244
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Prediction of the performance and exhaust emissions of ethanol-diesel engine using different neural network

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 4949-4963 | Received 12 May 2019, Accepted 03 Aug 2019, Published online: 22 Aug 2019

References

  • Aida, D.-S., G. A. Ratta, and C. Carmen. 2018. Barrios. Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using artificial neural network and symbolic regression [J]. Energy 149:675–83. doi:10.1016/j.energy.2018.02.080.
  • Ata, R. 2015. Artificial neural networks applications in wind energy systems: a review [J]. Renewable & Sustainable Energy Reviews 49:534–62. doi:10.1016/j.rser.2015.04.166.
  • Beale, R., and T. Jackson. 1990. Neural computing: a introduction. In Department of computer science [J], 1–38. Boca Raton: University of York.
  • Brown, M., and C. J. Harris. 1994. Neurofuzzy adaptive modelling and control,prentice hall [J]. New York: University of Southampton.
  • Erdi, T., K. Aydin, and M. Bilgili. 2016. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures [J]. Alexandria Engineering Journal 55:3081–89. doi:10.1016/j.aej.2016.08.011.
  • Fadare, D. A. 2009. Modelling of solar energy potential in Nigeria using an artificial neural network model [J]. Applied Energy 86 (9):1410–22. doi:10.1016/j.apenergy.2008.12.005.
  • Firat, M., and M. Gungor. 2009. Generalized regression neural networks and feed forward neural networks for prediction of scour depth around bridge piers [J]. Advances in Engineering Software 40 (8):731–37. doi:10.1016/j.advengsoft.2008.12.001.
  • Harish, K. G., and K. P. Radha. 2018a. Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique [J]. Journal of Environmental Management 223:566–75.
  • Harish, K. G., and K. P. Radha. 2018b. Investigation of thermal performance of unidirectional flow porous bed solar air heater using MLP, GRNN, and RBF models of ANN technique [J]. Thermal Science and Engineering Progress 6:226–35. doi:10.1016/j.tsep.2018.04.006.
  • Javad, R., M. Shahbakhti, B. Bahri, and A. A. Aziz. 2015. Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks [J]. Applied Energy 138:460–73. doi:10.1016/j.apenergy.2014.10.088.
  • Liu, H., H. Tian, X. Liang, and Y. Li. 2015. New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, mind evolutionary algorithm and artificial neural networks [J]. Renewable Energy 83:1066–75. doi:10.1016/j.renene.2015.06.004.
  • Parinet, J., M. Julien, P. Nun, Robins, R. J., Remaud, G., & Höhener, P. 2015. Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach [J]. Chemosphere 134:521–27. doi:10.1016/j.chemosphere.2014.10.079.
  • Pranav, K., and C. Shankararaman. 2010. Disinfection by-product formation following chlorination of drinking water: artificial neural network models and changes in speciation with treatment [J]. Science of the Total Environment 408 (19):4202–10. doi:10.1016/j.scitotenv.2010.05.040.
  • Prasada Rao, K., T. Victor Babu, G. Anuradha, and B. V. Appa Rao. 2017. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN) [J]. Egyptian Journal of Petroleum 26:593–600. doi:10.1016/j.ejpe.2016.08.006.
  • Qazi, A., H. Fayaz, A. Wadi,Raj RG, Rahim NA, Khan WA. 2015. The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review [J]. Journal of Cleaner Production 104:1–12. doi:10.1016/j.jclepro.2015.04.041.
  • Sakthivel, G., C. M. Sivaraja a, and B. W. Iku. 2019. Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA [J]. Energy 166:287–306. doi:10.1016/j.energy.2018.10.023.
  • Samet, U., and M. B. Celik. 2018. Prediction of engine emissions and performance with artificial neural networks in a single cylinder diesel engine using diethyl ether [J]. Engineering Science and Technology, an International Journal 21:1194–201. doi:10.1016/j.jestch.2018.08.017.
  • Taghavipour, A., M. S. Foumani, and M. Boroushaki. 2012. Implementation of an optimal control strategy for a hydraulic hybrid vehicle using CMAC and RBF networks [J]. Scientia Iranica B 19 (2):327–34. doi:10.1016/j.scient.2012.02.019.
  • Vahid, K. A., and Y. Farzin. 2015. Application of GRNN neural network in non-texture image inpainting and restoration [J]. Pattern Recognition Letters 62:24–31. doi:10.1016/j.patrec.2015.04.020.
  • Vinay, K. D., P. Ravi Kumar., and M. Santosha Kumari. 2013. Prediction of performance and emissions of a biodiesel fueled lanthanum zirconate coated direct injection diesel engine using artificial neural network [J]. Procedia Engineering 64:993–1002. doi:10.1016/j.proeng.2013.09.176.
  • Wong, B.-Y., K.-T. Shih, C.-K. Liang, and H. H. Chen. 2012. Single image realism assessment and recoloring by color compatibility [J]. IEEE Transactions on Multimedia 14 (3):760–69. doi:10.1109/TMM.2012.2188997.
  • Yunfei, C., G. Zhiqiang, Z. Qunxiong, and H. Yongming. 2017. Review: multi-objective optimization methods and application in energy saving [J]. Energy 125:681–704. doi:10.1016/j.energy.2017.02.174.
  • Yusuf, C. 2013. Prediction of a gasoline engine performance with artificial neural network [J]. Fuel 111:324–31. doi:10.1016/j.fuel.2012.12.040.
  • Zahedi, J., and M. M. Rounaghi. 2015. Application of artificial neural network models and principal component analysis method in predicting stock prices on tehran stock exchange [J]. Physica A Statistical Mechanics & Its Applications 438:178–87. doi:10.1016/j.physa.2015.06.033.
  • Zhang, J., B. Zhou, N. Lin, Q. Zhang, and J. Chen. 2013. Prediction of mid-long term load based on gray elman neural networks. Proceedings of the CSU-EPSA 25:4.
  • Zhang, K., X. Gao, D. Tao, and X. Li. 2012. Single image super-resolution with non-local means and steering kernel regression [J]. IEEE Transactions on Image Processing 21 (11):4544–56. doi:10.1109/TIP.2011.2169268.
  • Zhiqiang, G., Q. Lin, H. Yongming, and Z. Qunxiong. 2017. Energy saving and prediction modeling of petrochemical industries: a novel ELM based on FAHP [J]. Energy 122:350–62. doi:10.1016/j.energy.2017.01.091.

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.