Biomass and Bioenergy 149 (2021) 106073
Available online 28 April 2021
0961-9534/© 2021 Elsevier Ltd. All rights reserved.
Production of a sustainable fuel from microalgae Chlorella minutissima grown in a 1500 L open raceway ponds
Amit Kumar Sharma
a, Abhishek Sharma
b, Yashvir Singh
c,*, Wei-Hsin Chen
d,e,faDepartment of Chemistry and Biofuel Research Laboratory, Centre of Alternative and Renewable Energy Research, University of Petroleum and Energy Studies, Uttarakhand, India
bDepartment of Mechanical Engineering, G L Bajaj Institute of Technology and Management, Greater Noida, UP, India
cDepartment of Mechanical Engineering, Graphic Era Deemed to Be University, Dehradun, Uttarakhand, India
dDepartment of Aeronautics and Astronautics, National Cheng Kung University, Tainan, 701, Taiwan
eResearch Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, 407, Taiwan
fDepartment of Mechanical Engineering, National Chin-Yi University of Technology, Taichung, 411, Taiwan
A R T I C L E I N F O Keywords:
Chlorella minutissima Flat plate photobioreactor Transesterification Biodiesel yield Fuel properties RSM
A B S T R A C T
Energy demand is increasing continuously due to the progress of industrialization, the vehicular population of the world, and modernization in lifestyle. In the present study, Chlorella minutissima microalgae were cultured in a 1500 L raceway open pond using commercial fertilizer under semi-continuous mode. About 19.98 wt % lipid was extracted by the soxhlet extraction method from this microalgae biomass. To optimize the input reaction parameters (temperature, lipid to methanol ratio, microwave power, and reaction time) for improving trans- esterification yield, Response Surface Methodology (RSM) was employed. The output responses of the experi- ments were biodiesel yield along with density, kinematic viscosity, calorific value, cold flow properties, and oxidation stability. To validate the model, confirmation trials were carried out. All the fuel properties were found to be satisfying ASTM D6751 and EN 14214 standard specifications except for oxidation stability at 3.0 h. The oxidation stability was further improved by using appropriate antioxidants and improved up to 10.5 h with the addition of 1000 ppm propyl gallate.
1. Introduction
Energy is one of the most important inputs for the economic devel- opment of a country. The consumption and demand for energy are increasing day by day due to industrialization, urbanization, and modernization which led to developing countries in perilous energy crises. More than 80% of the primary energy demands are meet by petroleum-based products worldwide, out of which transportation sec- tors account for up to 60% of demand [1]. However, the sustainability of fossil fuels is under concern in terms of economics, the environment, and ecology. Furthermore, the combustion of fossil fuels releases carbon dioxide and other greenhouse gases that have adverse effects on envi- ronments and leads to global warming and climate change.
Over-exploitation of fossil fuels, fluctuation in fossil fuel price, dimin- ishing fossil fuel reserves and increasing effect of fossil fuel burnings are the major concerns that forced researchers to find new alternative, renewable, and green energy sources. In a developing country like India,
solar, wind hydro, and biomass energy have been studied and imple- mented as green and sustainable energy sources [2]. Among them, biomass-based fuels (e.g. bioethanol and biodiesel) have shown more potential to replace transportation fuel. According to the National bio- fuel policy 2018 (Date: 8/2/2019. GAIN Report Number: IN9069), India has planned to achieve a 20% blending share of ethanol with gasoline (E20) and 5% of biodiesel with diesel (B5) by 2030 [3]. Unfortunately, first and second-generation biofuels are unable to commercialize due to competition with food crops for agricultural land [4]. Currently, fat, waste cooking oil, grease, and microalgae have shown good potential for biodiesel production [5,6].
Microalgae biofuels are environment-friendly, sustainable, and clean energy sources which have 2–10 times more potential to fulfill global fuel demand in comparison to first and second-generation biofuels [7,8].
Microalgae may be rich sources of lipids, carbohydrates, or protein depending upon cultivation conditions [9]. Furthermore, the composi- tion of microalgae varies from one species to another. Therefore, the
* Corresponding author.
E-mail address: [email protected] (Y. Singh).
Contents lists available at ScienceDirect
Biomass and Bioenergy
journal homepage: http://www.elsevier.com/locate/biombioe
https://doi.org/10.1016/j.biombioe.2021.106073
Received 31 October 2020; Received in revised form 12 March 2021; Accepted 18 April 2021
microalgae biomass can be utilized for the production of different types of biofuels such as biodiesel, bioethanol, biogas, bio-hydrogen, and jet fuel.
The production of biodiesel from microalgae biomass is of more in- terest nowadays due to high lipid content (20–80%), high biomass productivity, high growth rates, and year-round production, ability to grow in waste water, avoiding competition for agricultural land, higher photosynthetic efficiency, and capability to fix more carbon dioxide than plant [10,11]. Researchers performed many experiments by implementing a new procedure to enhance biodiesel recovery from microalgae biomass. Almarashi et al., 2020 enhanced biodiesel recovery of the green microalga Chlorella vulgaris through pretreatment of inoc- ulum with low doses of cold atmospheric-pressure plasma (CAPP) [12].
Abomohra et al., 2020 worked on the effect of lipid-free microalgal biomass and waste glycerol on the growth and lipid production of Sce- nedesmus obliquus [13]. Sharma et al., 2016 grew Chlorella vulgaris microalgae in open raceway ponds for biodiesel production and ach- ieved biodiesel production yield up to 84.03% by optimizing reaction parameters such as catalyst concentration, methanol to lipid ratio, re- action time, and temperature [14]. Similarly, Joshi et al., 2016 opti- mized the reaction parameter such as catalyst concentration, methanol to lipid ratio, microwave power, and reaction time for maximizing biodiesel production from algae, jatropha, and polanga oil [10]. Also, Singh et al., 2019 carried out transesterification of microalgae biodiesel using mixed metal oxide as a heterogeneous catalyst and achieved 98.94% yield at the following optimized parameters: catalyst - 2.5 wt %, methanol to oil ratio - 1:18 stirring speed - 600 rpm speed, temperature - 65 ◦C and reaction time - 120 min [15]. However, the optimization of the process parameter by the empirical modeling method is considered more important.
Response surface methodology is one of them which are used to develop a relationship between controlled experimental factors and output responses (observed results). Kumar et al., 2016 employed Box- Behnken design type response surface methodology to optimize four reaction parameters i.e. catalyst concentration, the temperature of the reaction, methanol to oil ratio, and reaction duration to get maximum biodiesel yield from Chlorella protothecoides oil [16]. Jain et al., 2017 optimized the process parameters for transesterification of jatropha and algae oil mixture using Boxe Behnken experimental design [17] and obtained a biodiesel yield of 81.98% at 3:5 methanol/oil ratio, 0.9%
catalyst concentration, 180 min time, and 50 ◦C temperature. Makar- eviciene et al., 2017 used response surface methodology to optimize ethanol to oil molar ratio, reaction duration, temperature, and lipase concentration of microalgae oil for increasing efficiency of trans- esterification reaction [18]. Park et al., 2018 carried out in situ trans- esterification of dry and wet microalgae Nannochloropsis gaditana using ethyl acetate as both reactant and co-solvent by RSM [19]. Thir- ugnanasambandham 2019 performed experiments with Chlorella minu- tissima microalgae to optimize transesterification parameters by Box–Behnken design using a hydrodynamic-cavitation reactor [20].
Garg and Jain 2020 used response surface methodology and artificial neural networks modeling methods to optimize transesterification re- action parameters for improving algae biodiesel yield [21]. Yahya et al., 2020 transesterified waste cooking oil using Fe-exchanged montmor- illoniteK10 (Fe-MMT K10) as a catalyst and optimized the reaction conditions by RSM statistically optimization method [22].
Most of the research was carried out to optimize the reaction pa- rameters like alcohol to oil ratio, catalyst concentration, reaction time, mixing speed, and reaction temperature for maximizing biodiesel yield.
However, there were limited studies that focused also on fuel properties along with biodiesel yield. This investigation tries to demonstrate the effect of different reaction parameters i.e. Temperature, methanol to lipid ratio, microwave irradiations, and reaction time on biodiesel yield and their fuel properties like viscosity, density, calorific value, cold flow properties, and oxidation stability. The experiments were performed in a microwave reactor with a variation of methanol to lipid ratio, reaction
temperature, microwave irradiations, and reaction time. RSM was employed to find out the best biodiesel yield along with improved fuel.
2. Material and methods
2.1. Cultivation and lipid extraction from microalgae
Pure culture of Chlorella minutissima was provided by the Centre for Conservation and Utilization of Blue Green Algae, IARI New Delhi, India) and cultured in 1500 L open raceway ponds (5 m length x 2 m width x 0.5 m height) at semi-continuous mode using BG-11 +fertilizer media under nitrogen-deficient conditions. The nutrient media was composed of urea 250 mg/L, DAP (Di ammonium phosphate) 250 mg/L, potash (potassium chloride) 250 mg/L, magnesium sulfate 250 mg/L, sodium carbonate 20 mg/L, Ferric citrate 6 mg/L and micronutrient from BG-11 medium (half strength) [14]. Tap water treated with 5%
sodium hypochlorite and sodium thiosulphate was used for microalgae cultivation. The culture was created (having 0.1% CO2) at an 8 L/min rate using an aquarium pump during day time. During the experiments, the average temperature of day and night was observed as 29 ◦C and 14 ◦C respectively. Initially, the microalgae were grown for 15 days at batch mode and after that half microalgae culture was replaced by the same amount of fresh nutrient media at each five days interval. The experiment was conducted for 40 days with a total of five harvestings.
During the experiment, pH was maintained 6.5–7.5. Microalgae were harvested using alum as a flocculant and dried under solar radiation up to moisture content achieved less than 10%. Before extract, the lipid, microwave, and ultrasonic pretreatment method have been employed to rupture the cell wall of microalgae biomass that maximum lipids will be recovered. The extraction of lipid is performed in a soxhlet apparatus (250 ml capacity) at 60 ◦C with chloroform and methanol solvent mixture of 2:1 ratio [14]. The extracted lipid and solvent mixture is further separated by the rotary evaporator distillation method. Addi- tionally, the lipid mixture is again refined using the hexane extraction method. This refines microalgae lipid is stored in airtight borosilicate bottles for further use.
2.2. Microwave-assisted transesterification of refined microalgae lipid Free fatty acid plays a big role in the transesterification process and according to ASTM standards; FFA should be less than 2. Therefore, the FFA of the collected microalgae lipid was inspected by the acid-base titration method and it was observed to be 9.36%. Therefore, to reduce FFA, the microalgae lipid is esterified using 20% methanol and 2.5% sulfuric acid at 64 ◦C for 15 min in a microwave-assisted reactor.
After completion of the reaction, the product mixture divided into two solvent phases- the upper phase having impurities and alcohol while the lower phase contained lipid with reduced FFA. As the FFA of the microalgae lipid under the limit (less than 2%), it was utilized for bio- diesel production to optimize the reaction parameters such as reaction temperature, methanol to lipid ratio, microwave radiations, and reac- tion duration using response surface methodology (Table 1). However, the concentration of catalyst i.e. KOH is maintained the same i.e. 1.5%
w/v throughout the experiments. RSM based on the center composite experimental design was applied to study the optimum process param- eters (independent variables) and examine the effect of each parameter Table 1
Different coded stages for microalgae biodiesel production.
Factors or input parameters Symbol Coded Levels
− 2 −1 0 1 2
Temperature (oC) T 50 55 60 65 70
Alcohol to oil ratio 8:1 9:1 10:1 11:1 12:1
Microwave power (watt) MW 500 600 700 800 900
Reaction time (minute) Minute 1 3 5 7 9
on microalgae yield along with fuel properties i.e. density, viscosity, calorific value, cold properties, and oxidation stability. Since there are four process parameters with five coded levels for each parameter, the total number of experimental runs was31 as shown in Table 2. Each experiment was performed using 10 g refined lipids with a variety of process parameters data set as shown in Table 1. After each experiment, biodiesel is washed with double distilled water 2–3 times and centrifuge at 10,000 rpm using sodium sulfate to remove moisture impurities. Now pure biodiesel is carefully transferred in 5 mL vials.
2.3. Analysis of biodiesel composition
Nucon 5700 series Gas chromatograph is used to examine the composition of biodiesel. This GC was equipped with a flame ionization detector (FID) and a WAX column (serial no 5061; 30 m ×0.25 mm × 0.25 μm). The flow of carrier gas was 1 ml/min 99.999% Nitrogen gas was used as a carrier gas. The oven temperature was fixed initially at 150 ◦C for 2 min, followed by a 4 ◦C/min ramp up to 230 ◦C and reserved for 40 min. The injector and FID detector temperature was set at 240 ◦C and 220 ◦C, respectively. Supleco 37 FAME standard was used to iden- tify peaks.
2.4. Fuel properties of biodiesel
The fuel properties of microalgae biodiesel were analyzed using standard ASTM methods [23,24]. Viscosity, density, and calorific value of the microalgae biodiesel were examined by a Brookfield-made viscometer, Anton parr made density meter and Rajdhani made bomb calorimeter. Viscosity, density, and calorific value are the main pa- rameters that affect the combustion efficiency of fuel in an engine [2,23, 24]. Cold flow properties and oxidation stability were measured using pour and cloud point apparatus and Petro Oxy Oxidation Stability Tester respectively. Both the cold flow properties and oxidation stability are
also essential properties of fuel as cold flow properties indicate fuel ability to run in cold areas while oxidation stability shows that fuel storage life [25].
3. Result and discussion
3.1. Cultivation of Chlorella minutissima microalgae in open raceway pond under semi-continuous mode
Chlorella minutissima was cultured in a 1500 L open raceway pond at semi-continuous mode under outdoor conditions. The growth rate of microalgae under batch and semicontinuous mode is shown in Fig. 1.
After 15 days, the biomass production rate was increased up to 1.80 g/L with a lipid productivity of 24.16 mg/L/day. However, the average biomass and lipid productivity were achieved 162.0 mg/L/day and Table 2
The experimental result after implementing CCRD.
Experiment
Run Temperature
(oC) Alcohol to
oil ratio Microwave
power (watt) Reaction time
(minute) Biodiesel
Yield (%) Viscosity
(cSt) Density (g/cm3) CV
(MJ/
kg)
Cold properties (⸰ C)
Oxidation stability (h)
1 65 11:1 800 7 96.6 4.6 0.872 39.2 −6 2.5
2 60 10:1 700 5 83.2 5.6 0.881 38.2 −2 3
3 60 10:1 700 5 83.6 5.6 0.881 38.2 −2 3
4 60 10:1 900 5 88.1 5.5 0.88 37.9 −1 2.8
5 65 9:1 800 3 73.1 6.3 0.882 35.9 2 2.5
6 55 9:1 800 3 63.9 8.6 0.89 34.2 5 3.2
7 65 11:1 800 3 77.3 5.7 0.878 36.4 −1 2.3
8 55 9:1 600 7 76.6 8.1 0.889 36 3 3.7
9 55 9:1 800 3 63.8 8.6 0.89 34.2 5 3.2
10 60 10:1 700 1 43.6 8.1 0.891 33.9 6 2.2
11 60 10:1 700 5 83.9 5.7 0.881 38.2 −2 3
12 60 10:1 500 5 76.7 6.2 0.886 36.3 2 3.2
13 55 11:1 600 7 79.9 7.3 0.885 36.5 0 3.4
14 60 10:1 700 5 83.8 5.7 0.881 38.2 −2 3
15 60 10:1 700 9 86.6 5.5 0.882 37.9 0 3.1
16 65 11:1 600 3 70.9 6.4 0.883 35.4 2 2.5
17 55 11:1 800 7 86.8 6.9 0.88 37.5 −3 3.2
18 50 10:1 700 5 72.6 8.2 0.891 35.6 3 0.4
19 55 11:1 800 3 67.9 8.2 0.886 34.7 2 3
20 60 8:1 700 5 80.5 6.0 0.881 36.9 3 3.2
21 60 12:1 700 5 86.2 5.3 0.881 37.8 1 2.8
22 55 9:1 800 7 86.3 7.3 0.886 37 0 3.1
23 60 10:1 700 5 83.8 5.6 0.881 38.2 −2 3
24 60 10:1 700 5 84 5.7 0.881 38.2 −2 3
25 65 9:1 600 7 88.5 5.5 0.878 37.7 0 2.9
26 65 11:1 600 7 88.8 5.2 0.878 38.2 −3 2.7
27 65 9:1 800 7 92.5 5.0 0.878 38.7 −3 3.4
28 55 11:1 600 3 61.5 8.7 0.886 33.7 5 3.2
29 60 10:1 700 5 84.1 5.6 0.881 38.2 −2 3
30 65 9:1 600 3 70.5 6.8 0.878 34.9 5 2.7
31 70 10:1 700 5 88.9 5.7 0.88 38 2 2.5
Fig. 1.Open raceway pond used for cultivation of Chlorella minu- tissima microalgae.
31.43 mg/L/day respectively during semi-continuous mode that was 27.33% and 25.26% higher than the batch mode. Moreover, the specific growth rate of microalgae biomass was 0.2193 day−1 under semi- continuous mode. The maximum biomass productivity of this study was observed to be 162.0 mg/L/day which was higher than biomass productivity examined by Chlorella vulgaris (77 mg/L/d) by Sharma et al., 2016 but lower than B. sudeticus (23.7 g/m2/d) by Veeramuthu et al., 2015. However, some studies such as the biomass productivity in this study were lower than observed for Phaeodactylum (1380 mg/L/
day), Nannochloropsis sps (2400 mg/L/day), and Synechocystis aquatilis (1000 mg/L/day) [26]. The reason behind the low biomass productivity may be a higher variation in temperature during day and night in Dehradun, India region (see Fig. 2).
3.2. Extraction and pretreatment of microalgae lipid for biodiesel production
Microalgae biomass was harvested using alum as a flocculant and dried under sunlight. About 19.98% lipid (w/w) was extracted using a mixture of 2:1 methanol and chloroform as solvent by soxhlet extraction method. Biomass to the solvent mixture was maintained 1:8 and the
reaction was performed for 6 h was extracted under these conditions.
The extracted lipid is refined by the solvent-solvent extraction method using hexane as a solvent. The FFA of the refined lipid was 9.36%.
Therefore, it was reduced lower than 2% by the esterification method using 20% methanol as co-solvent and 2.5% sulfuric acid as catalyst. The reaction was conducted at 64 ◦C for 15 min in a microwave-assisted reactor [14]. After compilation of reaction, the product mixture was kept in a separating lask for 3 h and allowed to separate in two phases.
The upper phase contained methanol with impurities while the lower phase was composed of lipid with reduced FFA. The lower layer was separated carefully and utilized for process optimization of microwave-assisted transesterification by RSM methods.
Analysis of Variance (ANOVA) generates empirical knowledge regarding future benefits. The ANOVA results of production yield, vis- cosity, density, calorific value, cold properties, and oxidation stability are shown in Table 3 and Table 4. From the ANOVA results, the p-value is considered the most important factor. The p-value of various param- eters must be not more than 0.05 and the parameters having a p-value greater than 0.05 are considered as not important. The variables having p-values less than 0.05, indicate that the variable has a great impact on the developed model [27]. From Table 3, it clear that for production yield, the p-value of reaction temperature, A/O ratio, power, and reac- tion time is less than 0.05. The models have also been tested using a computational approach using the decision coefficient R2. It is believed that that R2 values below 1 suggest that the experimental effects are consistent with the findings of the model indicates that the reliability is very strong [28]. From Table 5, it was observed that the finding of R2 was 97.07%, 86.15%, 91.97%, 94.54%, 94.37%, and 94.18% for pro- duction yield, viscosity, density, calorific value, cold properties, and oxidation stability respectively. These R2 Values were nearly close to 1 which indicated that this mode gave reliable results.
Fig. 2. Cultivation of Chlorella minutissima in open raceway ponds under semi- continuous mode.
Table 3
ANOVA results in production yield, viscosity, and density.
Production yield Viscosity Density
Sources DF Adj. SS Adj. MS F-value P-value Adj. SS Adj. MS F-value P-value Adj. SS Adj. MS F-value P-value
Regression 4 2931.00 732.75 27.89 0.000 33.0895 8.2724 18.46 0.000 0.00448 0.00112 18.53 0.000
Temp. (oC) 1 498.46 498.46 18.97 0.000 23.0274 23.0274 51.39 0.000 0.00329 0.00329 54.32 0.014
A/O Ratio 1 41.22 41.22 1.57 0.021 1.0311 1.0311 2.30 0.041 0.00026 0.00026 4.32 0.048
Power (W) 1 187.54 187.54 7.14 0.013 0.8995 0.8995 2.01 0.068 0.00027 0.00027 4.40 0.046
RT (min) 1 2358.55 2358.55 89.77 0.000 9.2991 9.2991 20.75 0.000 0.00092 0.00092 15.19 0.001
Lack-of-fit 19 682.53 35.92 466.28 0.000 11.6327 0.6122 250.00 0.000 0.00157 0.00008 * *
Table 4
ANOVA results for calorific value, cold properties, and oxidation stability.
Calorific value Cold properties Oxidation stability
Sources DF Adj. SS Adj. MS F-value P-value Adj. SS Adj. MS F-value P-value Adj. SS Adj. MS F-value P-value
Regression 4 53.2345 13.3086 15.11 0.000 177.164 44.291 12.44 0.000 67.669 6.967 283.41 0.000
Temp. (oC) 1 13.6886 13.6886 15.54 0.001 26.334 26.334 7.40 0.011 5.2696 0.266 2.73 0.099
A/O Ratio 1 1.2927 1.2927 1.47 0.037 30.674 30.674 8.61 0.007 0.301 0.041 3.33 0.073
Power (W) 1 3.6445 3.6445 4.14 0.052 29.039 29.039 8.16 0.008 0.7860 1.760 29.38 0.000
RT (min) 1 37.7071 37.7071 42.82 0.000 108.578 108.578 30.49 0.000 6.409 63.19 67.80 0.000
Lack-of-fit 19 22.8965 1.2051 * * 92.578 4.873 * * 2.67 7.51 13.78 0.000
Table 5 Model summary.
Model S R2 (%) Reg. R2 (%) Pred. R2 (%)
Yield 2.37192 97.07 96.50 95.32
Viscosity 2.9206 86.15 85.90 82.35
Density 5.5552 91.97 91.05 90.68
Calorific value 0.2615 94.54 93.01 92.01
Cold properties 2.4753 95.37 92.16 91.05
Oxidation stability 2.3612 94.18 92.05 91.06
3.3. Optimization of transesterification parameters 3.3.1. Effect of process parameters on biodiesel yield
In transesterification reaction, 3 mol of alcohol e.g. methanol, ethanol, etc. react with 1 mol of triglyceride stoichiometrically and gives 3 mol of fatty acid methyl esters (biodiesel) along with 1 mol of glycerol as a by-product [29]. Acidic value of lipid, temperature, methanol quantity, catalyst concentration, stirring speed, and reaction time are some most important factors which not only affect the production yield but also fuel properties [30]. In the present study, microwave-assisted transesterification is optimized with a variety of different reaction pa- rameters such as temperature 50–70 ◦C, lipid to methanol ratio 8:1 to 12:1, microwave irradiations 500–900 W, and reaction duration 1–9 min using Response Surface Methodology (RSM). Here, potassium hy- droxide is used as a catalyst due to its less corrosive nature and the catalyst concentration was fixed throughout the experiment. RSM regression equation for microalgae biodiesel production yield is given below as equation no. 1. In the present investigation, all responses were generated using MINITAB 17.
Yield(%)=− 238.5+5.7∗Temp(C)+3.68∗
(A O )
Ratio+0.09∗Power(W)+17.1
∗RT(min.)− 0.03∗{Temp(C)}2− 0.17 {(A
O )
Ratio }2
− 0.000041{Power(W)}2− 1.18{RT(min.)}2− 0.02∗Temp(C)∗
(A O )
Ratio
− 0.0009∗Temp(C)∗Power(W)− 0.018∗Temp(C)∗RT(min.)+0.004
∗ (A
O )
Ratio∗Power(W)− 0.099∗
(A O )
Ratio∗RT(min.)+0.0025
∗Power(W)∗RT(min.)
(1) The effect of temperature and methanol ratio on biodiesel yield is plotted in Fig. 3a, b and 3e. The results revealed that with increasing the temperature, the yield of biodiesel was also found to be increased.
However, when the temperature was beyond 65 ◦C, the yield of biodiesel decreased sharply. This may be due to the reason that the boiling point of methanol is 64.5 ◦C. As the temperature rises more than 65 ◦C, the methanol vaporizes and reduces the moles of methanol participating in the reaction which results in a lower biodiesel yield [14,30]. The surface Fig. 3. Effect of process parameters on biodiesel production yield.
contour of biodiesel yield vs alcohol ratio and time is shown in Fig. 3d. It was observed that as the quantity of methanol increased, the biodiesel production yield also increased. This may be because that the trans- esterification reaction was an equilibrium reaction and a higher amount of methanol supported forward reaction [10]. Although, when the methanol to lipid ratio increased beyond 10:1, there was no significant improvement in the biodiesel yield. According to the literature, the excessive use of methanol also favors the increasing solubility of glycerol and makes a difficult separation of glycerol and biodiesel [31]. In addition, reaction time also plays a key role to optimize biodiesel pro- duction yield and the effect of reaction time on transesterification re- action is shown in Fig. 3d and e. Results indicated that the biodiesel production yield improved with increasing reaction time duration. The maximum yield was observed as 96.6% at 7 min. On the other hand, when the reaction was carried out for a long duration, the yield of biodiesel was found to be decreased. This may be due to that the transesterification is a reversible reaction and therefore, too long reac- tion time leads to the backward reaction which diminishes the biodiesel yield while too low time results in incomplete reaction [10,14].
In microwave-assisted transesterification, microwave radiation power is another important factor that highly influences the reaction yield economy. As shown in Fig. 3b and c, the yield of biodiesel was found to be increased with increasing microwave power. However,
microwave radiations had shown a negative effect on production yield when it increased above 700 W. This may be due to the degrading of triglycerides to FFA and making highly disordered and drastic molecular interactions at high microwave power [10].
3.3.2. Effect of reaction parameters on the kinematic viscosity of the biodiesel fuel
Viscosity is the measurement of a fluid’s resistance to flow. The quality of fuel improves with decreasing the viscosity as fuel spray characteristics are highly influenced by viscosity and density of the fuel.
Due to higher viscosity, poor atomization of the fuel spray takes place in CI engines which leads to incomplete combustion and carbon deposition on the injector and valve sheet resulting in serious engine problems.
Also, fuel pumps also face many problems with viscous fuel e.g. leakage problems, higher injected pressure, and injected fuel mass. The impact of temperature on the viscosity of fuel is shown in Fig. 4a, b, and 4e.
Increasing temperature favorers improved the viscosity of biodiesel. The reason behind this may be that higher temperature leads to more con- version of the triglycerides into fatty acid methyl esters [30]. Besides, with increasing methanol to lipid ratio and reaction time, the viscosity of biodiesel reduced however, too high methanol to lipid ratio results in poor viscosity as shown in Fig. 4c. Fig. 4d demonstrates that viscosity also increases with reaction time. As the reaction time reached beyond 7 Fig. 4.Effect of process parameters on the viscosity of biodiesel.
min, viscosity decreased due to dominating backward reaction which diminishes fatty esters in the product and increased FFA. Moreover, with rising microwave radiation, the viscosity of microalgae biodiesel was also found to be decreased which may be due to better interactions between reactant molecules due to microwave energy as shown in Fig. 4b and c. Regression Eq. (2) for kinematic viscosity of biodiesel finds out at different input process parameters.
3.3.3. Effect of reaction parameters on biodiesel density
Density plays a vital role in spray characteristics of fuel in CI/SI engines and directly affects combustion behavior. Dense fuel results in poor atomization, more heat absorption for vaporization of large fuel droplets, and poor mixing of the fuel vapor which leads to incomplete combustion with loss of fuel economy and increased exhaust emissions [23]. Generally, the density of a fuel is highly influenced by the presence Fig. 5.Effect of process parameter on density of biodiesel fuel.
K.V(cSt) =85.6− 2∗Temp(C) − 1.3∗
(A O )
Ratio− 0.006∗Power(W) − 0.7∗RT(min.)
+0.016∗{Temp(C)}2+0.09 {(A
O )
Ratio }2
+0.000014{Power(W)}2
+0.1{RT(min.)}2− 0.002∗Temp(C)∗
(A O )
Ratio− 0.00013∗Temp(C)
∗Power(W) − 0.0023∗Temp(C)∗RT(min.) − 0.00056∗
(A O )
Ratio
∗Power(W) − 0.018∗
(A O )
Ratio∗RT(min.) − 0.00034∗Power(W)
∗RT(min.)
(2)
of unsaturation in biodiesel and increases with increasing unsaturated FAME content in biodiesel [32]. Fig. 5a demonstrates the effect of temperature and methanol to lipid ratio on the density of the biodiesel fuel. With increasing temperature, the density of fuel decreases which may be again due to more conversion of triglycerides to methyl esters.
Also, high methanol to lipid content and reaction time favors improved
density as shown in Fig. 5c and d. However, a higher amount of meth- anol and long reaction time enhance the density of biodiesel fuel. The effect of microwave irradiation is plotted in Fig. 5b and c and it observed that the density has non-linear co-relation with MR.
The regression equation for microalgae biodiesel density with various reaction parameters is represented in Eq. (3).
Fig. 6. Effect of process parameter on the calorific value of microalgae biodiesel fuel.
Density (g
cc )
=0.98− 0.0057∗Temp(C) +0.008∗
(A O )
Ratio +0.000125∗Power(W)
+0.006∗RT(min.) +0.00003∗{Temp(C)}2− 0.00029 {(A
O )
Ratio }2
+{Power(W)}2+0.00027{RT(min.)}2+0.00013∗Temp(C)∗
(A O )
Ratio
− Temp(C)∗Power(W) − 0.000036∗Temp(C)∗RT(min.) − 0.000012
∗ (A
O )
Ratio∗Power(W) − 0.00037∗
(A O )
Ratio∗RT(min.) − 0.000005
∗Power(W)∗RT(min.)
(3)
3.3.4. Effect of reaction parameters on the calorific value of microalgae biodiesel
'The calorific value of a fuel is defined as the amount of heat released during the complete combustion of a specified fuel amount. The calorific value of fuel directly influences engine efficiency and exhaust emissions [33,34]. Higher will be the calorific value, better will be engine efficiency.
The presence of oxygen diminishes the calorific value of a fuel. Biodiesel has 10–12% more oxygen and therefore, its calorific value is always less than diesel. In the present study, the impact of temperature and methanol to lipid ratio on calorific value with holding power 700 W and reaction time 5 min is shown in Fig. 6a. Results indicated that the temperature had a non-linear co-relation with calorific value and was found to be increased
with rising temperature as shown in Fig. 6a, b, and 6e. This can be described by the fact that higher temperature results in the reduction of FFA and increase of fatty acid esters. Although, a sharp decrease in calo- rific value at 70 ◦C is due to the availability of less amount of methanol to react with lipids. Fig. 6c and d depicted the variation of methanol to lipid ratio and microwave radiations vs calorific value. Rising the methanol to lipid ratio leads to increasing calorific value, however, to increase meth- anol to lipid ratio results in microalgae biodiesel with a poor calorific value which may be due to the presence of methanol or dominating backward transesterification reaction. The regression equation for microalgae bio- diesel calorific value is given by Eq. (4).
Fig. 7. Effect of process parameters on the cold flow properties of microalgae biodiesel.
CV(MJ/kg) = − 73.3+2.04∗Temp(C) +5.1∗
(A O )
Ratio +0.035∗Power(W) +1.54
∗RT(min.) − 0.017∗{Temp(C)}2− 0.3 {(A
O )
Ratio }2
− 0.00004{Power(W)}2
− 0.17{RT(min.)}2+0.008∗Temp(C)∗
(A O )
Ratio+0.00016∗Temp(C)
∗Power(W) +0.0042∗Temp(C)∗RT(min.) +0.0008∗
(A O )
Ratio
∗Power(W) +0.02∗
(A O )
Ratio∗RT(min.) +0.0004∗Power(W)
∗RT(min.)
(4)
The effect of microwave radiation on the calorific value of micro- algae biodiesel is presented in Fig. 6b and c and demonstrated that with increasing microwave power, the calorific value was also improved.
Reaction duration has also a significant impact on microalgae biodiesel calorific value. Fig. 6d and e revealed that calorific value improved with increasing reaction time. The graph plotted with reaction time vs calo- rific value was observed to be non-linear. However, when reaction time reached above 7 min, the calorific value was also found to be decreased.
This can be explained by the fact that reverse conversion dominated to favor the production of fatty acids [31].
3.3.5. Impact of reaction parameters on cold flow properties of microalgae biodiesel
The cold flow properties of fuel indicate its ability to flow under the environment of lower temperature. Cold flow properties of the biodiesel fuels generally depend on the presence of saturated or unsaturated fatty acid alkyl esters. The presence of unsaturated fatty acid esters favors improved cold properties. Most of the biodiesel fuels have poor cold flow
properties in comparison to diesel [35]. During winter or cold condi- tions, the biodiesel tends to solidify and leads to gum formation and crystallization which chocks fuel filters and fuel lines causing failure of engines [10,36]. In this study, the behaviour of cold flow properties i.e.
Pour point with temperature, methanol to lipid ratio, reaction time and microwave power are presented in Fig. 7a, b, 7c, 7d and 7e. The results revealed that high temperature and methano to lipid ratio resulted into improved cold flow properties. This may due to more conversion of polyunsaturated fatty acids such as linoleic and linolenic acid methyl ester. Reaction time and microwave power also affected the cold flow properties and obseved to be enhanced with increasting rection time and microwave radiation power. However, too load reaction time duration (more than 7 min) led to poor cold flow properties due to the dominating saturated FFA. At the favorable condition of temperature, methanol to lipid ratio, microwave power, and reaction duration, the pour point of biodiesel is to be observed as -6OC. Cold properties of microalgae bio- diesel can also be calculated by following regression Equation (5) Fig. 8. Effect of process parameters on oxidation stability of microalgae biodiesel fuel.
3.3.6. Impact of reaction parameters on oxidation stability of biodiesel Oxidation stability is one of the most important concerns about biodiesel poor characteristics [14]. Biodiesel has a long carbon chain of mono unsaturated and poly unsaturated fatty acid esters which tend to oxidize with the duration of time resulting formation of sediment and gum formation. Due to this, the engine faces many serious problems
such as filter plugging, injector fouling, and depositions in the com- bustion chamber [37,38]. In the current study, the graphs of microalgae biodiesel oxidation stability against temperature, methanol to lipid ratio, microwave power and reaction time are plotted in Fig. 8a, b, 8c, 8d and 8e. The oxidation stability of microalgae biodiesel can also be measured by following regression Equation (6).
Fig. 9. Optimized process parameter for production of biodiesel from Chlorella minutissima microalgae.
CP(C) =230.7+5.7∗Temp(C) − 14.6∗
(A O )
Ratio− 0.03∗Power(W) − 1.9∗RT(min.)
+0.04∗{Temp(C)}2+0.8 {(A
O )
Ratio }2
+0.00004{Power(W)}2
+0.26{RT(min.)}2− 0.022∗Temp(C)∗
(A O )
Ratio− 0.00034∗Temp(C)
∗Power(W) − 0.011∗Temp(C)∗RT(min.) − 0.0017∗
(A O )
Ratio
∗Power(W) − 0.055∗
(A O )
Ratio∗RT(min.) − 0.00086∗Power(W)
∗RT(min.)
(5)
OxidationStability(h.)
= − 53.9+1.7∗Temp(C) +0.5∗
(A O )
Ratio+0.0049∗Power(W) +0.57
∗RT(min.) − 0.013∗{Temp(C)}2+0.07 {(A
O )
Ratio }2
+0.000007{Power(W)}2− 0.0043{RT(min.)}2− 0.018∗Temp(C)
∗ (A
O )
Ratio− 0.00004∗Temp(C)∗Power(W) +0.0022∗Temp(C)
∗RT(min.) − 0.001∗
(A O )
Ratio∗Power(W) − 0.03∗
(A O )
Ratio∗RT(min.)
− 0.00035∗Power(W)∗RT(min.)
(6) From Fig. 8a and c, it was observed that oxidation stability had a non-linear relationship with both temperature and methanol to the lipid ratio. The oxidation stability of microalgae biodiesel initially improved with increasing temperature and when the temperature reached more than 60 ◦C, it was observed no significant improvement as shown in Fig. 8a, b and 8e. This may be due to the formation of polyunsaturated fatty acid esters at a higher temperature. The effect of lipid to methanol ratio on oxidation stability of microalgae biodiesel is presented in Fig. 8c and d. With rising lipid to methanol ratio, the oxidation stability of microalgae biodiesel is found to be decreased due to more esters con- version with dominating polyunsaturated fatty acids (linolenic fatty acid esters C-18:3). The trends of oxidation stability against microwave power and reaction time are depicted in Fig. 8 b, 8c, 8d and 8e. As the reaction time increases, the conversion of triglycerides to unsaturated fatty acid esters also increased which favors poor oxidation stability.
Depending on the presence of saturated and unsaturated fatty acids composition in biodiesel, the oxidation stability and cold flow properties behave reversibly [39]. For example, with increasing unsaturated fatty acid esters in biodiesel composition, the cold flow properties improved while oxidation stability reduced.
3.4. RSM optimization response and validation
Fig. 9 displays the RSM optimizer to investigate the best input re- action parameter settings for Chlorella minutissima microalgae biodiesel.
The multi-objective optimization is performed with the same weightage on output responses. The objective of this research is to improve the biodiesel yield along with the fuel properties i.e. viscosity, density, calorific value, cold flow properties, and oxidation stability. The opti- mum values of input reaction parameters observed by RSM optimizer were Temperature (63.93 ◦C), methanol to lipid ratio (10.58), micro- wave power (713.87 Watt), and reaction time (6.16 min) (see Fig. 10).
At these reaction parameter settings, the following output responses are observed – microalgae biodiesel production yield 91.94%, Density of biodiesel 0.877 g/cc, the kinematic viscosity of biodiesel 4.75 cSt, CV of biodiesel 39.15 MJ/kg cold flow properties − 3.76 ◦C and oxidation stability 2.80 h. Except for density and oxidation stability, all the re- sponses approach almost to unity which indicates a good signal of process optimization.
To confirm the validity of the above RSM signals, the experiments are performed using the following optimized input parameters i.e. Tem- perature (64 ◦C), methanol to lipid ratio (10.5), microwave power (700 Watt), and reaction time (6.0 min), and the results are compared with RSM output responses. All the experimental results are presented in Table 6. The experimental finding showed that the yield of biodiesel production was 90.21% which is slightly lower than the predicted RSM model (91.94%). Also, other responses such as the kinematic viscosity, density, CV, cold flow properties, and Oxidation stability had an error value of − 5.47%, − 1.60%, 3.65%, and 3.99% respectively between predicted and experimental value (Table 6). Hence, it may be confirmed that the RSM model is found to be satisfactory for optimization of
microalgae biodiesel yield and fuel properties (density, kinematic vis- cosity, calorific value, cold properties, and oxidation stability) with four input parameters (temperature, alcohol to lipid ratio, microwave irra- diation, and reaction duration).
3.5. Composition of Chlorella minutissima microalgae biodiesel
The composition of optimized biodiesel is shown in Table 7. The results revealed that Chlorella minutissima microalgae biodiesel has Table 7
Composition of Chlorella minutissima Microalgae biodiesel.
Fatty acid methyl ester Carbon chain
length Composition
(%)
Caprylic acid methyl ester C-8.0 0.5943
Capric acid methyl ester C-10.0 2.8572
Lauric acid methyl ester C-12.0 2.1761
Myristic acid methyl ester C-14.0 2.341
Palmitic acid methyl ester C-16.0 19.906
Palmitoleic acid methyl ester C-16:1 9.4912
Stearic acid methyl ester C-18.0 6.0782
Oleic acid methyl ester C-18.1 28.5375
Linoleic acid methyl ester C-18.2 18.8802
Linolenic acid methyl ester C-18.3 7.4552
Arachidic acid methyl ester C-20 0.8792
Behenic acid methyl ester C-22 0.8039
Saturated fatty acid methyl esters – 35.6359
Mono unsaturated fatty acid methyl
esters – 38.0287
Poly unsaturated fatty acid methyl esters – 26.3354 Fig. 10.Effect of different antioxidants on the oxidation stability of micro- algae biodiesel.
Table 6
Validation of optimized response.
Biodiesel response at 64 ◦C with A/O Ratio 10.58, Power 887.87 (Watt) at RT7.46 min.
Predicted Actual % Error
Yield (%) 91.94 90.21 1.88
KV(cSt) 4.75 5.01 −5.47
Density (g/cc) 0.877 0.891 −1.60
CV(MJ/kg) 39.15 37.72 3.65
CP (C) −3.76 −3.61 3.99
Oxidation stability (h.) 2.80 2.7 3.57
35.63%, 38.02%, and 26.33% saturated monounsaturated and poly- unsaturated fatty acid methyl esters. The major quantity of fatty acid methyl ester was 28.53% (Oleic acid methyl ester), followed by 19.90%
(palmitic acid methyl ester) and 18.88% (linoleic acid methyl ester).
According to European standards EN14214, biodiesel should not contain more than 12% linolenic acid methyl ester (C18:3) and 1% poly- unsaturated fatty acid methyl esters (having more than 3 double bonds) [4,14]. In the present study, linolenic acid methyl ester was 7.45%.
Moreover, the composition of unsaturated fatty acid methyl ester was lower than saturated which supports its poor oxidation stability and superior cold properties nature.
3.6. Improving oxidation stability of biodiesel
Oxidation stability is the main fuel property which highly affects the quality of biodiesel fuels. In the present study, the oxidation stability of biodiesel was 2.7 h which neither satisfied European standards EN14214 (6 h) nor ASTM and Indian biodiesel standards (3 h). Therefore, to improve oxidation stability, some antioxidants such as t-butyl hydro- quinone (TBHQ), butyl-4-methyl phenol (BHT), butylated hydrox- yanisole (BHA), propyl gallate (PG) and pyrogallol (PY) were mixed with neat microalgae biodiesel in the range 200 ppm–1000 ppm. The evaluation of oxidation stability was carried out by Petrotest “PetroOXY (e)-VERSION: 10.08.2011” instrument (made in Germany) in the form of induction period (h). The results revealed that the oxidation stability of microalgae biodiesel increased with increasing concentration of anti- oxidants and found a maximum at 1000 ppm concentration of antioxi- dants (Fig. 9). Among different antioxidants, PG was found most effective followed by PY, BHA, TBHQ, and BHT. Maximum oxidation stability was achieved 10.5 h with PG antioxidants at 1000 ppm. The results are agreed with the literature that antioxidants with three –OH groups (PG and PY) are more significant to inhibit oxidation and pro- longed storage stability in comparison to BHT, BHA (one –OH groups) and TBHQ (two –OH groups) [40,41].
4. Conclusion
This study deals with the optimization of transesterification of microalgae lipid extracted from Chlorella minutissima grown in a 1500 L open raceway photobioreactor. Reaction factors such as temperature, alcohol to lipid ratio, microwave power, and reaction time were opti- mized using Response Surface Methodology (RSM) for maximum bio- diesel recovery and enhancement of fuel properties (density, kinematic viscosity, CV, CP, and OS). The optimized input parameter was observed as temperature (63.93 ◦C), methanol to lipid ratio (10.58), microwave power (713.87 Watt) and reaction time (6.16 min). Using these input parameter, the following experimental results were obtained: biodiesel yield (90.21%), density (0.891 g/cc), kinematic viscosity (5.01 cST), calorific value (37.72 MJ/kg), cold properties (− 3.61 ◦C), and oxidation stability (2.7 h) which have an error of 1–6% with the predicted value observed by RSM model optimizer and confirm the validity RSM model.
The results indicate that all the fuel properties except oxidation stability were improved significantly and satisfied ASTM standards. The oxida- tion stability of microalgae biodiesel was improved up to 10.5 h using 1000 ppm concentration of propyl gallate.
Declaration of competing interest
The authors declare no conflict of interest.
Acknowledgments
Authors are very thankful to Dr. S J Chopra, (Chancellor, UPES), Dr.
Sunil Rai (Vice-chancellor, UPES), Dr. D K Avasthi (Dean, R & D UPES), and Dr Bhawana Lamba (HOD chemistry UPES), for providing continued support and analysis facilities in UPES, Bidholi campus, Dehradun, UK,
India. The authors are also very thankful to Dr Sunil Pabbi, Centre for Conservation and Utilization of Blue-Green Algae, Division of Microbi- ology, ICAR-Indian Agricultural Research Institute, New Delhi-110 012 for providing pure microalgae species. The authors are also thankful of CIC, UPES for GC analysis.
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