation with cationic polymers was inhibited by the high ionicstrength of sea water. Figs. 1C and 2C show the flocculation effi-ciency and concentration factor of c-PGA with salinity levels of10, 20, 30, 40 and 50 g L�1 for C. vulgaris. Both the flocculation effi-ciency and concentration factor of c-PGA decreased significantly(P < 0.05) with increasing salinity and a maximum efficiency of88% and a maximum concentration factor of 17.4 were obtainedat a salinity of 10 g L�1, which was the lowest salinity tested. In or-der to study the effect of salinity on C. vulgaris flocculation withoutaddition of c-PGA, microalgal suspensions with a salinity of 10(CK1) and 50 g L�1 (CK2) were designed as controls. The resultsshowed that salinity had little effect on C. vulgaris flocculationwithout c-PGA (Figs. 1C, 2C and Fig. 4). Increasing salinity inhib-ited flocculation with c-PGA thus salinity was one of the mostimportant flocculation parameters for C. vulgaris. This result mightbe explained by increasing salinity affecting the conformation of c-PGA and higher sea salt concentration (ionic strength) causing thechain of c-PGA to adopt a random coil arrangement (He et al.,2000; Shih and Van, 2001), which induces a loose structure ofthe flocs, resulting in a decrease in flocculation efficiency (Bajajand Singhal, 2011).

3.2. Optimization of flocculation of C. vulgaris and C. protothecoideswith c-PGA

Since c-PGA dosage, biomass concentration and salinity hadhighly significant effects (P < 0.01) on flocculation of C. vulgarisand c-PGA dosage and biomass concentration had highly signifi-cant effects (P < 0.01) on flocculation of C. protothecoides withc-PGA, it was desirable to investigate the interaction betweenthe three most significant factors for C. vulgaris and the two most

significant factors for C. protothecoides and optimize them in an at-tempt to obtain higher flocculation efficiencies and concentrationfactors.

The results from the optimization experiments were analyzedby standard ANOVA and the central composite design was fittedwith the polynomial equations:

C. vulgaris

Flocculation efficiency ¼ ð0:3362þ 0:0489x1 � 0:0012X21

þ 0:0451x2 � 0:0237X22 þ 0:0034x3

� 0:0001X23 þ 0:0010x1x2 þ 0:0001X1X3

� 0:0035X2X3Þ � 100% ð6Þ

Concentration factor ¼ �17:5716þ 4:5617x1 � 0:1192X21

� 3:2846x2 � 1:4524X22 � 0:5508x3

� 0:0050X23 � 0:1400x1x2

þ 0:0205x1x3 þ 0:1400x2x3 ð7Þ

C. protothecoides

Flocculation efficiency ¼ ð0:5158þ 0:0441x1 � 0:0012X21

þ 0:0005x2 � 0:1075X22

þ 0:0060x1x2Þ � 100% ð8Þ

Concentration factor¼�25:5439þ6:9772x1�0:1967X21

�36:1743x2�2:2750X22þ1:4300x1x2 ð9Þ

where X1, X2 and X3 are c-PGA dosage, biomass concentration andsalinity (all for real values), respectively.

The fit of the models was checked by the coefficients of deter-mination R2, which were calculated to be 0.96, 0.99, 0.94 and0.99, implying that 96%, 99%, 94% and 99% of the variability inthe response could be explained by Eqs. (6)–(9) (Table 3). The sta-tistical significance of the model equations was evaluated by the F-test for ANOVA. The model F-values were more than 13.00 andtheir very low P-values (P < 0.05) indicated that all the modelswere significant. There was less than 5% chance that every modelwith an F-value this large could result from noise. The lack of fitF-values of less than 200.10 implied that there was no less than5% chance that every lack of fit F-value could occur due to noise.These results indicated that the models were suitable to describethe relationships between flocculation efficiency and the signifi-cant factors and between concentration factor and the significantfactors. The regression models developed can be represented in3-D response surface plots to gain a better understanding of theinteraction between the variables and to determine the optimumlevel of each variable for maximum response (SupplementaryFig. S2–S4).

In this study, with the aim of achieving high values of floccula-tion efficiency and concentration factor, a contradiction in param-eter settings is evident between the models. In practice, highefficiency is more important than a high concentration factor,otherwise biomass is lost (Bosma et al., 2003). Based on the resultsof RSM, flocculation was further optimized by the application ofthe global desirability function. The combinations predicted bythe application of the global desirability function were,22.03 mg L�1 c-PGA, 0.57 g L�1 biomass, and 11.56 g L�1 salinityfor C. vulgaris and 19.82 mg L�1 c-PGA and 0.60 g L�1 biomass forC. protothecoides. The values predicted for the responses wereflocculation efficiencies of 91 and 97% and concentration factorsof 20.7 and 29.5 for C. vulgaris and C. protothecoides, respectively.

In order to confirm the optimization results, flocculation wasstudied using the optimal flocculation parameters (c-PGA dosage

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22.03 mg L�1, biomass concentration 0.57 g L�1, and salinity11.56 g L�1 for C. vulgaris and 19.82 mg L�1 c-PGA and 0.60 g L�1

biomass for C. protothecoides). Maximum flocculation efficienciesunder optimal flocculation parameters were observed at 2 h of91 and 98% for C. vulgaris and C. protothecoides (Fig. 4), respectively.Their corresponding maximum concentration factors were 20.5and 29.8, respectively. These results were in good agreement withthe predicted values. Both flocculation efficiencies greater than90% and concentration factors exceeding 20.0 demonstrated the

feasibility of c-PGA as a promising microbial flocculant for harvest-ing microalgae.

3.3. Application of c-PGA as a microbial flocculant to other microalgalspecies

In an attempt to verify that the optimal flocculation parameterswith c-PGA were applicable to other microalgae, two marine mic-roalgal species (P. tricornutum and N. oculata LICME 002) and twofreshwater species ( C. vulgaris LICME 001 and B. braunii LICME003) were flocculated using the optimal flocculation parametersof marine C. vulgaris and freshwater C. protothecoides with c-PGA,respectively. The flocculation efficiencies and concentration factorsfor C. vulgaris LICME 001, B. braunii LICME 003, P. tricornutum andN. oculata LICME 002 were 90% and 20.1, 92% and 21.4, 97% and28.2, and 96% and 27.6, respectively, indicating effectiveness offlocculation with c-PGA for harvesting microalgae.

3.4. Effect of c-PGA on cell integrity

The harvesting process may cause cell disruption and affectdownstream processing and lipid recovery. In order to assess theimpact of c-PGA on the microalgal biomass harvest, the direct ef-fects of c-PGA on the cell wall of marine C. vulgaris (SupplementaryFig. S1A–D) and the other five microalgae (data not shown) wereobserved using scanning electron and light microscopes. Supple-mentary Fig. S1A and S1B show the state of microalgal cells beforethe addition of c-PGA, and Supplementary Fig. S1C and S1D showthe state of microalgal cells after flocculation with c-PGA. Compar-ing Supplementary Fig. S1A and S1C, it can be easily demonstratedthat c-PGA flocculates microalgal cells with very little visualchange in their morphology. In a previous study (Zheng et al.,2011), the structure of disrupted cells of marine C. vulgaris showedsignificant deformation (Supplementary Fig. S1E) compared withintact cells. In addition, c-PGA had very little effect on the

Table 3ANOVA for the response surface models.

Source Sum of squares DF Mean square F-value p-value

Marine Chlorella vulgarisFlocculation efficiencya

Model 500.62 9 55.62 16.43 0.0016Residual 20.32 6 3.39Lack of fit 19.82 5 3.96 7.93 0.2630Pure error 0.50 1 0.50Total 520.94 15Concentration factorb

Model 325.55 9 36.17 105.94 <0.0001Residual 2.05 6 0.34Lack of fit 2.03 5 0.41 20.29 0.1669Pure error 0.02 1 0.02Total 327.60 15Freshwater Chlorella protothecoidesFlocculation efficiencyc

Model 194.46 5 38.89 13.03 0.0138Residual 11.94 4 2.99Lack of fit 11.44 3 3.81 7.63 0.2587Pure error 0.50 1 0.50Total 206.40 9Concentration factord

Model 635.21 5 127.04 169.03 <0.0001Residual 3.01 4 0.75Lack of fit 3.00 3 1.00 200.09 0.0519Pure error 0.01 1 0.01Total 638.22 9

a The coefficient of determination (R2) of the model was 0.96.b The coefficient of determination (R2) of the model was 0.99.c The coefficient of determination (R2) of the model was 0.94.d The coefficient of determination (R2) of the model was 0.99.

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.20

10

20

30

40

50

60

70

80

90

100

Flo

ccul

atio

n ef

ficie

ncy

(%)

Flocculation time (h)

Fig. 4. Time course of the flocculation under the optimized parameters. (jflocculation of marine Chlorella vulgaris with c-PGA; d flocculation of freshwaterChlorella protothecoides with c-PGA; N Control: marine Chlorella vulgaris withoutthe addition of c-PGA; � Control: freshwater Chlorella protothecoides without theaddition of c-PGA).

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morphology of the other microalgal cells. These results indicatethat flocculated microalgal cells with c-PGA did not show lysis.Similar results were obtained by Divakaran and Pillai (2002) usingchitosan as a microbial flocculant for harvesting Spirulina, Oscillato-ria, Chlorella and Synechocystis. The lipids of the cells in our studywould not be lost during the flocculation process.

3.5. Comparison of microalgae harvesting efficiency with c-PGA andconventional harvesting methods

The optimal flocculation method with c-PGA evaluated in thiswork was compared with some conventional harvesting methods(Table 4). The harvesting efficiencies in this study showed no sig-nificant difference compared with those of the conventional har-vesting methods (P > 0.05). c-PGA was able to flocculate marineand freshwater microalgae. Moreover, the microalgal cells were in-tact and no metallic flocculants were used. The price of c-PGA ap-plied in this work is approximately 5 US dollars per kg, which issufficient to treat up to 45,000 L of microalgal suspensions. How-ever, it is also noteworthy to point out that the products of c-PGA from different bacterial species may have different harvestingperformances for different microalgae, an area requiring furtherresearch.

4. Conclusion

The work focused on optimizing flocculation parameters ofmarine C. vulgaris and freshwater C. protothecoides with c-PGA. Amaximum flocculation efficiency and concentration factor of 91%and 20.5 of C. vulgaris and 98% and 29.8 of C. protothecoides, respec-tively, were obtained. The optimal flocculation parameters of c-PGA dosage, biomass concentration and salinity for C. vulgarisand c-PGA dosage and biomass concentration for C. protothecoideswere successfully applied to harvest other microalgae. c-PGA hadlittle effect on microalgal cell integrity. Our results demonstratethat c-PGA has potential as an efficient and sustainable microbialflocculant for harvesting microalgae in biodiesel production.

Acknowledgements

This work was supported by the Major State Basic ResearchDevelopment Program of China (973 Project) (Grant Nos.2011CB200904 and 2011CB200906), and our sincere thanks toDr. Ailish O’Halloran from Institute of Technology Tallaght, Irelandfor her language assistance.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.biortech.2012.02.086.

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Table 4Comparison of harvesting efficiencies of different methods.

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Flocculation with c-PGA Chlorella protothecoides, Chlorella vulgarisLICME 001, and Botryococcus braunii LICME 003

Freshwater microalgae >95 Current study

Flocculation with c-PGA Chlorella vulgaris, Nannochloropsis oculata LICME 002,and Phaeodactylum tricornutum

Marine microalgae >90 Current study

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