Application of Response Surface Methodology in the Statistical Analysis of Biodiesel Production from Microalgae Oil
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1 Application of Response Surface Methodology in the Statistical Analysis of Biodiesel Production from Microalgae Oil Ikechukwu Fabian Ejim Chemical Engineering Department, Institute of Management and Technology, Enugu, Nigeria ABSTRACT: Incessant use of fuel from petroleum is found to unmanageable because of its contribution to the depletion of ozone layer thereby causing excessive accumulation of greenhouse gases in the environment. Biodiesel has become the surest alternative due to its environmental and economic advantages over crude diesel. In this study, response surface methodology was used to optimize the production methyl ester. The effects of five reaction variables: methanol/oil molar ratio (1), catalyst concentration (), temperature (3), time ( 4 ), and mixing rate ( 5 ) on transesterification of crude microalgae oil was investigated. A Central Composite Design (CCD) consisting five factors at five levels was used to analyze the transesterification of microalgae oil. Transesterification process was optimized as response to increase the yield. Thirty-four experimental runs resulted from the analysis. The biodiesel yield was characterized to determine its quality. A second order quadratic polynomial model was deduced to predict the methyl ester yield and the ANOVA test showed the developed model to be significant (P < 0.05). The R adjusted values of indicated that the regression model was a good one. RSM was also successfully applied to assess the effects of multiple variables, including the alcohol/oil molar ratio, catalyst concentration, temperature, rate of mixing, and reaction time, for the production of biodiesel from the crude algae oil. A statistical model predicted that optimal conditions were as follows: methanol/oil molar ratio 6.1; temperature, 55 C; time, 45min, catalyst concentration, 1.0%; and rate of mixing, 300rpm. These optimized conditions were validated and actual biodiesel yield of 94.36% confirming the efficacy of the model.the physicochemical analysis of the biodiesel oil from microalgae indicated it is reliable and usable for industrial uses having compared it with commercial diesel. KEYWORDS: response surface methodology; statistical analysis; transesterification; microalgae 1. INTRODUCTION Biofuel production has recently become a topic of intense interest due to increased concern regarding limited petroleum-based fuel supplies and the contribution of the use of these fuels to atmospheric CO levels (Asha et al; 011). Biofuel research is not just a matter of finding the right type of biomass and converting it to fuel, but it must also find environmentally and economically sound uses for the by-products of biofuel production Corresponding author, Ph.D. Student, iyke.ejim@yahoo.com 57
2 (Dragone et al., 010). Photosynthetic microorganisms like cyanobacteria and microalgae can potentially be employed for the production of biofuel in an economically effective and environmentally sustainable manner and at rates high enough to replace a substantial fraction of our society s use of fossil fuels (Li et al., 008). Microalgae commonly double their biomass within 4 hours (hr), and this duration during the exponential growth phase can be as short as 3.5 h (Harrison et al., 01; Chisti 007). The triglyceride composition of algae upon transesterification in which 3 mol of alcohol for each mole of triglyceride react to produce 1 mol of glycerol and 3 mol of methyl esters. The use of alkali-catalysts has been found to be useful for pre-treating high free fatty acid feedstocks but the reaction rates for converting triglycerides to methyl esters are very slow. The objectives of this work were to evaluate the effects of the reaction parameters such as temperature, reaction time, stirring rate, catalyst concentration and methanol/oil molar ratio of methanol to oil on the biodiesel yield and to optimize the reaction conditions using Response Surface Methodology (RSM). Response surface methodology is a collection of statistical and mathematical techniques useful to develop, improve and optimize process and product (Myers et al; 00) largely applied in industry, it addresses this issue by providing: (1) an understanding of how the test variables affect the selected process response; () determination of the interrelationships among the test variables; (3) and characterization of the combined effect that all influential test variables may have on the process response. This advantage has made RSM increasingly involved in biodiesel production.. METHODOLOGY.1 Designing of Experiments A 3-level-5-factor experiment was conducted using a central composite design (CCD) to examine the effects of methanol/oil molar ratio, catalyst concentration (%), temperature ( C), time (min), and rate of mixing (rpm), on yield of methyl ester. The CCD consisted of 34 experimental runs (1 axial point, and 8 replicated center points) and provided sufficient information to fit a full second-order polynomial model. The Design Expert 9.0 software was used to analyze the transesterification data for developing response equations, for analysis of variance (ANOVA), generate surface plots and determine optimum conditions using its optimization toolbox. 58
3 . Experiments and Methods..1 Acid Pre-treatment and Biodiesel Production Results from previous research (Ejim and Kamen, 013; Kamen et al, 015 and Anya et al, 01) were used for the extraction, characterization and esterification process of microalgae oil in this experiment. The transesterification process was carried out by using the centre point in table 3.1 which presents itself as the first run of the experiment (ml) of methanol was mixed with 1.0 (g) of sodium hydroxide solution (NaOH) in a beaker and stirred for 1 (min). The mixture was poured into a distillation flask containing pre-treated algae oil and heated to 55 o C with a thermostatic magnetic stirrer and maintaining the stirring rate at 300 (rpm) for 45 (min). The reaction mixture was poured into a separating funnel with a tap and this was allowed to stand overnight while phase separation occurred by gravity settling. The FAME was carefully decanted from the equilibrium mixture into the PET bottle leaving impurities and glycerol. After separating the biodiesel, hot distilled water of 50ml (10% by volume) was sprayed over the surface of biodiesel and stirred gently. Lower layer was discarded and yellow colour layer biodiesel was separated. Biodiesel was dried by evaporating the water with an electric oven and finally kept under the running fan for several minutes (>30 min) to reduce the temperature. Table 1 3 Level 5 Factor Experimental Design Plan Level Methanol/Oil Molar Ratio 1 1/4 1/6 1/8 Catalyst Oil (wt%) Temperature ( o C) Time (Hr) Stirring Speed (Rpm) Statistical Model Approach The function was approximated by a second degree polynomial model; 59
4 n n i i ii i Y (1) o i 1 i 1 Where Y is % methyl ester yield, i and j are the independent study factors, and 0, i, ii, and Bij are intercept, linear, quadratic, and interaction constant coefficients, respectively. An alpha ( ) level of 0.05 was used to examine the statistical significance of the fitted polynomial model. ij i j 3. RESULT AND DISCUSSION 3.1 Statistical Analysis of Data and Response Equation for Transesterification Reaction The effect of catalyst concentration, temperature, time, methanol/oil molar ratio and stirring speed on the oil yield (Y) is as shown on table 3. and this was subsequently used to fit the response equations for oil yield. Table 3.3 shows that the coefficient of determination (R ) for the responses yield was The coefficients of determination were high for response surfaces, and indicated that the fitted quadratic models accounted for more than 90% of the variance in the experimental data. Base on p-values, the regression coefficients that was significant at p < were selected for the models. Analysis Of Variance (ANOVA) was also conducted to evaluate the adequacy and consistency of the models using f-statistic. The Model F-value of 17.4 implies the model is significant. The "Lack of Fit F-value" of implies the Lack of fit is significant. There is only a 0.01% chance that a "Lack of Fit F-value" this large could occur due to disturbance. Table Table 3.CCD Experimental Matrix for the Fractional Factorial Design Run Yield Predicted Residual (%) (%) Value (%)
5 Table 3 ANOVA for Response Surface Quadratic Model Source Sum of Squares df Mean Square F Value p-value Prob > F Model < ** Residual Lack of Fit < **t 61
6 Pure Error Cor Total Adj R- Squared **=significant Based on p-values, table 3.4 shows the regression coefficients that were significant at p<0.05 as were used to form equation (); Y R 1 = () Where; 1,, 3, 4, and 5 are Methanol/Oil molar ratio, Catalyst Concentration, Temperature, Time, and Stirring Speed respectively. From table 3.4, it was clear that Methanol/Oil molar ratio (1) had quite high linear negative effect on yield than catalyst concentration, temperature, time and speed. The interaction of methanol/oil molar ratio and catalyst concentration had more positive effect on the yield than methanol/oil molar ratio and time, methanol/oil molar ratio and speed, temperature and time, and time and speed while other interactions had negative effects on yield, with catalyst concentration and temperature having the highest negative effects on yield. Time had the highest negative quadratic effects on yield followed by temperature, methanol/oil molar ratio, speed and lastly catalyst concentration respectively. Table 4 Regression Coefficients of Fitted Quadratic Equation Terms Regression Coefficient P-value Linear β β β β β Quadratic β β β β β Interaction 6
7 β β β β β β β β β β Response Surface Plot for Transesterification The response surface plots for the chosen model equations show the relationship between the independent and the dependent variables. The interaction terms between methanol/oil molar ratio and catalyst concentration, catalyst concentration and temperature, temperature and time, and time and stirring speed all had significant effects on the biodiesel yield at (p<0.05). Figure 3.1 shows a contour surface plot of percentage biodiesel yield, methanol/oil molar ratio and catalyst concentration (%wt of oil). This shows a clear increase and decrease in yield as methanol/oil molar ratio increases with increase in catalyst concentration. Figure 3.4 contour plot does not have any significant effect on percentage yield except for figure 3.5 contour plot which has respective increase and decrease in percentage yield as the reaction time increases. Figure 3.3 shows a 3D surface plot for temperature and reaction time indicating an increase in percentage yield as temperature increases. Figure 3. and 3.6 clearly demonstrate that there is no significant effect on yield with temperature and catalyst concentration interaction plot but a respective increase and decrease in percentage yield as reaction time increases in stirring speed and reaction time interaction plot. 63
8 International Journal Series in Engineering Science (IJSES), Vol. 1, No. 1, 015, B: Catalyst Concentration (%wt of oil) Prediction C: Temturepera (oc) 0.7B: Catalyst Concentration (%wt of oil) A: Methanol/Oil Molar Ratio (-) Figure 3.1: Contour surface plot for methanol/oil molar ratio and catalyst concentration Figure 3.: 3D surface plot for catalyst concentration and temperature D: Time (mins) Prediction D: Time (mins) C: Temturepera (oc) C: Temturepera (oc) Figure 3.3: 3D surface plot for temperaturefigure 3.4: Contour surface plot for temperature and and reaction time reaction time 64
9 E: Speed (Rpm) Prediction D: Time (mins) E: Speed (Rpm) D: Time (mins) Figure 3.5: Contour surface plot for Figure 3.6: 3D surface plot for reaction time and temperature and stirring speed stirring speed 4. CONCLUSIONS O Biodiesel produced by transesterification of pre-treated microalgae oil with alcohol, is the newest form of energy that has attracted the attention of many researchers due to various advantages associated with its usages. Response surface methodology, based on a 3-level-5-factor of central composite design was used to analyze the interaction effect of the transesterification reaction variables such as methanol/oil molar ratio, catalyst concentration, temperature, reaction time and stirring speed on biodiesel yield. It was observed that methanol/oil molar ratio, catalyst concentration, temperature and reaction time are the four main variables that have significant effect on transesterification reaction of microalgae. In conclusion, the maximum percentage yield of 98.77% of biodiesel was observed at methanol/oil molar ratio of 6.1/1, catalyst concentration of 1.0, temperature of 55 o C, reaction time of 45 (mins) and stirring speed of 300 (rpm). The condition was validated and actual yield confirmed. ACKNOWLEDGMENTS The author is grateful to Institute of Management and Technology, Enugu, Nigeria for the help rendered and to design Expert 9.0 trial Software for permission to use the trial version. The authors want also to thank Director, Pymotech, Enugu, for his encouragement. 65
10 REFERENCES Adewoye, T. L., and Ogunleye, O. O. (01), Optimization of neem seed oil extraction process using response surface methodology, Journal of Natural Sciences Research, (6), AL-Harbawy, A. W., and AL-Mallah, M. K. (014), Production and characterization of biodiesel from seed oil of castor (ricinus communis L.) plants. International Journal of Science and Technology, 3(9) Anya, A. U., Chioma, N. N., and Obinna, O. (01), Optimized reduction of free fatty acid content on neem seed oil, for biodiesel production. Journal of Basic and Applied Chemistry, (4), 1-8. Chisti, Y. (007), Biodiesel from microalgae. Biotechnology Advances, 5(3), Dale, M. R., Dixon, P., Fortin, M., Legendre, P., Myers, D. E., and Rosenberg, M. S. (00), Conceptual and mathematical relationships among methods for spatial analysis. Ecography, 5(5), Demirbaş, A. (003), Biodiesel fuels from vegetable oils via catalytic and non-catalytic supercritical alcohol transesterifications and other methods, Energy Conversion and Management, 44(13), Dragone, G., Fernandes, B. D., Vicente, A. A., and Teixeira, J. A. (010), Third generation biofuels from microalgae, Formatex, Portugal. Duan, P., and Savage, P. E. (011), Upgrading of crude algal bio-oil in supercritical water, Bioresource Technology, 10(), Ejim, I. F., and Kamen, F. L. (013), Physicochemical characterization of microalgae oil from Nike lake Enugu, Journal of Engineering and Applied Sciences, Vol.5, No.1, Pp Fan,. (008), Optimization of biodiesel production from crude cottonseed oil and waste vegetable oil: Conventional and ultrasonic irradiation methods, Ph.D. Dissertation, Clemson University, South Carolina. Fukuda, H., Kondo, A., and Noda, H. (001), Biodiesel fuel production by transesterification of oils, Journal of Bioscience and Bioengineering, 9(5), Griffiths, M. J., van Hille, R. P., and Harrison, S. T. (01), Lipid productivity, settling potential and fatty acid profile of 11 microalgal species grown under nitrogen replete and limited conditions Journal of Applied Phycology, 4(5), Kamen, F. L., Ejim, I. F., Nwakaudu, M. S., and Onyelucheya, O. E. (015), Statistical modeling and optimization of algae oil extraction using response surface methodology, American International Journal of Contemporary Scientific Research. Vol..Issue.3. Pp Li, Y.., Yang, J. W., Hui, F. L., Fan, W. W., and Yang, Y. (013), Optimization of biodiesel production from rice bran oil via immobilized lipase catalysis, African Journal of Biotechnology, 10(7), Mansourpoor, M., and Shariati, A. (01). Optimization of biodiesel production from sunflower oil using response surface methodology,j Chem Eng Process Technol, 3, 141. Parmar, A., Singh, N. K., Pandey, A., Gnansounou, E., and Madamwar, D. (011), Cyanobacteria and microalgae: A positive prospect for biofuels, Bioresource Technology, 10(),
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