Bioresource Technology 170 (2014) 90–99

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Biocapture of CO2 from biogas by oleaginous microalgae for improving methane content and simultaneously producing lipid Wassa Tongprawhan, Sirasit Srinuanpan, Benjamas Cheirsilp ⇑ Department of Industrial Biotechnology, Faculty of Agro-Industry, Prince of Songkla University, Hat Yai, Songkhla 90112, Thailand

h i g h l i g h t s  Several oleaginous microalgae were able to capture CO2 from biogas.  Marine Chlorella sp. was most suitable for capturing CO2 and producing lipid.  The medium and operating conditions for capturing CO2 from biogas were optimized.  The produced microalgal lipid was suitable for being used as a biodiesel feedstock.

a r t i c l e

i n f o

Article history: Received 2 June 2014 Received in revised form 19 July 2014 Accepted 23 July 2014 Available online 1 August 2014 Keywords: Biogas CO2 capture Lipid Methane Oleaginous microalgae

a b s t r a c t This study aimed to use oleaginous microalgae to capture CO2 from biogas for improving methane content and simultaneously producing lipid. Several microalgae were screened for their ability to grow and produce lipid using CO2 in biogas. A marine Chlorella sp. was the most suitable strain for capturing CO2 and producing lipid using biogas (50% v/v CO2 in methane) as well as using 50% v/v CO2 in air. The medium and operating conditions were optimized through response surface methodology (RSM). The optimal concentrations of KNO3 and K2HPO4 were 0.80 g L1 and 0.06 g L1, respectively. The optimal operating conditions were: initial pH of 7.8, initial cell concentration of 107.5 cells mL1, light intensity of 4500 lux and gas flow rate of 0.03 L min1. After optimization, 89.3% of CO2 was removed from biogas and the methane content was increased up to 94.7%. The lipid productivity was 94.7 mg L1 day1. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Recycling CO2 into energy is an interesting concept that could help to avoid a critical energy crisis due to the exhaustion of fossil fuels. Green microalgae could make a big contribution to this process via their photosynthesis ability to fix CO2 into biomass and a possible energy source (Berberoglu et al., 2009). Microalgae efficiently use CO2 when they grow rapidly and can be readily incorporated into normal engineered systems, such as photobioreactors (Chiu et al., 2008; Carvalho et al., 2006). In recent times, microalgae have been promoted for CO2 mitigation as they can be grown in wastewater and capture released CO2 at high CO2 concentration such as that are present in flues and flaring gas at 5–15% (Chinnasamy et al., 2010; Hsueh et al., 2007). In addition, microalgae can provide several different types of renewable biofuels. These include methane produced by anaerobic microbial digestion of the produced microalgal biomass (Spolaore et al., 2006), ⇑ Corresponding author. Fax: +66(74) 44 6727. E-mail address: [email protected] (B. Cheirsilp). http://dx.doi.org/10.1016/j.biortech.2014.07.094 0960-8524/Ó 2014 Elsevier Ltd. All rights reserved.

biodiesel derived from microalgal lipid (Chisti, 2007; Chiu et al., 2009; Ho et al., 2010; Lv et al., 2010) and photobiologically produced biohydrogen (Gavrilescu and Chisti, 2005; Fedorov et al., 2005). The biocapture of CO2 by microalgae can be applied to improve the quality of biogas by reducing the CO2 content as this would lead to an increase in the methane content (Mann et al., 2009). Biogas is a fuel derived from the microbial degradation of organic matters under anaerobic condition. Biogas contains 50–70% of methane, 28–50% of CO2 and the remaining being 2% of gaseous nitrogen and hydrogen sulfide. However, high contents of CO2 and hydrogen sulfide in the biogas cause it to have restrictions on its use and its efficiency. Therefore, the removal of those gases is a key to improving the quality of biogas. To date research work that has been associated with removing CO2 from biogas by a biological process has been limited. One research group has evaluated the possibility to use microalgae Chlorella sp. to reduce the CO2 content of biogas (Mann et al., 2009). Converti et al. (2009) have also investigated the purification of biogas using cyanobacterium Arthrospira platensis. They established that there was a linear

W. Tongprawhan et al. / Bioresource Technology 170 (2014) 90–99

relationship between the rate of bacterial growth and CO2 removal. Recently, Yan and Zheng (2013) determined the effect of photoperiods and light intensities on the growth of Chlorella sp. and its ability to remove CO2 from biogas. However, no attempt has been tried to optimize the process for biocapturing CO2 from biogas and simultaneously producing lipid by the microalgae. Therefore, this has been the first study that attempted to use oleaginous microalgae to capture CO2 from biogas and simultaneously produce lipid for being used as biodiesel feedstock. Several green microalgae were screened for their ability to grow and accumulate lipid using 50% v/v CO2 in air and in biogas. The medium and operating conditions for capturing CO2 from biogas and producing lipid by the selected microalgae were then optimized through response surface methodology (RSM). The fatty acid compositions of the microalgal lipid were also determined.

91

based on multiple linear regressions that took into account the main, quadratic and interactive effects, in accordance with the following equation:

Y ¼ b0 þ Rbi xi þ Rbii x2i þ Rbij xi xj

ð1Þ

where Y is the predicted response, xi and xj represent the variables or parameters, b0 is the offset term, bi is the linear effect, bij is the first order interaction effect and bii is the squared effect. The goodness of the fit of the model was evaluated by the coefficient of determination (R2) and the analysis of variance (ANOVA). Response surface plots were developed to indicate an optimum condition using the fitted quadratic polynomial equations obtained by holding one of the independent variables at a constant value and changing the levels of the other two variables. 2.4. Analytical method

2. Methods 2.1. Microalgal strain Five green microalgal strains: Chlorella vulgaris TISTR 8580, Chlorella protothecoides TISTR 8243, Chlorococcum sp. TISTR 8416, Chlorella sp. TISTR 8263 and Scenedesmus armatus TISTR 8653 were obtained from the microbiological resources center of the Thailand Institute of Scientific and Technological Research (TISTR) and one strain of marine Chlorella sp. was obtained from the National Institute of Coastal Aquaculture, Thailand. 2.2. Media The modified Chu13 medium used as the basic medium for this study consisted of KNO3 0.2 g as a nitrogen source, K2HPO4 0.04 g as a phosphorus source, citric acid 0.1 g, Fe citrate 0.01 g, MgSO4 7H2O 0.1 g, NaHCO3 0.036 g, and 1 mL of trace metal solution per 1 L. The trace metal solution consisted of H3BO3 2.85 g, MnCl24H2O 1.8 g, ZnSO47H2O 0.02 g, CuSO45H2O 0.08 g, CoCl26H2O 0.08 g, and Na2MoO42H2O 0.05 g per 1 L (Largeau et al., 1980). 2.3. Culture conditions and experimental design Experiments for selection of the microalgae were conducted in 1 L Duran bottles with 900 mL working volume and agitation was performed by a magnetic stirrer. The culture was bubbled with filtered air (0.03% CO2), 50% CO2 in air and 50% CO2 in methane at a gas flow rate of 0.01 L min1 during the illumination period of 16 h a day. The light intensity was 3000 lux provided by cool-white fluorescent lamps. The initial cell concentration was 106 cells mL1. The cultivation time lasted until the cell growth entered the stationary phase: 24 days while being aerated with air and 8 days while being aerated with 50% CO2 in air and 50% CO2 in methane. Optimizations of the medium and operating conditions for biocapture of CO2 and lipid production by the selected microalgae were performed through Response Surface Methodology (RSM) using the Box–Behnken experimental design (BBD). The three variables in the medium components (KNO3, K2HPO4 and pH) and the three variables in the operating conditions (initial cell concentration, light intensity and gas flow rate) at three levels were followed to determine the response pattern and also to determine the synergetic effects of the variables. According to this design, 15 runs for each optimization were conducted containing three replications at the central point for estimating the purely experimental uncertainty variance. The relationship of the variables was determined by fitting a second order polynomial equation to the data obtained from the 15 runs. The response surface analysis was

Cell growth was determined as cell concentrations by a direct microscopic count method using a hemocytometer and turbidimetrically at 540 nm using a spectrophotometer. The dry cell weight was determined as follows: 20 mL of microalgal suspension was centrifuged at 4000g for 20 min and the precipitate was dried at 60 °C until constant weight. The lipid content of the dry microalgal biomass was determined using liquid extraction with a mixed solvent solution of methanol and chloroform (2:1 v/v). Dry microalgal biomass was mashed and mixed with the solvent solution before sonication for 30 min. The suspension was centrifuged at 4000g for 20 min. The supernatant was collected and the precipitate was extracted twice more with the same solvent. After extraction, the solvent solution was evaporated overnight and the extracted lipid was determined gravimetrically. The compositions of biogas was determined using a Gas Chromatograph (GC 7890A) with a thermal conductivity detector (TCD: Model Number Restek 19808 Shincarbon-ST) and helium was the carrier gas. All experiments were performed in triplicates. The results are expressed as a mean plus standard deviations. Analysis of variance was performed to calculate significant differences in treatment means, and the least significant difference was used to separate means, using SPSS software. 3. Results and discussion 3.1. Screening of microalgae for biocapture of CO2 The growth of six green microalgae strains: C. vulgaris TISTR 8580 (Cv), C. protothecoides TISTR 8243 (Cp), Chlorococcum sp. TISTR 8416 (Cc), Chlorella sp. TISTR 8263 (Csp), S. armatus TISTR 8653 (Sa) and marine Chlorella sp. (Cs) in modified Chu13 medium aerated with normal air (0.03% CO2) and 50% CO2 in air are shown in Fig. 1A and B, respectively. Under aeration with normal air, all microalgae grew and showed the highest cell density at 18 days of cultivation. Chlorella sp. TISTR 8263 (Csp) grew fastest and showed the highest cell density at 4.29  107 cells mL1 followed by marine Chlorella sp. (Cs), C. vulgaris TISTR 8580 (Cv) and C. protothecoides TISTR 8243 (Cp). S. armatus TISTR 8653 (Sa) and Chlorococcum sp. TISTR 8416 (Cc) showed a very low growth rate and reached a very low cell density (Fig. 1A). It was interesting to note that although the cell numbers of S. armatus TISTR 8653 (Sa) and Chlorococcum sp. TISTR 8416 (Cc) were lower than the other four strains, since they are big in size their dry cell dry weights were close to those of the other four strains (Table 1). As shown in Table 1, Chlorella sp. TISTR 8263 and marine Chlorella sp. also displayed the maximum specific growth rate of 0.097 ± 0.007 day1 and 0.093 ± 0.006 day1, respectively. Chlorococcum sp. TISTR

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B 50

Cell concentration (106 cells mL-1)

Cell concentration (106 cells mL-1)

A Cv Cp Cc Csp Sa Cs

40 30 20 10 0 0

3

6

9 12 15 18 21 24 Time (day)

50

Cv Cp Cc Csp Sa Cs

40 30 20 10 0 0

1

2

3 4 5 Time (day)

6

7

8

Fig. 1. Cell concentration of six green microalgae strains cultivated in modified Chu13 medium and aerated with (A) normal air (0.03% CO2) and (B) 50% CO2 in air. Cv: C. vulgaris TISTR 8580, Cp: C. protothecoides TISTR 8243, Cc: Chlorococcum sp. TISTR 8416; Csp: Chlorella sp. TISTR 8263, Sa: S. armatus TISTR 8653 and Cs: marine Chlorella sp. Data are means of triplicates.

Table 1 Specific growth rate (l), dry cell weight (DCW), lipid content and lipid productivity of six green microalgae strains cultivated in modified Chu13 medium and aerated with normal air (0.03% CO2). Microalgae strain

l (day1)

DCW (mg L1)

Lipid content (% DCW)

Lipid productivity (mg L1 day1)

Cv: C. vulgaris TISTR 8580 Cp: C. protothecoides TISTR 8243 Cc: Chlorococcum sp. TISTR 8416 Csp: Chlorella sp. TISTR 8263 Sa: S. armatus TISTR 8653 Cs: Marine Chlorella sp.

0.071 ± 0.009c 0.080 ± 0.007bc 0.065 ± 0.005cd 0.097 ± 0.007a 0.053 ± 0.002d 0.093 ± 0.006a

351.1 ± 43.7b 451.1 ± 34.7a 375.0 ± 25.7b 434.6 ± 31.4a 445.3 ± 20.2a 461.9 ± 28.9a

28.5 ± 3.3ab 23.4 ± 3.7b 32.0 ± 3.5a 25.8 ± 2.3ab 22.7 ± 3.9b 27.6 ± 3.3ab

4.17 ± 0.52c 4.39 ± 0.34bc 5.01 ± 0.34ab 4.67 ± 0.34b 4.22 ± 0.19c 5.30 ± 0.33a

Data in the same column with different superscript letters are significantly different (P < 0.05).

8416 showed the highest lipid content of 32 ± 3.5% while those of other strains were in the range of 22–28%. Among the strains tested, the lipid productivity of marine Chlorella sp. was the highest at 5.30 ± 0.33 mg L1 day1. By increasing the CO2 content in the air up to 50% to imitate the CO2 content in the biogas, the cell growth of all microalgae were enhanced (Fig. 1B). The cell concentration increased rapidly and reached the stationary phase within 6 days of cultivation. This was about 3 times faster than those using normal air (Fig. 1A). The specific growth rates of all microalgae were also much enhanced (Table 2). Chlorella sp. TISTR 8263 showed the highest specific growth rate of 0.469 ± 0.052 day1 which was about 5 times greater than that using normal air but its final cell density was not much different from that using normal air. This indicated that the increased CO2 content did promote a faster growth rate of the microalgae but had less affect on the final cell concentration. In addition to the availability of the CO2, the final cell concentration would also depend on other essential nutrients, i.e. nitrogen source. The increase in the CO2 content also increased the lipid productivity by 2–4 times while the lipid content did not significantly change (Tables 1 and 2). Among the strains tested, Chlorella sp.

TISTR 8263 was the most suitable strain for cultivation using high concentration of CO2 (50% in air) followed by marine Chlorella sp. and C. protothecoides TISTR 8243 based on their relatively high specific growth rates and high dry cell weights. The maximum cell concentration of marine Chlorella sp. (601 ± 34.7 mg L1) was slightly higher than that previously reported using a CO2 tolerant Chlorella littorale (500 mg L1) aerated with 50% CO2 for 14 days (Ota et al., 2009) but lower than those of Scenedesmus obliquus SJTU-3 (820 mg L1) and Chlorella pyrenoidosa SJTU-2 (690 mg L1) (Tang et al., 2011). Yoo et al. (2010) compared three species of microalgae including Botryococcus braunii, C. vulgaris and Scenedesmus sp. The lipid productivity of Scenedesmus sp. was the highest at 20.65 mg L1 day1 followed by C. vulgaris (6.91 mg L1 day1) under condition aerated with 10% CO2 at 0.3 vvm. Many studies also confirmed that these two species were able to grow when aerated with high CO2 content of 12–18% (de Morais and Costa, 2007a,b). Tang et al. (2011) reported that these two species could even grow at 50% CO2 but with a lower growth rate compared to those at 10% CO2. Aeration with CO2 at higher levels was also possible when using a lower aeration rate or an intermittent feeding strategy. A

Table 2 Specific growth rate (l), dry cell weight (DCW), lipid content and lipid productivity of six green microalgae strains cultivated in modified Chu13 medium and aerated with 50% CO2 in air. Microalgae strain Cv: C. vulgaris TISTR 8580 Cp: C. protothecoides TISTR 8243 Cc: Chlorococcum sp. TISTR 8416 Csp: Chlorella sp. TISTR 8263 Sa: S. armatus TISTR 8653 Cs: Marine Chlorella sp.

l (day1)

DCW (mg L1) bc

0.372 ± 0.030 0.413 ± 0.032ab 0.309 ± 0.054c 0.469 ± 0.052a 0.317 ± 0.025c 0.457 ± 0.026a

c

361.1 ± 29.1 454.5 ± 34.7b 382.3 ± 66.2bc 433.9 ± 48.2b 400.9 ± 31.4bc 601.8 ± 34.7a

Data in the same column with different superscript letters are significantly different (P < 0.05).

Lipid content (% DCW) ab

28.1 ± 3.9 22.9 ± 4.3b 31.8 ± 2.8a 25.7 ± 5.7b 21.4 ± 2.3b 28.2 ± 3.9ab

Lipid productivity (mg L1 day1) 12.9 ± 1.1bc 13.3 ± 1.0bc 15.4 ± 2.7b 13.9 ± 1.5b 10.7 ± 0.8c 21.3 ± 1.2a

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different approach of increasing an initial cell concentration could also increase the tolerance against high levels of CO2 and reduce the time for adaptation (Lee et al., 2002).

Table 4 CO2 removal by selected green microalgae strains cultivated in modified Chu13 medium and aerated with 50% CO2 in air and in biogas. Microalgae strain

3.2. Screening of microalgae for biocapture of CO2 from biogas

Cp: C. protothecoides TISTR 8243 68.5 ± 1.16a Csp: Chlorella sp. TISTR 8263 70.3 ± 2.03a Cs: Marine Chlorella sp. 70.4 ± 2.06a

In the application of the microalgae for biocapture of CO2 from biogas, the tolerance to both high CO2 content and high methane content are of great importance. Based on the performance using 50% CO2 in air, three microalgae showing the high growth rates were selected. These included C. protothecoides TISTR 8243, Chlorella sp. TISTR 8263 and marine Chlorella sp. They were then cultivated using imitated biogas in which CO2 was mixed with CH4 at the ration of 50:50 v/v. The effects of using biogas on the cell concentration and dry cell weight are shown in Fig. 2. Chlorella sp. TISTR 8263 showed the highest cell concentration at day 6 of cultivation (Fig. 2A), while marine Chlorella sp. showed the highest dry cell weight at day 8 of cultivation followed by Chlorella sp. TISTR 8263 and C. protothecoides TISTR 8243 (Fig. 2B, Table 3). Although the cell concentration of Chlorella sp. TISTR 8263 was higher than that of marine Chlorella sp. (Fig. 2A), its lipid productivity was much lower (Table 3). This was because marine Chlorella sp. could fix CO2 into its biomass and gave higher lipid content than did Chlorella sp. TISTR 8263. It should be noted that the performance of marine Chlorella sp. using biogas was similar to that using 50% CO2 in air. This indicated that methane did not negatively affect this microalga at the aeration rate tested. Table 4 shows the percentage of CO2 removal by the three green microalgae strains aerated with 50% CO2 in air and in biogas. The maximum CO2 removal of about 70% was achieved by Chlorella sp. TISTR 8263 and marine Chlorella sp. in the culture aerated with 50% CO2 in air. The CO2 removal only slightly decreased to 68–69% when the culture was aerated with 50% CO2 in biogas. The reduction in CO2 removal by these two microalgae when using 50% CO2 in biogas was less than that by C. vulgaris TISTR 8580. Based on both CO2 removal ability and lipid productivity, the marine Chlorella sp. was considered to be the most suitable strain for the

64.8 ± 1.56b 67.8 ± 1.28a 68.9 ± 1.40a

Data in the same column with different superscript letters are significantly different (P < 0.05).

biocapture of CO2 from biogas. These results indicated that the CO2 removal and lipid productivity were greatly dependent on the ability of the microalgae to fix CO2 into their biomass and lipid. 3.3. Optimization of the medium components and pH for the biocapture of CO2 and lipid production Response surface methodology was used to optimize the medium components and pH for the biocapture of CO2 from biogas and lipid production by the selected marine Chlorella sp. The independent variables for the process were KNO3 as a nitrogen source (g L1; A), K2HPO4 as a phosphorus source (g L1; B), and pH (C). Their values were changed in the ranges shown in Table 5 and coded at three levels between 1 and +1. The experimental results were concerned with CO2 removal from biogas (%; Y1) and lipid productivity (mg L1 day1; Y2) using the three-factor BBD experimental design are shown in Table 5. The conditions at the center point were KNO3 concentration at 0.5 g L1, K2HPO4 concentration at 0.06 g L1, and pH at 6.8. The responses Y1 and Y2 were fitted using second order polynomial Eqs. (2) and (3), respectively.

Y 1 ¼ 37:6  73:2A  214:8B þ 13:1C þ 30:1A2 þ 395:8B2  1:17C2 þ 158:3AB þ 8:58AC þ 18:75BC

ð2Þ 2

2

Y 2 ¼ 176:5  140:8A  457:5B þ 64:9C þ 67:3A þ 1135:4B  5:27C2 þ 179:2AB þ 21:8AC þ 45:0BC

ð3Þ

B

A

700

50

Cp Csp Cs

40

Dry cell weight (mg L-1)

Cell concentration (106 cells mL-1)

CO2 removal (%) CO2 removal (%) from 50% CO2 in air from 50% CO2 in biogas

30 20 10 0

Cp Csp Cs

600 500 400 300 200 100 0

0

1

2

3 4 5 Time (day)

6

7

8

0

1

2

3 4 5 Time (day)

6

7

8

Fig. 2. (A) Cell concentration and (B) dry cell weight of three green microalgae strains cultivated in modified Chu13 medium and aerated with 50% CO2 in biogas. Cp: C. protothecoides TISTR 8243, Csp: Chlorella sp. TISTR 8263 and Cs: marine Chlorella sp. Data are means of triplicates.

Table 3 Specific growth rate (l), dry cell weight (DCW), lipid content and lipid productivity of selected green microalgae strains cultivated in modified Chu13 medium and aerated with 50% CO2 in biogas. Microalgae strain

l (day1)

DCW (mg L1)

Lipid content (% DCW)

Lipid productivity (mg L1 day1)

Cp: C. protothecoides TISTR 8243 Csp: Chlorella sp. TISTR 8263 Cs: Marine Chlorella sp.

0.383 ± 0.040b 0.422 ± 0.029a 0.409 ± 0.040ab

330.8 ± 34.7b 382.1 ± 65.1b 590.8 ± 57.2a

26.5 ± 3.8a 25.4 ± 2.7a 28.3 ± 2.9a

10.9 ± 1.1b 12.1 ± 2.1b 20.9 ± 2.0a

Data in the same column with different superscript letters are significantly different (P < 0.05).

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The equations represent the quantitative effects of the individual factors and their interactions on CO2 removal from biogas and lipid productivity. The multiple correlation coefficients or R2 of the regression equation for CO2 removal from biogas and lipid productivity obtained from analysis of variance (ANOVA) were 0.9873 and

0.9881, respectively indicating that these quadratic equations could appropriately describe the relationships between the factors and the responses. The ‘‘lack of fit’’ compares the residual error to the pure error from the replicated experimental design points. The P-values, greater than 0.05, for the two responses indicated that

Table 5 Experimental design and results of CO2 removal from biogas and lipid productivity by marine Chlorella sp. Run

KNO3 (g L1)

K2HPO4 (g L1)

pH ()

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.8 0.8 0.2 0.8 0.5 0.2 0.5 0.2 0.2 0.5 0.5 0.8 0.5 0.5 0.5

0.08 0.04 0.06 0.06 0.08 0.06 0.08 0.04 0.08 0.04 0.04 0.06 0.06 0.06 0.06

6.8 6.8 7.8 7.8 7.8 5.8 5.8 6.8 6.8 5.8 7.8 5.8 6.8 6.8 6.8

(+1) (1) (0) (0) (+1) (0) (+1) (1) (+1) (1) (1) (0) (0) (0) (0)

(0) (0) (+1) (+1) (+1) (1) (1) (0) (0) (1) (+1) (1) (0) (0) (0)

Actual

Predicted

Actual

Predicted

86.7 82.5 69.0 87.7 75.5 67.8 70.6 68.2 68.6 70.5 73.9 76.2 73.9 73.0 74.0

85.7 82.2 67.8 87.8 76.4 67.7 70.4 69.2 68.9 69.6 74.1 77.4 73.6 73.6 73.6

79.6 74.8 21.5 80.6 45.7 18.6 32.5 20.1 20.6 31.0 40.6 51.5 42.6 40.6 43.6

77.0 72.0 17.6 82.1 46.7 17.0 31.2 22.6 23.5 30.0 41.9 55.4 42.2 42.2 42.2

B

88 88

88

CO removal (%)

87

77.5 77.5

2

2

CO removal (%)

A

(+1) (+1) (1) (+1) (0) (1) (0) (1) (1) (0) (0) (+1) (0) (0) (0)

Lipid productivity (mg L1 day1)

CO2 removal (%)

6767

7.8

7.30

0.65 6.80

6.8

67

0.80 0.8

68

0.08

0.80 0.8 0.07

0.65

0.50

6.30

0.35 5.80

0.20

5.8

0.2

C

76

CO2 removal (%)

C: ppH H C:

77.5

0.06

0.50

0.06

0.5

A:KNO33(g L-1) B:B:KK2 HPO44 (g L-1) A: KNO 2HPO

0.05

0.35 0.04

0.20

0.04 0.2

0.5

KNO33 (g L-1) A:A:KNO

72.5

69 7.8

0.08 0.08 7.30

C: pH C: pH

0.07 6.80 6.8

0.06

6.30

0.05 5.80 5.8

0.06

B: K2HPO4 (g L-1)

0.04

0.04

Fig. 3. 3D surface plots of CO2 removal from biogas versus KNO3 concentration and pH (A), K2HPO4 concentration and KNO3 concentration (B), and K2HPO4 concentration and pH (C).

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the lack of fit for the model was insignificant. It should be noted that the effect of each variable on the response was a combination of the coefficients and variables as well as the contribution of the joint effect of variables that cannot be observed by traditional optimization methods. The statistical analysis showed that the KNO3 and pH were significant factors for both CO2 removal and lipid

B 83 83 66.5

5050 33.5

17

17

7.80 7.8

0.80 0.8 7.30

0.65 6.80 6.8

C: pH C: p H

Lipid productivity (mg L-1day--1)

Lipid productivity (mg L-1day--1)

A

productivity at P-value less than 0.01 while the effect of K2HPO4 was less significant in the tested range. Furthermore, the interaction between KNO3 and pH also showed a significant effect on both responses. Three dimensional response surfaces were generated to visualize the combined effects on CO2 removal from biogas and lipid

80 80 65

5050 35

2020

0.08 0.08

0.80.80 0.07

0.65 0.06 0.06

0.50

6.30

0.35

5.85.80 0.20 0.2

p p (mg L-1yday--1) Lipid productivity

C

0.5

A:K NO3 -1 A: KNO 3 (g L )

K2HP4O(g 4L ) B: KB2:HPO -1

0.50

0.05

0.35 0.04 0.20 0.04 0.2

0.5

A:K O3L-1) A: KNO (g 3N

4848 43.25 38.5 38.5 33.75

29 29

7.80 7.8

0.08 0.08 7.30

0.07 6.80 6.8

C:CpH : pH

0.06 6.30

0.05 5.80 0.04 5.8 0.04

0.06

B:BK HPO : K22 HPO44 (g L-1)

Fig. 4. 3D surface plots of lipid productivity versus KNO3 concentration and pH (A), K2HPO4 concentration and KNO3 concentration (B), and K2HPO4 concentration and pH (C).

Table 6 Experimental design and results of CO2 removal from biogas and lipid productivity by marine Chlorella sp. Run

Cell concentration (cells mL1)

Light intensity (lux)

Flow rate (L min1)

CO2 removal (%) Actual

Predicted

Actual

Predicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

108 (+1) 108 (+1) 106 (1) 108 (+1) 107.5 (0) 106 (1) 107.5 (0) 106 (1) 106 (1) 107.5 (0) 107.5 (0) 108 (+1) 107.5 (0) 107.5 (0) 107.5 (0)

6000 (+1) 3000 (1) 4500 (0) 4500 (0) 6000 (+1) 4500 (0) 6000 (+1) 3000 (1) 6000 (+1) 3000 (1) 3000 (1) 4500 (0) 4500 (0) 4500 (0) 4500 (0)

0.03 0.03 0.05 0.05 0.05 0.01 0.01 0.03 0.03 0.01 0.05 0.01 0.03 0.03 0.03

65.5 64.3 77.5 69.3 64.2 76.5 73.3 78.9 57.4 80.4 74.3 77.1 87.5 85.9 89.3

65.7 63.8 76.3 68.4 64.9 77.4 71.8 78.6 57.9 79.8 75.7 78.3 87.6 87.6 87.6

67.5 60.5 76.4 72.5 67.9 81.0 76.2 83.8 59.5 84.8 78.4 82.4 90.9 92.7 94.7

68.8 62.3 78.0 70.6 68.1 82.6 76.4 82.4 57.7 84.5 78.2 80.8 92.7 92.7 92.7

(0) (0) (+1) (+1) (+1) (1) (1) (0) (0) (1) (+1) (1) (0) (0) (0)

Lipid productivity (mg L1 day1)

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the same as follows: KNO3 concentration of 0.8 g L1, K2HPO4 concentration of 0.06 g L1 and pH of 7.8. Under these conditions, the maximum CO2 removal of 87.7% and the maximum lipid productivity of 80.56 mg L1 day1 were achieved. In many microalgae, an increase in the carbon to nitrogen ratio results in an accumulation of lipid. Although a deficiency of nitrogen favors lipid accumulation, nitrogen is required for growth.  Studies with nitrogen supplied as NO 3 , NO2 , and NH3 reveal that the primary factor regulating nitrogen metabolism in microalgae was the nitrate uptake system (Lupi et al., 1994). The use of NH+4 as the nitrogen source caused the culture pH to decline to less than pH 4, the nitrate reductase enzyme becomes inactive and this also damages the microalgal cells. The NH+4 related toxicity manifests itself in the late exponential phase of growth. Nitrogen then is generally supplied as nitrate salts (Yang et al., 2004) and KNO3 has been found to be a suitable nitrogen source for cultivation of microalgae (Wu et al., 2013; Dayananda et al., 2006). A variation in the pH of a culture affects the solubility and the bioavailability of nutrients, transport of substrates across the cytoplasmic membrane and enzyme activity and electron transport in photosynthesis and respiration (Carrion et al., 2001). The pH change during the cultures of microalgae is due to the

productivity (Figs. 3 and 4). When the effects of the two factors were plotted, the other factor was set at its center point. Fig. 3A shows the effects of KNO3 concentration and pH on CO2 removal. It was obvious that the KNO3 concentration affected CO2 removal greater than did the pH. The CO2 removal increased with increasing KNO3 concentration. Although the pH had less effect on the CO2 removal at a low concentration of KNO3 but when KNO3 concentration was increased the effect of pH on CO2 removal became more obvious. This indicated the positive interaction between these two factors. Fig. 3B depicts the interaction between the concentrations of KNO3 and K2HPO4 on CO2 removal when the pH was fixed at 6.8. The effect of K2HPO4 on CO2 removal was similar to that of the pH. No significant effect of K2HPO4 was observed at a low concentration of KNO3. When comparing the effect of pH and K2HPO4 in Fig. 3C, the effect of pH was more obvious than that of K2HPO4. Therefore, the important degree of influence by the 3 factors on CO2 removal was: KNO3 concentration > pH > K2HPO4 concentration. Fig. 4 shows the effects of these factors on the lipid productivity by the microalgae. The effect of each factor on the lipid productivity showed a similar trend to that on the CO2 removal. This result indicated that the two responses were linked together and the optimal conditions for both responses were found to be

A

B 90

CO removal (%)

73.5

79

2

2

CO removal (%)

90

57 108

6000

108

0.05

107.5

4500

B: Light intensity (lux)

68

3000 106

CO2 removal (%)

C

107.5

0.03 -1

A: Cells (cells mL )

C: Flow-1rate (L min )

0.01

106

A: Cells (cells mL-1)

90

77

64

6000

0.05 0.03

C: Flow-1rate (L min )

4500 0.01

3000

B: Light intensity (lux)

Fig. 5. 3D surface plots of CO2 removal from biogas versus cell concentration and light intensity (A), cell concentration and gas flow rate (B), and light intensity and gas flow rate (C).

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solubilization and consumption of CO2 and other substrates and the production of metabolites (Grima et al., 1999). In this study, since CO2 is removed by microalgal metabolism at a rate depending on their photosynthetic activity, hydroxide ions (OH) are formed, and the pH becomes more alkaline (data not shown). It has been reported that a neutral pH is the optimal pH for production of maximum biomass by green microalgae (Banerjee et al., 2002). The microalgae can grow in the pH range of 6.0–8.5 and the optimal pH for cell growth was 6.0. But the lipid content in the microalgae was not much affected by the pH (Dayananda et al., 2006).

three-factor BBD experimental design and are shown in Table 6. The operating conditions at the center point were a cell concentration of 107.5 cells mL1, a light intensity of 4500 lux, and a gas flow rate of 0.03 L min1. The responses Y1 and Y2 were fitted using second order polynomial Eqs. (4) and (5), respectively.

Y 1 ¼ 2:80 þ 7:85  108 D þ 0:040E þ 53:3F  3:88  1015 D2  5:13  106 E2  74:3F2 þ 7:64  1011 DE  2:22  107 DF  2:50  103 EF

ð4Þ

Y 2 ¼ 1:15 þ 9:25  109 D þ 0:045E þ 38:3F  4:84  1015 D2  5:82  106 E2  71:2F2 þ 1:05  1010 DE  1:34  107 DF

3.4. Optimization of the operating conditions for biocapture of CO2 and lipid production

 1:58  103 EF

The operating conditions for the biocapture of CO2 from biogas and the lipid production by the selected marine Chlorella sp. were optimized through RSM. The operating conditions included initial cell concentration (cells mL1; D), light intensity (lux; E) and gas flow rate (L min1; F). Their values were changed in the ranges shown in Table 6 and coded at three levels between 1 and +1. The experimental results were concerned with CO2 removal from biogas (%; Y1) and lipid productivity (mg L1 day1; Y2) using the

The multiple correlation coefficients or R of the regression equations for CO2 removal from biogas and lipid productivity obtained from ANOVA were 0.9866 and 0.9833, respectively. These values indicated that the quadratic equations could appropriately describe the relationships between the factors and the responses. The P-values, greater than 0.05, for the two responses indicated that any lack of fit for the model was insignificant. The statistical analysis showed that the cell concentration was significant for

B Lipid productivity (mg L-1day-1)

A Lipid productivity (mg L-1day-1)

ð5Þ 2

95

76

57 108

6000

82.5

70 108

0.05

107.5

4500

B: Light intensity (lux)

95

3000 106

107.5

0.03 -1

A: Cells (cells mL )

C: Flow-1rate (L min )

0.01

106

A: Cells (cells mL-1)

Lipid productivity (mg L-1day-1)

C 95

81

67

6000

0.05 0.03

C: Flow-1rate (L min )

4500 0.01

3000

B: Light intensity (lux)

Fig. 6. 3D surface plots of lipid productivity versus cell concentration and light intensity (A), cell concentration and gas flow rate (B), and light intensity and gas flow rate (C).

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both the CO2 removal and lipid productivity at P-values less than 0.05 while the light intensity and gas flow rate were significant at P-values less than 0.01. In addition, the interaction between the cell concentration and the light intensity showed a significant effect on both responses. Three dimensional response surfaces were generated to visualize the combined effects on CO2 removal from biogas and lipid productivity (Figs. 5 and 6). When the effects of two factors were plotted, the other factor was set at its center point. Figs. 5A and 6A show the interactive effects of the cell concentration and light intensity on CO2 removal and lipid productivity, respectively. The shapes of the response surface curves showed a positive interaction between these two variables for both CO2 removal and lipid productivity. The two responses were found to increase with the simultaneous increase of both factors. The CO2 removal increased with increasing cell concentration and light intensity to its peak, but then decreased with a further increase in both factors. The initial cell concentration was a fundamental parameter that controlled the growth rate and the fermentation time. The larger initial cell concentration would give a faster growth rate and hence high CO2 removal and high lipid productivity. However, no further enhancement was observed at an initial cell concentration larger than 107.5 cells mL1. This could possibly be because the larger initial cell concentration than the optimum level might lead to the high cell density at initial and reduce the light penetration. Light intensity has an important role on the photosynthetic activity of microalgae. The CO2 removal and lipid productivity increased with increasing light intensity but sharply decreased when the light intensity was further increased higher than 4500 lux. When the light intensity was insufficient, the microalgae could not efficiently use CO2 and it might even consume its storage lipid (Yan and Zheng, 2013; Jeong et al., 2013). In contrast, when the light intensity was increased above the light saturation limit, the photoinhibition effect occurred. However an excessive light intensity produced an overloaded photosystem, bleached pigments, and destroyed photosystems (Jeong et al., 2013). Fig. 5B and C depicts the interactive effect of the gas flow rate with cell concentration and light intensity on CO2 removal when the pH was fixed at 6.8. It was found that the variations of gas flow rate were less important. The optimal gas flow rate was at 0.03 L min1. The effect of gas flow rate on the lipid productivity was also similar to that on the CO2 removal (Fig. 6B and C). The optimal conditions for both responses were: initial cell concentration of 107.5 cells mL1, light intensity of 4500 lux, and gas flow rate of 0.03 L min1, which gave the maximum CO2 removal of 89.3% and the maximum lipid productivity of 94.7 mg L1 day1. With these optimal conditions, the methane content in the biogas was increased from 50% up to 94.7%. In addition, the lipid produced by this microalga mainly consisted of long-chain fatty acids of 16 and 18 carbon atoms, and the two major fatty acids were palmitic acid (C16:0, 72%) and stearic acid (C18:0, 13%). In a similar experiment, high contents of palmitic acid were found in C. vulgaris and Nannochloropsis oculata (47–63%) (Converti et al., 2009) and Nannochloropsis sp. (>70%) (Doan et al., 2011). The high content of saturated acids found in the microalgal lipid would provide a higher cetane number (CN), lower NOx emissions, shorter ignition delay time, and higher oxidative stability. The similarity of the fatty acids of microalgal lipid to those of plant oils indicated its potential use as a biodiesel feedstock.

4. Conclusion In this study, the marine Chlorella sp. was found to be the most suitable strain for the biocapture of CO2 from biogas and simultaneously producing lipid. The optimum medium composition and

operating conditions have been determined. After optimization, the maximum CO2 capture of 89.3% corresponding to a final methane content of 94.7% and a maximum lipid productivity of 94.7 mg L1 day1 were achieved. This study has shown that it was possible to simultaneously upgrade biogas and produce lipid by the cultivation of microalgae. The microalgal lipid was also suitable for being used as a biodiesel feedstock. Acknowledgements This work was financial supported by Prince of Songkla University and Thai Government under Grant No. AGR550003S. The first author was supported by Thai Research Fund in the fiscal year of 2011–2012 under Grant WI535S087. Also thanks to Dr. Brian Hodgson Faculty of Pharmaceutical Science, Prince of Songkla University for assistance with the English. References Banerjee, A., Sharma, R., Chisti, Y., Banerjee, U.C., 2002. Botryococcus braunii: a renewable source of hydrocarbons and other chemicals. Crit. Rev. Biotechnol. 22, 245–279. Berberoglu, H., Gomez, P.S., Pilon, L., 2009. Radiation characteristics of Botryococcus braunii, Chlorococcum littorale, and Chlorella sp. used for CO2 fixation and biofuel production. J. Quant. Spectrosc. Radiat. Transfer 17, 1879–1893. Carrion, C.P., Valiente, E.F., Pinas, F.F., Leganes, F., 2001. Acclimation of photosynthetic pigments and photosynthesis of the cyanobacterium Nostoc sp. strain UAM206 to combined fluctuations of irradiance, pH and inorganic carbon availability. J. Plant Physiol. 158, 1455–1461. Carvalho, A.P., Meireles, L.A., Malcata, F.X., 2006. Microalgal reactors: a review of enclosed system designs and performances. Biotechnol. Prog. 22, 1490–1506. Chinnasamy, S., Bhatnagar, A., Hunt, R.W., Das, K.C., 2010. Microalgae cultivation in a wastewater dominated by carpet mill effluents for biofuel applications. Bioresour. Technol. 101, 3097–3105. Chisti, Y., 2007. Biodiesel from microalgae. Biotechnol. Adv. 25, 294–306. Chiu, S.Y., Kao, C.Y., Chen, C.H., Kuan, T.C., Ong, S.C., Lin, C.S., 2008. Reduction of CO2 by a high-density culture of Chlorella sp. in a semicontinuous photobioreactor. Bioresour. Technol. 99, 3389–3396. Chiu, S.Y., Kao, C.Y., Tsai, M.T., Ong, S.C., Chen, C.H., Lin, C.S., 2009. Lipid accumulation and CO2 utilization of Nannochloropsis oculata in response to CO2 aeration. Bioresour. Technol. 100, 833–838. Converti, A., Oliveira, R.P.S., Torres, B.R., Lodi, A., Zilli, M., 2009. Bio-gas production and valorization by means of a two-step biological process. Bioresour. Technol. 100, 5771–5776. Dayananda, C., Sarada, R., Shamala, T.R., Ravishankar, G.A., 2006. Influence of nitrogen sources on growth, hydrocarbon and fatty acid production by Botryococcus braunii. Asian J. Plant Sci. 5, 799–804. de Morais, M.G., Costa, J.A.V., 2007a. Biofixation of carbon dioxide by spirulina sp. and scenedesmus obliquus cultivated in a three-stage serial tubular photobioreactor. J. Biotechnol. 129, 439–445. de Morais, M.G., Costa, J.A.V., 2007b. Carbon dioxide fixation by chlorella kessleri, c. vulgaris, scenedesmus obliquus and spirulina sp. cultivated in flasks and vertical tubular photobioreactors. Biotechnol. Lett. 29, 1349–1352. Doan, T.T.Y., Sivaloganathan, B., Obbard, J.P., 2011. Screening of marine microalgae for biodiesel feedstock. Biomass Bioenerg. 35, 2534–2544. Fedorov, A.S., Kosourov, S., Ghirardi, M.I., Seibert, M., 2005. Continuous H2 photo production by Chlamydomonas reinhardtii using a novel two-stage, sulfatelimited chemostat system. Appl. Biochem. Biotechnol. 121–124, 403–412. Gavrilescu, M., Chisti, Y., 2005. Biotechnology – a sustainable alternative for chemical industry. Biotechnol. Adv. 23, 471–499. Grima, E.M., Fernandez, F.G.A., Camacho, F.G., Chisti, Y., 1999. Photobioreactors: light regime, mass transfer and scale up. J. Biotechnol. 70, 231–247. Ho, S.H., Chen, W.M., Chang, J.S., 2010. Scenedesmus obliquus CNW-N as a potential candidate for CO2 mitigation and biodiesel production. Bioresour. Technol. 101, 8725–8730. Hsueh, H.T., Chu, H., Yu, S.T., 2007. A bath study on the bio-fixation of carbon dioxide in the absorbed solution from a chemical wet scrubber by hot spring and marine algae. Chemosphere 66, 878–886. Jeong, H., Lee, J., Cha, M., 2013. Energy efficient growth control of microalgae using photobiological methods. Renew. Energ. 54, 161–165. Largeau, C., Caradevall, E., Berkaloff, C., Dhamliencourt, P., 1980. Sites of accumulation and composition of hydrocarbons in Botryococcus braunii. Phytochemistry 19, 1043–1051. Lee, J.S., Kim, D.K., Lee, J.P., Park, S.C., Koh, J.H., Cho, H.S., Kim, S.W., 2002. Effect of SO2 and NO growth of Chlorella sp. KR-1. Bioresour. Technol. 82, 1–4. Lupi, F.M., Fernandes, H.M.L., Tomme, M.M., Sa Correia, I., Novais, J.M., 1994. Influence of nitrogen source and photoperiod on exopolysaccharide synthesis by the microalga Botryococcus braunii. Enzyme Microb. Technol. 16, 546–551. Lv, J.M., Cheng, L.H., Xu, X.H., Zhang, L., Chen, H.L., 2010. Enhanced lipid production of Chlorella vulgaris by adjustment of cultivation conditions. Bioresour. Technol. 101, 6797–6804.

W. Tongprawhan et al. / Bioresource Technology 170 (2014) 90–99 Mann, G., Schlegel, M., Schumann, R., Sakalauskas, A., 2009. Biogas-conditioning with microalgae. Agronomy Res. 7, 33–38. Ota, M., Kato, Y., Watanabe, H., Watanabe, M., Sato, Y., Smith, R.L., Inomata, H., 2009. Fatty acid production from a highly CO2 tolerant alga, Chlorocuccum littorale, in the presence of inorganic carbon and nitrate. Bioresour. Technol. 100, 5237–5242. Spolaore, P., Joannis, C.C., Duran, E., Isambert, A., 2006. Optimization of Nannochloropsis oculata growth using the response surface method. J. Chem. Technol. Biotechnol. 81, 1049–1056. Tang, D., Han, W., Li, P., Miao, X., Zhong, J., 2011. CO2 biofixation and fatty acid composition of Scenedesmus obliquus and Chlorella pyrenoidosa in response to different CO2 levels. Bioresour. Technol. 102, 3071–3076.

99

Wu, L.F., Chen, P.C., Lee, C.M., 2013. The effects of nitrogen sources and temperature on cell growth and lipid accumulation of microalgae. Int. Biodeter. Biodegr. 85, 506–510. Yan, C., Zheng, Z., 2013. Performance of photoperiod and light intensity on biogas upgrade and biogas effluent nutrient reduction by the microalgae Chlorella sp.. Bioresour. Technol. 139, 292–299. Yang, S., Wang, J., Cong, W., Cai, Z., Ouyang, F., 2004. Utilization of nitrate as a nitrogen source by Botryococcus braunii. Biotechnol. Lett. 26, 239–243. Yoo, C., Jun, S.Y., Lee, J.Y., Ahn, C.Y., Oh, H.M., 2010. Selection of microalgae for lipid production under high levels carbon dioxide. Bioresour. Technol. 101, S71–S74.