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Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties Jibu Thomas n, E.V. Jayachithra Centre for Algae Biomass Research, School of Biotechnology & Health Sciences, Karunya University, Coimbatore, Tamil Nadu 641114, India

art ic l e i nf o

a b s t r a c t

Article history: Received 8 November 2014 Received in revised form 5 March 2015 Accepted 9 March 2015

Economically viable production facilities for microalgae depend on the optimization of growth parameters with regard to nutrient requirements. Using microalgae to treat industrial effluents containing heavy metals presents an alternative to the current practice of using physical and chemical methods. Present work focuses on the statistical optimization of growth of Chlorococcum humicola to ascertain the maximum production of biomass. Plackett Burman design was carried out to screen the significant variables influencing biomass production. Further, Response Surface Methodology was employed to optimize the effect of inoculum, light intensity and pH on net biomass yield. Optimum conditions for maximum biomass yield were identified to be inoculum at 15%, light intensity to be 1500 lx and pH 8.5. Theoretical and predicted values were in agreement and thus the model was found to be significant. Gas chromatography analyses of the FAME derivatives showed a high percentage of saturated fatty acids thereby confirming the biofuel properties of the oil derived from algal biomass. & 2015 Elsevier Inc. All rights reserved.

Keywords: Plackett Burman Response surface methodology Chlorococcum humicola Biomass Optimization Microalgae

1. Introduction Bio-fuel has received considerable attention in recent years, it is biodegradable, renewable and non-toxic (Hallenbeck and Benemann, 2002). It contributes no net carbon dioxide or sulfur to the atmosphere and emits less gaseous pollutants (Vicente et al., 2004). Biodiesel is recognized as an ideal recyclable energy carrier, and as a possible primary energy source (Chisti, 2007). Some of the raw materials currently being exploited for bio-fuel production are soybean (Freedman et al., 1986), sunflower (Antolin et al., 2002), palm (Darnoko and Cheryan, 2000), rapeseed (Kusdiana and Saka, 2001), canola (Zou and Atkinson, 2003), cotton seed (Lee et al., 1995) and others (Singh and Singh, 2010). As the prices of edible vegetable oils are competing with diesel fuel, waste vegetable oils, non-edible sources such as pongamia, jatropha (Jain and Sharma, 2010), microalgae (Ritu Tripathi et al., 2015), neem, karanja, rubber seeds (Morsheda et al., 2011) are preferred as potential low priced biodiesel sources (Demirbas, 2009). Among the various sources used for large scale biofuel production, microalgae has been found to be the most promising feedstock in terms of their biomass productivity, high oil content, strong adaptive capacity to adverse environments like high salinity, heavy metals, toxicants, high CO2 concentration and no competition with cultivable land (Chisti, 2007), ability to grow in

waste streams, lower nutrient requirements and with higher photosynthetic efficiency (Pienkos and Darzins, 2009). Advantages of microalgae are that they can be cultivated even under difficult agro-climatic conditions and are able to produce a wide range of commercially interesting by-products such as fats, oils, sugars and functional bioactive compounds (Banerjee et al., 2002) besides they are of particular interest in the development of future renewable energy. Certain microalgae are effective in the production of hydrogen and oxygen through the process of biophotolysis while others naturally manufacture hydrocarbons which are suitable for direct use as high-energy fuels (Akkerman et al., 2002). Using micro algae to treat industrial effluents containing heavy metals presents an alternative to the current practice of using other biosorbents and physical and chemical methods. The objective of the present study is to optimize the growth conditions of microalgae Chlorococcum humicola using Plackettt Burman Design and Response Surface Methodology for the production of high calorific biomass with biofuel properties and to estimate the fatty acid methyl esters (FAME) by Gas chromatography.

2. Materials and methods 2.1. Chemicals

n

Corresponding author. Fax: þ 91 422 2615615. E-mail address: [email protected] (J. Thomas).

Chemicals used in present study are of analytical grade (AR)

http://dx.doi.org/10.1016/j.ecoenv.2015.03.008 0147-6513/& 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Thomas, J., Jayachithra, E.V., Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties. Ecotoxicol. Environ. Saf. (2015), http://dx.doi.org/10.1016/j.ecoenv.2015.03.008i

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and are obtained from Himedia labs (Mumbai, India). 2.2. Algae cultivation and growth conditions Microalgae, C. humicola strain ISP05 was obtained from the culture collection of Vivekananda Institute of Algal technology (VIAT), Chennai, Tamil Nadu, India and grown under controlled conditions. Culture was maintained as mother culture in one litre sterilized conical flashes containing CFTRI medium with regular attention avoiding any bacterial contamination (Venkataraman and Becker, 1985), incubated at 2771 °C in a thermo-statically controlled room illuminated with cool white fluorescent lamps at an intensity of 1000 lx in a 12/12 h light/ dark cycle and with a subculture interval of 6–7 weeks. Necessary dilutions as working culture were made as and when inoculums is required for the lab scale bioreactor (Aditya and Bruce, 2010). 2.3. Experimental design Two step approach was employed to optimize the culture conditions for C. humicola. Effects of individual media components influencing the biomass productivity were studi.ed using the Plackett–Burman (PB) design. After determining the contributing parameters, surface and contour plots were obtained by Central Composite Design (CCD). 2.4. Experimental design and data analysis Optimization was done using Plackett–Burman (PB) experimental design to establish the relationship among the components of medium on biomass production (Plackett and Burman, 1946). Total number of experiments carried out according to PB was K þ1, where K is the number of variables (medium components and environmental factors). Each variable is represented at a high level denoted by ‘H’ and a low level denoted by ‘L’. PB design was analyzed using Design Expert version 8.0.7.1 (STAT-EASE Inc., Minneapolis, USA) to estimate the significant factors. The Pareto chart of standardized effects was drawn to detect the most significant variables in the experiment. Analysis of variance (ANOVA) was performed to obtain p values. A total of 11 variables that influences the growth rate of Chlorococcum sp. were selected for the PB analysis (Hui et al., 2000; Prabakaran and Ravindran, 2012). Variables A to J represents medium components and environmental factors at high and low levels whereas K, L represents photo period and temperature respectively. The experimental design followed for screening the growth parameters are shown in Table 1. Each experimental set up was carried out in photobioreactor with a working volume of 1 l of CFTRI medium and were subjected to different conditions defined by the experimental design. Specified conditions were maintained for a period of 30 days. 2.5. Response surface methodology (RSM) A standard RSM design called a Central Composite Design (CCD), which is a five level optimization technique, was applied to develop the experimental design for optimizing the biomass production. Three main operating conditions that affected biomass production as evident from PB design viz., pH, light intensity and inoculum concentration were subjected to a series of 20 runs each containing one litre of medium in a table top bioreactor with different combinations to determine the possible interactions. Trials were performed by varying three critical parameters and keeping all the other medium and environmental conditions constant. Levels and ranges of the three independent variables studied are inoculum 5, 10, 15, 20 and 25%, light intensity 500, 1000, 1500, 2000 and 2500 lx, pH 8, 8.5, 9, 9.5 and 10. The

Table 1 The Plackett–Burman experimental design matrix for screening growth of Chlorococcum sp. Notation

Variable

High (h)

Low (l)

A B C D E F G H J K L

Carbon (sodium carbonate) Nitrogen (urea) pH Phosphate (calcium phosphate) Inoculum Salinity (NaCl) Light intensity Sulfate (CuSO4) Aeration Light period Temperature

0.125% 0.025% 10 0.025% 10% 0.2% 3000 lx 0.05% 90 min 12 h 270 °C

0.018% 0.0025% 8 0.0025% 1% 0.05% 2000 lx 0.0125% 30 min 12 h 270 °C

Variables A–J are represented at 2 levels-a high concentration level (h) and a low concentration (l) level. The variables K and L are dummy variables. There is no I in Design Expert version 8.0.7.1.

variables/ranges were selected based on results obtained from preliminary studies and literatures (Wan and Hameed, 2011). For three variables (n¼ 3), the total number of experiments were 20, which was determined by the expression 2n (8 factorial points), 2n (6 axial points), six (center points, six replications). Axial points are located at ( 7a, 0, 0), (0, 7a, 0) and (0, 0, 7 a), where ‘a’ is the distance of the axial points from center. In this study, the value of ‘a’ for this CCD is fixed at two (Montgomery, 2001). The complete design matrixes of the experimental runs conducted are given elsewhere. All the parameters at zero level represent the center points. Experimental runs were randomized to minimize the effects of uncontrolled factors. RSM analyzed the experimental data obtained from the above procedure by following second-order polynomial as shown by the equation of Montgomery (2001) is given below:

Y = bo +

n

∑i + 1

bixi +

n

∑i + 1

biixi2 +

n−1

n

∑i = 1 ∑ j = i + 1

biji xixj

where Y is the predicted response; bo, bi, bii, and bij are constant, linear, quadratic and interaction coefficients respectively whereas xi and xj are the uncoded independent variables. Regression analysis and analysis of variance (ANOVA) were performed using Design Expert version 8.0.7.1 (STAT-EASE Inc., Minneapolis, USA). The fitted quadratic polynomial equation obtained from regression analysis was used to develop the response surfaces and contour plots according to Wan and Hameed (2011). 2.6. Harvesting and oil extraction Microalgal biomass was harvested from the culture media by vacuum filtration and the dry weight of the biomass was determined after drying at 80 °C for 4 h and expressed as g/L. Five gram of the dried microalgal biomass was extracted with 250 ml of hexane in a soxhlet extraction apparatus for 8 h at 55° C. Extracts were evaporated using rotary vacuum evaporator to separate out the oil from the hexane fraction. 2.7. Transesterification and gas chromatography analyses For transesterification, the oil obtained from the algal biomass were mixed with methanol in the molar ratio of 1:10. Sulfuric acid (5%, v/v) was added as the catalyst and the mixture was stirred at 150 rpm at 65° C for 4 h in a reflux condenser (Obadiah et al., 2012). After transesterification resultant mixture was allowed to separate out into two layers in a separating funnel. The upper layer was subjected to rotary vacuum evaporation to recover excess methanol. The remaining FAME fractions collected from the upper

Please cite this article as: Thomas, J., Jayachithra, E.V., Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties. Ecotoxicol. Environ. Saf. (2015), http://dx.doi.org/10.1016/j.ecoenv.2015.03.008i

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layer were subjected to gas chromatography analyses using FID detector where oven temperature was retained at 220° C. Chromatograph obtained was compared with the elution profile of C4– C24 FAME standards. The relative distribution of the individual FAME fractions were computed and presented.

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Table 3 ANOVA for Response Surface Quadratic Model to determine the significance of variables and their interactions.

3. Results and discussion 3.1. Screening of medium constituents for biomass production by PB design An experimental design with 11 variables and 12 combinations was used for screening the influence of medium constituents by PB design. Design does not consider the interaction effects between the variables and is used to screen the important variables affecting biomass production (Montgomery, 1997). The high level (h) of each variable is far enough from the low level (l) so that every significant effect that exists are likely to be detected (Sivakiran et al., 2010). Experimental trials were carried out for 20 days and the yield obtained by vacuum filtration was determined gravimetrically and was found to range from 0.041 to 0.266 g dry weight biomass per litre of medium (Table 2). Results clearly indicated that the variables had a significant influence on the productivity of biomass. Similar correlation was reported by Harwati et al. (2012). Yield obtained was subjected to obtain the Pareto chart, which is a simple and powerful way of identifying the significant variables and it constructs a histogram of variables highlighting the significant variables that crosses the p-value at 0.05 per cent. Pareto chart of standardized effects is drawn to detect the most significant variables in the experiment. The p-values were calculated by performing analysis of variance (ANOVA). The Pareto chart results revealed that pH (C), inoculum (E) and light intensity (G) had higher significance (p o0.05) that crosses the p-line and was considered to significantly influence biomass production in the present study. Further optimization of these 3 parameters at 5 levels was done using Central Composite Design.

Source

Sum of squares

df

Mean square

F value

p-Value

Model A-Inoculum B-Light intensity C-pH AB AC BC A2 B2 C2 Residual Lack of fit Pure error

0.16 0.011 0.031 0.082 0.0064 0.016 0.00095 0.0065 0.0053 0.0023 0.003 0.0029 0.00012

9 1 1 1 1 1 1 1 1 1 10 5 5

0.018 0.011 0.031 0.082 0.0064 0.016 0.00095 0.0065 0.0053 0.0023 0.0003 0.00058 0.00002

59.49 38.10 104.57 273.76 21.43 54.80 3.15 21.54 17.77 7.53

o 0.0001 0.0001 o 0.0001 o 0.0001 0.0009 o 0.0001 0.1064 0.0009 0.0018 0.0207

28.47

0.0011

Dry weight in g/L (Y)¼ 0.22þ 0.029  inoculumþ0.048  light intensityþ 0.078XpHþ 0.028  inoculum  light intensity 0.045  inoculum  pHþ 0.011  light intensity  pH 0.021  inoculum2  0.019  light intensity 2  0.013  pH2.

analysis yielded in an empirical model that relates the response measured to the independent variables. The optimal concentrations of the critical variables were obtained by analyzing 3D plots. The statistical analysis of the model was represented in the form of analysis of variance (ANOVA) in Table 3. Regression analysis of the experimental data showed the following second order regression equation for biomass production in terms of dry weight as the function of inoculum, light intensity and pH. 3.3. Model interpretation Stastistical significance for the response surface quadratic model is given in Table 4. F-value (59.49) of the model implies that the model is significant at 0.01% level. Values of “Prob4 F” less than 0.05 indicates that the model terms are significant. In this case A, B, C, AB, AC, A2, B2, C2 are significant model variables that affect biomass production. Values greater than 0.10 indicates that the model terms have no direct significance. Values for lack of fit for the F-value (28.47) was also found to be significant.

3.2. Optimization by response surface methodology and regression analysis

Standard deviation Mean C.V. (%) R2 Adjusted R2 Predicted R2 Adequate precision

RSM utilizes mathematical and statistical techniques to perform modeling and analysis of problems in which a response of interest is influenced by several variables. The three independent variables at sub-optimal and supra-optimal level were considered at five different levels, with a set of 20 experiments and were subjected to multiple regression analysis. Multiple regression

0.017 0.18 9.38 0.9817 0.9652 0.8555 25.215

Table 2 Effect of selected variables on growth of Chlorococcum humicola. Trialno.

A %

B %

C pH

D %

E %

F %

G lx

H %

J min

K h

L °C

Yield g/L

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

0.125 0.018 0.018 0.125 0.018 0.018 0.125 0.125 0.018 0.018 0.125 0.125

0.0250 0.0025 0.0025 0.0250 0.0250 0.0250 0.0025 0.0025 0.0250 0.0025 0.0025 0.0250

8 10 8 10 8 10 10 10 10 8 8 8

0.0025 0.0025 0.0025 0.0250 0.0250 0.0025 0.0025 0.0250 0.0250 0.0250 0.0250 0.0025

10 1 1 1 1 10 10 1 10 10 10 1

0.20 0.20 0.05 0.05 0.05 0.05 0.05 0.20 0.20 0.20 0.05 0.20

2000 3000 2000 2000 3000 2000 3000 2000 3000 2000 3000 3000

0.050 0.050 0.0125 0.050 0.050 0.0125 0.050 0.0125 0.0125 0.050 0.0125 0.0125

30 90 30 90 30 90 30 30 30 90 90 90

12 12 12 12 12 12 12 12 12 12 12 12

27 27 27 27 27 27 27 27 27 27 27 27

0.146 0.152 0.041 0.213 0.172 0.123 0.183 0.147 0.150 0.266 0.217 0.053

A – Carbon (%); B – Nitrogen (%); C – pH; D – Phosphate (%); E – Inoculum (%); F – Salinity (%); G – Light intensity (lx); H – Sulfate (%); J – Aeration duration (min); K – Light period (h); L – Temperature (°C); Yield-dry weight (g/L).

Please cite this article as: Thomas, J., Jayachithra, E.V., Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties. Ecotoxicol. Environ. Saf. (2015), http://dx.doi.org/10.1016/j.ecoenv.2015.03.008i

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Table 4 Theoretical and predicted values obtained in terms of yield. Trial/variable

Inoculum X1

Light intensity X2

pH X3

Theoretical Predicted Yield (dry weight)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 1 1 0 2 0 0 2 0 1 1 0 0 0 0 1 0 1 0 1

1 1 1 0 0 0 0 0 2 1 1 0 0 2 0 1 0 1 0 1

1 1 1 0 0 0 0 0 0 1 1 2 0 0 0 1 2 1 0 1

0.25 0.16 0.30 0.22 0.14 0.22 0.22 0.19 0.24 0.09 0.21 0.06 0.22 0.10 0.23 0.01 0.31 0.32 0.22 0.002

0.228 0.142 0.292 0.220 0.078 0.220 0.221 0.194 0.241 0.098 0.230 0.012 0.221 0.048 0.221 0.024 0.324 0.316 0.221 0.008

Adequate precision measures the signal to noise ratio and the value greater than 4 is desirable. Obtained ratio of 25.215 indicates an adequate signal so as to navigate the design space. The coefficient of determination, R2 (98.17%) indicates that the obtained model gives a good system response estimates within the studied range. Similar results were reported by Wan and Hameed, (2011). A relatively lower value of the coefficient of variation (9.38%) indicated good precision and reliability of the experimental runs. 3.4. Graphical interpretation The plot of residuals versus predicted values of biomass dry weight showed a random scattering without any obvious patterns, implying that the model proposed is adequate. The normal probability plot, a graphical tool for comparing a data set with the normal distribution was used with the standardized residual of the linear regression model to confirm that the error term is actually normally distributed or not. Result indicated that the normal probability plot between internally studentized residuals and normal % probability wherein the residuals falls on a straight line indicating that errors are randomly distributed for all the responses. Predicted values obtained were close to the theoretical values (Table 4), signifying that the regression analysis is

reproducible. Thus, it can be concluded that the model is significant and the effect of inoculum, light intensity and pH can be directly correlated to biomass yield with a minimum of nutrient concentration. Results of regression analysis showed that biomass yield was significantly affected by higher order terms of inoculum, light intensity and pH (Fig. 1). The interaction effect of inoculum and light intensity as well as the interaction effect of inoculum and pH was also found to be significant. Fig. 2 represents the three dimensional response surface plots in terms of dry weight between the factors inoculum and light intensity, inoculum and pH and light intensity respectively. The third variable is kept at zero level in all the cases. With increase in the inoculum concentration and light intensity, the biomass yield was found to increase and a maximum yield was obtained with inoculum (15%) and light intensity of 1500 lx (Fig. 2). Biomass yield also increased with increase in inoculum percentage and much higher productivities were observed at higher pH of 8.5 (Fig. 2). From optimization studies and three dimensional surface plot, the optimized values of the variables for growth were obtained as: pH 8.5, inoculum 15% and light intensity 1500 lx. The experimental design also showed a close concordance between the expected and obtained activity levels. The results are in concurrence with the report by Mahapatra et al. (2014). 3.5. Gas chromatography analysis of fatty acids The fatty acid methyl ester composition values of the biofuel were determined by comparing the retention times of C4–C24 standards. Elution profile revealed that main components of oil are Caprylic acid (18:0), Myristic acid (14:0), Palmitic acid (16:0), Stearic acid (18:0), Oleic acid (18:1), Linoleic acid (18:2) and Linolenic acid (18:3). As the ignition quality, heat of combustion and melting point increase with increase in number of carbon atoms and decrease with increase in unsaturation, the fatty acids present indicated its biofuel properties. Unsaturated fatty acid esters are oxidatively unstable in comparison to saturated fatty acid esters as double bonded fatty acids react with oxygen more readily (Kumar et al., 2011). In the present study, gas chromatography analysis of oil from C. humicola shown the presence of high percentage of saturated fatty acids and lesser concentration of linoleic and linolenic acids, thereby confirming the oxidative stability of biofuel thus obtained.

4. Conclusion C. humicola was used in the present study to investigate the growth parameters by Plackett–Burman design and response surface methodology. PB design confirmed the relative importance

Fig. 1. Correlation of variables on biomass. (a) Normal plot of residuals. (b) predicted versus actual yield in terms of dry weight.

Please cite this article as: Thomas, J., Jayachithra, E.V., Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties. Ecotoxicol. Environ. Saf. (2015), http://dx.doi.org/10.1016/j.ecoenv.2015.03.008i

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Fig. 2. Three dimensional surface plot of dry weight with respect to (a) inoculum and light intensity and (b) inoculum and pH.

of medium components on growth rate. Among the variables, inoculum percentage, light intensity and pH were found to be the most significant variables. Optimized values of the variables were found to be pH 8.5, inoculum 15% and light intensity of 1500 lx. Experimental design showed a close concordance between the expected and obtained activity level. Theoretical and predicted values were in agreement and thus the model was found to be significant. Also, the gas chromatography results confirmed the presence of biofuel precursors in the oil obtained from the biomass of C. humicola, thus revealing its commercial importance.

Acknowledgements Authors express thanks to the initiatives of W2E and BESI, USA for a collaborative project on microalgae. Special thanks to Dr. V. Sivasubramaniam, Phycologist for providing the authentic algae cultures and technical support. Financial support of DSTSERB (SERB:S.O. No./SB/FT/LS-389/2012) project is thankfully acknowledged. Guidance of Dr. E.J. James and Dr. Patrick Gomez, Karunya University are thankfully acknowledged. Facilities and support extended by Karunya University is greatly acknowledged.

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Please cite this article as: Thomas, J., Jayachithra, E.V., Growth kinetics of Chlorococcum humicola – A potential feedstock for biomass with biofuel properties. Ecotoxicol. Environ. Saf. (2015), http://dx.doi.org/10.1016/j.ecoenv.2015.03.008i