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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2018, Vol. 12 Issue (4) : 14    https://doi.org/10.1007/s11783-018-1075-2
RESEARCH ARTICLE
Mixed culture of Chlorella sp. and wastewater wild algae for enhanced biomass and lipid accumulation in artificial wastewater medium
Kishore Gopalakrishnan, Javad Roostaei, Yongli Zhang()
Department of Civil and Environmental Engineering, Wayne State University, Detroit, MI 48201, USA
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Abstract

RSM is used to explore the impact of different parameter on algal growth response.

Mixed algal culture promotes algal biomass and lipid accumulation.

Optimized conditions achieve maximum productivity of algal biomass and lipid.

The purpose of this work is to study the co-cultivation of Chlorella sp. and wastewater wild algae under different cultivation conditions (i.e. CO2, light intensity, cultivation time, and inoculation ratio) for enhanced algal biomass and lipid productivity in wastewater medium using Response Surface Methodology (RSM). The results show that mixed cultures of Chlorella sp. and wastewater wild algae increase biomass and lipid yield. Additionally, findings indicate that CO2, light intensity and cultivation time significantly affect algal productivity. Furthermore, CO2 concentration and light intensity, and CO2 concentration and algal composition, have an interactive effect on biomass productivity. Under different cultivation conditions, the response of algal biomass, cell count, and lipid productivity ranges from 2.5 to 10.2 mg/mL, 1.1 × 106 to 8.2 × 108 cells/mL, and 1.1 × 1012 to 6.8 × 1012 total fluorescent units/mL, respectively. The optimum conditions for simultaneous biomass and lipid accumulation are 3.6% of CO2 (v/v), 160 µmol/m2/s of light intensity, 1.6/2.4 of inoculation ratio (wastewater-algae/Chlorella), and 8.3 days of cultivation time. The optimal productivity is 9.8 (g/L) for dry biomass, 8.6 E+ 08 (cells/mL) for cell count, and 6.8 E+ 12 (Total FL units per mL) for lipid yield, achieving up to four times, eight times, and seven times higher productivity compared to non-optimized conditions. Provided is a supportive methodology to improve mixed algal culture for bioenergy feedstock generation and to optimize cultivation conditions in complex wastewater environments. This work is an important step forward in the development of sustainable large-scale algae cultivation for cost-efficient generation of biofuel.
Keywords Algal biofuels      Algal mixed cultures      Algal biomass      Algal lipid      Wastewater      Response surface methodology     
Corresponding Author(s): Yongli Zhang   
Issue Date: 17 August 2018
 Cite this article:   
Kishore Gopalakrishnan,Javad Roostaei,Yongli Zhang. Mixed culture of Chlorella sp. and wastewater wild algae for enhanced biomass and lipid accumulation in artificial wastewater medium[J]. Front. Environ. Sci. Eng., 2018, 12(4): 14.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-018-1075-2
https://academic.hep.com.cn/fese/EN/Y2018/V12/I4/14
Run Factors (Coded) Factors (Un-coded levels)
a b c d A B C D
1 0 0 0 0 3.5 150 2:2 9
2 + 1 + 1 0 0 6 250 2:2 9
3 + 1 0 0 + 1 6 150 2:2 15
4 +1 + 1 0 0 1 250 2:2 9
5 0 -1 0 + 1 3.5 50 2:2 15
6 +1 -1 0 0 1 50 2:2 9
7 + 1 0 -1 0 6 150 1:3 9
8 0 0 -1 -1 3.5 150 1:3 3
9 0 -1 -1 0 3.5 50 1:3 9
10 1 0 0 + 1 1 150 2:2 15
11 0 + 1 0 + 1 3.5 250 2:2 15
12 0 -1 + 1 0 3.5 50 3:1 9
13 + 1 -1 0 0 6 50 2:2 9
14 0 + 1 -1 0 3.5 250 1:3 9
15 0 0 0 0 3.5 150 2:2 9
16 0 + 1 + 1 0 3.5 250 3:1 9
17 0 -1 0 -1 3.5 50 2:2 3
18 0 0 + 1 -1 3.5 150 3:1 3
19 0 + 1 0 -1 3.5 250 2:2 3
20 + 1 0 + 1 0 6 150 3:1 9
21 0 0 0 0 3.5 150 2:2 9
22 + 1 0 0 -1 6 150 2:2 3
23 0 0 0 0 3.5 150 2:2 9
24 0 0 -1 + 1 3.5 150 1:3 15
25 0 0 0 0 3.5 150 2:2 9
26 0 0 + 1 + 1 3.5 150 3:1 15
27 +1 0 -1 0 1 150 1:3 9
28 +1 0 0 -1 1 150 2:2 3
29 +1 0 + 1 0 1 150 3:1 9
Tab.1  Table 1(a)Experimental design by using three factorial Box-Behnken model
Factor 1 Factor 2 Factor 3 Factor 4 Response1 Response2 Response3
Run CO2 concentration(%) Light intensity(µmol/m2/s) Inoculum ratio Harvest time(days) Cell count(Cells/mL) Lipid(FL units) Dry weight(g/L)
1 3.5 150 2:2 9 8.18E+ 08 5.85E+ 12 10.1
2 6 250 2:2 9 8.34E+ 07 5.89E+ 12 9.9
3 6 150 2:2 15 1.23E+ 06 2.34E+ 12 4.4
4 1 250 2:2 9 3.03E+ 08 4.99E+ 12 9.2
5 3.5 50 2:2 15 4.17E+ 06 1.74E+ 12 3.9
6 1 50 2:2 9 5.27E+ 08 6.05E+ 12 10.2
7 6 150 1:3 9 2.44E+ 08 5.38E+ 12 10.9
8 3.5 150 1:3 3 3.63E+ 06 3.73E+ 12 4.4
9 3.5 50 1:3 9 8.36E+ 08 6.60E+ 12 10.2
10 1 150 2:2 15 1.07E+ 07 2.14E+ 12 3.3
11 3.5 250 2:2 15 2.37E+ 06 1.67E+ 12 3.1
12 3.5 50 3:1 9 7.54E+ 08 6.23E+ 12 9.4
13 6 50 2:2 9 1.01E+ 08 4.96E+ 12 8.3
14 3.5 250 1:3 9 6.12E+ 08 5.08E+ 12 8.6
15 3.5 150 2:2 9 8.18E+ 08 5.85E+ 12 10.1
16 3.5 250 3:1 9 5.30E+ 08 5.17E+ 12 8.7
17 3.5 50 2:2 3 1.66E+ 06 3.46E+ 12 3.9
18 3.5 150 3:1 3 3.46E+ 06 3.46E+ 12 2.8
19 3.5 250 2:2 3 1.35E+ 06 3.39E+ 12 3.3
20 6 150 3:1 9 2.06E+ 08 5.02E+ 12 10.6
21 3.5 150 2:2 9 8.18E+ 08 5.85E+ 12 10
22 6 150 2:2 3 1.07E+ 06 4.37E+ 12 5
23 3.5 150 2:2 9 8.18E+ 08 5.85E+ 12 9.7
24 3.5 150 1:3 15 2.70E+ 06 2.27E+ 12 3.1
25 3.5 150 2:2 9 8.18E+ 08 5.85E+ 12 9
26 3.5 150 3:1 15 2.02E+ 06 1.96E+ 12 2.5
27 1 150 1:3 9 4.14E+ 08 5.48E+ 12 10.2
28 1 150 2:2 3 1.40E+ 06 3.36E+ 12 3.1
29 1 150 3:1 9 3.32E+ 08 5.11E+ 12 10.1
Tab.2  Table 1(b)Level of different factors maintained and the response of cell count, lipid content and dry weight
Source Sum of
Squares		 df Mean
Square		 F
Value		 p-value
Prob>F		 Significance
Model 228.61 14 16.33 12.05 <0.0001 Significant
A-CO2 2.842E-014 1 2.842E-014 2.098E-014 1.0000
B-Light 0.70 1 0.70 0.52 0.4838
C-Inoculum 0.44 1 0.44 0.33 0.5774
D-time 3.333E-003 1 3.333E-003 2.461E-003 0.9611
AB 10.24 1 10.24 7.56 0.0157 Significant
AC 1.21 1 1.21 0.89 0.3606
AD 0.36 1 0.36 0.27 0.6142
BC 1.10 1 1.10 0.81 0.3822
BD 1.000E-002 1 1.000E-002 7.382E-003 0.9327
CD 0.25 1 0.25 0.18 0.6740
A2 3.59 1 3.59 2.65 0.1257
B2 9.06 1 9.06 6.69 0.0216 Significant
C2 0.28 1 0.28 0.20 0.6580
D2 208.47 1 208.47 153.90 <0.0001 Significant
Residual 18.96 14 1.35
Lack of Fit 18.10 10 1.81 8.34 0.0278 Significant
Pure Error 0.87 4 0.22
Corrected Total 247.57 28
Tab.3  Variance analysis of response surface quadratic model for algal dry biomass  
Source Sum of
Squares		 df Mean
Square		 F
Value		 p-value
Prob>F		 Significance
Model 202.16 14 14.44 72.11 <0.0001 Significant
A-CO2 3.39 1 3.39 16.91 0.0011 Significant
B-Light 0.40 1 0.40 1.98 0.1811
C-Inoculum 0.080 1 0.080 0.40 0.5384
D-time 0.67 1 0.67 3.33 0.0894
AB 0.032 1 0.032 0.16 0.6932
AC 7.180E-004 1 7.180E-004 3.585E-003 0.9531
AD 0.89 1 0.89 4.45 0.0534
BC 4.111E-004 1 4.111E-004 2.053E-003 0.9645
BD 0.031 1 0.031 0.16 0.6989
CD 0.015 1 0.015 0.074 0.7894
A2 4.75 1 4.75 23.74 0.0002 Significant
B2 1.13 1 1.13 5.65 0.0323 Significant
C2 0.034 1 0.034 0.17 0.6856
D2 189.17 1 189.17 944.69 <0.0001 Significant
Residual 2.80 14 0.20
Lack of Fit 2.80 10 0.28
Pure Error 0.000 4 0.000
Corrected Total 204.96 28
Tab.4  Table 2(b) Variance analysis of response surface quadratic model for algal cell count 
Source Sum of
Squares		 df Mean
Square		 F
Value		 p-value
Prob>F		 Significance
M odel 7.247E+ 025 14 5.176E+ 024 9.05 <0.0001 Significant
A-CO2 1.029E+ 024 1 1.029E+ 024 1.80 0.2012
B-Light 1.288E+ 023 1 1.288E+ 023 0.23 0.6424
C-Inoculum 5.201E+ 022 1 5.201E+ 022 0.091 0.7674
D-time 1.092E+ 025 1 1.092E+ 025 19.09 0.0006 Significant
AB 2.500E+ 023 1 2.500E+ 023 0.44 0.5192
AC 2.250E+ 022 1 2.250E+ 022 0.039 0.8456
AD 1.654E+ 023 1 1.654E+ 023 0.29 0.5992
BC 4.694E+ 020 1 4.694E+ 020 8.208E-004 0.9775
BD 9.000E+ 022 1 9.000E+ 022 0.16 0.6976
CD 5.444E+ 020 1 5.444E+ 020 9.520E-004 0.9758
A2 3.142E+ 024 1 3.142E+ 024 5.49 0.0344 Significant
B2 5.607E+ 024 1 5.607E+ 024 9.80 0.0074 Significant
C2 1.524E+ 024 1 1.524E+ 024 2.66 0.1249
D2 5.874E+ 025 1 5.874E+ 025 102.71 <0.0001 Significant
Residual 8.007E+ 024 14 5.719E+ 023
Lack of Fit 7.495E+ 024 10 7.495E+ 023 5.86 0.0516
Pure Error 5.120E+ 023 4 1.280E+ 023
Corrected Total 8.047E+ 025 28
Tab.5  Table 2(c) Variance analysis of response surface quadratic model for algal lipid content.  
Fig.1  The fitness of the actual and predicted results. These graphs show the high closeness of the fitted regression between the actual and predicted biomass (BM), cell count (CC) and lipid content (LC). (a) the fitness of the actual and predicted algal dry biomass; (b) the fitness of the actual and predicted algal cell count; (c) the fitness of the actual and predicted algal lipid content
Optimal condition Response Optimum outcome Desirability
CO2 LI IR HT
3.4 180 1.2:2.8 8.4 Biomass (g/L) 9.9 0.961
2.2 187 1.5:2.5 8.7 Cell count (Cells/mL) 8.4 × 108 0.921
4.1 153 2.0:2.0 7.7 Lipid content (Total FL) 6.9 × 1012 1.000
3.6 159 1.6:2.4 8.3 Overall biomass (g/L) 9.9 0.985
Overall cell count (Cells/mL) 8.6 × 108
Overall lipid content (Total FL/mL) 6.8 × 1012
Tab.6  Optimal conditions for three outputs (biomass, cell count and lipid content) individually and together with desirability values
Fig.2  The response of algal dry biomass to different cultivation parameters. 3D surface response and contour line of Box-Behnken Design showing the mutual effect of different parameters on algal biomass (BM) with maximum response value in boxes. IR, inoculation ratio of wastewater algae to Chlorella (1:3, 2:2, 3:1), with the higher number indicating a high ratio of wastewater algae; LI, light intensity, 50– 50 µmol/m2/s; HT, harvesting time, 3–15 days; CO2, CO2 concentration, 1%–6% (v/v). (a)the response of algal dry biomass to LI and CO2; (b) the response of algal dry biomass to IR and CO2; (c) the response of algal dry biomass to HT and CO2; (d) the response of algal dry biomass to IR and LI; (e) the response of algal dry biomass to HT and LI; (f) the response of algal dry biomass to HT and IR
Fig.3  The cross lines of CO2 and light intensity, and CO2 and inoculation ratio demonstrate the cross-interactive impact of these parameters on algal biomass. (a) Interaction plot of CO2 concentration with respect to light intensity on biomass response. (b) Interaction plot of CO2 concentration with respect to inoculation ration on biomass response
Fig.4  The response of algal cell count to different cultivation parameters.3D surface response and contour line of Box-Behnken Design showing the mutual effect of different parameters on algal cell count (CC) with maximum response value in the boxes above each parabola. IR, the inoculation ratio of wastewater algae to Chlorella (1:3, 2:2, 3:1), with the higher number indicating a high ratio of wastewater algae; LI, light intensity, 50–250 µmol/m2/s; HT, harvesting time, 3–15 days; CO2, CO2 concentration, 1%–6% (v/v). (a) the response of algal cell count to LI and CO2; (b) the response of algal cell count to IR and CO2; (c) the response of algal cell count to HT and CO2; (d) the response of algal cell count to IR and LI; (d) the response of algal cell count to IR and LI; (e) the response of algal cell count to HT and LI; (f) the response of algal cell count to HT and IR
Fig.5  The response of algal lipid productivity to different cultivation parameters. 3D surface response and contour line of Box-Behnken Design showing the mutual effect of different parameters on algal lipid content (LC) with the maximum response value in the box above eachfigure. IR, the inoculation ratio of wastewater algae to Chlorella (1:3, 2:2, 3:1), with the higher number indicating high ratio of wastewater algae; LI, light intensity, 50–250 µmol/m2/s; HT, harvesting time, 3–15 days; CO2, CO2 concentration, 1%–6% (v/v). (a) the response of algal lipid productivity to LI and CO2; (b) the response of algal lipid productivity to IR and CO2; (c) the response of algal lipid productivity to HT and CO2; (d) the response of algal lipid productivity to IR and LI; (e) the response of algal lipid productivity to HT and LI; (f) the response of algal lipid productivity to HT and IR
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