References
- Abbaszaadeh, A., B. Ghobadin, M. R. Omidkhah, and N. Gholamhassan. 2012. Current biodiesel production technologies: A comparative review. Energy Conversion and Management 63:138–48. doi:10.1016/j.enconman.2012.02.027.
- Asri, M., R. N. Rahman, A. Ebrahimpour, A. B. Salleh, E. R. Gunawan, and M. B. Rahman. 2007. Comparison of estimation capabilities of response surface methodology (RSM) with Artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester. BMC Biotechnology 7:53. doi:10.1186/1472-6750-7-53.
- Baharak, S., D. Meysam, A. R. Abdul Aziz, and I. Shaliza. 2016. Analysis and optimization of ultrasound-assisted alkaline palm oil transesterification by RSM and ANN-GA. Chemical Engineering Communications 204:365-381. doi:10.1080/00986445.2015.1135427.
- Betiku, E., and S. O. Ajala. 2014. Modelling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: A case of artificial neural network vs. response surface methodology. Industrial Crops and Products 53:314–22. doi:10.1016/j.indcrop.2013.12.046.
- Bhowmik, S., R. S. Panua, D. Debroy, and A. Paul. 2017. Artificial neural network prediction of diesel engine performance and emission fueled with diesel-kerosene-ethanol blends: A fuzzy based optimization. Journal of Energy Resources Technology 139:4. doi:10.1115/1.4035886.
- Chakraborty, R., and H. Sahu. 2014. Intensification of biodiesel production from waste goat tallow using infrared radiation: Process evaluation through response surface methodology and artificial neural network. Applied Energy 114:827–36. doi:10.1016/j.apenergy.2013.04.025.
- Chen, Y. H., T. H. Chiang, and J. H. Chen. 2012. An optimum biodiesel combination: Jatropha and soapnut oil biodiesel blends. Fuel 92:377–80. doi:10.1016/j.fuel.2011.08.018.
- Das, S., A. Bhattacharya, S. Haldar, A. Ganguly, G. Sai, Y. P. Ting, and P. K. Chatterjee. 2015. Optimization of enzymatic saccharification of water hyacinth biomass for bio-ethanol: Comparison between artificial neural network and response surface methodology. Sustainable Materials and Technologies 3:17–28.
- Haykin, S. 1998. Neural networks. Englewood Cliffs, New Jersey, USA: Prentice Hall.
- Himmelblau, D. 2000. Applications of artificial neural networks in chemical engineering. Korean Journal of Chemical Engineering 17:373–92. doi:10.1007/BF02706848.
- Kara-Togun, N., and S. Baysec. 2010. Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks. Applied Energy 87:349–55. doi:10.1016/j.apenergy.2009.08.016.
- Kisi, O. 2010. River suspended sediment concentration modeling using a neural differential evolution approach. Journal of Hydrology 389:227–35. doi:10.1016/j.jhydrol.2010.06.003.
- Kline, S. J., and F. A. McClintock. 1953. Describing uncertainties in single-sample experiments. Mechanical Engineering 78:3–8.
- Kumar, S., S. Jain, and H. Kumar. 2017. Process parameter assessment of biodiesel production from a Jatropha–Algae oil blend by response surface methodology and artificial neural network. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 22:2119–215. doi:10.1080/15567036.2017.1403514.
- Lahiri, S. K., and K. C. Ghanta. 2009. Artificial neural network model with the parameter tuning assisted by a differential evolution technique: The study of the hold up of the slurry flow in a pipeline. Chemical Industry & Chemical Engineering Quarterly 15:103–17. UDC 621.643.2:517.9:621.6.07. doi:10.2298/CICEQ0902103L.
- Lane, D. 2011. Online statistics education. In International encyclopedia of statistical science, ed. M. Lovric. New York, NY: Springer.
- Lawrynczuk, M. 2008. Modelling and nonlinear predictive control of a yeast fermentation biochemical reactor using neural networks. Chemical Engineering Journal 145:290–307. doi:10.1016/j.cej.2008.08.005.
- Lee, H. V., R. Yunus, J. C. Juan, and Y. H. TaufiqYap. 2011. Process optimization design for jatropha-based biodiesel production using response surface methodology. Fuel Process Technology 92:2420–28. doi:10.1016/j.fuproc.2011.08.018.
- Montgomery, D. C. 2008. Design and analysis of experiments. New York, NY, USA: John Wiley & Sons.
- Mustafa, B. 2011. Potential alternatives to edible oils for biodiesel production. A review of current work. Energy Conversion and Management 52:1479. doi:10.1016/j.enconman.2010.10.011.
- Nagy, Z. K. 2007. Model based control of a yeast fermentation bioreactor using optimally designed artificial neural networks. Chemical Engineering Journal 127:95–109. doi:10.1016/j.cej.2006.10.015.
- Patil, P. D., and S. Deng. 2009. Optimization of biodiesel production from edible and non-edible vegetable oils. Fuel 88:1302–06. doi:10.1016/j.fuel.2009.01.016.
- Rasimoğlu, N., and H. Temur. 2014. Cold flow properties of biodiesel obtained from corn oil. Energy 68:57–60. doi:10.1016/j.energy.2014.02.048.
- Rezaei, J., M. Shahbakhti, B. Bahri, and A. A. Aziz. 2015. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied Energy 138:460–73. doi:10.1016/j.apenergy.2014.10.088.
- Sebayang, A., H. H. Masjuki, H. C. Ong, S. Dharma, A. S. Silitonga, F. Kusumo, and J. Milano. 2017. Optimization of bioethanol production from sorghum grains using artificial neural networks integrated with ant colony. Industrial Crops and Products 97:146–55. doi:10.1016/j.indcrop.2016.11.064.
- Wadumesthrige, K., J. C. Smith, J. R. Wilson, S. O. Salley, and K. Y. Simon. 2008. Investigation of the parameters affecting the cetane number of biodiesel. Journal of the American Oil Chemists’ Society 85:1073–81. doi:10.1007/s11746-008-1290-2.
- Xin, Y. 1999. Evolving artificial neural networks. Proceedings of the IEEE.87: 1423–47.
- Yuan, W., A. C. Hansen, and Q. Zhang. 2005. Vapor pressure and normal boiling point predictions for pure methyl esters and biodiesel fuels. Fuel 84:943–50. doi:10.1016/j.fuel.2005.01.007.
- Zeynab, A., O. HwaiChyuan, D. M. Harrison., F. Kusumo., M. Hoora, and I. Zul. 2017. Biodiesel production by lipase-catalyzed transesterification of Ocimum basilicum L. (Sweet Basil) Seed Oil. Energy Conversion and Management 132:82–90. doi:10.1016/j.enconman.2016.11.017.