Synthesis of Biodiesel from Low-Cost Vegetable Oil and Prediction of

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Synthesis of Biodiesel from Low-Cost Vegetable Oil and Prediction of the Fuel Properties of a Biodiesel−Diesel Mixture Pratiksha R. Kale, Anand D. Kulkarni, and Somnath Nandi* Department of Petroleum and Petrochemical Engineering, Maharashtra Institute of Technology, Paud Road, Kothrud, Pune 411 038, India S Supporting Information *

ABSTRACT: The depletion of crude reserves and turbulent global and local economy leads to ever increasing prices of petroleum-based fuels. Refineries all across the globe are forced to process heavy crude and high sulfur crude which in effect embraces pollution problems on combustion front unless advanced technologies are implemented. All of these factors point toward the search of sustainable substitutes for conventional energy sources. In this scenario, the prospect of using biodiesel as an environment-friendly and sustainable energy source is very promising. Several countries are opting for a blend of 5−20% of biodiesel with conventional petro-based diesel. In this study, a generalized model is proposed for the prediction of important fuel properties of a biodiesel−diesel mixture. The model utilizes properties of each component along with its fractional composition in the mixture. Performance of the model has been evaluated for various biodiesel−diesel blends, where biodiesel were synthesized from diversified sources namely soybean, sunflower, ricebran, cottonseed oil etc. It has been clearly observed that the developed model is able to predict density, viscosity, cetane number, and pour point quite accurately irrespective of the source of biodiesel. The simple model can be utilized to predict the blending proportions of biodiesel with petro-based diesel in order to meet ASTM specification of the blended fuel.

1. INTRODUCTION Renewable energy sources are receiving increased attention in the present day scenario of escalating crude oil prices and market volatility. The added advantages of these new generation fuel are their environmental friendliness, as they are essentially free from sulfur, producing lower exhaust emissions than conventional fuels. Biodiesel is a nontoxic, biodegradable, and renewable fuel which can be blended with petroleum-based diesel fuel in any proportions. Generally biodiesel contains 10−12% oxygen by weight which increases the combustion efficiency as well as enhances lubricity and hence results in longer engine component life.1−3 Industrial scale production of biodiesel started in 1992 in the European Union (EU), and commercialization started in the United States (USA) in 1993. Presently more than 380 plants being operated all across the globe produce more than 14 000 million liters of biodiesel annually.1 Biodiesel can be blended with diesel fuels and the resultant blend can be utilized in diesel engines without any modification. Biodiesel has different fuel properties compared to petroleum-based diesel variants; hence, the overall fuel properties of biodiesel−diesel blends vary with the amount of biodiesel present in the fuel mixture.4,5 The most common feedstock for biodiesel is rapeseed oil in Europe and soybean oil in the USA. Other vegetable oils such as sunflower oil, corn oil, palm oil, canola oil, cottonseed oil, etc. are also used in various parts of world.1 Nowadays usage of various nonedible oils namely jatropa, karanja, camelina etc. and waste cooking oil are gaining importance to meet sustainability requirements.1,4,5 The fuel properties of biodiesel must meet EN-14214 specifications in Europe or the American Society of Testing and Materials (ASTM) D-6751 specifications in the USA (refer to Table 1). Biodiesel quality depends on the © XXXX American Chemical Society

Table 1. Specifications of Biodiesel and Petro-Based Diesel biodiesel fuel properties

EN-14214

density (gm/cm3) viscosity (mm2/s) flash point (°C) cetane number (---)

0.86−0.9 3.5−5.0 min 120 min 51

diesel

ASTM-D6751

EN-590

1.9−6 min 130 min 47

0.82−0.845 2−4.5 min 55 min 51

vegetable oil (feedstock) utilized and the operating conditions; hence, biodiesels obtained from a dissimilar source have different fuel properties. The need of blending biodiesel at increasing ratios imposes the need to estimate the fuel properties of the final mixture before hand. Various types of linear and nonlinear models are available for the prediction of important fuel properties of diesel−biodiesel blends. Several researchers utilized Kay’s mixing rule for density prediction.6−8 Benjumea and coworkers have extended the relation for the prediction of heating value, cloud point, and cetane index for a palm oil-based biodiesel and diesel mixture.9 Complex relations like the Grunberg−Nissan mixing rule is commonly employed for the prediction of viscosity of the blended mixture.10,11 Meng et al. (2014) have utilized nonlinear models encompassing neural net architecture based on the mass fraction of fatty acid methyl Special Issue: Energy System Modeling and Optimization Conference 2013 Received: March 15, 2014 Revised: July 10, 2014 Accepted: August 21, 2014

A

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ester (FAME) for the prediction of the viscosity of biodiesel.12 A literature search reveals that the predictions of various properties require different types of models. Hence, our work focuses on the development of a unified model to be utilized for the estimation of various fuel properties of the biodiesel− diesel blend. The applicability of such a model will be enormous as this single model will be able to predict various important fuel properties of a biodiesel−diesel mixture within reasonable accuracy. The model can be effectively utilized for predicting blending proportions in order to meet ASTM specifications, irrespective of the different routes and scales in which the biodiesel has been synthesized.

Table 2. Properties of Neat Biodiesel Synthesized in the Laboratory and the Diesel Used for Blending soybean oil based biodiesel

properties

2. EXPERIMENTAL SECTION 2.1. Synthesis of Biodiesel. Three low-cost vegetable oils namely soybean, cottonseed oil, and ricebran oil were purchased from a local market and utilized for biodiesel production. The in-house experiments revealed that the acid numbers of these vegetable oils were in the range of 0.5−1.3 mg KOH/g, indicating a negligible proportion of free fatty acid in the refined oil; hence, pretreatment to the feedstock was not performed. Methanol (CH3OH) was used as the alcohol, and sodium hydroxide (NaOH) was utilized as the homogeneous catalyst for the transesterification reaction. Alcohol and vegetable oil were taken in the mole ratio of 6:1, and the catalyst amount was 1.5% of the vegetable oil charged. The experiments were performed in a 1000 mL reaction flask equipped with reflux condenser, stirring arrangement, and thermometer. The reactor was operated in a PID controlled water bath in order to maintain a temperature constant at 62 °C (±0.5). The catalyst sodium hydroxide was first dissolved in methanol by stirring the mixture in a small flask. Accurately measured 500 mL of vegetable oil was added to the reaction flask and heated. When the temperature reached 60 °C, the mixture of alcohol and catalyst was added slowly into the hot oil, and then the final temperature of the mixture was closely maintained at 62 °C. The reaction mixture was stirred continuously for 2 h 30 min. Then the mixture was allowed to cool down and the glycerin layer was separated in a separating funnel. The ester layer was twice washed with warm water. Commercially available petro-based diesel fuels were purchased locally (from Bharat Petroleum Petrol Pump, normal diesel) to prepare the blends for further study. The synthesized biodiesel and its various blends with petroleum-based diesel were characterized by determining their viscosity, density, pour point, in the Refining Process Laboratory of the Department of Petroleum and Petrochemical at Maharashtra Institute of Technology, Pune. The fuel properties of the biodiesels and diesel fuels are presented in Table 2. 2.2. Measurement of Density. The densities of the neat biodiesel as well as its various blends were measured in laboratory using a specific gravity bottle and electronic weighing balance. The data reported here are the average values of five measurements taken for each sample. 2.3. Measurement of Viscosity. A Brookfield viscometer (model DVII+ Pro) was used for the measurement of viscosities of the biodiesel and its various blends. The data reported here are the average values of the three measurements for each sample. 2.4. Measurement of Pour Point. The pour points of the samples were measured using a pour point apparatus as per ASTM standard D97. The fuel samples were placed in sample

cottonseed oil based biodiesel

color

pale yellow

yellow

density (gm/cm3) dynamic viscosity (cP) pour point (°C)

0.891

ricebran oil based biodiesel

petro-based diesel light yellow

0.892

yellowish brown 0.896

4.81

5.19

5.86

3.1

1 (±0.5)

5 (±0.5)

2.3 (±0.5)

− 8 (±0.5)

0.83

tubes kept in a freezing mixture. The temperature was measured with a thermometer having an accuracy level of ±0.5 °C. Pour-point data reported here are the average values of the three sets of measurements for each sample.

3. RESULTS AND DISCUSSION 3.1. Development of the Unified Model for the Prediction of Fuel Properties. Biodiesels are green and sustainable fuels which can be blended in any proportions with petroleum-based diesel in order to reduce the impact of depleting crude reserves and to have a smaller carbon footprint. It has been observed that individual properties of petroleum-based diesel and biodiesel as well as their proportions in the fuel blend govern the resultant fuel properties of the mixture. Several studies are available in the open literature indicating that properties of blended fuel are additive in nature.6,8,9,13 Kay’s mixing rule14 is most commonly used for predicting the density of a hydrocarbon mixture as well as the density of a blended diesel−biodiesel mixture: n

ρblend =

∑ xiρi

(1)

i=1

where ρblend is the density of the blended mixture, ρi is the density of an individual component, and xi denotes molar fractions. The viscosity of liquid mixtures is predicted by the Grunberg−Nissan mixing rule15 as represented below: n

ln ηblend =

n

n

∑ xi ln ηi + ∑ ∑ xixjGij i=1

i=1 j=1

(2)

where ηblend is the overall viscosity of the blended mixture, ηi is the viscosity of the pure ith component, xi and xj are mole fractions of the ith and jth components. Gij denotes an interaction parameter (for a similar type of components, i ≈ j hence, Gij = 0) and n denotes total number of components present in the mixture. Biodiesel and diesel are completely miscible in all proportions, and their volumes are practically additive in mixture. In addition to this, they are practically nonpolar;9 hence, the interaction parameter Gij is negligible for a biodiesel−diesel mixture, and the second part of right-hand side of eq 2 can be neglected for most of the practical purposes. The resultant equation also indicates the additive nature of individual fuel properties on prediction of viscosity of the blended mixture. Joshi and Pegg16 have developed a nonlinear regressionbased model using MINITAB for the prediction of cloud point and pour point of various proportions of diesel and biodiesel blends, and the resultant equations are as shown below: B

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Table 3. Performance of Knowledge-Based Simple Additive Model Proposed in This Study vs Regression-Based Model (MINITAB)16 for Cloud Point and Pour Point of Diesel−Biodiesel Blends cloud point, CP (K)

% variation in CP

pour point, PP (K)

% variation in PP

blended fuel

expt

proposed model#

MINITAB$

proposed model#

MINITAB$

expt

proposed model#

MINITAB$

proposed model#

MINITAB$

B100 B80 B60 B40 B20 B0

279.0 273.0 269.0 265.0 261.0 256.0

279.0 274.4 269.8 265.2 260.6 256

278.6 273.8 269.2 264.7 260.5 256.4

0 0.51 0.29 0.07 −0.15 0

−0.14 0.29 0.07 −0.11 −0.19 0.16

276.5 270.0 266.5 262.0 258.5 253.5

276.5 271.9 267.3 262.7 258.5 253.5

275.9 270.9 266.3 261.9 257.8 253.9

0 0.7 0.3 0.27 0 0

−0.22 0.33 −0.07 −0.04 −0.27 0.16

# The proposed model indicates additive type mechanistic model (eq 6) discussed in this study. $MINITAB-based nonlinear regression model developed by Joshi and Pegg (2007).16 The experimental data are from Joshi and Pegg (2007).16

Table 4. Prediction of Important Fuel Properties of Biodiesel Blends Synthesized in the Laboratory density (gm/cm3) veg. oil used soy bean oil

cotton seed oil

rice bran oil

dynamic viscosity (cP)

fract. of biodiesel

fract. of diesel

expt

model

dev %

expt

model

dev %

0 0.02 0.05 0.1 0.15 1.0 0 0.02 0.05 0.1 0.15 1.0 0 0.02 0.05 0.1 0.15 1.0

1.0 0.98 0.95 0.9 0.85 0 1.0 0.98 0.95 0.9 0.85 0 1.0 0.98 0.95 0.9 0.85 0

0.83 0.8486 0.8492 0.8516 0.854 0.891 0.83 0.847 0.8486 0.8505 0.8534 0.892 0.83 0.8458 0.8486 0.8498 0.8515 0.8961

0.83 0.8312 0.833 0.8361 0.8391 0.891 0.83 0.8312 0.8331 0.8362 0.8393 0.892 0.83 0.831 0.8333 0.8366 0.8399 0.8961

0 −2.05 −1.9 −1.82 −1.74 0 0 −1.91 −1.83 −1.68 −1.65 0 0 −1.71 −1.8 −1.55 −1.36 0

3.1 3.13 3.165 3.195 3.295 4.81 3.1 3.15 3.195 3.26 3.38 5.19 3.1 3.162 3.225 3.26 3.395 5.86

3.1 3.134 3.185 3.271 3.356 4.81 3.1 3.142 3.204 3.309 3.41 5.19 3.1 3.155 3.238 3.376 3.514 5.86

0 0.134 0.648 2.379 1.866 0 0 −0.26 0.297 1.503 0.842 0 0 −0.31 0.403 3.558 3.505 0

Tcp = 256.4 + 0.1991VB + 2.23 × 10−04VB2

(3)

Tpp = 253.9 + 0.1865VB + 3.35 × 10−04VB2

(4)

present in the blended mixture. This model predicts both cloud point and pour point quite accurately for the available data16 for all the proportions of blending. The model predictions vis-à-vis earlier published nonlinear regression-based predictions in MINITAB16 are reported in Table 3. As evident from the table, the simple mechanistic model (eq 5) is of the same accuracy level for most practical purposes (within 99.3% accuracy). On the basis of these observations, we are proposing the simple mechanistic model where individual fuel properties (of petro-based diesel and biodiesel) and their respective concentrations are utilized to predict fuel property of the final mixture. We have utilized the following simple blending equation:

where Tcp and Tpp indicates cloud point and pour point, respectively, of the blended mixture, and VB is the volume fraction of biodiesel present in the mixture. A close look at these equations reveals that for most practical purposes the effect of the last term (i.e., term containing the square of VB) is negligible and the values of the constant term, 256.4 for cloud point and 253.9 for pour points, are practically the same as the measured values of cloud point (256 K) and pour point (253.5 K).16 So instead of a regression-based model which has limited applicability (depends heavily on the specified data range utilized to develop the model), a knowledge-based model can be tried. Here, a simple knowledge-based model is proposed: blend bd pd T prop = (x bd × T prop ) + (x pd × Tprop )

blend pd propM = x × propbd + (1 − x) × prop M M

(6)

where propblend denotes the predicted property M for the M biodiesel−diesel blend, propbd M is the value of property M for biodiesel, proppd M indicates property M of petroleum-based diesel and x denotes the fraction of biodiesel present in the blended mixture. The simple equation proposed can be utilized for estimation of the important properties like density, viscosity, pour point, flash point, etc. of biodiesel−diesel mixture. The model can also be utilized to forecast the maximum quantity of biodiesel blending permissible in order to have all important

(5)

where Tblend prop indicates a property, namely cloud point or pour point, of the blended mixture, Tbd prop is that property (cloud point or pour point as the case may be) of the neat biodiesel, and xpd denotes volume fraction of the petro-diesel present in the overall blend. Each of the components should have their contribution on the overall property of the mixture, and its strength will depend on the proportion of the component C

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Figure 2. Performance of the model developed in this study for four different biodiesel blends of Ozcanli and Serin (2011).4 Plot (a) represents prediction of kinematic viscosity and plot (b) denotes estimation of pour points.

blended mixture at low biodiesel concentration. The performance of the model is also judged for prediction of pour point of the blended mixture, results of which are reported in Figure 1a−c. Figure 1a indicates that the model predicted pour-point value of soybean-based biodiesel blend is less than that of the actual fuel blend. A similar trend is observed for Figure 1 panels b and c for the biodiesel synthesized from cottonseed and ricebran oil. Proper purification of the synthesized biodiesel may reduce the gap, as it was evident that a small turbidity present in the biodiesel may act as seed and increase the pour point of the fuel by a few degrees. 3.3. Prediction of Fuel Properties of Biodiesel Blends from Open Literature. 3.3.1. Prediction of Fuel Quality of Soybean/Canola/Palm Biodiesel Mixture. Ozcanli and Serin4 have produced biodiesel through transesterification of soybean, canola, and palm oils separately using methyl alcohol and sodium hydroxide as reactant and catalyst, respectively. They have utilized four different blends of biodiesel−diesel mixture and measured their fuel properties. All blends comprised equal proportions of soybean-, canola-, and palm oil-based biodiesel mixture with varying proportions of petroleum-based diesel mixed in it. Blend 1 was neat biodiesel mixture without any petro-based diesel in it, whereas blends 2, 3, and 4 had 70%, 40%, and 25% of petro-based diesel in them, respectively. The developed model (eq 6) was utilized for estimation of fuel properties for all these blends. Predicted fuel properties, namely, density, cetane number, and higher heating value, are

Figure 1. Comparison of pour-point prediction of three types of biodiesel synthesized in the laboratory: (a) various blends of soybean based biodiesel, (b) blends of cottonseed based biodiesel, and (c) blends of ricebran based biodiesel.

fuel properties of the mixture well within specified limits, as per EN-14214 or ASTM D-6751 standards. 3.2. Prediction of Fuel Properties of Synthesized Biodiesel Blends. Biodiesels obtained from different sources have different physical properties as evident from the data reported in Table 2. The developed model (eq 6) is utilized to estimate important fuel properties such as density, viscosity, and pour point of the various blends (2%, 5%, 10%, and 15%). The predictions of the model matches well for most of the biodiesels blended in various proportions as reported in Table 4. The maximum deviation is 2% for density and 3.5% for the viscosity of the fuel blends. Average error for density prediction is 1.16% and 0.9% for the viscosity prediction which is really encouraging. Similarly, dynamic viscosity predicted by the simple model is quite accurate for most of the blends except biodiesel obtained from ricebran oil as evident from Table 4. The results obtained indicate that the simple additive-type relation holds good even for the viscosity prediction of a

Table 5. Predictions of the Developed Model for Properties of Blended Fuel. Experimental Results Are from Diesel−Biodiesel Blends for Ozcanli and Serin (2011)4 density (g/cm3) fuel Blend Blend Blend Blend

No. No. No. No.

1 2 3 4

cetane number (−)

higher heating value (kJ/kg)

experimental data taken from ref 4

prediction of model developed in this study

experimental data taken from ref 4

prediction of model developed in this study

experimental data taken from ref 4

prediction of model developed in this study

0.881 0.852 0.864 0.870

0.879 0.852 0.864 0.868

50.5 54.8 53.9 53.0

51.37 55.8 53.9 52.95

38843 43370 41908 40752

39139 42909 41293 40485

D

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Figure 3. Estimation of density (plot a) and kinematic viscosity (plot b) for various different biodiesel blends; the prediction is based on the model developed in this study, and experimental data are from Alptekin and Canakci (2008)1

reported in Table 5, along with the reported experimental values. A maximum deviation in the prediction of density is 0.19%, of cetane number is 1.82%, and that for higher heating value is 1.47%. Similarly model-based estimations for kinematic viscosity and pour point are plotted along with experimental data points as depicted in Figure 2a,b. It is clearly evident that all the predicted values are in good agreement with the experimental findings. Earlier, Benjumea et al., (2008)9 demonstrated the applicability of the simple additive-type Kay’s mixing rule for prediction of a lower heating value, cloud point, and cetane index based on palm oil biodiesel−diesel blends in various proportions. The proposed model-based predictions indicate the generalization capacity of the simple model for various properties even at higher proportions of biodiesel (Blend No. 1 is neat biodiesel mixture and Blend No. 4 is 75% biodiesel−25% diesel mixture). 3.3.2. Estimation of Fuel Properties of Six Different Biodiesel Mixtures. Alptekin and Canakci1 reported laboratory-scale biodiesel synthesis from six different vegetable oils, namely, waste palm oil (WPOB), sunflower oil (SFOB), soybean oil (SOB), corn oil (CROB), canola oil (COB), and cottonseed oil (CSOB). Biodiesels were blended with the diesel fuel at 2%, 5%, 10%, 20%, 50%, and 75% on a volume basis. Fuel properties were measured using standard methods: density by ASTM D941 and viscosity through ASTM D445 techniques. Authors had reported experimentally determined values of various blends for density (at 15 °C) and viscosity (at 40 °C). We have utilized eq 6 for prediction purposes; density was predicted with 99.9% and viscosity with 97.47% accuracy (details of all the results are tabulated in Supporting Information, Table S1). Results obtained are in good agreement with the previously published regression-based

Figure 4. Performance of model proposed in this study for pilot plant scale data: (a) results for kinematic viscosity, (b) cloud point, (c) pour point, and (d) estimates of flash point for Karanja- and Jatropa-based biodiesel at various blending proportions. Experimental data are from Sahu et al. (2011).18

model, in which density and viscosity were predicted with 99.8% and 97.75% accuracy, respectively.1 The density prediction is plotted in Figure 3a which indicates that the model can predict the density of blended fuel quite accurately up to a 50% biodiesel (remainder diesel) mixture irrespective of the source of the biodiesel. In the same note it is evident from Figure 3b that the generalized model can effectively be used for a kinematic viscosity prediction of various biodiesel−diesel blends with a biodiesel fraction of 0.2 and below, which is quite E

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Notes

good enough for all practical purposes as most of the countries follow allowable biodiesel blending below 20% as per ASTM D 6751 specifications.17 The result indicates that the simple additive type relationship holds better for density and deviates for viscosity at higher proportions (above 20%) of biodiesel. 3.3.3. Evaluation of Fuel Quality for Pilot Plant Scale Biodiesel Production Facility. Recently a pilot plant scale study on biodiesel production from karanja and jatropa oils was reported by Sahu and his co-workers.18 They have reported viscosity, cloud point, pour point and flash point of both the biodiesel varieties as well as their various blends. The proposed model (eq 6) was utilized to estimate the diversified fuel properties, and the same has been plotted along with the experimentally determined data points for effective comparison (see Figure 4a−d). As evident from these plots (Figure 4), the trend of variation for kinematic viscosity, cloud point, flash point, and pour point were well picked up, and these important fuel properties can be predicted with reasonable accuracy even up to higher proportion of biodiesel blended with petro-based diesel. These results are quite encouraging as the simple model can quite effectively predict diversified important fuel properties namely viscosity, cloud point, and pour point as well as flash point for the blended fuel obtained from two sufficiently viscous vegetable oils namely karanja and jatropa. The robustness of the simple mechanistic model is evident from the results (Figure 4) as it can predict most of the important fuel properties namely viscosity, cloud point, pore point, and flash point for pilot plant scale studies even with the batch size of 50 L of vegetable oil processed.

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Comments from anonymous reviewers helped to enhance the technical content of the manuscript. (1) Alptekin, E.; Canakci, M. Determination of the Density and Viscosities of Biodiesel−Diesel Fuel Blends. Renew. Energy 2008, 33, 2623−2630. (2) Lotero, E.; Liu, Y.; Lopez, D. E.; Suwannakarn, K.; Bruce, D. A.; Goodwin, J. G., Jr. Synthesis of Biodiesel via Acid Catalysis. Ind. Eng. Chem. Res. 2005, 44, 5353−5363. (3) Mushrush, G. W.; Hughes, J. M.; Willauer, H. D. Blends of Soybean Biodiesel with Petroleum Diesel: Advantages. Ind. Eng. Chem. Res. 2013, 52, 1764−1768. (4) Ozcanli, M.; Serin, H. Evaluation of Soybean/Canola/Palm Biodiesel Mixture as Alternative Diesel Fuel. J. Sci. Ind. Res. 2011, 70, 466−470. (5) Altun, S. Fuel Properties of Biodiesels Produced from Different Feedstocks. Energy Educ. Sci. Technol. Part A 2011, 26, 165−174. (6) Nita, I.; Geasai, S.; Iulian, O. Measurements and Correlations of Physico-chemical Properties to Composition of Pseudo-binary Mixtures with Biodiesel. Renew. Energy 2011, 36, 3417−3423. (7) Kashinath, S. A. A; Manan, Z. A.; Hashim, H.; Wan Alwi, S. R. Design of Green Diesel from Biofuels Using Computer Aided Technique. Comput. Chem. Eng. 2012, 41, 88−92. (8) Alam Fahd, M. E.; Lee, P. S.; Chow, S. K.; Wenming, Y.; Yap, C. Experimental Study and Empirical Correlation Development of Fuel Properties of Waste Cooking Palm Biodiesel and Its Diesel Blends at Elevated Temperatures. Renew. Energy 2014, 68, 282−288. (9) Benjumea, P.; Agudelo, J.; Agudelo, A. Basic Properties of Palm Oil Biodiesel−Diesel Blends. Fuel 2008, 87, 2069−2075. (10) Allen, C. A. W; Watts, K. C.; Ackman, R. G.; Pegg, M. J. Predicting the Viscosity of Biodiesel Fuels from Their Fatty Acid Ester Composition. Fuel 1999, 78, 1319−1326. (11) Tat, M. E.; Van Gerpen, J. H. The Kinematic Viscosity of Biodiesel and Its Blends with Diesel Fuels. J. Am. Oil Chem. Soc. 1999, 76, 1511−1513. (12) Meng, X.; Jia, M.; Wang, T. Neural Network Prediction of Biodiesel Kinematic Viscosity at 313 K. Fuel 2014, 121, 133−140. (13) Dmytryshyn, S. L.; Dalai, A. K.; Chaudhari, S. T.; Mishra, H. K.; Reaney, M. J. Synthesis and Characterization of Vegetable Oil Derived Esters Evaluation for their Diesel Additive Properties. Biores. Technol. 2004, 92, 55−64. (14) Kay, W. B. Density of Hydrocarbon Gases and Vapors. Ind. Eng. Chem. 1936, 28, 1014−1019. (15) Grunberg, L.; Nissan, A. H. Mixture Law for Viscosity. Nature 1949, 164, 799−800. (16) Joshi, R. M.; Pegg, M. J. Flow Properties of Biodiesel Fuel Blends at Low Temperatures. Fuel 2007, 86, 143−151. (17) Guidance for Biodiesel Producers and Biodiesel Blenders/Users; EPA420-B-07-019; United States Environmental Protection Agency: Washington, DC, November 2007; www.epa.gov/otaq/ renewablefuels/420b07019.pdf (accessed on 8th July 2014). (18) Sahu, G.; Das, L. M.; Sharma, B. K.; Naik, S. N. Pilot Plant Study on Biodiesel Production from Karanja and Jatropa Oils. AsiaPacif. J. Chem. Eng. 2011, 6, 38−43.

4. CONCLUSION Biodiesel has been synthesized in the laboratory from soybean, cottonseed, and ricebran oils using sodium hydroxide as the homogeneous catalyst. Important fuel properties like density, viscosity, and pour point are measured for the neat biofuels as well as their different blends with petro-based diesel. A simple additive-type model for property prediction is proposed. It has been demonstrated that the developed model predicted diversified fuel properties namely density, viscosity, cetane number, pour point, and cloud point of a biodiesel−diesel mixture reasonably well. The efficacy of the model has been demonstrated for various lab-scale and pilot plant-scale biodiesel synthesized from diversified feedstocks. Estimation of various fuel properties is possible with the simple generalized model, and it can be extended for a commercial scale biodiesel production plant for the estimation of fuel properties of blended mixtures. It can even further be utilized for optimization of various available blends in a commercial scale operation.

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ASSOCIATED CONTENT

* Supporting Information S

The results of estimation of fuel properties of six different biodiesel at various blending proportions using the developed model for the experimental data obtained from Alpetkin and Canakci (2008)1 are provided in Table S1. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Tel: +91-20-3027-3400. Fax: +91-20-2544-2770. E-mail: [email protected]. F

dx.doi.org/10.1021/ie5011146 | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX