Energy Fuels 2010, 24, 2120–2126 Published on Web 02/09/2010
: DOI:10.1021/ef901488g
Application of Taguchi Method to Investigate the Effects of Process Parameters on the Transesterification of Soybean Oil Using High Frequency Ultrasound Naresh N Mahamuni and Yusuf G. Adewuyi* Chemical Engineering Department North Carolina Agricultural and Technical State University Greensboro, North Carolina 27411 Received December 4, 2009. Revised Manuscript Received January 23, 2010
This paper utilizes the Taguchi optimization methodology (L9 orthogonal array) to optimize various parameters for the ultrasound-assisted, KOH-catalyzed transesterification of soybean oil with methanol. The statistical tool used in the Taguchi method to analyze the results is the analysis of variance (ANOVA), which gives the relative contribution of the factors varied to the change in the dependent variable (i.e., FAME or biodiesel yield). It is observed that catalyst loading is the most influential parameter, with ∼42.56% contribution toward variation in biodiesel yield, followed by ultrasonic power with ∼39.95%, and oil/methanol molar ratio with 11.40%. Ultrasonic frequency is found to have negligible influence on the biodiesel yield in the range of the present investigation. The optimum conditions are determined to be 581 kHz, 143 W, 0.75% (w/w) KOH loading at 1:6 oil/methanol molar ratio, resulting in more than 92.5% biodiesel yield in less than 30 min. Confirmation experiments have been performed to prove the effectiveness of the Taguchi technique after the optimum levels of process parameters are determined.
catalyst (1%), and at 65 °C for about 1 h. However, this conventional approach is hampered by process limitations such as feedstock issues and product separation problems. Basecatalyzed transesterification of triglycerides is affected by a number of parameters such as free fatty acid and water content in the oil, reaction temperature, molar ratio of alcohol to oil, type of catalyst, type/chemical structure of alcohol, amount/ concentration of catalyst, reaction time, intensity of mixing (rpm), and use of cosolvents.3 Since the oil and alcohol are immiscible with each other, the base-catalyzed transesterification is mass transfer controlled in the initial phases of the reaction.4 Investigators have used intensification methods such as ultrasonic or microwave irradiations, supercritical conditions, and addition of cosolvents to eliminate or minimize the mass transfer resistance and improve the biodiesel synthesis processes.5-7 Stavarache et al.8-12 and Hanh et al.13-16 have
1.0. Introduction Biodiesel is an oxygenated renewable fuel manufactured from vegetable oils, animal fats, and recycled cooking oils. Biodiesel offers many attributes such as its renewability, nontoxicity, biodegradability, energy efficiency, possible substitution for petroleum-derived diesel fuels, capability of reducing global warming gas emissions, and its ready adaptability to use in most diesel equipments such as compression-ignition (CI) engines, fuel oil and heating oil boilers, and turbines with minor modifications.1 Biodiesel can be used (in 1-2% v/v) as an additive to improve the lubricity of ultra low sulfur diesel from petroleum fuels that have poor lubricating properties. In addition, biodiesel can also be used as fuel in place of diesel (B100) or in combination with diesel (B5-B20) for most applications that use diesel fuels, where “B” indicates the percentage of biodiesel in a gallon of fuel, and the reminder represents No. 1 or No. 2 diesel, kerosene, jet A, JP8, heating oil, or any other distillate fuel.1 ASTM D6751 standards define the standard specifications of the biodiesel to be used as fuel in the USA. Biodiesel is also known as fatty acid methyl ester (FAME) and is produced by transesterification of triglycerides or esterification of long chain fatty acids. Biodiesel can be synthesized by transesterification using different types of catalysts such as base, acid, or lipase, but base-catalyzed transesterification is most prevalent in the industry owing to easier, faster, and cheaper processing.2 Biodiesel produced by base-catalyzed transesterification of vegetable oil is usually performed in batch reactors where the required energy is provided by heating accompanied by mechanical mixing. Using this technique, the best yields (up to 92-98%) are obtained using a methanol/oil molar ratio of 6:1, potassium or sodium hydroxide as the
(3) Enweremadu, C.; Mbarawa, M. Renew. Sustain. Energy Rev. 2009, 13 (9), 2205–2224. (4) Meher, L.; Vidya Sagar, D.; Naik, S. Renew. Sustain. Energy Rev. 2006, 10 (3), 248–268. (5) Vyas, A.; Verma, J.; Subrahmanyam, N. Fuel 2009, 89 (1), 1–9. (6) Mahamuni, N.; Adewuyi, Y. Energy Fuels 2009, 23 (5), 2757–2766. (7) Caglar, E., Biodiesel Production Using Co-solvent; European Congress of Chemical Engineering (ECCE-6) 2007, Copenhagen, Sept. 16-20, 2007. (8) Stavarache, C.; Vinatoru, M.; Nishimura, R.; Maeda, Y. Ultrasonics-Sonochem. 2005, 12 (5), 367–372. (9) Stavarache, C.; Vinatoru, M.; Nishimura, R.; Maeda, Y. Chem. Lett. 2003, 32 (8), 716–717. (10) Stavarache, C.; Vinatoru, M.; Maeda, Y.; Bandow, H. Ultrasonics-Sonochem. 2007, 14 (4), 413–417. (11) Stavarache, C.; Vinatoru, M.; Maeda, Y. Ultrasonics-Sonochem. 2007, 14 (3), 380–386. (12) Stavarache, C.; Vinatoru, M.; Maeda, Y. Ultrasonics-Sonochem. 2006, 13 (5), 401–407. (13) Hanh, H.; Dong, N.; Starvarache, C.; Okitsu, K.; Maeda, Y.; Nishimura, R. Energy Convers. Manage. 2008, 49 (2), 276–280. (14) Hanh, H.; Dong, N.; Okitsu, K.; Nishimura, R.; Maeda, Y. Renew. Energy 2009, 34 (3), 766–768. (15) Hanh, H.; Dong, N.; Okitsu, K.; Maeda, Y.; Nishimura, R. J. Jpn. Pet. Inst. 2007, 50 (4), 195–199. (16) Hanh, H. Jpn. J. Appl. Phys. 2007, 46, 4771.
*To whom correspondence should be addressed. Phone: (336)3347564, ext 107. Fax: (336)334-7417. E-mail:
[email protected]. (1) U.S. Department of Energy, E.E.a.R.E. Biodiesel Handling and Use Guidellines: DOE/GO-102006-2358; September 2006, Third Ed. (2) Ma, F.; Hanna, M. Bioresour. Technol. 1999, 70 (1), 1–16. r 2010 American Chemical Society
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: DOI:10.1021/ef901488g
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extensively studied the transesterification and esterification reactions to produce biodiesel in the presence of ultrasound. Ultrasonication provides an effective way to attain the required mixing while providing the necessary activation energy. It is well-known that transesterification by ultrasound offers a lot of advantages over the conventional classical procedure.17 It is found to be efficient (biodiesel yield up to 98-99%) and offers time and energy savings due to dramatic reduction of reaction time to 5 min compared to 1 h or more using conventional batch reactor systems, as well as a remarkable reduction in static separation time to 25 min compared to 8 h.17 In addition, higher reaction rates, lower reaction time, higher yields, lesser reagents, and better byproduct are some of the characteristics of the ultrasonic biodiesel synthesis processes.6,8 The current interest in the application of ultrasound to intensify biodiesel synthesis presents a need to optimize the ultrasound-assisted synthesis process to reduce the cost of operation and to make it economically attractive for largescale applications. We recently reported that high-frequency ultrasound-assisted, KOH-catalyzed transesterification of soybean oil with methanol achieved very high biodiesel conversions (>90%) with relatively low-energy inputs.6 Although ultrasound has been found to intensify the biodiesel formation, the science of biodiesel formation using ultrasound is not exactly known. The exact influence of individual operating parameters such as catalyst loading, frequency of ultrasound, ultrasonic power, oil/methanol molar ratio, and interactions among them is not known. The conventional approach of experimenting with one variable (or one factor) at a time is labor-intensive, time-consuming, and results in waste of materials. Also, the optimum operating conditions using this technique are generally achieved by trial and error and intuition. We recently optimized the ultrasound-assisted biodiesel synthesis process using the one-variable-at-a-time approach.6 This reduces the robustness of the process to consistently achieve higher biodiesel yields. However, there are no reports in the open literature that quantify the contribution of individual parameters or relative significance of individual parameters toward biodiesel formation. Hence, there is a need to understand the simultaneous effects of various parameters such as frequency, power, oil/methanol molar ratio, and catalyst loading on biodiesel synthesis in the presence of ultrasound. Also, to aid cost-effectiveness and commercialization of the process, an understanding of the effects of multiple factors as well as the influence of the individual factors on the overall biodiesel yield is essential to establish a more robust approach for obtaining the optimal conditions for the process. 1.1. Statistical Design. The classical method (full factorial design) used in statistical design of experiments requires a large number of experiments to be carried out when the number of process parameters increases. For a full factorial design, the number of possible designs of experiments, N, is N = Lm, where L is the number of levels for each factor and m is the number of factors. For example, to study the effect of four parameters (frequency, power, catalyst loading, and molar ratio) at three different levels, 81 (34) different combinations of parameters are possible. Also, it is very difficult to identify and quantify the interactions among different parameters and the contribution of individual parameters. Hence, there was an absolute need for a design of experiments strategy that can reduce the number of
experiments as well as identify and quantify the interactions among different parameters affecting the process. Dr. Genichii Taguchi18 designed a system of specific orthogonal arrays to be chosen and applied in suitable conditions to describe a large number of experimental situations. This fractional factorial design optimization technique uses the Taguchi orthogonal design matrix, where only a fraction of the combination of variables are considered, and hence, minimizing the number of experiments while covering a wide range of operating conditions and keeping all the information/data intact. The quantified and comparative analysis of the effect of parameters is the second advantage of this approach. Usually, with the aid of range analysis, analysis of variance (ANOVA), or analysis of signal-to-noise ratio (S/N ratio), the key factors that have significant effects on the response can be identified and the best factor levels for a given process can be determined from the predetermined factor levels. The Taguchi methodology uses several design arrays, such as L4, L8, L9, L12, L16, L18, L27, and L64, which focuses on the main effects and increases the efficiency and reproducibility of small-scale experiments. This is a quick yet accurate way of determining optimization, details of which can be found in the books by Roy.19,20 Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the parameter design. We previously adapted this technique to optimize the sonochemical oxidation of carbon disulfide to sulfate as the main product.21 In this paper, the optimization of biodiesel synthesis is accomplished by evaluating the simultaneous effects of key process variables on the biodiesel yield using Taguchi statistical approach to design the experiments and analyze the results with the view of determining the percent contribution of each experimental variable to the ultimate biodiesel yield and arriving at a set of conditions that results in optimal yield in a shorter time. 2.0. Experimental Section 2.1. Materials and Experimental Methods. Pure soybean oil was obtained from MP Biomedicals, LLC (Solon, OH). The certificate of analysis from the supplier shows that FFA content of the oil was 0.03%, iodine value was 130 g/100 g of soybean oil, and peroxide value was 0.2 meq/g. Methanol of ACS grade was obtained from Fisher Scientific (Somerville, NJ). Potassium hydroxide (pellets) of ACS reagent grade was obtained from Aldrich Chemical Co., Inc. (Milwaukee, WI). Biodesel standards, such as linoleic acid methyl ester, palmitic acid methyl ester, cis-11-eicosenoic acid methyl ester, arachidic acid methyl ester, oleic acid methyl ester, linolenic acid methyl ester, and stearic acid methyl ester were obtained from Sigma-Aldrich, Inc. (Atlanta, GA). The biodiesel standards were needed for calibration of biodiesel using our newly developed Fourier transform infrared spectroscopy (FTIR) analytical method to monitor transesterification reaction and discussed in an earlier study.22 (18) Taguchi, G. Introduction to Quality Engineering; UNIPUB/Kraus International: White Plains, 1986. (19) Roy, R. A Primer on the Taguchi Method; Van Nostrand Reinhold: New York, 1990. (20) Roy, R. Design of Experiments Using the Taguchi Approach: 16 Steps to Product and Process Improvement; Wiley-Interscience: 2001. (21) Adewuyi, Y.; Oyenekan, B. Ind. Eng. Chem. Res. 2007, 46 (2), 411–420. (22) Mahamuni, N.; Adewuyi, Y. Energy Fuels 2009, 23 (7), 3773– 3782.
(17) Refaat, A. A., Elsheltawy, S. T. WIT Trans. Ecol. Environ. 2008, 109 (Waste Management and the Environment IV), 133-140.
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Table 1. Selected Parameters/Factors and Their Levels
Table 2. L9 Array for Design of Experiment by Taguchi Method
level
frequency (kHz)
power (W)
catalyst loading (w/w)
molar ratio
1 2 3
581 611 1300
46 79 143
0.25 0.50 0.75
1:4 1:6 1:9
operating parameters
2.2. Experimental Design. The first important step in the design of experiments is the proper selection of factors and their levels. In this study, effects of four parameters (factors), namely, ultrasonic frequency, ultrasonic power, KOH loading, and soybean oil/methanol molar ratio were investigated at three different levels as shown in Table 1. The factors and their levels have been chosen based upon our earlier study6 and literature reports.8,13 For Taguchi design of experiments with four factors and three levels of each factor, a standard L9 orthogonal array19,20 was employed as shown in Table 2. Each row of the matrix represents one run at specified conditions. The sequence in which these runs were carried out was randomized to avoid the systematic bias. No interactions among different parameters were considered for this preliminary study. The statistical analysis of the results was carried out using analysis of variance (ANOVA). All experiments were carried out in a multifrequency, variable power ultrasonic reactor details of which are given in our earlier published paper.6 2.3. Experimental Procedure and Analysis. The experiments were started in the ultrasonic reactor initially at room temperature (26 ( 1 °C). Since the temperature of the reactor was not controlled, the temperature rose up to 45 ( 2 °C during the 1 h duration of the experiment depending on the power setting. The initially required amount of potassium hydroxide was added to methanol and mixed well. After the mixture was magnetically stirred for about 5 min, potassium hydroxide dissolved completely in methanol. This mixture was then added into the ultrasonic reactor, which was already filled with the required amount of soybean oil. Ultrasound was simultaneously started, and the time was recorded. One milliliter samples were taken out of the reactor at regular intervals of 5 min and quenched at 5 °C, followed by the addition of 1 mL of 4% acetic acid solution in methanol to stop the reaction completely. The layer of FAME was then taken and further washed with water to remove any dissolved impurities, byproducts, or catalyst. The samples were then separated using centrifugation at 10 000 rpm. The pure samples thus obtained were then analyzed using Fourier transform infrared (FTIR) spectroscopy as described in detail earlier.22The biodiesel yield was expressed in percent wt/wt basis. The reaction mixture was always maintained around 450 mL to maintain a constant power density at the start of the reaction for all of the experiments.
exp. No.
frequency (kHz)
power (W)
catalyst loading (w/w)
molar ratio
1 2 3 4 5 6 7 8 9
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
Table 3. Measured Response (Biodiesel Yield) for Each Experiment exp. No 1 2 3 4 5 6 7 8 9
frequency power catalyst molar biodiesel (kHz) (W) loading (w/w) ratio (M) yield (% wt/wt) 581 581 581 611 611 611 1300 1300 1300
46 79 143 46 79 143 46 79 143
0.25 0.50 0.75 0.50 0.75 0.25 0.75 0.25 0.50
1:4 1:6 1:9 1:9 1:4 1:6 1:6 1:9 1:4
7.89 71.69 94.39 5.58 82.78 60.59 61.67 8.34 76.99
to investigate and model the relationship between a response variable and one or more independent variables. However, ANOVA differs from regression in two ways: the independent variables are qualitative and discrete, and no assumptions are made about the nature of the relationship. The strategy of the ANOVA calculations is to statistically analyze the variation that each factor cause relative to the total variation observed in the results.19,20,23,24 The ANOVA statistical table also screens the significant factors from those with less significance. There are many statistical terms in an ANOVA table as can be seen from Tables 4 and 5, a few of which are important to understand the analysis. The F-ratio is the variance ratio which is the variance of the factor divided by the error variance.19 The F-ratio is a criterion for distinguishing the important factors from those with less significance and depends upon the degree of freedom of the numerator and the denominator and can be found in the standard books by Roy.19,20 The details of various terms used in Taguchi method are discussed by Roy.19,20 If the F-ratio of a control parameter is almost equal to or greater than 9 (confidence level of 90%), then the factor has an important influence on the response.19 It should be emphasized that the interpretation of ANOVA is valid just in the range of the levels considered for the factors. If the F-ratio for a factor is greatly less than 9, it does not mean that the factor has no effect on the response absolutely; instead, it just means that the variation in the response due to changes in the factor levels is insignificant compared with the errors in the range of the selected levels. That is the reason why the selection of levels is also vital to the design of experiments using the Taguchi method. The error term in the last row of the ANOVA table contains information about three sources of variability of the results: uncontrollable (noise) factors, factors that are not considered in the experiments, and the
3.0. Results and Discussion 3.1. Analysis of Experimental Data. Experiments were carried out in random according to the experimental conditions as shown in Table 3. The results including biodiesel yields of each experiment at 20 min used for the Taguchi analysis are summarized in Tables 3-5 and Figures 1-6. In the Taguchi method, the results are statistically analyzed using analysis of variance (ANOVA) to determine the percentage contribution of individual parameters to the response (average biodiesel yield of number of experiments at that level). ANOVA is similar to regression, which is used
(23) Ross, P. Taguchi Techniques for Quality Engineering; McGrawHill: New York, 1988. (24) Rao, R.; Kumar, C.; Prakasham, R.; Hobbs, P. Biotechnol. J. 2008, 3 (4), 510.
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Table 4. ANOVA Table for Biodiesel Yield (Without Pooling) factors
degree of freedom
sum of squares
variance
frequency power catalyst loading molar ratio error total
2 2 2 2 0 8
150.798 4118.309 4377.912 1283.088 0 9930.108
75.399 2059.154 2188.956 641.544 0 4965.054
variance ratio
pure sum of squares
percent contribution 1.518 41.472 44.087 12.921 100
Table 5. Modified ANOVA Table for Biodiesel Yield (With Pooling) source frequency power catalyst loading molar ratio error total a
degree of freedom 2 2 2 2 8
sum of squares 4118.309 4377.912 1283.088 150.798 9930.108
variance 2059.155 2188.956 641.544 75.399
variance ratioa 27.310 29.031 8.508 1.000
pure sum of squares pooled 3967.511 4227.114 1132.29 603.19
percent contribution 39.954 42.568 11.40 6.074 100
Tabulated F-ratio at 90% confidence level: F10(2,2) = 9.0019.
Figure 4. Effect of oil/methanol molar ratio on biodiesel yield (main effects).
Figure 1. Effect of ultrasonic power on biodiesel yield (main effects).
Figure 2. Effect of ultrasonic frequency on biodiesel yield (main effects).
Figure 5. Percentage contribution of individual factors on variation in biodiesel yield.
experimental error.20 The main effects of factors are determined using average values of response at each level. For example, to compute the average performance for the factor frequency (F) at level 1, that is, for F1, the responses for experiments including F1 are added and then divided by the number of such trials. Since the F1 level occurs in experiment numbers 1, 2 and 3, the average response of F1 is calculated using eq1. F 1 ¼ ðF1 þ F2 þ F3Þ=3 ð1Þ where, F1, F2 and F3 are the responses of experiments 1, 2, and 3 respectively. The main effects of the various parameters such as ultrasonic frequency, ultrasonic power, catalyst (KOH)
Figure 3. Effect of catalyst loading on biodiesel yield (main effects).
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Figure 6. Actual experimental data.
loading, and oil/methanol molar ratio ascertained using similar calculations are shown in Figures 1-4. Tables 4 and 5 show the ANOVA tables for biodiesel yield with and without pooling. The process of disregarding an individual factor’s contribution and then subsequently modifying the contribution of the other factors is known as “pooling”. The various terms in the ANOVA table such as sum of squares (S), variance (V), variance ratio (F), pure sum of squares (S0 ), and percentage contribution (P) can be expressed using eqs 2-6. SF ¼
F1 2 F2 2 F3 2 þ þ NF1 NF2 NF3
from 46 to 79 to 143 W. It can also be seen from Table 4, which shows the ANOVA table for the biodiesel yield, that ultrasonic power has a predominant effect for controlling the biodiesel yield. As depicted in Figure 5, the percentage contribution of ultrasonic power toward biodiesel formation is ∼41.47%. It is well-known that as the ultrasonic power increases, the size of the cavitation bubbles increase leading to more intense collapse of bubble, which causes better emulsion formation of oil and methanol resulting into higher interfacial surface area for mass transfer and hence the higher biodiesel yield. Singh et al.25 have also observed similar effects of power on biodiesel yield in the presence of ultrasound. They found that as the ultrasonic power increased from 100 to 400 W, the biodiesel yield increased from 95 to 99% in 5 min. Chand et al.26 have also observed an increase in the biodiesel yield from 87 to 96% and then a decrease to 92% as the ultrasonic power was increased by varying the amplitude of the ultrasound from 60 to 120 μm and then to 180 μm, respectively. Ji et al.27 also studied the effect of ultrasonic power on biodiesel yield at three levels of 100, 150, and 200 W and found that optimum conversion of 100% was obtained at the intermediate power level of 150 W using 6:1 molar ratio of methanol/soybean oil in the presence of base catalyst at 45 °C. Mahamuni and Adewuyi6 also observed similar effects in their work on biodiesel formation using high frequency ultrasound. Adewuyi and co-workers28,29 explained in detail as to why reaction yields generally increase with increase in ultrasonic power and why there exists an optimum ultrasonic power for the best reaction yields.
ð2Þ
VF ¼ SF =fF
ð3Þ
FF ¼ VF =Ve
ð4Þ
SF 0 ¼ SF -fF Ve
ð5Þ
PF ¼ SF 100=ST
ð6Þ
where, for example, SF is the sum of squares; F1 = (F1 þ F2 þ F3) is the sum of responses of experiments involving F1; NF1 is the number of experiments containing F1; VF is the variance of the factor, and fF is the degrees of freedom of the factor; Ve is the variance of the error, and SF0 is the pure sum of squares for the factor; while PF is the percentage contribution of the factor; and ST = (sum of square of all trial run results - CF), where CF is a correction factor. The correction factor is defined by eq 7. square of the sum of all trial run results CF ¼ ¼ number of all trial runs
PN ¼9 2 i ¼1 yi ð7Þ N
(25) Singh, A.; Fernando, S.; Hernandez, R. Energy Fuels 2007, 21 (2), 1161–1164. (26) Priyanka Chand, C. V. R., Verkade, J. G. Grewell, D. In Enhancing Biodiesel Production from Soybean Oil using Ultrasonics; ASABE Annual International Meeting Rhode Island, American Society of Agricultural and Biological Engineers: RI, 2008. (27) Ji, J.; Wang, J.; Li, Y.; Yu, Y.; Xu, Z. Ultrasonics 2006, 44, 411– 414. (28) Adewuyi, Y. Ind. Eng. Chem. Res. 2001, 40 (22), 4681–4715. (29) Owusu, S.; Adewuyi, Y. Ind. Eng. Chem. Res. 2006, 45 (13), 4475– 4485.
3.2. Effect of Ultrasonic Power on Biodiesel Yield. The effects of ultrasonic power on biodiesel yield are illustrated in Table 4 and in Figure 1. It can be seen from Figure 1 that as the ultrasonic power increases from level 1 to levels 2 and 3, there was an increase in the response accordingly. Thus, biodiesel yield increases with increasing ultrasonic power 2124
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3.3. Effect of Ultrasonic Frequency on Biodiesel Yield. The frequency of the ultrasound has a significant effect on the cavitation process because it alters the critical size of the cavitation bubble, which in turn changes the intensity of the collapse of the cavitation bubbles. The detail explanation of the effect of ultrasonic frequency on reaction medium is given by Thompson and Doraiswamy.30 It can be seen from Figure 2 that as the frequency of ultrasound changes from level 1 to level 2 and then to level 3, there was a decrease in the response accordingly. Hence, as the ultrasonic frequency changes from 581 to 611 kHz and then to 1300 kHz, the response decreases from 58 to 50 and then to 49. However, as shown in Figure 5, the contribution of change of frequency toward the total variation observed in the response appeared to be insignificant. The percentage contribution of frequency was only 1.52% compared to greater than 41% for ultrasonic power. These results can probably be explained by considering the absolute critical bubble sizes at these frequencies. Brotchie et al.31 reported that as frequency changes from ∼515 to 647 kHz and then to ∼1100 kHz the resonance bubble size changes from ∼5.8 to 4.6 μm and then to ∼2.7 μm. This would imply that there would not be a significant change in the intensity of collapse of the cavitation bubbles due to change in frequency alone in this range. However, depending upon the level of the ultrasonic power, the intensity of collapse might change as increase in power would result in an increase in the bubble size at a given frequency.31 It is obvious from this discussion that there is an interaction between ultrasonic power and ultrasonic frequency that affects the cavitation bubble collapse, which in turn affects the mass transfer characteristics and hence biodiesel yield. The percentage contribution of this interaction needs to be further investigated. However, investigation of possible interactions is beyond the scope of the current study and is the subject of a future study. 3.4. Effect of Catalyst Loading. It is well-known that increase in the catalyst (KOH) loading in the transesterification reaction increases the biodiesel yield.6,13,15 It can be seen from Figure 3 that as the level of catalyst loading increases from level 1 to level 2 and then to level 3, the response increases linearly. This implies that as the amount of KOH increases from 0.25 to 0.50% and then to 0.75% (w/w), the biodiesel yield follows similar trend. This observation could easily be explained using eq 8. The detailed mechanisms for the base-catalyzed transesterification of triglyceride with methanol have been discussed in our previous study.6 As the amount of KOH increases, the concentration of methoxide anions, which are responsible for nucleophilic attack on the triglyceride molecules to produce biodiesel, also increase, resulting in higher biodiesel yield. KOH þ CH3 OH f CH3 OK þ H2 O T Kþ þ CH3 O - ð8Þ
32
Stavarache et al., and Armenta et al. have observed similar effects. 3.5. Effect of Oil/Methanol Molar Ratio. As oil and methanol are not miscible into each other, they form a heterogeneous reaction mixture and mass transfer between these two phases becomes important for the transesterification reaction. The presence of ultrasound can help increase the mass transfer between the two phases by the formation of a fine emulsion, which increases the interfacial area between the two phases. Ultrasound can also increase the mass transfer coefficient due to the presence of acoustic streaming and jet formations at the end of cavitation bubble collapse near the phase boundary between oil and methanol phases. However, the amount of increase in the mass transfer coefficient as well as interfacial area depends upon the amount of oil as well as methanol. Kalva et al.33 have explained in detail that as the oil/methanol molar ratio increases, the emulsion system changes from dispersion of methanol into oil toward dispersion of oil into methanol. This transformation results in an increase in the interfacial area up to a point above which the interfacial area starts to decrease as the cavitation in methanol phase is much easier than in oil phase due to viscosity differences. It can be seen from Figure 4 that as the level of oil/methanol molar ratio increases the response increases up to second level but decreases at the subsequent level. Thus, the biodiesel yield increased with increase in molar ratio of oil/methanol from 1:4 to 1:6 but decreased when it was further increased to 1:9. Colluci et al.34 observed that the biodiesel yield increased from 82.48 to 93.96% and then decreased slightly to 92.45% when the oil/methanol molar ratio increased from 1/3 to 1/6 and then to 1/9. Hanh et al.13 also observed an optimum at the 1/6 oil/methanol molar ratio. As shown in Figure 5, the percentage contribution of variation in oil/methanol molar ratio is about 12.92% of the total variation observed in biodiesel yield. 3.6. Optimum Conditions and Experimental Verification. Since we are investigating the effects of different parameters at varied levels to determine the best design parameters that produce maximum biodiesel yield, the “the bigger the better” quality characteristic was used to find the optimum parameters.19-21 As observed from Figures 1-4 the maximum response is obtained at first level of ultrasonic frequency, third level of ultrasonic power, third level of catalyst loading, and second level of oil/methanol molar ratio. Thus, F1-P3C3-M2 condition is most likely to produce the best result and therefore presents the optimum condition except for the possible effect of interactions between the factors. A confirmation experiment was performed to prove the effectiveness of the Taguchi method and the validity of the observed results. As can be observed from Figure 6 which shows the actual experimental data, the results from the experiment carried out at optimum conditions gives the most biodiesel formation under the range of parameter conditions investigated in this study with 92.51% biodiesel yield obtained in just 30 min at the optimal conditions. 3.7. Modified ANOVA Table with Pooling. As the effect of frequency was found to be negligible and was in the range of experimental error and the error variance was indeterminate
It can be seen from Figure 5 that catalyst loading has the highest percentage (44.09%) contribution in the total variation in biodiesel yield, which signifies its dominant role in the biodiesel formation. Several researchers such as Hanh et al.,13,15 (30) Thompson, L.; Doraiswamy, L. Ind. Eng. Chem. Res. 1999, 38 (4), 1215–1249. (31) Brotchie, A.; Grieser, F.; Ashokkumar, M. Phys. Rev. Lett. 2009, 102 (8), 84302. (32) Armenta, R.; Vinatoru, M.; Burja, A.; Kralovec, J.; Barrow, C. J. Am. Oil Chem. Soc. 2007, 84 (11), 1045–1052.
(33) Kalva, A.; Sivasankar, T.; Moholkar, V. Ind. Eng. Chem. Res. 2008, 48 (1), 534–544. (34) Colucci, J.; Borrero, E.; Alape, F. J. Am. Oil Chem. Soc. 2005, 82 (7), 525–530.
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: DOI:10.1021/ef901488g
Mahamuni and Adewuyi
(F-ratios cannot be calculated), the effect of frequency was pooled with error to produce improved percentage contribution of individual parameters. The detailed procedure for pooling and the criteria used to determine its use are discussed elsewhere.19 Table 5 shows the modified ANOVA table for biodiesel yield with pooling of frequency effect. It can be observed from the values of F-ratio of various parameters that the catalyst loading is the most dominant factor followed by ultrasonic power and oil/methanol molar ratio. The percentage contribution of individual factors has changed by small amount due to the pooling effect, and the statistical significance of individual parameters remains the same due to the very small change. As shown in Tables 4 and 5 the percentage contribution of ultrasonic power, catalyst loading and molar ratio changed from 41.47 to 39.95%, 44.09 to 42.56%, and 12.92 to 11.40%, respectively, due to the pooling effect. As the F-ratio should be equal to or greater than 9,19 it is evident from the values of F-ratio that catalyst loading has the most dominant influence on biodiesel formation followed by ultrasonic power and oil/ methanol molar ratio.
followed by ultrasonic power. (3) Ultrasonic frequency has a negligible effect on biodiesel formation in the range of the frequencies investigated in this study. (4) Percentage contributions of individual parameters on variation in biodiesel formation are: catalyst loading, 42.56%; ultrasonic power, 39.95%; oil/methanol molar ratio, 11.40%; and frequency, negligible. (5) Optimum conditions for biodiesel formation in the presence of high frequency ultrasound are: 581 kHz; 143 W; 0.75% KOH (w/w); and 1/6 soybean oil/methanol molar ratio, resulting in more than 92.5% biodiesel yield in less than 30 min. Nomenclature F1 = the first level of the parameter frequency, i.e., 581 kHz F1, F2, F3 = responses (biodiesel yield) of the three experiments which contain F1, i.e., responses of experiments 1, 2, and 3 in Table 3 P1 = the first level of the parameter power, i.e., 46 W P1, P2, P3 = responses (biodiesel yield) of the three experiments which contain P1, i.e., responses of experiments 1, 4, and 7 in Table 3 C1 = the first level of the parameter catalyst loading, i.e., 0.25% (w/w) C1, C2, C3 = responses (biodiesel yield) of the three experiments which contain C1, i.e., responses of experiments 1, 6, and 8 in table 3 M1 = the first level of the parameter molar ratio, i.e., 1:4 M1, M2, M3 = responses (biodiesel yield) of the three experiments which contain M1, i.e., responses of experiments 1, 5, and 9 in Table 3
4.0. Conclusions For batch transesterification of soybean oil with methanol and KOH in the presence of high frequency ultrasound, the influence of four operating variables (ultrasonic frequency, ultrasonic power, catalyst loading, and oil/methanol molar ratio) on the biodiesel yield was statistically analyzed using Taguchi experimental design methodology. The optimum conditions were arrived at based on the quality characteristic of the bigger-the-better. The main conclusions of the study based only on the range of variable levels and systems investigated are as follows: (1) Very few experiments are required using Taguchi method to get insight into the batch transesterification process under the influence of ultrasound. (2) Catalyst loading was found to be the most important parameter that affects the biodiesel formation process
Acknowledgment. The authors are grateful to the College of Engineering at North Carolina Agricultural and Technical State University for partial support of this project. Partial support made possible through the National Science Foundation (NSF) under Award CBET-0651811 is also acknowledged.
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