Comparison of linear regression and artificial neural network technique for prediction of a soybean biodiesel yield

References

• Achanta, A. S., J. G. Kowalski, and C. T. Rhodes. 1995. Artificial neural networks: Implications for pharmaceutical sciences. Drug Development and Industrial Pharmacy 21:119–55. doi:10.3109/03639049509048099.
   Web of Science ®Google Scholar
• Azam, M. M., A. Waris, and N. M. Nahar. 2005. Prospects and potential of fatty acid methyl esters of some non-traditional seed oils for use as biodiesel in India. Biomass & Bioenergy 29:293–302. doi:10.1016/j.biombioe.2005.05.001.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Bourquin, J., H. Schmidli, P. Hoogevest, and H. Leuenberger. 1998. Advantages of artificial neural networks (ANNs) as alternative modeling technique for data sets showing nonlinear relationships using data from a galenical study on a solid dosage for. European Journal of Pharmaceutical Sciences 7:5–16. doi:10.1016/S0928-0987(97)10028-8.
   PubMed Web of Science ®Google Scholar
• Boz, N., and O. Sunal. 2009. Biodiesel production from canola oil over zinc oxide supported catalysts. Journal of the Faculty of Engineering and Architecture of Gazi University 24:389–95.
   Web of Science ®Google Scholar
• Ceyla, Ö., and T. Erdi. 2017. Prediction of density and kinematic viscosity of biodiesel by artificial neural networks. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2017.1280563.
   Google Scholar
• Erdi, T., A. Kadir, and B. Mehmet. 2016. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal. doi:10.1016/j.aej.2016.08.011.
   Web of Science ®Google Scholar
• Jain, S., and S. Kumar. 2018. A REVIEW ON BIODIESEL PRODUCTION TECHNIQUE. The Engineering Journal of Application & Scopes 3:1–4.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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 39:2119–25.
   Web of Science ®Google Scholar
• Kumar., S., S. Jain., and H. Kumar. 2019. Prediction of jatropha-algae biodiesel blend oil yield with the application of artificial neural networks technique. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2018.1548507.
   Google Scholar
• Lin, Y., D. Ying, S. Chaitep, and S. Vittayapadung. 2009. Biodiesel production from crude rice bran oil and properties as fuel. Applied Energy 86:681–88. doi:10.1016/j.apenergy.2008.06.002.
   Web of Science ®Google Scholar
• Ramón, P. R., S. B. Yisel, L. Magin, G. P. Leonardo, and V. Sebastian. 2013. Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. Energy Conversion and Management 65:255–61. doi:10.1016/j.enconman.2012.07.023.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Roy, S., R. Banerjee, and P. K. Bose. 2014. Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network. Applied Energy 119:330–40. doi:10.1016/j.apenergy.2014.01.044.
   Web of Science ®Google Scholar
• Sajjadi, B., A. A. A. Raman, and H. Arandiyan. 2016. A comprehensive review on properties of edible and non-edible vegetable oil-based biodiesel: Composition, specifications and prediction models. Renewable and Sustainable Energy Reviews 63:62–92. doi:10.1016/j.rser.2016.05.035.
   Web of Science ®Google Scholar
• Stanmore, B. R., J. F. Brilhac, and P. Gilot. 2001. The oxidation of soot: A review of experiments, mechanisms and models. Carbon 39:2247–68. doi:10.1016/S0008-6223(01)00109-9.
   Web of Science ®Google Scholar
• Sunil, K., J. Siddharth, and K. Harmesh. 2018. Performance evaluation of adaptive neurofuzzy inference system and response surface methodology in modeling biodiesel synthesis from jatropha–Algae oil. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2018.1515277.
   Google Scholar
• Yang, H., Z. Ring, Y. Briker, N. McLean, W. Friesen, and C. Fairbridge. 2002. Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC–MS. Fuel 81:65–74. doi:10.1016/S0016-2361(01)00121-1.
   Web of Science ®Google Scholar