Process parameter optimization for biodiesel production from mixed feedstock using empirical model

Process parameter optimization for biodiesel production from mixed feedstock using empirical model

Sustainable Energy Technologies and Assessments 28 (2018) 54–59 Contents lists available at ScienceDirect Sustainable Energy Technologies and Assess...

2MB Sizes 0 Downloads 27 Views

Sustainable Energy Technologies and Assessments 28 (2018) 54–59

Contents lists available at ScienceDirect

Sustainable Energy Technologies and Assessments journal homepage:

Original article

Process parameter optimization for biodiesel production from mixed feedstock using empirical model


G. Antony Miraculasa, , N. Boseb, R. Edwin Raja a b

Department of Mechanical Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil 629003, India Department of Mechanical Engineering, Mepco Schlenk Engineering College, Sivakasi 626005, India


Different feedstocks are available at various quantities in different regions, which are to be exploited to meet the growing demand for biodiesel production. In the present study, raw oil from three non-edible feedstock like Calophyllum Inophyllum, Jatropha curcas and Pongamia Pinnata are mixed in equal proportion and the biodiesel process parameters optimized. Since the acid value of the mixed feed stock is high, two stage esterification-transesterification processes is applied to produce biodiesel. Multi variant empirical model is employed to optimize the influencing process parameters and it is found that methanol concentration is the most significant process parameter followed by the catalyst concentration. Around 98% biodiesel yield is obtained at the optimized condition. The optimized process conditions for the transesterification process are oil/methanol ratio (2.5v/v), catalyst concentration (1.17%w/v), duration (95 min) and temperature (53 °C). The optimum process parameters are validated by experiments and the biodiesel properties tested for its compliance with ASTM standard in order to use it in the existing diesel engine.

Introduction Growing demand and insufficient availability of crude oil has forced India to look for alternate fuels to sustain its economic development. Moreover the increase in the consumption of electricity and oil for transportation has increased the external debt and environmental degradation. The centralized power generation system is inadequate to meet the energy needs of the decentralized communities of rural India, due to low load density and long distribution lines. These aspects have forced researchers to find viable, environmental friendly and sustainable alternate fuel [1]. Raw vegetable oils if used directly in diesel engine may cause problems like chocking of injector, carbon deposits on the engine cylinder and sticking of piston due to its high viscosity and low volatility [2]. These effects can be minimized when they are transesterified to biodiesel. Biodiesel is a mono-alkyl esters of long chain fatty acids derived from lipid feed stocks such as vegetable oils and animal fats. Extraction of edible vegetable oil for biodiesel production is not a viable solution for countries like India, where there is a dare need for edible vegetable oil of cooking purpose itself. Biodiesel produced from non-edible feedstock can positively supplement the rapidly increasing energy requirements of the world; especially for the countries which have limited fossil fuel resources. There are various sources of non-

Corresponding author. E-mail address: [email protected] (G. Antony Miraculas). Received 24 June 2017; Received in revised form 16 February 2018; Accepted 27 June 2018 2213-1388/ © 2018 Elsevier Ltd. All rights reserved.

edible renewable vegetable oils to augment the fuel source for the huge requirements of diesel in this vast nation. Biodiesel is extracted from various non-edible seeds like castor [3], tamanu [4], rubber seed [5], jatropha curcas [6], neem [7], pongamia pinnata [8], mahua [9], cottonseed [10], silybum marianum [11], rhazya stricta decne [12], prunus armeniaca [13], yucca aloifolia [14], phoenix dactylifera [15] etc, which can be a possible solution to the present crisis. The feedstock like Calophyllum Inophyllum, Jatropha curcas and Pongamia Pinnata plants are commonly available in India. Calophyllum Inophyllum belongs to the mangosteen family and are normally planted in coastal areas to prevent soil erosion. They grow to a height of 2–3 m with thick rough trunk having cracked barks. The average diameter of the seed is around 25 mm and weighs approximately 7 g, which is surrounded by a smooth layer called epidermis and followed by a hard cover [16]. The average oil content of the seed is between 65 and 75% by weight [17]. Jatropha curcas is a tropical and subtropical plant which belongs to the family Euphorbiaceae. It is a poisonous shrub that grows in desert, due to its high degree of resistance to aridity. The average weight of a seed is 0.6 g and contains about 80% unsaturated fatty acid [18]. The oil content ranges amongst 43 and 59% [16]. Pongamia pinnata belongs to Leguminosae family. This is a nitrogen fixing tree which is grown as an ornamental tree and its dense network of lateral roots prevent soil erosion. The seeds have oil content ranging

Sustainable Energy Technologies and Assessments 28 (2018) 54–59

G. Antony Miraculas et al.

between 30 and 40% [19]. The outer shells of the sun dried seeds are broken to separate the kernels and are further screw pressed to extract oil. The prepared mixed feedstock oil has an acid value of 32 mg KOH/ g. The process parameters that affect the transesterification reactions are oil/alcohol ratio, catalyst concentration, reaction time and temperature [4,5]. High oil/alcohol ratio will reduce the conversion efficiency of triglycerides to biodiesel. Since transesterification is a reversible process excess alcohol will always enhance the completion of the reaction [21]. The homogeneous catalysts that are normally used during alkali catalyzed transesterification reaction are KOH and NaOH. Biodiesel yield increases with catalyst concentration. However, at high catalyst concentration emulsions are formed which reduce the yield of biodiesel [22]. Transesterification reaction speeds up with increase in temperature. However at high temperature the hydrolysis of fatty acid methyl ester to corresponding alcohol and acid results in reduction of biodiesel yield [23]. It is evident that the process parameter has to be kept at an optimum value for maximizing the biodiesel yield. It is necessary to consider more than one potential feedstock to meet the huge requirement of biodiesel production. Obtaining sufficient quantity of a particular non edible oil source from a certain region is highly uncertain. Moreover, biodiesel derived from a single feed stock has its own strengths and weaknesses. Oils having high saturated fatty acids have better oxidation stability whereas oils having high unsaturated fatty acids have good cold flow properties [24]. Under these circumstances, collecting raw vegetable oil from different sources and extracting biodiesel from mixed feedstock is a better option. This needs to be investigated in order to commercialize biodiesel in large scale. In this paper, the three major potential non edible biodiesel sources, having different fatty acid contents are mixed and processed by a two stage esterification process where an acid catalyst, H2SO4 is used in the first stage and an alkaline catalyst, potassium hydroxide in the second stage. The process parameters of the extraction process are optimized by using multi-variant empirical model approach.

Table 1 Process parameter range for the acid esterification. Process Parameters

Low (−)

High (+)

Axial (−α)

Axial (+α)

Oil/Methanol (v/v) H2SO4 (%v/v) Temperature (°C) Time (min)

2 1.06 50 82

3 1.68 60 127

1.5 0.75 45 60

3.5 2 65 150

Therefore, the design of experiment (DOE) is employed to conduct a designed set of experiments with an alpha value of 2 to observe and identify the influential process parameters and to derive objective conclusions. Pilot experiments are carried out for the acid esterification stage to identify the significant process parameters and its range of influence. The parameters and the identified value using design expert software are tabulated in Table 1. The response surface methodology is a statistical tool to analyze the influence of all the considered significant parameters simultaneously. The model enables to predict and map the outcome three dimensionally in the designed domain. 50 ml of raw mixed feedstock oil is taken in a round bottom conical flask and heated to the designed temperature. The metered amount of sulfuric acid and methanol are then added and stirred at 800 rpm using magnetic stirrer for the intended time period. The reactants are then allowed to separate in a separating funnel where the impurities and excess alcohol separates at the top and the oil is collected from the bottom. The acid values of the oil are determined for every experiment by titrating it against KOH and noted, since the objective of this stage is to minimize the acid value. Sufficient quantity of oil is prepared with the optimized process condition of the acid esterification stage, where the FFA of the extracted oil is less than 1 mg KOH/g. It is further processed through alkaline esterification process to maximize the yield percentage. Here also, pilot experiments are done to design the parameter and range of study in corroboration with literature (Table 2). 50 ml of acid esterified oil is taken in a round bottom flask and heated to the required temperature. The designed quantity of methanol and KOH are then added into the reactor and the reactants are stirred at constant speed by means of a magnetic stirrer, for a predefined time period. The products are then allowed to separate under the influence of gravity in a separating funnel. Methyl ester separates at the top and the heavy glycerol settles at the bottom. After that, the biodiesel is collected and the percentage yield is calculated for each trial.

Synthesis of biodiesel The experimental procedure from the collection of raw seed to biodiesel production and characterization is shown in Fig. 1. The high FFA content of raw vegetable oil deters its direct usage in IC engines. Esterification method is a proven robust comprehensive method for conditioning the raw vegetable oil for its applications in internal combustion engines [4,5,7,20]. Studies conducted on esterification of heavy vegetable oil indicated that the process parameters like alcohol/ oil ratio, catalyst, reaction time and reaction temperature are the major influencing parameters. It is quite complex to assess the reason for the observed changes in the outcome, as many parameters are involved in the process.

Result and discussion Process parameter optimization for acid esterification A series of pilot trials were conducted to assess the relevance of various process parameters in reducing the acid value. After finalizing the influential parameters and its range, experiments are conducted as per the design by varying the input parameters and the acid value is noted and tabulated in Table 3. The response surface quadratic model is selected and the analysis of variance is done for the acid esterification model and its result is shown in Table 4. Before drawing any inference, the model has to be validated satisfactorily. The model p-value is less than 0.0001 which indicates that the model terms considered are significant, and especially the most significant model term is the ratio of Table 2 Process parameter range for the alkaline esterification.

Fig. 1. Biodiesel synthesis process from mixed feedstock. 55

Process Parameters

Low (−)

High (+)

Axial (−α)

Axial (+α)

Oil/Methanol (v/v) KOH (%W/v) Temperature (°C) Time (min)

2.27 0.94 45 67

3.2 1.31 55 102

2 0.75 40 50

4 1.5 60 120

Sustainable Energy Technologies and Assessments 28 (2018) 54–59

G. Antony Miraculas et al.

Table 3 Experimental plan for acid esterification process with process data and the FFA response. Run

Oil/Methanol (v/v)

H2SO4 (%v/v)

Temperature (°C)

Time (min)

FFA (mgKOH/g)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

3 3.5 2.5 2.5 3 3 3 3 2 1.5 3 2 2.5 3 3 2 2.5 2 2.5 2.5 2.5 2.5 2 2 2 2 2.3

1.69 1.38 1.38 1.38 1.69 1.69 1.06 1.06 1.69 1.38 1.06 1.69 2.00 1.06 1.69 1.06 1.38 1.69 1.38 1.38 1.38 0.75 1.06 1.69 1.06 1.06 1.38

60 55 55 55 50 60 50 50 50 55 60 60 55 60 50 50 55 50 45 65 55 55 50 60 60 60 55

128 105 105 105 128 83 128 83 83 105 128 83 105 83 83 128 60 128 105 105 150 105 83 128 83 128 105

4.94 16.34 1.73 1.68 5.64 6.27 9.56 10.65 1.21 3.73 8.48 1.1 2.25 9.73 7.45 2.13 2.78 1.23 2.34 0.96 1.48 6.42 2.84 0.92 2.45 1.83 1.70

Fig. 2. Perturbation chart for acid esterification method.

Table 4 ANOVA result for acid esterification method. Source

Sum of Squares


Mean Square

F Value

P-value Prob > F

Model A-Oil/Methanol B-H2SO4 C-Temperature D-Time AB AC AD Residual Total

379.1 229.6 30.9 2.5 3.8 5.4 0.5 1.0 0.9 379.9

10 1 1 1 1 1 1 1 15 25

37.9 229.6 30.9 2.5 3.8 5.4 0.5 1.0 0.1

650.5 3940 531 42.9 65.5 93.4 8.2 17.1

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0117 0.0009

Fig. 3. Response surface model for acid esterification method.

to acid value is shown in Fig. 3, while keeping the temperature and time of reaction at the center point. It can be observed that the low acid value is accomplished by reducing the oil/methanol ratio and by increasing the H2SO4 concentration. Adequate quantity of oil was esterified using the optimized process parameters (Table 5) for further processing in the second stage with alkaline catalyst. The model equation for predicting the acid value in the acid esterification stage is given in equation (1). The plot drawn between actual acid value and the predicted value shows a close relation amongst them (Fig. 4).

oil to methanol (factor A) with the F-value of 3940. The amount of acid catalyst, H2SO4 is also a significant parameter with the F-value of 531, whereas other factors such as reaction time and temperature have less influence to reduce the acid value of the raw oil. The “Pred. R-Squared” value of 0.9914 is in reasonable agreement with the “Adj. R-Squared” of 0.9962. “Adeq Precision” which measures the signal to noise ratio and has a value of 99.6, which indicates that the signals are adequate. The analysis of variance signifies that the design space can be navigated with the present model. The perturbation plot compares the influence of various factors at a specific point in the design space one at a time, while keeping the other parameters as constant (Fig. 2). A positive slope for the factor A, which is the oil/methanol ratio indicates that the acid value decreases with decrease in oil/methanol ratio. The negative slope for H2SO4, reaction time and temperature implies that the acid value decreases with increase in acid concentration, time and temperature. The interactive effect between oil/methanol ratio (A), H2SO4 concentration (B), reaction temperature (C) and time (D) are significant as the p-value of AB, AC and AD are less than 0.05. Since the quantity of the alcohol and the catalyst are the most influencing parameters, the three dimensional response plot of oil/methanol ratio and percentage of H2SO4 in relation

AcidValue = 37.5−24.76A−13.6B + 0.11C −0.02D−3.7AB−0.1AC −0.02AD (1) + 8.4A2 + 7B2 + 2.7D 2

Process parameter optimization for transesterification Similar to the first stage, pilot experiments are carried out to design the process parameters and its range (Table 6). The percentage yield of biodiesel is calculated based on volume, as the ratio of biodiesel yield to the volume of raw oil. Analysis of variance is done to test the significance of the model and the result is shown in Table 7. The ratio of Table 5 Optimum process parameters for minimising FFA content by acid esterification.






Acid Value

2.3 v/v


55 °C

110 min

0.64 mg KOH/g

Sustainable Energy Technologies and Assessments 28 (2018) 54–59

G. Antony Miraculas et al.

Fig. 4. The plot showing the correlation between the observed and predicted acid value plot. Table 6 Experimental plan for transesterification process with process data and the percentage yield. Run Oil/Methanol (v/v) KOH (%w/v) Time (min)

Temperature (°C) Yield (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

45 45 55 50 40 50 50 55 50 55 50 55 45 45 55 45 60 50 55 50 50 45 45 45 55 55 50

3.2 2.3 3.2 2.6 2.6 2.6 4.0 3.2 2.6 2.3 2.0 2.3 3.2 2.3 3.2 2.3 2.6 2.6 3.2 2.6 2.6 3.2 3.2 2.3 2.3 2.3 2.6

0.94 1.31 0.94 0.75 1.13 1.13 1.13 1.31 1.13 0.94 1.13 0.94 0.94 1.31 0.94 0.94 1.13 1.13 1.31 1.13 1.50 1.31 1.31 0.94 1.31 1.31 1.13

68 103 103 85 85 120 85 68 50 68 85 103 103 68 68 103 85 85 103 85 85 68 103 68 68 103 85

Table 7 ANOVA results for transesterification method.

75.8 90.2 82.7 76.7 88.7 96.6 71.2 84.8 87.8 88.2 85.0 91.3 79.4 84.2 78.6 88.0 97.0 94.2 94.5 90.8 83.3 79.5 84.7 83.8 90.5 95.2 94.5


Sum of Squares


Mean Square

F Value

p-value Prob > F

Model A-Oil/Methanol B-KOH C-Time D-Temperature AB BC BD Residual Total

1359.3 287.7 85.5 123.8 119.3 13.1 3.0 5.0 0.6 1362.8

11 1 1 1 1 1 1 1 5 25

123.6 287.7 85.5 123.8 119.3 13.1 3.0 5.0 0.1

631.7 1470.9 437.1 632.7 609.7 67.2 15.2 25.3

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0010 < 0.0001

0.9958. The “Adeq Precision” value of 93.4 indicates the adequacy of signal which ensures that the model can be used to navigate the design space. The sharp negative slope of Oil/Methanol ratio (factor A) in the perturbation chart clearly shows that it is the most significant factor when compared with the other process parameters (Fig. 5). The biodiesel yield increases with methanol and KOH concentration to a certain extent and then gets saturated. The three dimensional response surface plot of Oil/Methanol ratio and the percentage weight to volume ratio of KOH in relation to yield is plotted by keeping the temperature and time of reaction at the center point is shown in Fig. 6(a). It is evident from the Fig. 6(a) & (b) that higher yield is obtained for higher concentration of methanol and KOH to a certain extent and then reverses due to saponification. The optimum process parameter for getting higher yield, predicted and tested by the empirical model is shown in Table 8. The final equation in terms of actual process factors for predicting the biodiesel yield by transesterification process is given in equation (2). The empirical model was tested with the experimental data and is plotted in Fig. 7.

maximum to minimum response (yield) is 1.37, which is less than 10, there is no requirement to transform the variables. The response surface quadratic model is selected and the model F-value of 631.7 and a pvalue less than 0.0001, implies the significance of the model. In this model also, the factors such as oil/methanol ratio and KOH concentration has high influence on the percentage yield. High F-value and a low p-value (< 0.05) of the interactive process variables AB, BC and BD denotes significant interactive effect between the process variables. These interactive effects are taken care of by the response surface model, which cannot be done by conventional methods. The “Pred RSquared” value of 0.9912 is close to the “Adj R-Squared” value of

BiodieselYield = −284.3 + 921.8 × A + 229.3B + 0.3C + 1.3D −77.3 × AB + 0.13BC + 0.59BD + 1039A2 −103B2−1.8C 2 (2)


Sustainable Energy Technologies and Assessments 28 (2018) 54–59

G. Antony Miraculas et al.

Fig. 7. The plot showing good correlation between experimental and predicted yield.

Fuel properties

Fig. 5. Perturbation chart of transesterification process for percentage biodiesel yield.

The extracted biodiesel with the optimized process parameters are purified and characterized for confirming its standard with ASTM. The entrained traces of catalyst and glycerol are removed by washing in warm water and drying with silica gel. The purified biodiesel is tested for its chemical composition using gas chromatograph, GC 200-3A-CIC. The major fatty acid components in esters are Palmitic acid, Stearic acid, Oleic acid and Linoleic acid (Fig. 8). Oleic acid oils are resistant to oxidation, which enables to store the oil for usage as an alternative fuel. The viscosity is measured using Brookfield LV-DV-II+ Pro viscometer at 40 °C in atmospheric pressure condition. The viscosity of raw oil has reduced from 41.4 mm2/s to 4.28 mm2/s after the two stage esterification process. The calorific value of biodiesel measured with 6772 calorimetric thermometer of Parr Instrument. A Pensky Martin’s closed cup apparatus is used to determine the flash point of the biodiesel. The other properties like density, pour point, Cetane number, Iodine number and saponification value are determined using standard test procedures and found to be analogous with the ASTM D6751-02 standard. The properties of biodiesel is listed in Table 9. Conclusion Non edible oil from three different sources Calophyllum Inophyllum, Jatropha curcas and Pongamia Pinnata are mixed together in equal proportion for biodiesel extraction. The mixed feed stock is investigated to optimize the major influencing parameters such as oil/ methanol ratio, catalyst concentration, reaction time and temperature

Fig. 6. Response surface model for alkaline esterification method. Table 8 The optimum process parameter for maximum biodiesel yield by transesterification method. Oil/Methanol





2.5 v/v

1.17 %w/v

95 min

53 °C


Fig. 8. Fatty acid profile for methyl esters of biodiesel by GC analysis. 58

Sustainable Energy Technologies and Assessments 28 (2018) 54–59

G. Antony Miraculas et al.

Table 9 Properties of biodiesel in comparison with diesel. Properties

Test method


Kinematic viscosity @ 40 °C (mm /s) Flash point (°C) Specific gravity Calorific value (kJ/kg) Pour point (°C) Cetane number Iodine number saponification value (mg KOH/g oil) Acid value (mg KOH/g)

D445 D93 D6890 EN14214 D2500 D6890 EN 14104 D5558 D664


3.18 72 0.839 43400 −20 49.6 – –

Biodiesel Standard ASTM D6751-02

1.9–6.0 Min 120 0.87–0.90 – −15 to 16 Min 47 Max.120 – < 0.5

by multi-variant design of experiment technique. Due to high viscous nature of mixed oil, two stage acid and alkaline catalyst induced esterification is carried out to extract biodiesel. Oil/methanol ratio is found to be the major influencing parameter in both the stages. In the first stage, higher concentration of methanol reduces the acid value to a large extent and is optimum at 2.3 v/v, whereas in the second stage it increases the biodiesel yield and maximum yield is obtained at 2.5 v/v. The other three parameters such as catalyst concentration, reaction time and temperature also have significant influence in reducing the acid value and maximizing the biodiesel yield. In the second stage alkaline catalyst concentration increases the biodiesel yield to a certain extent and becomes insignificant with further addition. The optimized empirical model parameters are validated by experiments and the biodiesel properties tested for IC engine compatibility with ASTM test procedure.

Biodiesel JME




4.18 178 0.872 38,800 5.8 57.4 88 193 0.78

4.78 144 0.877 39,100 4.3 58.9 91 187 0.45

4.2 110 0.885 40,800 3 60.2 105 196 0.34

4.28 124 0.875 40,200 3.7 59.7 96 194 0.38

[7] Anyanwu CN, Mbajiorgu CC, Ibeto CN, Ejikeme PM. Effect of reaction temperature and time on neem methyl ester yield in a batch reactor. Energy Convers Manage 2013;74:81–7. [8] Godugula Veeraprasad, Srinivas I. The ethanolosis of Pongamia pinnata oil by a two-stage acid-base catalyst transesterification process for production of biodiesel. Energy Sources Part A 2012;34(16):1550–8. [9] Manjunath H, Hebbal Omprakash, Hemachandra Reddy K. Process optimization for biodiesel production from Simarouba, Mahua, and waste cooking oils. Int J Green Energy 2015;12(4):424–30. [10] Georgogianni KG, Kontominas MG, Pomonis PJ, Avlonitis D, Gergis V. Alkaline conventional and in situ transesterification of cottonseed oil for the production of biodiesel. Energy Fuels 2008;22:2110–5. [11] Fadhil AB, Ahmed KM, Dheyab MM. Silybum marianum L. seed oil a novel feedstock for biodiesel production. Arabian J Chem 2017;10:S683–90. [12] Nehdi IA, Sbihi HM, Al-Resayes SI. Rhazya stricta Decne seed oil as an alternative, non-conventional feedstock for biodiesel production. Energy Convers Manage 2014;81:400–6. [13] Fadhil AB. Evaluation of apricot (Prunus armeniaca L.) seed kernel as a potential feedstock for the production of liquid bio-fuels and activated carbons. Energy Convers Manage 2017;133:307–17. [14] Nehdia IA, Sbihi HM, Mokbli S, Rashid U, Al-Resayes SI. Yucca aloifolia oil methyl esters. Ind Crops Prod 2015;69:257–62. [15] Fadhil Abdelrahman B, Alhayali Mohammed A, Saeed Liqaa I. Date (Phoenix dactylifera L.) palm stones as a potential new feedstock for liquid bio-fuels production. Fuel 2017;210:165–76. [16] Dweck AC, Meadowsy T. Tamanu (Calophyllum inophyllum) – the African, Asian, Polynesian and Pacific Panacea. Int J Cosmet Sci 2002;24:341–8. [17] No SY. Inedible vegetable oils and their derivatives for alternative diesel fuels in CI engines: a review. Renew Sustain Energy Rev 2011;15(1):131–49. [18] Bora Dilip Kumar, Baruah DC. Assessment of tree seed oil biodiesel: a comparative review based on biodiesel of a locally available tree seed. Renew Sustain Energy Rev 2012;16:1616–29. [19] Balat M, Balat H. Progress in biodiesel processing. Appl Energy 2010;87(6):1815–35. [20] Mythili R, Venkatachalam P, Subramanian P, Uma D. Production characterization and efficiency of biodiesel: a review. Int J Energy Res 2014;38:1233–59. [21] Jain S, Sharma MP. Kinetics of acid base catalyzed transesterification of Jatropha curcas oil. Bioresour Technol 2010;101:7701–6. [22] Nautiyal P, Subramanian KA, Dastidar MG. Kinetic and thermodynamic studies on biodiesel production from Spirulina platensis algae biomass using single stage extraction–transesterification process. Fuel 2014;135:228–34. [23] Verma P, Sharma MP. Review of process parameters for biodiesel production from different feedstocks. Renew Sustain Energy Rev 2016;62:1063–71. [24] Verma P, Sharma MP, Dwivedi G. Evaluation and enhancement of cold flow properties of palm oil and its biodiesel. Energy Rep 2016;2:8–13.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at References [1] Cao X. Climate change and energy development; implications for developing countries. Resour Policy 2013;29:61–7. [2] Dwivedi Jain S, Sharma MP. Impact analysis of biodiesel on engine performance—a review. Renew Sustain Energy Rev 2011;15:4633–41. [3] Berman Paula, Nizri Shahar, Wiesman Zeev. Castor oil biodiesel and its blends as alternative fuel. Biomass Bioenergy 2011;35:2861–6. [4] Antony Miraculas G, Bose N, Edwin Raj R. Optimization of process parameters for biodiesel extraction from tamanu oil using design of experiments. J Renewable Sustainable Energy 2014;6:033120. [5] Melvin Jose DF, Edwin Raj R, Durga Prasad D, Robert Kennedy Z, Mohammed Ibrahim A. A multi-variant approach to optimize process parameters for biodiesel extraction from rubber seed oil. Appl Energy 2011;88(6):2056–63. [6] Kumar Sunil, Singh Jasvinder, Nanoti SM, Garg MO. A comprehensive life cycle assessment (LCA) of Jatropha biodiesel production in India. Bioresour Technol 2012;110:723–9.