Bioresource Technology Volume 112 Issue None 2012 [Doi 10.1016%2Fj.biortech.2012.02.086] Hongli Zheng; Zhen Gao; Jilong

8/18/2019 Bioresource Technology Volume 112 Issue None 2012 [Doi 10.1016%2Fj.biortech.2012.02.086] Hongli Zheng; Zhe…

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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 2011

Received in revised form 7 February 2012Accepted 13 February 2012

Available online 27 February 2012

Keywords:

Microalgae

Microbial flocculant

Response surface methodology

Biomass harvest

a b s t r a c t

In an effort to search for an efficient and environmentally friendly harvesting method, a commercially

available 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. Application

of 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 cells

showed 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 of

fossil fuels and climate change. Microalgae are among the most

primitive forms of plant life able to capture CO2. In addition, some

microalgae can produce lipids suitable for biodiesel (Chiu et al.,

2009; Sialve et al., 2009). Compared with other energy crops, the

advantages of deriving biodiesel from microalgae include rapid

growth rates and a high per-acre yield. In addition, biodiesel has

low toxicity, is highly biodegradable and contains no sulfur (Hsieh

and Wu, 2009; Fu et al., 2009). Considering all the steps involved in

the biodiesel production from microalgae, harvest is a particularly

important step. Harvesting of microalgae is challenging because of

low cell concentrations (


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2.2. Microalgal strains and cultivation conditions

Species of microalgae were obtained from the Culture Collection

of Algae at the University of Texas at Austin (Chlorella prototheco-

ides UTEX 255 and Phaeodactylum tricornutum UTEX 640), the

China Center for Type Culture Collection at Wuhan in China (mar-

ine Chlorella vulgaris, strain CCTCC M 209256), and our laboratory

isolations (freshwater C. vulgaris LICME 001, Nannochloropsisoculata LICME 002, and Botryococcus braunii LICME 003). Marine

C. vulgaris and N. oculata were grown in medium composed of (in

mg L 1): KNO3, 100; KH2PO4, 10; Na2EDTA, 10; FeSO47H2O, 2.5;

MnSO4, 0.25; Vitamin B1, 0.006; Vitamin B12, 0.00005; instant

ocean synthetic sea salt (Aquarium Systems, Inc., USA), 26,000.

Freshwater C. vulgaris LICME 001, C. protothecoides, and B. braunii

LICME 003 were grown in BG-11 medium. P. tricornutum was

grown in F/2 medium. The media were autoclaved at 121 C for

20 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 culture

temperature of 25 C was regulated by water recycled in the outer

layer of the photobioreactor. Ten fluorescent lamps were arranged

around the photobioreactor to supply continuous illumination of

80 lmol photons m2 s1 with a 12/12 h light/dark cycle. At the

bottom of the reactor, there was a gas sparger. CO 2 of 3.0% was

prepared with a combination of room air and pure CO2 from a com-

pressor and an aeration rate of 200 mL min1 was carried out. The

cultivation 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) of

cultures at 680 nm according to the following equations: marine or

freshwater C. vulgaris BC = 0.560 OD680 (R2 = 0.986); N. oculata

BC = 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 the

250 mL glass beakers, and the salinity of the media was adjusted

by addition of instant ocean synthetic sea salt or distilled water

to 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 adjusted

to 6.5, 7.0, 7.5, 8.0, and 8.5 with 0.5 M HCl or 0.5 M NaOH for the

pH effect experiment, otherwise the pH was kept at 7.5. The initial

optical density of the microalgal suspension in the beakers was

measured 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 the

clarified layer and the sludge was measured. The flocculation effi-ciency was defined as the ratio of the mass of cells recovered to the

total mass of cells and the concentration factor was the ratio of the

final product concentration to the initial concentration (Bosma

et al., 2003). The flocculation efficiency and concentration factor

were calculated as:

Flocculation efficiency ð%Þ ¼A0V 0 A1V 1

A0V 0 100 ð1Þ

Concentration factor ¼A2A0

ð2Þ

where A1 is OD680 of the supernatant from half the height of the

clarified layer after flocculation, A2 is OD680 of the sludge after

flocculation, and A0 is OD680 of the microalgal suspension before

flocculation. V 0 is the volume of microalgal suspension before floc-

culation, and V 1 is the volume of microalgal supernatant after

flocculation.

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 for

marine and freshwater microalgae, respectively. The effects of

the flocculation parameters such as c-PGA dosage, biomass

concentration, pH and salinity on C. vulgaris and c-PGA dosage, bio-

mass concentration and pH on C. protothecoides were individually

investigated by analyzing flocculation efficiency and concentration

factor.

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. vulgaris

and 1.2 g L 1 biomass and pH 7.5 for C. protothecoides) and those

of the flocculated suspensions (obtained from the above c-PGA

dosage experiment containing 20 and 30 mg L 1 c-PGA, respec-

tively) were measured with a Zeta Potential Analyzer utilizing

phase analysis light scattering (Brookhaven Instruments Corpora-

tion, USA).

2.4.2. Optimization of flocculation of C. vulgaris and C. protothecoides

with c-PGATo improve flocculation efficiencies and concentration factors,

the interaction between the three most significant factors (c-PGA

dosage, biomass concentration and salinity) for C. vulgaris and that

between the two most significant factors (c-PGA dosage and bio-

mass concentration) for C. protothecoides identified by preliminary

evaluation experiments were studied. Since it is known that RSM

can evaluate the interaction between the significant factors of an

experiment and optimize them (Ghosh and Hallenbeck, 2010; Jiet al., 2009), RSM using central composite design was applied to

determine the optimal levels of the three selected variables for C.

vulgaris and the two selected variables for C. protothecoides, which

significantly affected the flocculation efficiency and concentration

factor. The three independent factors with five different levels

(1.682, 1, 0,+1,+1.682) of C. vulgaris and the two independent

factors with five different levels (1.414, 1, 0,+1,+1.414) of C.

protothecoides were investigated and the experimental designs

are shown in Tables 1 and 2. The factors were coded according to

the following equation:

xi ¼ X i X 0D X

; i ¼ 1; 2; 3; . . . ;k ð3Þ

where xi is the coded independent factor, X i is the real independentfactor, X 0 is the value of X i at the center point and D X is the step

change value.

The flocculation efficiencies and concentration factors of c-PGA

were fitted using a polynomial equation and four multiple regres-

sions of the data were carried out to obtain four empirical models

related to the three and two most significant factors in the case of

C. vulgaris and C. protothecoides, respectively. The general form of

the polynomial equation is:

Y ¼ b0 þX

biXiþX

bii X 2i þX

bij X i X j; . . . i; j ¼ 1; 2; 3; . . . ;k ð4Þ

where Y is the predicted response, X i and X j are independent factors,

b0 is the intercept, bi is the linear coefficient, bii is the quadratic

coefficient, and b ij is the interaction coefficient.

H. Zheng et al. / Bioresource Technology 112 (2012) 212–220 213


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To maximize the two response variables flocculation efficiency

and concentration factor simultaneously (m = 2), an optimization

using the global desirability function (D) was performed forC. vulgaris and C. protothecoides, respectively, which consisted in

converting each response into a single desirability function (di)

ranging from 0 to 1 (0 6 di 6 1) (Derringer and Suich, 1980). The

individual desirability’s were then combined using the geometric

mean, which gives the overall desirability D:

D ¼ Ym

i¼1

di !1=m

ð5Þ

Microalgal biomass samples harvested at optimal flocculation

parameters and cells harvested before flocculation (control), were

examined microscopically using a scanning fiber-optic microscope

(Quanta 200, FEI Company, USA) and a Leica microscope (Leica DM

1000, Leica Microsystems, Germany). The powder forms of micro-

algae were sputter-coated with gold by the JFC-1600 auto fine

coater (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 of the experimental data. The experiment was designed and carried

out at random. All the treatments were repeated three times and

data 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 of

82% 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 anoptimalc-PGA dosage of 20 mg L 1 with a floc-

culation efficiency of 90% and a concentration factor of 23.7. Godos

et al. (2011) reported similar results for above and below optimum

dosages of five polymeric flocculants including chitosan. Our result

indicated 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

F l o c c u l a t i o n e f f i c i e n c y ( % )

γ -PGA dosage (mg L-1

)

aab

bc

d

A

10 15 20 25 30 0.4 0.8 1.2 1.6 2.0

0

20

40

60

80

100


Biomass concentration (g L-1

)

ab

c

d

e

B

0

20

40

60

80

100C


CK2

Salinity (g L-1

)

10 20 30 40 50 CK1

a a

b

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 a

significant 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 salt

with 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 thatc-PGA could adsorbat the surface of themicro-

algae andsuch adsorption causeda reductionof surface potential by

charge neutralization and a resulting destabilization of the

10 15 20 25 30

0

2

4

6

8

10

12

14

16

18


)

C o n c e n t r a

t i o n f a c t o r

a

b

c

d

e

A

0.4 0.8 1.2 1.6 2.00

2

4

6

8

10

12

14

16

18

20B


)

C o n c e n t r a t i o n f a c t o r

a

bc

d

e

0

2

4

6

8

10

12

14

16

18

20

CK2

Salinity (g L-1

)

10 20 30 40 50 CK1

C


a

b c

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 1

The 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

X 1 P (mg L 1) X 2 B (g L

1) X 3 S (g L 1)

1 1 15 1 0.5 1 10 86 ± 2 18.6 ± 0.6

2 1 15 1 0.5 1 30 79 ± 3 11.2 ± 0.4

3 1 15 1 1.5 1 10 82 ± 4 11.9 ± 0.5

4 1 15 1 1.5 1 30 70 ± 2 5.4 ± 0.8

5 1 25

1 0.5

1 10 88 ± 1 18.8 ± 0.66 1 25 1 0.5 1 30 85 ± 4 13.6 ± 0.7

7 1 25 1 1.5 1 10 87 ± 3 8.8 ± 0.3

8 1 25 1 1.5 1 30 75 ± 2 8.3 ± 0.4

9 1.682 11.59 0 1.0 0 20 74 ± 3 8.1 ± 0.5

10 1.682 28.41 0 1.0 0 20 86 ± 2 9.3 ± 0.3

11 0 20 1.682 0.16 0 20 90 ± 2 20.4 ± 0.6

12 0 20 1.682 1.84 0 20 83 ± 4 11.8 ± 0.5

13 0 20 0 1.0 1.682 3.18 90 ± 3 18.1 ± 0.4

14 0 20 0 1.0 1.682 36.82 78 ± 2 13.3 ± 0.3

15 0 20 0 1.0 0 20 87 ± 2 16.2 ± 0.4

16 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 and

C. protothecoides

Flocculation efficiency and concentration factor of c-PGA as a

function 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 flocculation

efficiency decreased from 89% to 65% and the concentration factor

decreased from 17.1 to 9.8. Similar results were found for

C. protothecoides flocculation (Fig. 3B and D). The flocculation

mechanisms of microbial flocculants were not well established

(Esser and Kues, 1983), but a series of flocculation mechanisms

of microbial flocculants, like charge neutralization, bridging,

sweep-out and precipitation enmeshment (Divakaran and Pillai,2002; Salehizadeh and Shojaosadati, 2001; Strand et al., 2002),

Table 2

The 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

X 1 P (mg L 1) X 2 B (g L

1)

1 1 15 1 0.5 92 ± 2 27.5 ± 0.8

2 1 15 1 1.5 80 ± 2 8.9 ± 0.6

3 1 25 1 0.5 93 ± 1 24.8 ± 0.94 1 25 1 1.5 87 ± 2 20.5 ± 0.5

5 1.414 12.93 0 1.0 87 ± 3 10.7 ± 0.6

6 1.414 27.07 0 1.0 90 ± 1 19.6 ± 0.7

7 0 20 1.414 0.29 96 ± 2 32.9 ± 0.8

8 0 20 1.414 1.71 82 ± 1 14.8 ± 0.4

9 0 20 0 1.0 94 ± 1 25.4 ± 0.5

10 0 20 0 1.0 94 ± 2 25.5 ± 0.6

0

20

40

60

80

100



)

a abc

d

A

10 15 20 25 30 0.4 0.8 1.2 1.6 2.00

20

40

60

80

100



)

a ab

cd

B

0

4

8

12

16

20

24

28

γ -PGA dosage (mg L-1)


a

b

c

d

e

C

10 15 20 25 30 0.4 0.8 1.2 1.6 2.0

0

4

8

12

16

20

24

28

32D

Biomass concentration (g L-1)


a

ab

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 in

the 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).



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have been proposed. Individual microalgal cells were visible in the

microalgal suspensions (Supplementary Fig. S1A and S1B) and the

cells were interlaced with c-PGA in flocs, indicating inter-cell

bridging between microalgal cells (Supplementary Fig. S1C and

S1D). Based on the observations of zeta potentials and SEM images

of the suspensions, the flocculation mechanisms were likely

mainly cell aggregation by charge neutralization and bridging with

c-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. protothecoides

The surface electric property of the particles for flocculation in

the suspension changed with pH, which influences flocculation

with microbial flocculants (Chaiwong and Nuntiya, 2008). c-PGA

is a homopolymer of D- and L-glutamic acid units produced by B.

subtilis (Shih and Van, 2001), and the dissolution of c-PGA in

microalgal suspension may be influenced by pH. Microalgae were

harvested at the late logarithmic phase of growth and the pH

values of the culture media for C. vulgaris and C. protothecoides were

approximately 8.4 and 7.8, respectively. In order to investigate the

effect of pH on flocculation efficiency and concentration factor

with 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% and

concentration factors were approximately 15.2 with pH values

ranging from 6.5 to 8.5. Flocculation efficiencies (89%) and

concentration factors (23.6) of C. protothecoides varied little with-

in the same pH range. This demonstrates that pH had little effect

on 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 the

pH changed from 6.0 to 8.0.

3.1.4. Effect of salinity on flocculation of C. vulgaris

High 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 ionic

strength of sea water. Figs. 1C and 2C show the flocculation effi-ciency and concentration factor of c-PGA with salinity levels of

10, 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 of

88% and a maximum concentration factor of 17.4 were obtained

at 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 without

addition of c-PGA, microalgal suspensions with a salinity of 10

(CK1) and 50 g L 1 (CK2) were designed as controls. The results

showed that salinity had little effect on C. vulgaris flocculation

without c-PGA (Figs. 1C, 2C and Fig. 4). Increasing salinity inhib-

ited flocculation with c-PGA thus salinity was one of the most

important flocculation parameters for C. vulgaris. This result might

be explained by increasing salinity affecting the conformation of c-PGA and higher sea salt concentration (ionic strength) causing the

chain of c-PGA to adopt a random coil arrangement (He et al.,

2000; Shih and Van, 2001), which induces a loose structure of

the flocs, resulting in a decrease in flocculation efficiency (Bajaj

and Singhal, 2011).

3.2. Optimization of flocculation of C. vulgaris and C. protothecoides

with c-PGA

Since c-PGA dosage, biomass concentration and salinity had

highly significant effects (P < 0.01) on flocculation of C. vulgaris

and c-PGA dosage and biomass concentration had highly signifi-

cant effects (P < 0.01) on flocculation of C. protothecoides with

c-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 concentration

factors.

The results from the optimization experiments were analyzed

by standard ANOVA and the central composite design was fitted

with the polynomial equations:

C. vulgaris

Flocculation efficiency ¼ ð0:3362 þ 0:0489 x1 0:0012 X 21

þ 0:0451 x2 0:0237 X 22 þ 0:0034 x3

0:0001 X 23 þ 0:0010 x1 x2 þ 0:0001 X 1 X 3

0:0035 X 2 X 3Þ 100% ð6Þ

Concentration factor ¼ 17:5716 þ 4:5617 x1 0:1192 X 21

3:2846 x2 1:4524 X 22 0:5508 x3

0:0050 X 23 0:1400 x1 x2

þ 0:0205 x1 x3 þ 0:1400 x2 x3 ð7Þ

C. protothecoides

Flocculation efficiency ¼ ð0:5158 þ 0:0441 x1 0:0012 X 21

þ 0:0005 x2 0:1075 X 22

þ 0:0060 x1 x2Þ 100% ð8Þ

Concentration factor¼25:5439þ6:9772 x1 0:1967 X 21

36:1743 x2 2:2750 X 22 þ1:4300 x1 x2 ð9Þ

where X 1, X 2 and X 3 are c-PGA dosage, biomass concentration and

salinity (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 and

0.99, implying that 96%, 99%, 94% and 99% of the variability in

the 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 and

their very low P -values (P < 0.05) indicated that all the models

were significant. There was less than 5% chance that every model

with an F -value this large could result from noise. The lack of fit

F -values of less than 200.10 implied that there was no less than

5% chance that every lack of fit F -value could occur due to noise.

These results indicated that the models were suitable to describe

the relationships between flocculation efficiency and the signifi-

cant factors and between concentration factor and the significant

factors. The regression models developed can be represented in

3-D response surface plots to gain a better understanding of the

interaction between the variables and to determine the optimum

level of each variable for maximum response (Supplementary

Fig. 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, high

efficiency is more important than a high concentration factor,

otherwise biomass is lost (Bosma et al., 2003). Based on the results

of RSM, flocculation was further optimized by the application of

the global desirability function. The combinations predicted by

the 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 salinity

for 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 were

flocculation efficiencies of 91 and 97% and concentration factors

of 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 salinity

11.56 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 efficiencies

under optimal flocculation parameters were observed at 2 h of

91 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 with

the predicted values. Both flocculation efficiencies greater than

90% 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 parameters

with c-PGA were applicable to other microalgae, two marine mic-

roalgal species (P. tricornutum and N. oculata LICME 002) and two

freshwater species ( C. vulgaris LICME 001 and B. braunii LICME

003) were flocculated using the optimal flocculation parameters

of marine C. vulgaris and freshwater C. protothecoides with c-PGA,

respectively. The flocculation efficiencies and concentration factors

for C. vulgaris LICME 001, B. braunii LICME 003, P. tricornutum and

N. oculata LICME 002 were 90% and 20.1, 92% and 21.4, 97% and

28.2, and 96% and 27.6, respectively, indicating effectiveness of

flocculation with c-PGA for harvesting microalgae.

3.4. Effect of c-PGA on cell integrity

The harvesting process may cause cell disruption and affect

downstream processing and lipid recovery. In order to assess the

impact of c-PGA on the microalgal biomass harvest, the direct ef-

fects of c-PGA on the cell wall of marine C. vulgaris (Supplementary

Fig. S1A–D) and the other five microalgae (data not shown) were

observed using scanning electron and light microscopes. Supple-

mentary Fig. S1A and S1B show the state of microalgal cells before

the addition of c-PGA, and Supplementary Fig. S1C and S1D show

the state of microalgal cells after flocculation with c-PGA. Compar-

ing Supplementary Fig. S1A and S1C, it can be easily demonstrated

that c-PGA flocculates microalgal cells with very little visual

change in their morphology. In a previous study (Zheng et al.,

2011), the structure of disrupted cells of marine C. vulgaris showed

significant deformation (Supplementary Fig. S1E) compared withintact cells. In addition, c-PGA had very little effect on the

Table 3

ANOVA for the response surface models.

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

Marine Chlorella vulgaris

Flocculation efficiencya

Model 500.62 9 55.62 16.43 0.0016

Residual 20.32 6 3.39

Lack of fit 19.82 5 3.96 7.93 0.2630

Pure error 0.50 1 0.50Total 520.94 15

Concentration factorb

Model 325.55 9 36.17 105.94


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morphology of the other microalgal cells. These results indicate

that flocculated microalgal cells with c-PGA did not show lysis.

Similar results were obtained by Divakaran and Pillai (2002) using

chitosan as a microbial flocculant for harvesting Spirulina, Oscillato-

ria, Chlorella and Synechocystis. The lipids of the cells in our study

would not be lost during the flocculation process.

3.5. Comparison of microalgae harvesting efficiency with c-PGA and

conventional harvesting methods

The optimal flocculation method with c-PGA evaluated in this

work 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 marine

and 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 is

sufficient 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 harvesting

performances for different microalgae, an area requiring further

research.

4. Conclusion

The work focused on optimizing flocculation parameters of

marine C. vulgaris and freshwater C. protothecoides with c-PGA. A

maximum 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. vulgaris

and c-PGA dosage and biomass concentration for C. protothecoides

were successfully applied to harvest other microalgae. c-PGA had

little effect on microalgal cell integrity. Our results demonstrate

that c-PGA has potential as an efficient and sustainable microbial

flocculant for harvesting microalgae in biodiesel production.

Acknowledgements

This work was supported by the Major State Basic Research

Development Program of China (973 Project) (Grant Nos.

2011CB200904 and 2011CB200906), and our sincere thanks to

Dr. Ailish O’Halloran from Institute of Technology Tallaght, Ireland

for 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 4

Comparison of harvesting efficiencies of different methods.

Methods Microlgal species Marine/freshwater microalgae Harvesting

efficiencies (%)

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Flocculation with c-PGA Chlorella protothecoides, Chlorella vulgaris

LICME 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

Flocculation with chitosan Thalassiosira pseudonana Marine microalga 90 Heasman et al. (2000)Flocculation with AlCl3 Chlorella minutissima Freshwater microalga >90 Papazi et al. (2009)

Centrifugation Phaeodactylum tricornutum Marine microalga 94 Heasman et al. (2000)


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Bioresource Technology Volume 112 Issue None 2012 [Doi 10.1016%2Fj.biortech.2012.02.086] Hongli Zheng; Zhen Gao; Jilong