INTERNATIONAL JOURNAL OF R&D IN ENGINEERING, SCIENCE AND MANAGEMENT Vol.3, Issue 7, April 2016, p.p.297-301, ISSN 2393-865X Analysis of Mahua Biodiesel Production with Combined Effects of Input Trans-Esterification Process Parameters Sunil Dhingra Assistant professor, Mechanical Engineering Department, UIET, Kurukshetra University, Kurukshetra, Haryana, India-136119 ABSTRACT The present work used madhuca indica oil for the enhancement of biodiesel yield by appling respose surface methodology based on central composite rotatable design. Further, interaction effects of input process parameters on mahua biodiesel yield are also discussed in detail. It is observed that mahua biodiesel yield is different at different combination of input parameters. Keywords: Madhuca indica oil, Interaction effects of input process parameters, response surface methodology 1. INTRODUCTION The non-edible oils are readily used as fuel by trans-esterification in the presence of catalyst (homogeneous/heterogeneous). Various researchers [Silitonga et al., 2013; Dhingra et al., 2013a; Dhingra et al., 2013b; Dhingra et al., 2014a; Dhingra et al., 2014b; Dhingra et al., 2014c; Dhingra et al., 2014d; Dhingra et al., 2016] have worked in the field of alternate fuels in the production of biodiesels. The current research deals with enhancement of mahua biodiesel through response surface methodology. Also the analysis of interaction effects of input process parameters on mahua biodiesel is also studied. 2. APPLICATION OF RESPONSE SURFACE METHODOLOGY IN PREDICTING BEHAVIOR OF MAHUA BIODIESEL WITH INPUT PARAMETERS Firstly the input process parameters are selected from various research studies [Chen et al., 2008; Abdullah et al., 2009; Jeong and Park, 2009] along with their ranges. Table 1 shows the design matrix of input process parameters for mahua biodiesel production. The value of each biodiesel yield is measured at various combinations of input parameters. Available at :www.rndpublications.com/journal Page 297 R&D Publications
Table 1: Design matrix for mahua biodiesel yield based on response surface methodology S. No. EC Rt RT CC MS MBY 1. 22.5 50 44 2 240 30 2. 22.5 30 56 2 240 35 3. 17.5 50 56 1 465 40 4. 22.5 50 56 1 240 45 5. 22.5 50 44 1 465 35 6. 22.5 30 44 2 465 40 7. 17.5 30 56 2 465 50 8. 17.5 50 44 2 465 40 9. 22.5 30 56 1 465 35 10. 17.5 50 56 2 240 45 11. 17.5 30 44 1 240 40 12. 15 40 50 1.5 350 30 13. 25 40 50 1.5 350 45 14. 20 20 50 1.5 350 50 15. 20 60 50 1.5 350 40 16. 20 40 40 1.5 350 35 17. 20 40 60 1.5 350 40 18. 20 40 50 0.5 350 45 19. 20 40 50 2.5 350 35 20. 20 40 50 1.5 150 50 21. 20 40 50 1.5 550 40 22. 20 40 50 1.5 350 44 Legend: EC: Ethanol concentration (% by weight of oil), Rt: Reaction time in minutes, RT: Reaction temperature in C, CC: Catalyst concentration on % by weight of oil, MS: Mixing speed n rpm, MBY- Mahua biodiesel yield (% by weight) The interaction effects of input process parameters on mahua biodiesel yield are shown in figures 1 and 2. These response surface graphs are obtained from RSM approach in design expert 6.0.8.Figure 1 for mahua biodiesel yield indicates that at lower values of ethanol concentration biodiesel yield increases with increase in reaction time while reverse trend is observed at higher values of ethanol concentration. Also at lower values of reaction time mahua biodiesel yield increases with increase in ethanol concentration while at higher values biodiesel yield remains almost constant with increase in ethanol concentration. The maximum biodiesel yield of mahua oil is observed at the highest value of ethanol concentration and the lowest value of reaction time considered. Page 298
Figure 1: Interaction effects of ethanol concentration and reaction time on mahua biodiesel yield Figure 2 for mahua biodiesel yield indicates that at lower values of reaction temperature biodiesel yield decreases with increase in catalyst concentration while at higher values yield remains almost constant with increase in catalyst concentration. At lower values of catalyst concentration biodiesel yield remains almost same with increase in reaction temperature. However the yield increases with increase in reaction temperature at higher values of catalyst concentration. Maximum biodiesel yield is observed at the highest values of reaction temperature and catalyst concentration. Page 299
Figure 2: Interaction effects of reaction temperature and catalyst concentration on mahua biodiesel yield 3. CONCLUSION I. Design matrix obtained from response surface methodology helps in analyzing the mahua biodiesel yield at various combinations of input process parameters. II. The maximum and minimum values of mahua biodiesel yields are predicting from the response surface graphs (figures 1 and 2) REFERENCES [1] Chen, X., Du, W., & Liu, D. (2008). Response surface optimization of biocatalytic biodiesel production with acid oil. Biochemical Engineering Journal, 40(3), 423-429. [2] Dhingra, S., Bhushan G., & Dubey, K. K. (2013a). Development of a combined approach for improvement and optimization of karanja biodiesel using response surface methodology and genetic algorithm. Frontiers in Energy, 7(5), 495 505 Page 300
[3] Dhingra, S., Bhushan G., & Dubey, K. K. (2013b). Performance and emission parameters optimization of mahua (madhuca indica) based biodiesel in direct injection diesel engine using response surface methodology. Journal of Renewable and Sustainable Energy, 5, 063117, DOI: 10.1063/1.4840155. [4] Dhingra, S., Bhushan G., & Dubey, K. K. (2014a). Understanding the interactions and evaluation of process factors for biodiesel production from waste cooking cottonseed oil by design of experiments through statistical approach. Frontiers in Energy (in press). [5] Dhingra, S., Bhushan G., & Dubey, K. K. (2014b). Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-ii. Frontiers of Mechanical Engineering,9(1), 81-94 [6] Dhingra, S., Dubey, K. K., & Bhushan, G. (2014c). A Polymath Approach for the Prediction of Optimized Transesterification Process Variables of Polanga Biodiesel. Journal of the American oil Chemist s Society, 91(4), 641-653 [7] Dhingra, S., Dubey, K. K., & Bhushan, G. (2014d). Enhancement in Jatropha-based biodiesel yield by process optimization using design of experiment approach. International Journal of Sustainable Energy, 33 (4), 842-853. [8] Dhingra, S., Bhushan G., & Dubey, K. K. (2016). Validation and enhancement of waste cooking sunflower oil based biodiesel production by the trans-esterification process. Energy Sources, part A, 38(10), 1448-1454. [9] Jeong, G.-T., & Park, D.-H. (2009). Optimization of Biodiesel Production from Castor Oil Using Response Surface Methodology. Applied Biochemistry and Biotechnology, 156(1-3), 1-11. [10] Silitonga, A. S., Masjuki, H. H., Mahlia, T. M. I., Ong, H. C., & Chong, W. T. (2013). Experimental study on performance and exhaust emissions of a diesel engine fuelled with Ceiba pentandra biodiesel blends. Energy Conversion and Management, 76, 828-836. Page 301