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Statistical Evaluation and Modeling of Cheap SubstrateBased Cultivation Medium of Chlorella vulgaris to Enhance Microalgae Lipid as New Potential Feedstock for Biolubricant a

a

b

M. A. Mohammad Mirzaie , M. Kalbasi , S. M. Mousavi & B. Ghobadian a

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Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran

b

Biotechnology group, Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran c

Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran Accepted author version posted online: 06 Apr 2015.

Click for updates To cite this article: M. A. Mohammad Mirzaie, M. Kalbasi, S. M. Mousavi & B. Ghobadian (2015): Statistical Evaluation and Modeling of Cheap Substrate-Based Cultivation Medium of Chlorella vulgaris to Enhance Microalgae Lipid as New Potential Feedstock for Biolubricant, Preparative Biochemistry and Biotechnology, DOI: 10.1080/10826068.2015.1031398 To link to this article: http://dx.doi.org/10.1080/10826068.2015.1031398

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Statistical Evaluation and Modeling of Cheap Substrate-Based Cultivation Medium of Chlorella vulgaris to Enhance Microalgae Lipid as New Potential Feedstock for Biolubricant M. A. Mohammad Mirzaie1, M. Kalbasi1, S. M. Mousavi2, B. Ghobadian3 1

Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran, 2Biotechnology group, Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran, 3Biosystems Engineering Department, Tarbiat Modares University, Tehran, Iran

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Corresponding author: Mansour Kalbasi, E-mail address: [email protected]

Abstract Chlorella vulgaris (C. vulgaris) microalgae was investigated as a new potential feedstock for the production of biodegradable lubricant. In order to enhance microalgae lipid for biolubricant production, mixotrophic growth of C. vulgaris was optimized using statistical analysis of Plackett-Burman (P-B) and response surface methodology (RSM). Cheap substrate-based medium of molasses and corn steep liquor (CSL) was used instead of expensive mineral salts to reduce the total cost of microalgae production. The effects of molasses and CSL concentration (cheap substrates) and light intensity on the growth of microalgae and their lipid content were analyzed and modeled. Designed models by RSM showed to have good compatibility with a 95 % confidence level when compared to the cultivation system. According to the models, optimal cultivation conditions were obtained with biomass productivity of 0.123 g.L-1.day-1 and lipid dry weight of 0.64 g.L-1 as 35 % of dry weight of C. vulgaris. The extracted microalgae lipid presented useful fatty acid for biolubricant production with viscosities of 42.00 cSt at 40 °C and 8.500 cSt at 100 °C, viscosity index of 185, flash point of 185 °C and pour point of -6 °C. These

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properties showed that microalgae lipid could be used as potential feedstock for biolubricant production.

KEYWORDS: microalgae-based biolubricant; mixotrophic growth; Chlorella vulgaris; response surface methodology; agricultural waste medium; statistical evaluation.

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1.

INTRODUCTION

Petrochemical-based lubricants with their inherent toxicity and non-biodegradable nature cause a constant threat to the ecology and vast ground water reservoirs when a large proportion of them, i.e 50-60 %, come in contact with soil, water and air [1]. As a replacement, the production of green bio-based lubricants using natural feedstock is a subject of scientific and industrial interest [2]. Some research works have recently focused on the production of biolubricants using plant oils [1, 3, 4] and waste cooking oils [5]. The main restriction to the wide application of plant oil-based biodegradable lubricants is their relatively high cost of production compared to the mineral oil-based lubricants [6]. Application of cheap new feedstock such as microalgae could be a solution to reduce the total cost of biolubricant production. Microalgae lipid has previously been used for the production of biodiesel [7], however it has not been used for the production of biolubricant. Many species of microalgae have oil content in the range of 20-50 % of biomass dry weight and do not compromise the production of food and other products derived from crops [8].

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For the reliable production of microalgae for biolubricant, it is important to cultivate microalgae with the highest biomass productivity and lipid content. Some research works have focused on autotrophic cultivation based on photosynthesis [9, 10] where CO2 and sunlight are used as carbon and energy sources for microalgae growth, respectively. However, it is difficult to reach a high density of microalgae biomass in this cultivation medium. As a feasible alternative, heterotrophic [7] and mixotrophic [11] growth regimes

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have been proposed. In heterotrophic cultivation, organic carbon sources are used as a sole carbon source and in mixotrophic cultivation, inorganic (CO2) and organic compounds are utilized simultaneously as a carbon source. Therefore, mixotrophic cultivation has both photosynthetic and heterotrophic metabolism at a high production rate [12, 13]. However, the cost of organic substrates could be 80 % of the total cultivation medium cost [11] and this is the main reason for the small usage of mixotrophic cultivation [14]. Using cheap substrates like industrial dairy waste [11], Jerusalem artichoke [7], sweet sorghum [15], agricultural waste medium [16] and molasses [17] could reduce the total cost of mixotrophic cultivation of microalgae.

During the last few years, different methods have been used for modeling and optimization of microalgae growth conditions based on different variables to reach the maximum biomass and lipid production [18, 19]. Statistical methods of Plackett-Burman and response surface methodology have also been applied to optimize the significant independent factors [20, 21]. These methods have a high ability to analyze the results of experiments and to drive mathematical models. In these studies, mixotrophic growth of

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microalgae using cheap substrates was not considered and expensive substrates, like glucose, were used as a carbon source in mixotrophic growth.

In the present work, microalgae of C. vulgaris were investigated as the potential feedstock for production of biolubricant. The presence of useful fatty acids for the production of biolubricant in the extracted lipid from microalgae was analyzed and the

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viscosities, flash point and pour point of the lipid were also measured. To reduce the total cost of microalgae production, microalgae were cultivated in a cheap agricultural waste medium using cheap substrates of cane molasses and CSL with the elimination of expensive traditional mineral salts. To cultivate microalgae with the highest biomass productivity and lipid content, analytical methods were used for optimizing the mixotrophic cultivation condition.

2. 2.1.

MATERIALS AND METHODS

Microorganism And Culture Media

Chlorella vulgaris (CCAP 211/11B) was obtained from the culture collection of algae and protozoa (CCAP, Scotland). The basic medium for cultivation was Rudic’s culture medium [22] which contains (per liter of distilled water): 300 mg KNO3, 20 mg KH2PO4, 80 mg K2HPO4, 20 mg NaCl, 47 mg CaCl2, 10 mg MgSO4.7H2O and trace elements consisting of 0.1 mg ZnSO4.7H2O, 1.5 mg MnSO4.H2O, 0.08 mg CuSO4.5H2O, 0.3 mg H3BO3, 0.3 mg (NH4)6Mo7O24.4H2O, 17 mg FeCl3.6H2O, 0.2 mg Co(NO3)2.H2O and 7.5 mg EDTA (this combination of mineral salts named after this as salt concentration or SC). Waste industries of cane molasses (Karaj sugar company in Iran) and corn steep

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liquor (CSL) (Gloucosan Corn industry in Iran) were used as cheap substrates and also as carbon and nitrogen sources. Composition of these 2 sources was shown in table 1 (composition of crude waste). For the experiments, 20 mL as-received molasses was dissolved in 1 L of distilled water and after centrifugation in 2650 g for 10 min, supernatant solution was used as molasses solution. Also, as-received CSL was dried and 10 g of dried CSL was dissolved in 1 L of distilled water at 50 ºC and centrifuged similar

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to molasses. Supernatant solution was used as CSL solution.

Different experiments were carried out in sterile conditions according to tables S1 and 2. Experiments have been carried out in 500 mL photobioreactors containing 100 mL of medium that were equipped with air sparger. Aeration was done via an air pump with sterilization using 0.22 µm filters. Temperature was fixed at 30 °C using an electrical heater. The volume of inoculum in photobioreactors was 10 % and initial pH of all cultivation media was 7.5.

All photobioreactors were externally illuminated with a 25 W tungsten lamp. The light intensity of 400-1400 lux was used in different experiments.

2.2.

Screening Of More Effective Parameters

Cane molasses and corn steep liquor are complex substrates that have a useful composition for microorganism growth (table 1). Therefore, mineral salts were selected as P-B variables to study the possibility of their elimination from the culture medium which reduce the final production cost. Light intensity, cane molasses concentration, CSL

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concentration and the concentration of each mineral salt as KNO3, MgSO4.7H2O, K2HPO4, KH2PO4, NaCl, FeCl3.6H2O, CaCl2, and trace elements in mineral salt composition were chosen as 11 factors in P-B design and experiments were carried out in the photobioreactors as indicated in table S1.

2.3.

Statistical Evaluation Using RSM

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For optimization of biomass growth and lipid content, central composite design (CCD) was used. Cane molasses concentration (X1), CSL concentration (X2) and light intensity (X3) were chosen as independent variables according to P-B results for significant parameters. Each factor was examined at five different levels (-α, -1, 0, +1, +α). In fact, three sets of experimental runs were performed based on the levels of factors: (i) fractional factorial runs in which the factors varied in two levels (+1, -1); (ii) center points in which the factors were kept in their medium levels; and (iii) axial points, the same as center point except for one factor which took on the values below or above the median of the two factorial levels [23]. For the purpose of statistical calculations, the selected independent variables were coded according to the following equation: xi

Xi

X0 X

(1)

where xi, is a coded value of the variable, Xi is the actual value of the variable, X0 is the actual value of Xi at the center point, and ΔX, is the step change of the variable [24]. The experimental runs and levels of the variables are shown in table 2. The behavior of the system is explained by a second-order polynomial empirical model:

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n

Y

0

n i

i 1

Xi

n ii

i 1

Xi

n

2 ij

(2)

Xi X j

i 1 j 1

where Y is the response, β0 is the model constant, Xi is non-coded variables, βi is the linear interaction coefficient, βii is the quadratic interaction coefficient, βij is the second-order interaction coefficient and ε is residual for each experiment [25].

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The experimental plan consisted of 20 trials according to table 2, including six replicates at center point in order to determine the experimental error. According to the experiment results, contribution of variables and their interactions were determined and mathematical models were generated. Analysis of variance (ANOVA) was used to evaluate the interaction between the process variables and the responses. The quality of the model fit was examined by the coefficient of determination (R2). The significance of the models and variables was checked by P value. The models were validated by conducting the experiment on the given optimal conditions of the models.

2.4.

Microalgae Productivity And Lipid Content

The biomass productivity was measured spectrophotometrically. In order to calculate the dry weight of microalgae from optical density, an excellent regression relationship between the dry weight and OD550 of microalgae was derived for 10 separate concentrations of microalgae cells. For the microalgae dry weight measurement, the cells were collected and washed by centrifuge and then the pellets were dried at 50 ºC for 1 day. Accordingly, the dry weight of microalgae (Xd) had a relation with OD550 as: X d g.L-1 = 0.49 × OD550 – 0.0215

(3)

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In this way, the cell density (optical density) of microalgae in the visible light region (wavelength of 550 nm) was measured in the first day of experiments and also after 15 days (last day). Optical density for each sample was measured twice and the mean was used for measurement. Biomass productivity was measured as:

P = Xdf – Xd1 /t

(4)

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where P is biomass productivity (g.L-1.day-1); Xdf is dry weight of microalgae on the last day (g.L-1); Xd1 is dry weight of microalgae on the first day (g.L-1) and t is time (day).

Lipid content was measured using FTIR analysis and according to the references [26, 27]. FTIR involves the measurement of infrared absorption in a range of molecular vibrational modes for different functional groups which in this case, includes proteins, lipids and carbohydrates. For analysis using FTIR, first the lipid of microalgae was extracted using a mixture of chloroform-methanol. After treatment with chloroform:methanol (2:1 v/v), the cell wall was broken and intracellular materials were exited from the cell. Then, after removing the organic phase containing lipid, the pellet was washed twice with 0.9 % NaCl. The spectra for each sample were measured twice over the wavenumber range of 4000–600 cm−1 and were corrected with baseline correction and normalization of lipid band (2945 cm−1) to amide band (1652 cm−1) to give the lipid:amide peaks ratio. The ratio of lipid to amide peaks was expressed as lipid content in this paper. It should be noted that there is no true lipid content, just the relative content as obtained by FTIR.

2.5.

Microalgae-Based Biolubricant

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The fatty acid composition of the extracted microalgae oil were analyzed by gas chromatographic analysis using PerkinElmer, Clarus 580 (USA) with flame ionization detector and capillary column (30 m × 0.32 mm × 0.25 µm, CP 9080 wax). The analysis carried out with the following conditions: keeping the oven temperature at 60 ºC for 2 min, then raising it to 200 ºC at a rate of 10 ºC/min; raising the temperature again to 240 ºC at a rate of 5 ºC/min; and keeping this temperature for 7 minutes. The carrier gas was

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helium.

Kinematic viscosity testing was performed according to the ASTM D445 standard using SVM–3000 Stabinger Viscometer (Anton Paar, Austria). Pour point was measured based on the standard ASTM D 97 using the P592 apparatus (Analyze, Belgium). Flash point was measured on the basis of standard ASTM D 93 using the FLPH (CCCFP) Continuously Closed Cup apparatus (Grabner, Austria).

3. 3.1.

RESULTS AND DISCUSSION

Screening Of The Effective Factors By The Plackett–Burman Design

P–B design was performed according to table S1 in order to evaluate the significance of each variable on the microalgae productivity. The results of microalgae productivity were obtained after 15 days of growth and then analyzed with the software. The results showed that the model was significant (P value= 0.0156) with R2 = 0.996. The results of P-B in estimating effects of variables with the software (Fig. 1) indicated that light intensity, CSL concentration and molasses concentration were the first, second and third significant variables on biomass productivity and mineral salts had less effect. Therefore, the first

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three variables were selected for experimental design by RSM. These three important variables had positive effects (Fig. 1) on productivity as the increase in their content raised the productivity of microalgae. Among all the used mineral salts, only K2HPO4 had a significant effect and KH2PO4 was possibly significant (P value a little more than 0.05). However the -1 level of these two salts which was attributed to the absence of these salts was significant. This means that the presence of such salts was detrimental on

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biomass growth. The insignificance of mineral salts or their detrimental effect on the growth could mean that biomass used the existing mineral salts in molasses or CSL (table 1) to a great extent. Therefore, all mineral salts were eliminated from the culture medium.

The effect of the medium composition on the product of microalgae has also been investigated previously. Ramnani and Gupta [28] screened the composition of cultivation medium of Bacillus licheniformis RG1 using Plackett–Burman design. They found that glucose, peptone and glutathione affected the keratinase production intensely and calcium was deleted from the cultivation medium due to its negative effect. Kong et al. [29] selected glucose, MgSO4.7H2O, KNO3 and NaCl as the most critical mixotrophic nutrients based on the Plackett–Burman design. In these research works, non-significant factors were kept at constant levels. The above authors did not investigate the deletion of mineral salts and use of agricultural wastes as nutrients, whilst, these parameters are studied in the present research work.

3.2.

Statistical Modeling And Optimization Using RSM

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In order to optimize the productivity and lipid content of C. vulgaris by RSM, light intensity, molasses and CSL concentration were selected as the main parameters. A number of twenty experiments were carried out according to table 2. The responses of the system for biomass productivity and lipid content were measured as described in section 2.2. ANOVA for the presented models is shown in table 3. P values are an important factor for determining the significance of models. If P value is less than 0.05, it indicates

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that the model is significant. According to the results observed, the models for biomass productivity and lipid content are both significant and useful for determining the behavior of the system. The model F values of 28.70 and 19.23 for biomass productivity and lipid content implied that these models were significant.

P values of the model factors are shown in table S2. P values less than 0.05 revealed that the effect of model factor is significant. According to the P values in table S2, factors A (light intensity), B (molasses concentration) and C (CSL concentration) were all significant parameters and A2, B2 and C2 were also taken as significant terms for biomass productivity. The coefficient of determination (R2) value of the presented model for biomass productivity is shown in table S2. The significant terms A, B, C, A2, B2 and C2 were placed in the model and the insignificant terms AB, AC and BC were dropped from the model. Fig. 2 shows the predicted values versus experimental results of the productivity.

In addition, the factors B (molasses concentration), C (CSL concentration) and A2 were taken as significant terms for lipid content. The terms B2, C2, AB and AC were dropped

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from the model for being insignificant. Factor A (light intensity) was not a significant parameter, but it was kept in the model due to the significant effect on mixotrophic growth. The coefficients estimated are presented in table S2. The represented models based on the significant terms as a function of the coded factors are as follows: Biomass productivity= 0.120 + 0.011A + 0.013B – 0.01C – 0.026A 2 – 0.013B2 – 0.021C2 (5)

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Lipid content= 1.95 – 0.07A + 0.37B – 0.64C – 0.22BC – 0.37A 2

(6)

The response surface plots for biomass productivity are shown in Fig. 3. Fig. 3a shows the simultaneous influence of light intensity (A) and molasses concentration (B) on biomass productivity when the CSL concentration was kept in its center level of 22.5 mL.L-1. Fig. 3b shows the simultaneous influence of light intensity (A) and CSL concentration (C) when molasses concentration (B) was kept in its center level of 22.5 mL.L-1. According to the figures, the highest biomass productivity could be obtained in the center of the circles. The center of the circles in these figures was near the middle content of each factor. This means that increasing the CSL or molasses concentration or light intensity to more than their center level had a negative effect on the biomass productivity. This seems reasonable due to the effects of these factors on the growth. The lower levels of the substrates could not produce sufficient nutrients for microalgae growth and also, lower levels of light intensity could not produce sufficient energy. Higher levels of these factors also showed the inhibition effect for the growth. Therefore, the center level of the factors had a better influence on the microalgae growth. Meanwhile, the productivity for each combination of factors could be measured from the figures.

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The simultaneous influence of the factors for the lipid content is shown in Fig. 4. A discussion on the surface and contour plots of lipid content is similar to the ones for biomass productivity. It should be noted that higher levels of molasses and lower levels of CSL resulted in the increment of lipid content. This result may show that a higher C/N ratio will increase lipid content [30], but not necessarily result in the increment of microalgae growth. Furthermore, the best situation was the one which produced both

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higher growth and lipid content which resulted in the higher overall lipid productivity.

According to the presented model for biomass productivity and lipid content, the predicted conditions to reach the maximum productivity and lipid content were obtained according to table 4. As the final aim in the lipid production in the microalgae growth was to achieve a higher lipid value, which was obtained by simultaneous maximum biomass production (microalgae growth) and lipid content, an optimization was performed using the developed models to reach the maximum biomass productivity and lipid content. In order to validate the model accuracy, three tests were performed according to the optimum conditions in order to compare the actual and predicted responses. The results confirmed the significance of the models with 95 % confidence interval and its ability for production of the new situation.

Another mean for optimizing the responses was shown in Fig. 5 as an overlay plot. Using this plot, an optimum region (yellow region) was identified based on two critical responses, biomass productivity of 0.11 g.L-1.day-1 and lipid content of 3. The reason for

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selecting these values for responses was acceptable biomass productivity and high lipid content which resulted in a high lipid value.

3.3.

Microalgae Growth

The results of biomass productivity and lipid content in table 2 are in good agreement with the mixotrophic cultivation and were significantly influenced by the nutritional

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conditions and light intensity. The highest biomass productivity was 0.134 when microalgae were cultivated under mixotrophic conditions using 965 lux light intensity and a mixture of 26.3 mL.L-1 molasses and 20.65 mL.L-1 CSL as organic carbon and nitrogen sources. Light intensity had an important effect on the productivity as energy source, with the significance of molasses as a carbon source confirming that the cultivation metabolism was mixotrophic. Light energy is used for CO2 fixation and carbon assimilation in mixotrophic cultures and therefore, mixotrophy provides higher energetic efficiency than other cultivation modes [11].

Maximum lipid content was obtained at 950 lux light intensity, 28.5 mL.L-1 molasses and 15.05 mL CSL concentration using FTIR. For analysis using FTIR, first the lipid of microalgae was extracted using a mixture of chloroform-methanol. Microalgae cells before treatment and lipid extraction from a cell wall after treatment were shown in optical micrographs in Fig. S1 a and b, respectively. After treatment with chloroformmethanol, the cell wall was broken and intracellular materials were exited from the cell. FTIR spectrum of the sample from the maximum lipid condition is shown in Fig. S2. There are some distinct absorption bands assigned to specific molecular groups according

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to the references [26, 27]. These absorption bands are attributed to vibrational modes of residual water (3382 cm−1); lipid CH2 (2945 cm−1); amide (1652 cm−1) and carbohydrate (1028 cm−1). The ratio of lipid peak to amide peak was used for normalization of FTIR spectra for all samples and the relative lipid content was measured [26, 27]. In addition to FTIR, in optimized conditions of both maximum biomass productivity and lipid content, the weight of the lipid was measured gravimetrically from the chloroform extract by the

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Bligh and Dyer method according to Ref. [31]. Lipid weight in this method was obtained as 0.64 g.L-1 which results in 35 % lipid of biomass dry weight.

Biomass productivity and lipid content of microalgae in this research work were comparable to the results reported by previous studies. Delavari Amrei et al. [32] reported the highest biomass productivity of 0.05 g.L-1.day-1 in autotrophic growth of Chlorella sp. (PTCC 6010). Bhatnagar et al. [33] compared the biomass productivity of different algae in autotrophic, heterotrophic and mixotrophic media and reported the maximum biomass productivity of 0.2176 g.L-1 per 7 days of incubation for mixotrophic growth. The lipid content obtained in the present investigation (35 % of dry weight) was higher than those obtained by Oh et al. [34] (the highest content of 19.3 %) for Porphyridium cruentum grown under different culture conditions. The lipid content in this research was close to the highest values reported by Liang et al. [35] for mixotrophic growth of Chlorella vulgaris (38%).

After using microalgae for extraction of intracellular lipids, there is some useful residual carbon in microalgae cellular debris. Therefore, it seems that microalgae cellular debris

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could itself be used as substrate for the production of other useful products like bioethanol [36].

3.4.

Properties Of Extracted Microalgae Lipid

The fatty acid composition of the extracted microalgae lipid was obtained according to the gas chromatography analysis (table 5, Fig. S3) and was similar to the previously

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reported plant oils used as feedstock for biolubricant production [37, 38]. The lubricant properties of the extracted lipid were measured (table 5). The viscosity of the biolubricant was very important, since this property determines the fluidity of the lubricant and its film protection. Pour point which is the lowest temperature that oil can still pour from a tilted jar is one of the most critical properties which determine the low temperature fluidity of the lubricants. The flash point is the minimum temperature at which the vapor above oil forms an ignitable mixture with air and hence, the highest flash point is desirable. The viscosities at 40 °C, and 100 °C, viscosity index, pour point and flash point of the extracted microalgae lipid were 42.00 cSt, 8.500 cSt, 185, -6 °C and 185 ºC respectively; comparable to the value reported for plant based oils (table 5) [37-40]. The composition of fatty acid and its measured properties showed that the microalgae lipid could be a potential reliable feedstock for biolubricant production [38, 41, 42].

4.

CONCLUSION

Optimization of biomass productivity of C. vulgaris and its lipid production were successfully carried out using statistical analysis based on Placket-Burmann and response surface methodology. Significant factors on these parameters were determined as light

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intensity, molasses concentration and CSL concentration and it was shown that mineral salts had no significant effect. Therefore, these expensive salts were omitted from the system and the cost of growth media was reduced. The growth system was optimized based on these parameters and the best condition for lipid value was obtained. According to the designed models for biomass productivity and lipid content, the highest lipid value was 35 % dry weight of biomass. Designed models for the biomass productivity and lipid

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content had good accuracy in the prediction of optimized conditions with 95 % confidence interval.

The properties of the extracted microalgae lipid as viscosity at 40 °C, viscosity at 100 °C, viscosity index, flash point and pour point were measured. These properties have values of 42.00 cSt, 8.500 cSt, 185, 185 ºC and -6 °C, respectively. These properties make the microalgae lipid a potential reliable feedstock for biolubricant production.

ACKNOWLEDGEMENT The authors are grateful to Stat-Ease, Minneapolis, MN, USA, for the provision of the Design-Expert 7.0.0 package.

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Table 1. Cane Molasses and corn steep liquor (CSL) composition

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Molasses

CSL

Major

% in dry

component

mass

Ash

14.5

Crude protein

mg per 1 L

Major

% in dry

molasses

component

mass

Ca

780

Ash

13

K

6200

2.5

K

2512

Crude Protein

49

Fe

20.6

Crude fiber

0.1

Fe

20

Fat

0.4

Na

200

NDF*

0.8

Mg

100

Lactic Acid

21

Cu

1.6

ADF**

0.5

Na

644

Phytic Acid

6.6

Ni

0.6

Lignin

0.3

Mn

0.8

Nitrogen

7.5

P

0.6

Total sugars

68.2

Zn

1.5

Total Sugars

2.5

Cl

100

P

180

Cl

10

Sugar

%

Glucose

10

Fructose

4

Sucrose

51

Other Sugars

35

Mineral

*Natural detergent fiber **Acid detergent fiber

24

Mineral

mg per 1 L CSL

Table 2. RSM design experiments and the results of biomass productivity (g.L-1.day-1) and lipid content Variables

X1: Light intensity

Low axial (-

Low factorial

Centre

High factorial

High axial

α:1.68)

(-1)

(0)

(+1)

(+α:1.68)

400

600

900

1200

1400

10

15

22.5

30

35

10

15

22.5

30

35

X1

X2

X3

Biomass productivity (g.L-

Lipid

1

content

0.063± 0.0076

1.892±

(lux) X2: Molasses

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volume (mL.L-1) X3: CSL volume (mL.L-1) Run

.day-1)

1

600

15

15

0.0932 2

1200

30

15

0.096± 0.0036

2.650± 0.1568

3

900

22.5

35

0.037± 0.0027

0.482± 0.0732

4

1200

30

30

0.081± 0.0092

1.014± 0.0451

5

600

30

30

0.074± 0.0063

2.986± 0.0693

6

900

22.5

22.5

0.132± 0.0058

2.237± 0.1749

7

900

22.5

22.5

25

0.122± 0.0094

1.567±

0.2435 8

900

22.5

22.5

0.120± 0.0028

1.743± 0.1739

9

1400

22.5

22.5

0.054± 0.0008

0.630± 0.0143

10

600

15

30

0.021± 0.0037

1.289± 0.0483

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11

900

22.5

22.5

0.128± 0.0085

1.985± 0.3275

12

900

22.5

10

0.072± 0.0007

2.919± 0.6382

13

400

22.5

22.5

0.029± 0.0046

0.581± 0.0200

14

1200

15

30

0.070± 0.0047

1.051± 0.0749

15

900

22.5

22.5

0.111± 0.0084

2.122± 0.2759

16

900

10

22.5

0.049± 0.0004

0.860± 0.1004

17

1200

15

15

0.080± 0.0009

1.865± 0.0765

18

600

30

30

0.057± 0.0036

1.445± 0.0500

19

900

35

22.5

0.108± 0.0076

2.654± 0.0820

26

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20 900 22.5 22.5

27 0.119± 0.0058 1.876±

0.0265

Table 3. ANOVA for response surface models applied. Responses

Sum of

Degree of

Mean

Squares

freedom

squares

Model

0.021

6

3.48×10-3

Residual

1.576×10-3

13

1.21×10-4

Model

9.89

5

1.98

Residual

1.44

14

0.10

F–value

P–value

28.70

Statistical evaluation and modeling of cheap substrate-based cultivation medium of Chlorella vulgaris to enhance microalgae lipid as new potential feedstock for biolubricant.

Chlorella vulgaris (C. vulgaris) microalga was investigated as a new potential feedstock for the production of biodegradable lubricant. In order to en...
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