Biodiesel production from Spirulina microalgae feedstock using direct transesterification near supercritical methanol condition

Biodiesel production from Spirulina microalgae feedstock using direct transesterification near supercritical methanol condition

Accepted Manuscript Biodiesel Production from Spirulina Microalgae Feedstock Using Direct Transesterification Near Supercritical Methanol Condition Ha...

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Accepted Manuscript Biodiesel Production from Spirulina Microalgae Feedstock Using Direct Transesterification Near Supercritical Methanol Condition Hamed Mohamadzadeh Shirazi, Javad Karimi-Sabet, Cyrus Ghotbi PII: DOI: Reference:

S0960-8524(17)30577-1 http://dx.doi.org/10.1016/j.biortech.2017.04.073 BITE 17968

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

12 March 2017 12 April 2017 18 April 2017

Please cite this article as: Shirazi, H.M., Karimi-Sabet, J., Ghotbi, C., Biodiesel Production from Spirulina Microalgae Feedstock Using Direct Transesterification Near Supercritical Methanol Condition, Bioresource Technology (2017), doi: http://dx.doi.org/10.1016/j.biortech.2017.04.073

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Biodiesel Production from Spirulina Microalgae Feedstock Using Direct Transesterification Near Supercritical Methanol Condition

Hamed Mohamadzadeh Shirazia,*, Javad Karimi-Sabetb, Cyrus Ghotbia a

Department of Petroleum and Chemical Engineering, Sharif University of Technology, Tehran, Iran b NFCRS, Nuclear Science and Technology Research Institute, Tehran, Iran (*) Corresponding author: [email protected] & [email protected]

Abstract: Microalgae as a candidate for production of biodiesel, possesses a hard cell wall that prevents intracellular lipids leaving out from the cells. Direct or in-situ supercritical transesterification has the potential for destruction of microalgae hard cell wall and conversion of extracted lipids to biodiesel that consequently reduces the total energy consumption. Response surface methodology combined with central composite design was applied to investigate process parameters including: Temperature, Time, Methanol-to-dry algae, Hexane-to-dry algae, and Moisture content. Thirty two experiments were designed and performed in a batch reactor, and biodiesel efficiency between 0.44% and 99.32% was obtained. According to fatty acid methyl ester yields, a quadratic experimental model was adjusted and the significance of parameters was evaluated using analysis of variance (ANOVA). Effects of single and interaction parameters were also interpreted. In addition, the effect of supercritical process on the ultrastructure of microalgae cell wall using scanning electron spectrometry (SEM) was surveyed.

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Keywords: Biodiesel; Supercritical methanol; Direct transesterification, Microalgae; Spirulina

1. Introduction In recent decades, due to a decrease fossil resources and negative effects of these fuels on the environment, researchers have been trying to produce an alternative fuel[1]. Recently, biodiesel fuel has attracted public attention because of having renewable, sustainable and environmentallyfriendly properties[2]. At first, biodiesel was produced from eatable feedstock such as soybean, palm, peanut, rapeseed; but over time and by appearing problems such as interference with food preparation, second generation using non-edible feedstock (for instance animal fat, used vegetable oils, karanja oil) for biodiesel production evolved[3]. Likewise first generation, this method was not economical because of costly feedstock and unsustainability of sources[3]. In the third generation, usage of microalgae as feedstock was suggested due to some advantages such as high productivity of lipid, non-eatable source, high growth rate, growth in water (freshwater or seawater) and environmental benefits of diminishing CO2 from air and bioremediation of wastewater from pollutants[4-8]. Conventionally, biodiesel is produced by extracting lipid from dry microalgae and subsequently reacting with an alcohol in the presence of an alkaline catalyst[9]. This method has some difficulties such as difficulty in lipid extraction from biomass is difficult, because the alga’s cell wall is so rigid, and therefore, this process is costly[10]. Drying the microalgae, which significantly affects the biodiesel yield in further processes, can consist up to 59% of overall cost[11]. In addition, a study conducted on life cycle assessment (LCA) of biodiesel production showed that drying and extraction processes accounted for up to 90% of total cost[10]. Thereupon, an intensification process came up to surmount these issues and produce biodiesel by

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integration of extraction and reaction processes called direct transesterification or in-situ transesterification. This scheme has so far investigated by many researchers, [12-14], but effective implementation of this approach still has some barriers. Drawbacks of this method are the need for separation of used catalyst from products and treatment of wastewater. Moreover, free fatty acids (FFAs) and water interfere with transesterification reaction mechanism and catalyst performance[11]. Water cause a great impediment in extracting oil from microalgae since it forms a hydrated layer and prevents lipids leaving out from the microalgae cell[15]. Hence, to address these issues, an interesting approach allowing concurrent extraction and non-catalytic transesterification in which water and FFAs do not disrupt the efficiency of reaction has been introduced by researchers. In this procedure, called supercritical in-situ transesterification, methanol is in its supercritical condition; in such situation the breakage of algae’s resistant cell and solvent diffusion to reach the lipids occur simultaneously [11, 16, 17]. This methodology is simple and has the advantages regarding environmentally-friendly properties, high conversion within a short time, no need for using acid or base catalysts and so consequently, no need for posttreatment[18, 19]. Chen et al.[14] carried out direct supercritical transesterification with Chlamydomonas sp. JSC4 microalgae and found that the conversion reaches 100% after feed is totally dried and microwave cell disruption is applied. Lee et al.[20] used in-situ supercritical ethanol with microalgae feedstock and reported that besides FAEE (fatty acid ethyl ester), which its yield was more than 90%, a few valuable chemicals formed. They also tested the reaction parameters such as temperature, solvent and alcohol volume, water content and catalyst dosage and founded that by decreasing the amount of water and increasing chloroform, ethanol and sulfuric acid quantity, the yield of all the products raised. Janjhu et al.[21] studied different solvents involving methanol, ethanol and isopropanol affecting biodiesel yield at supercritical

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condition. Souhir et al.[17] investigated different parameters of supercritical transesterification with washed and unwashed Nanochloropsis microalgae feedstock consisting 80% moisture. They observed that the maximum yield of 0.46-0.48 (gr FAME/gr biomass) can be reached under the condition: Temperature (255-2650C), Time (50 min), and methanol to dry microalgae ratio (10:1 ml/gr). They also showed that the difference between yield of biodiesel fuels produced by wet and dry microalgae are insignificant. Abedini et al.[16] tested different co-solvents in supercritical transesterification reaction with Chlorella sp. microalgae and concluded that n-hexane with optimal ratio of 6:1 (vol. /wt.) has the greatest impact on biodiesel yield. Besides, they realized that moisture content up to 80% has an inappreciable effect on final production. Feng et al.[22] examined several Lewis acid catalysts together with methanol at 3500C among all of them, ZnCl2 had the best performance on biodiesel production. He also investigated other parameters affecting the reaction yield and founded that initial amount of water is the most important factor in efficiency of biodiesel. Most of the research under supercritical condition accomplished in recent years were carried out with various oils such as Chlorella protothecoides[23], waste pig fat[24], Jatropha curcas L. oil[25], camelina sativa oil[26] and edible waste oil [27]. However researchers have rarely used supercritical synthesis of biodiesel directly from microalgae. Most of aforementioned studies have used microalgae Chlorella and Nannochloropsis; nevertheless, only a few work have been published until now with Spirulina microalgae. In the present study, the effect of most important parameters and their interactions with each other on performance of direct sub/supercritical methanolysis of Spirulina platensis algae biomass has been examined, which to our knowledge has not been investigated with this vision until now. Although this method is considered to be an economical approach, the economic assessment is not the main purpose of the present study. In fact, qualitative analysis of biodiesel production

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using supercritical methanolysis was investigated. The reason why Spirulina platensis was employed in this study lies in the fact that no other types of algae were more cost effective and available in the place where the research was conducted. Experiments were conducted on dry and wet microalgae, and the efficiencies affected by variable parameters which include Temperature (200-3000C), Reaction time (10-50 min), Alcohol to dry algae ratio (4-12 vol./wt.), Co-solvent to dry algae ratio (0-8 vol./wt.), and Moisture content(0-80% wt./wt.) was monitored. Several tests have been planned and done to optimize the process parameters. Gas chromatography mass spectrometry was used to detect and analyze the fatty acid methyl esters (FAMEs). Moreover, scanning electron microscopy (SEM) was employed to monitor the structure of microalgae cell wall before and after supercritical transesterification process.

2. Materials and method 2.1. Materials Dried and powdered biomass of algae S. platensis was purchased from a knowledge-based company located at Science and Technology Park in Gilan, Iran. The solvents used in this work included methanol (99%, Merck), n-Hexane (96%, Merck), and chloroform (99%, Merck). Also deionized water (Merck) was provided to be added to dry algae for attaining desired moisture content. Methyl heptadecanoate (99%, Sigma–Aldrich) was used as an internal standard for FAMEs quantification.

2.2. Experimental procedure Direct supercritical transesterification procedures were done in a 157 milliliter tubular batch reactor made of stainless steel. The reactor was heated with an electric heating jacket while the temperature was sensed using a thermocouple. For adjusting and monitoring the reaction

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temperature, a PID controller was used with delicacy of 10C. As is clear, the pressure differs for each experimental run in a batch reactor respecting to reaction temperature, amount of methanol, co-solvents and water. Therefore, to address this issue, since it is concluded that pressure has inconsiderable effect on transesterification reaction under supercritical condition[28], and even it was observed that the time for reaching the equilibrium condition is much shorter than the total reaction time, pressure was fixed at equilibrium pressure of 12MPa in all the experiments in order to eliminate the pressure effect on biodiesel production. The pressure inside the reactor was not controlled directly and instead calculated by applying the SRK equation of state (EOS) while the amounts of solvents were changed for each experimental run [29]. The impact of biomass was neglected, and interaction parameters in SRK EOS between methanol and n-Hexane, methanol and water, and n-Hexane and water was obtained 0, -0.9 and 0.51090, respectively using Aspen-Hysys software databank. For instance, to reach the desired pressure for condition: Methanol-to-dry algae=10, Co-solvent-to-dry algae=6, Moisture content=20% and Temperature=2750C , the amounts of methanol, n-Hexane and water are 25 ml, 15 ml and 0.5009ml, respectively. Critical temperature and pressure of n-Hexane is lower than methanol (234.50C, 3.02MPa for n-Hexane and 2400C, 7.95MPa for methanol); so, adding this co-solvent would decrease the critical temperature and pressure of the system. The columns of the penultimate in Table 3 shows the critical temperature and pressure of the operating conditions, which were calculated according to Lorentz-Berthelot-type mixing rules[30] and by ignoring biomass effect. At the end, the reactor was brought out of the heating jacket and was immersed into an ice water bath to quench the reaction immediately. Then, all the products and contents inside the reactor were taken out and filtered on a filter paper in order that the remaining solid residues of algae be

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removed. The reactor was washed several times with methanol and n-Hexane to assure no leftover of products. Next, in order to mix the products well, the liquid mixture was vortexed before transferred to separatory funnel. The upper layer in brown color consisted of FAMEs and nHexane while the lower black color layer was rich in mainly methanol, glycerol and other polar compounds. The upper layer was segregated and retained, and the lower layer was again subjected to n-Hexane. This extraction for waste layer was done twice more to ensure all the FAMEs are transferred to n-Hexane phase. Finally all the hexane phases were mixed together to obtain the final product.

2.3. Analytical method For FAME analysis, 10 μl of methyl heptadecanoate solution (C17:0 methyl ester in n-Hexane) with concentration of 130 gr/L as internal standard was added to extracted solution, then the solution was analyzed by gas chromatography-mass spectroscopy. The apparatus Agilent Technologies, 7890A equipped with a CP-Still 8 CB. H (99.9%) was used as carrier gas at flow rate of 1ml/min and splitless mode was applied. The operating temperature program of oven was: initially at 1200C, held for 3 minutes, then raised to 2800C at 70C/min. The temperature of detector and injector was kept at 2600C. Device database (NIST05a and Wiley 7n) was used to identify the products. FAME yield was calculated according to the following equation:

total weight of FAMEs  . mass of algae FAME Yield wt./wt.  = × 100% total maximum of FAMEs   .! mass of algae

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(1)

In which S.C and B.D stand for sub/supercritical condition and Bligh & Dyer method, respectively. Total maximum of FAMEs which were measured from modified Bligh & Dyer method is described as follows.

2.4. Total lipid of Spirulina platensis microalgae and total maximum FAMEs Bligh and Dyer method with some modification was used just to find total lipid content of Spirulina microalgae capable to convert to FAMEs; this method was used and proved by some researchers to be potent to show the maximum conversion of fatty acids to biodiesel [31]. Therefore, the purpose of this study is not to control the variable parameters of Bligh & Dyer method. For that, 0.5 gr dry powdered microalgae was exposed to 15 ml mixture of methanol and chloroform (2:1 vol. /vol.) in a flask. The sample was ultrasonicated at 25 KHz and 600 W for 20 minutes at ambient condition in order that the cell wall of microalgae be disrupted and lipids come out of cell. Then, the sample was put on a magnetic stirrer for 2 hour at 180 rpm at 600C for agitation and extraction process. After that, the mixture was centrifuged at 5000 rpm for 10 minutes and the supernatant was collected and kept. This procedure was repeated for another two times on the remaining biomass to ensure all the lipids were extracted. Then, the collected solutions were mixed and solvents were evaporated using a rotary evaporator device. After total vaporization, the remaining amount of lipids in the flask were weighted and measured as 16% of powdered microalgae’s weight. To measure the total lipids that are capable to convert to FAMEs, extracted lipids firstly reacted with 2 ml methanol containing 2% (wt. /vol.) KOH for 15 minutes at 650C; then, 2 ml methanol containing 5% (vol. /vol.) H SO( was added to the solution and the flask was again heated for 15 minutes at 650C. After that, the FAMEs were extracted using n-Hexane. 10 μL of methyl heptadecanoate solution with concentration of 50 gr/ml was added into the sample as

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internal standard. The sample obtained was analyzed using gas chromatography-mass spectrometry (GC-MS) as described in section 2.3. Moreover, the composition of free fatty acids and the percentage of saturation of compositions are shown in Table 1.

Table 1: Free fatty acid composition of Spirulina microalgae and percentage of saturation of fatty acid chain C14:0 (%)

C16:0 (%)

C16:1 (%)

C18 (%)

C18:2 (%)

C18:3 (%)

Saturated (%)

Unsaturated (%)

0.33

49.70

7.62

1.86

23.51

17.00

52.00

48.00

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2.5. Design of experiment In order to evaluate the effects of reaction temperature, reaction time, methanol-to-dry microalgae ratio, co-solvent-to-dry microalgae ratio, and moisture content and their binary interaction on biodiesel production efficiency, a central composited design (CCD) with five factors and five levels was applied due to experiment design. This design included 16 factorial points, 10 axial points and 6 control points. Thirty two experiments were produced according to the experiment matrix shown in Table 2.

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Table 2: Experimental process parameters and their levels in CCD.

Levels Parameters

Symbols

-2

-1

0

+1

+2

Temperature (0C)

A

200

225

250

275

300

Time (min)

B

10

20

30

40

50

Alcohol-to-algae ratio (ml/gr)

C

4

6

8

10

12

Co-solvent-to-algae ratio (ml/gr)

D

0

2

4

6

8

Moisture content (%)

E

0

20

40

60

80

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Table 3: Experimental conditions and alkyl esters yields

CoMethanolRun

Tempe

solventTime

Order

to-dry

rature

to-dry

Critical

Critical

FAME

Temperature

Pressure

yield

(0C)

(MPa)

(%)

Moisture content

algae algae

1

275

40

6

6

20

240.89

5.50

29.92

2

275

20

10

2

60

248.54

7.77

24.89

3

250

30

8

0

40

249.14

8.78

11.32

4

225

40

10

2

60

248.54

7.77

3.68

12

5

225

40

10

6

20

240.58

6.09

8.46

6

250

30

12

4

40

243.61

6.95

12.61

7

275

20

6

6

60

247.48

5.97

8.25

8

250

30

8

4

40

244.89

6.61

14.91

9

300

30

8

4

40

244.88

6.61

99.32

10

225

20

10

6

60

245.53

6.44

0.44

11

225

40

6

2

20

243.61

6.95

0.98

12

250

30

8

4

40

244.89

6.61

8.13

13

225

40

6

6

60

247.48

5.97

7.35

14

200

30

8

4

40

244.89

6.61

1.78

15

275

40

10

2

20

242.35

7.31

73.49

16

225

20

10

2

20

242.35

7.31

9.64

17

250

30

4

4

40

247.48

5.97

11.33

18

225

20

6

6

20

244.89

5.50

0.49

19

250

30

8

4

40

244.89

6.61

11.57

20

275

40

6

2

60

252.56

7.63

95.75

21

250

30

8

4

40

244.89

6.61

16.55

22

250

30

8

4

0

238.16

6.13

11.05

23

250

30

8

8

40

242.61

5.62

4.22

24

275

40

10

6

60

245.53

6.44

25.03

25

275

20

6

2

20

243.61

6.95

24.77

26

250

10

8

4

40

244.89

6.61

1.59

27

250

50

8

4

40

244.89

6.61

41.10

13

28

250

30

8

4

80

250.93

7.07

15.01

29

225

20

6

2

60

252.56

7.63

0.54

30

250

30

8

4

40

244.89

6.61

15.24

31

250

30

8

4

40

244.89

6.61

20.46

32

275

20

10

6

20

240.58

6.09

60.94

2.6. Regression model Design Expert software version 10.0 was applied to analyze the experimental data. The quadratic polynomial equation used for the response surface design simulation in this study is shown below:

)=

0

0

*+ + - *. /. + - *.. /. .12 .12

(

0

+ - - *.3 /. /3 .12 31.42

(2)

In this equation, Y indicates response variable (%FAME yield), X is in place of independent parameters and *+ , *. , *.. , *.3 are the intercept, linear, quadratic and interaction coefficients, respectively. Analysis of variance (ANOVA) was used to evaluate the statistical significance of the model and parameters.

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3. Results and Discussion 3.1

Regression model improvement

The results of thirty two experiments done in accordance with experiment design is shown in Table 3. The FAME yield ranges between 0.44% and 99.32%. These results were modeled using a quadratic model as shown below:

YFAME = 14.32 + 21.11A + 8.07B + 1.71C - 4.46D - 1.45E + 6AB + 0.8AC - 6.04AD - 1.73AE 5.33BC - 7.09BD + 5.04BE + 3.7CD - 9.64CE - 4.67DE + 9.18A2 + 1.87B2 - 0.47C2 - 1.52D2 -

(3)

0.2E2

In which A, B, C, D and E are process parameters i.e. Temperature, Time, Methanol-to-dry algae, Co-solvent to-dry algae and Moisture content, respectively. Summary of parameters and levels is specified in Table 2. ANOVA of response surface is also shown in Table 4. The term with P-value lower than 0.05 indicates that such term is considerable at 95% confidence interval (CI) which in turn, shows that the term has an efficient impact on the process. As is clear from Table 4, P-value of the model is far less than 0.05 which means the corresponding model can predict the process very well. Determining coefficient (R2), equal to 0.9789, is close to unity which indicates that the predictive model is precise enough to explain a relation between process parameters and FAME yields. In addition, predicted values agreed very well with experimental data.

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Table 4: Response surface quadratic model analysis of variance (ANOVA)

Varied

Degree of Sum of square

parameters

Mean square

F-value

p-value

freedom

Model

20521.80

20

1026.09

25.51

0.0001

A

10690.65

1

10690.95

265.83

0.0001

B

1563.64

1

1563.64

38.88

0.0001

C

70.32

1

70.32

1.75

0.2129

D

477.58

1

477.58

11.87

0.0055

E

50.58

1

50.58

1.26

0.2660

AB

575.76

1

575.76

14.32

0.0030

AC

10.24

1

10.24

0.25

0.6238

AD

583.95

1

583.95

14.52

0.0029

AE

47.75

1

47.75

1.19

0.2992

16

BC

453.69

1

453.69

11.28

0.0064

BD

804.01

1

804.01

19.99

0.0009

BE

406.81

1

406.83

10.12

0.0088

CD

219.04

1

219.04

5.45

0.0396

CE

1486.49

1

1486.49

36.96

0.0001

DE

348.94

1

348.94

8.68

0.0133

5

2469.79

1

2469.79

61.41

0.0001

6

103.09

1

103.09

2.56

0.1377

7

6.45

1

6.45

0.16

0.6964

8

67.69

1

67.69

1.68

0.2211

9

1.22

1

1.22

0.030

0.8648

R2 = 0.9789.

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3.2

Effects of single parameters

Analyze of ANOVA for determining the significance of each reaction parameter is shown in Table 4. As it stands, P-value for Temperature (A), Time (B), and the ratio of Hexane-to-dry microalgae (D) is less than 0.05, while the P-value for the ratio of alcohol-to-dry microalgae (C) and Moisture content (E) is more than 0.05. These results show the significant impact of temperature, Time and the ratio of hexane-to-dry microalgae on fatty acid methyl ester production efficiency while the effects of alcohol-to-dry microalgae ratio and Moisture content are insignificant under current parameter ranges. In addition, the coefficients of quadratic model provided for the parameters of Temperature, Time and the ratio of alcohol-to-dry microalgae are positive and for Hexane-to-dry microalgae ratio and water content are negative which indicate the positive and negative impact on biodiesel production yield, respectively. Also, both the quadratic model and ANOVA analysis

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show that the greatest impact on efficiency of biodiesel production is attributed to parameters of Temperature and Time.

3.1.1. Temperature effect The temperature effect has been presented in many researches and it can be seen in Figure 1a, that increasing temperature, due to the endothermic reaction, let the reaction equilibrium to produce more biodiesel. In addition, raising the Temperature will increase the reaction rate, which consequently results in further conversion. It also helps the homogeneity of mixture of oil and alcohol[32]. However, the high temperature causes decomposition of alkyl esters. According to a previous research, alkyl ethers tend to decompose at temperatures above 3500C and the degree of decomposition increases with increasing temperature[33]. Therefore, to ensure no decomposition of products, maximum temperature was set to 3000C.

3.1.2. Reaction Time effect As can be seen in Figure 1b, alkyl ester efficiency increases with increasing reaction time because more time gives transesterification reaction the opportunity to go further toward more conversion. Hnjin et al. have done the in-situ transesterification reaction between 30 and 120 minutes with the interval of 10 minutes and observed that after 30 minutes the effect of reaction time is very significant. They also found that after 90 minutes, the reaction reaches its equilibrium value[11].

3.1.3. Methanol-to-dry microalgae ratio effect Methanol in supercritical process acts both as catalyst and solvent in addition to its reactant role in reaction medium[13]. In this study, the ratio of methanol to dry microalgae between 4: 1 and 12: 1 was used to obtain maximum production of alkyl ester. The positive impact of increasing

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methanol ratio on biodiesel yield is depicted in Figure 1c. According to the stoichiometry of transesterification reaction, three moles of alcohol and one mole of triglyceride is required in order to produce three moles of fatty acid methyl ester and one mole glycerol. Here, more methanol to immerse the microalgae and drive the reaction toward more production of biodiesel was required. So, the increase of alkyl ester efficiency may be due to increased contact between the alcohol and extracted lipid. Moreover, increasing the methanol as reactant can increase the reaction rate[17].

3.1.4. Hexane-to-dry microalgae ratio effect In this study, as it is clear from equation (3) and Figure 1d, the presence of additional n-hexane as a co-solvent had a negative impact on the efficiency of biodiesel production. This result contrasts with the results of some articles but is very interesting and meaningful[10]; it can be hypothesized that the presence of Hexane as a Co-solvent cannot be beneficial in all situations. Abedini et al. demonstrated that the use of hexane can help the production of biodiesel at supercritical condition with microalgae Chlorella as feed[16]. In addition, the noteworthy result is that in supercritical condition, methanol plays as solvent in addition to its reactant role; so in these circumstance there is no need for another solvent and the additional solvent just reduces the concentration and density of the reactants.

3.1.5. Moisture content effect The results of the experiments conducted in this study showed that with increasing percentage of humidity from 0 to 80% by weight of the dry microalgae, fatty acid methyl ester yield decreased. The negative impact of the presence of water which is showed in Figure 1e could be due to the following reasons :(1) Fatty acid methyl ester production reaction is a reversible reaction and

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water can reverse the esterification reaction toward methanol and free fatty acid production (2) Water can form a hydrated layer around the biomass and prevent the lipid from bulk releasing into the reaction medium (3) Triglyceride hydrolysis reaction may occur instead of transesterification reaction. These results are consistent with the results obtained by Sathish et al. [34]. Also some other studies suggest hydrolysis and esterification reactions simultaneously with the main transesterification reaction [35, 36]. In a same study, in-situ transesterification of dry microalgae N. Oceanica with relative humidity of 65% was performed and high conversion of 91.1% was achieved under the condition 950C and 90 minutes[11].

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22

Figure 1: Effect of single parameters on biodiesel yield

3.3

Effects of interaction between parameters

As it can be seen from Table 4, there are eight interaction parameters that have significant effect on efficiency of alkyl esters including: Temperature and Time (AB), Temperature and ratio of nhexane (AD), Time and ratio of methanol (BC), Time and ratio of n-hexane (BD), Time and Moisture content (BE), ratio of methanol and ratio of n-hexane (CD), ratio of methanol and Moisture content (CE), and finally ratio of hexane and Moisture content (DE). In Figure 2, the rest of the parameters are held at their zero levels. Figure 2a shows the effect of interaction between Temperature and reaction time on the yield of alkyl esters. In all the Temperature and Time ranges, the yield rises with increasing these parameters which indicates the positive effect of Temperature and Time on FAMEs. The effect of increasing Time on biodiesel yield at temperatures below the critical temperature of methanol (200, 2250C) is less than the effect of increasing time above the critical conditions (250, 275, 3000C) which can be interpreted as follows. Biodiesel production process consists of two successive processes consisting extraction and transesterification reaction. At temperatures below the critical point, lipid extraction is the rate-determining step of alkyl ester production because in this situation the solubility potency of methanol is poor; therefore, sufficient lipid for transesterification reaction with methanol is not available and thus the rate of biodiesel production is low. But in supercritical conditions, because of availability of lipid, increasing reaction time can lead to produce more products. As can be seen in Figure 2b, increasing the Hexane amount can reduce the alkyl ester efficiency due to some reasons including diluting the reaction medium and hindering reactants from being in contact with each other. But as it turns out, this effect is not so tangible at temperatures below

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the critical point. The reason could be due to hexane collaboration with methanol for lipid extraction process in subcritical conditions in which, as mentioned, methanol does not have enough ability to extract total lipid. But this is in contrast to its negative effect on transesterification reaction. Therefore, even in subcritical condition negative impact of the presence of hexane dominates its positive effect. In Figure 2c, the effect of interaction between Time of reaction and Methanol-to-dry microalgae ratio is depicted. At times less than 30 minutes, more methanol to increase the contact between methanol and lipid and consequently more lipid extraction can be fruitful. But at times more than 30 minutes, reducing the fatty acid methyl ester yield with increasing the Methanol-to dry microalgae ratio can be observed. The reason for this phenomenon is that the triglyceride density decreases by increasing the amount of methanol, and this effect intensifies when time passes. In addition, by increasing the ratio of methanol, critical pressure of the reaction mixture rises that causes trouble with forming a homogenous single phase mixture. As seen from Figure 2d, at times more than 30 minutes, because probably lipid extraction process has been completed, increasing the amount of hexane had negative effect on biodiesel yield, but this trend is not tangible at lower times. The reason could be according to hexane assistance with methanol to extract lipids in subcritical conditions. But this positive effect is in contrast with its negative effect as a barrier against transesterification reaction. The results of the interaction between reaction time and Moisture content is noteworthy. As can be seen in Figure 2e, the lack of moisture at low times can be ideal because otherwise, a hydrated layer forms around the microalgae cell and prevents the solvent penetration into the cell. But at more times (more than 30 minutes), because some lipids react with water by hydrolysis reaction,

24

the methanol-to-lipid ratio and subsequently the methanol-to-microalgae ratio rises as time passes. This will benefit the reversible transesterification reaction leading to more production of biodiesel. The effect of interaction between the hexane and methanol to microalgae ratio on alkyl ester yield can be seen in Figure 2f. Hexane had a negative effect on the efficiency of biodiesel production in all ranges, but this effect intensified at low ratio of methanol to dry microalgae situations. The reason is that in these situations hexane causes the dilution of methanol in reaction medium and therefore the required methanol amount for lipid is not accessible. Also, the results of the analysis of the interaction between Methanol-to-microalgae ratio and Moisture content is significant. As seen in Figure 2g, in low ratios of Methanol-to-microalgae, moisture can help increase the efficiency by doing the hydrolysis reaction. At Methanol-to-dry microalgae ratios more than 8:1, adding more water to reaction medium can cause the reverse reaction of esterification and thus reducing the efficiency of alkyl ester. This explanation shows that in the presence of water, the amount of methanol required to achieve high efficiency of biodiesel decreases. As is clear from Figure 2h, increasing each of two parameters, ratio of hexane to dry algae and moisture content, reduces the efficiency of biodiesel production. The result obtained from this graph points out removing water and hexane from reaction medium. Of course, this action is costly and definitely requires necessary measures for optimization.

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Figure 2: 3-D response surface plots (FAMEs) of interaction effects between: temperature and time (a), temperature and hexane ratio (b), time and methanol ratio (c), time and hexane (d), time and moisture content (e), methanol ratio and hexane ratio (f), methanol ratio and moisture content (g), hexane ratio and moisture content (h). Other parameters are held at zero level (250 centigrade, 30min, 8:1, 4:1, and 40 wt. % water).

26

4. Model verification The optimum yield, which is the maximum yield of biodiesel production, was obtained as 99.32 percent using optimization module of Design Expert software in the range of variable parameters. However, in order to verify the developed model specified to predict the process, a target of yield was chosen at random and the amount of each parameter was determined due to the quadratic model by using the optimization module of Design Expert software. The experimental run with determined condition was conducted and the corresponding yield was achieved as shown in Table 5. For analysis, the same concentration of internal standard as the other direct or in-situ processes was added to the sample and gas chromatography mass spectrometry was applied. The experimental yield under specified condition, 24.46 is very close to the target yield, 24.5. The relative error is 0.16% that is acceptable and shows the high accuracy of the developed model.

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Table 5: determined target condition and verification of yield

Target A (0C)

257

B (min)

32

C (ml/gr)

8

D (ml/gr)

Obtained

E (%)

4

39

28

Err. (%) yield

yield

24.5

24.46

0.16

5. Scanning electron microscopy of microalgal biomass

To investigate the impact of in-situ supercritical transesterification process on the structure of Spirulina platensis cell walls, scanning electron microscopy (SEM) observations were performed. From the SEM analysis of dry algae before the process, it was found that single algae cells have either oval or spherical shape. The smooth and intact surface of the cell wall before process represents that the algae cells did not change and break yet. The solvent must pass through cell membranes to reach intracellular content. The SEM image of cells after transesterification process under supercritical condition (run number 9) showed that the cell membranes were completely destroyed. Therefore, despite of the rigid structure of microalgae’s cell wall, supercritical treatment broke successfully the microalgae’s cell wall, thus allowing intracellular lipids to come out of the cell. The result of scanning electron microscopy image analysis reveals the high potential of direct supercritical methanolysis process for biodiesel production.

6. Conclusions and perspectives

This work examined the impact of most important parameters on biodiesel yield in direct supercritical transesterification with Spirulina microalgae as feedstock. By carrying out various tests at different Temperature, Time, Methanol-to-microalgae ratio, Co-solvent-to-microalgae ratio and Moisture content, positive and negative effects of single and interacted parameters were interpreted. Maximum yield of 99.32% in comparison with the reference method (Bligh & Dyer), without implementing the drying and extraction processes, was achieved that presents a promising technique for biofuel production from microalgae. Study various aspects of biodiesel

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production, by the method described, in order to reduce operating costs and industrialize this procedure can be considered as future work.

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Tables’ legends: Table 1: Free fatty acid composition of Spirulina microalgae and percentage of saturation of fatty acid chain Table 2: Experimental process parameters and their levels in CCD. Table 3: Experimental conditions and alkyl esters yields

34

Table 4: Response surface quadratic model analysis of variance (ANOVA) Table 5: determined target condition and verification of yield

Figures’ legends: Figure 1: Effect of single parameters on biodiesel yield Figure 2: 3-D response surface plots (FAMEs) of interaction effects between: temperature and time (a), temperature and hexane ratio (b), time and methanol ratio (c), time and hexane (d), time and moisture content (e), methanol ratio and hexane ratio (f), methanol ratio and moisture content (g), hexane ratio and moisture content (h). Other parameters are held at zero level (250 centigrade, 30min, 8:1, 4:1, and 40 wt. % water).

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In-situ transesterification of microalgae was proposed for biodiesel production.



Single and interaction effect of most important parameters on biodiesel production were investigated.



High efficiency of 99.32% was achieved under supercritical condition.



Observation of SEM photos after supercritical process showed total disruption of alga’s cells.

36

37