239
Views
20
CrossRef citations to date
0
Altmetric
Original Articles

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

, , , &

References

  • Al-Mulali, U. 2014. The impact of biofuel energy consumption on GDP growth, co2 emission, agricultural crop prices, and agricultural production. International Journal of Green Energy 12(11):1100–06.
  • Baños, R., F. Manzano-Agugliaro, F.G. Montoya, C. Gil, A. Alcayde, and J. Gómez. 2011. Optimization methods applied to renewable and sustainable energy: A review. Renewable and Sustainable Energy Reviews 15(4):1753–66.
  • Cheng, M.-Y., and K.-Y. Huang. 2009. K-means clustering and chaos genetic algorithm for nonlinear optimization. Paper read at The 26th International Symposium on Automation and Robotics in Construction (ISARC 2009), Austin, TX.
  • Cheng, M.-Y., and K.-Y. Huang. 2010. Genetic algorithm-based chaos clustering approach for nonlinear optimization. Journal of Marine Science and Technology 18 (3):435–41.
  • Cheng, M.-Y., and Y.-W. Wu.2009. Evolutionary support vector machine inference system for construction management. Automation in Construction 18 (5):597–604.
  • Ching-Piao, T., H. Ching-Her, H. Chien, and C. Hao-Yuan. 2012. Study on the wave climate variation to the renewable wave energy assessment. Renewable Energy 38(1):50–61.
  • Chuah, T.G., A.G.K. Wan Azlina, Y. Robiah, and R. Omar. 2006. Biomass as the renewable energy sources in Malaysia: An overview. International Journal of Green Energy 3 (3):323–46.
  • Demirbas, A. 2007. Recent developments in biodiesel fuels. International Journal of Green Energy 4(1):15–26.
  • Dorado, M.P., E. Ballesteros, F.J. López, and M. Mittelbach. 2004. Optimization of alkali-catalyzed transesterification of Brassica Carinata oil for biodiesel production. Energy and Fuels 18(1):77–83.
  • Freedman, B., E.H. Pryde, and T.L. Mounts. 1984. Variables affecting the yields of fatty esters from transesterified vegetable oils. Journal of the American Oil Chemists’ Society 61(10):1638–43.
  • Gaurav, K., R. Srivastava, and R. Singh. 2012. Exploring biodiesel: Chemistry, biochemistry, and microalgal source. International Journal of Green Energy 10(8):775–96.
  • Goldberg, D. E., K. Deb, H. Kargupta, and G. Harik. 1993. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. Paper read at Proceedings of the Fifth International Conference on Genetic Algorithms, Champaign, Illinois.
  • Hoekman, S.K., A. Broch, C. Robbins, E. Ceniceros, and M. Natarajan. 2011. Review of biodiesel composition, properties, and specifications. Renewable and Sustainable Energy Reviews 16(1):143–69.
  • Hsu, C.W., C.C. Chang, and C.J. Lin. 2003. A practical guide to support vector classification. Available at www.csie.ntu.edu.tw/~cjlin/papers/guide/guide. pdf
  • Huang, C.-L., and C.-J. Wang.2006. A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31(2):231–40. doi:10.1016/j.eswa.2005.09.024.
  • Kalogirou, S.A. 2001. Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews 5(4):373–401.
  • Kohavi, R. 1995. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Paper read at Proceedings of the 14th international joint conference on Artificial intelligence, Montreal, Canada.
  • Noam, L. 2010. Energy resources and use: The present (2008) situation and possible sustainable paths to the future. Energy 35(6):2631–38.
  • Ozgul-Yucel, S., and S. Turkay. 2002. Variables affecting the yields of methyl esters derived from in situ esterification of rice bran oil. Journal of the American Oil Chemists’ Society 79(6):611–14.
  • Puig-Arnavat, M., J.C. Bruno, and A. Coronas. 2010. Review and analysis of biomass gasification models. Renewable and Sustainable Energy Reviews 14(9):2841–51.
  • Rajendra, M., P.C. Jena, and H. Raheman. 2009. Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel 88(5):868–75.
  • Ramadhas, A.S., S. Jayaraj, C. Muraleedharan, and K. Padmakumari. 2006. Artificial neural networks used for the prediction of the cetane number of biodiesel. Renewable Energy 31(15):2524–33.
  • Shiu, P.-J., S. Gunawan, W.-H. Hsieh, N.S. Kasim, and Y.-H. Ju. 2010. Biodiesel production from rice bran by a two-step in-situ process. Bioresource Technology 101 (3):984–89.
  • Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. New York: Springer.
  • Wakil, M.A., A. Kalam, H.H. Masjuki, and I.M. Rizwanul Fattah. 2016. Rice bran: A prospective resource for biodiesel production in Bangladesh. International Journal of Green Energy 13(5):497–504.
  • Yuste, A.J., and M. Pilar Dorado. 2005. A neural network approach to simulate biodiesel production from waste olive oil. Energy and Fuels 20(1):399–402.
  • Yustianingsih, L., S. Zullaikah, and Y.H. Ju. 2009. Ultrasound assisted in situ production of biodiesel from rice bran. Journal of the Energy Institute 82(3):133–37.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.