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The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach

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  • Al-Hinti, I.
  • Samhouri, M.
  • Al-Ghandoor, A.
  • Sakhrieh, A.

Abstract

This paper uses a neuro-fuzzy interface system (ANFIS) to study the effect of boost pressure on the efficiency, brake mean effective pressure (BMEP), and the brake specific fuel consumption (BSFC) of a single cylinder diesel engine. Experimental data were used as inputs to ANFIS to simulate the engine performance characteristics. The experimental as well as the model results emphasize the role of boost pressure in improving the different engine characteristics. The results show that the ANFIS technique can be used adequately to identify the effect of boost pressure on the different engine characteristics. In addition, different data points that were not used for ANFIS training were used to validate the developed models. The results suggest that ANFIS can be used accurately to predict the effect of boost pressure on the different engine characteristics.

Suggested Citation

  • Al-Hinti, I. & Samhouri, M. & Al-Ghandoor, A. & Sakhrieh, A., 2009. "The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach," Applied Energy, Elsevier, vol. 86(1), pages 113-121, January.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:1:p:113-121
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    7. Salah A. M. Elmoselhy & Waleed F. Faris & Hesham A. Rakha, 2021. "Validated Analytical Modeling of Diesel Engines Intake Manifold with a Flexible Crankshaft," Energies, MDPI, vol. 14(5), pages 1-20, February.
    8. Hountalas, D.T. & Papagiannakis, R.G. & Zovanos, G. & Antonopoulos, A., 2014. "Comparative evaluation of various methodologies to account for the effect of load variation during cylinder pressure measurement of large scale two-stroke diesel engines," Applied Energy, Elsevier, vol. 113(C), pages 1027-1042.
    9. Payri, F. & Olmeda, P. & Martín, J. & García, A., 2011. "A complete 0D thermodynamic predictive model for direct injection diesel engines," Applied Energy, Elsevier, vol. 88(12), pages 4632-4641.
    10. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    11. Ma, Zetai & Xie, Wenping & Xiang, Hanchun & Zhang, Kun & Yang, Mingyang & Deng, Kangyao, 2023. "Thermodynamic analysis of power recovery of marine diesel engine under high exhaust backpressure by additional electrically driven compressor," Energy, Elsevier, vol. 266(C).
    12. Togun, Necla & Baysec, Sedat, 2010. "Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine," Applied Energy, Elsevier, vol. 87(11), pages 3401-3408, November.
    13. Avola, Calogero & Copeland, Colin D. & Burke, Richard D. & Brace, Chris J., 2017. "Effect of inter-stage phenomena on the performance prediction of two-stage turbocharging systems," Energy, Elsevier, vol. 134(C), pages 743-756.
    14. Saravanan, S. & Kaliyanasunder, R. & Rajesh Kumar, B. & Lakshmi Narayana Rao, G., 2020. "Effect of design parameters on performance and emissions of a CI engine operated with diesel-biodiesel- higher alcohol blends," Renewable Energy, Elsevier, vol. 148(C), pages 425-436.
    15. Tauzia, Xavier & Maiboom, Alain, 2013. "Experimental study of an automotive Diesel engine efficiency when running under stoichiometric conditions," Applied Energy, Elsevier, vol. 105(C), pages 116-124.
    16. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    17. Mavropoulos, G.C., 2011. "Experimental study of the interactions between long and short-term unsteady heat transfer responses on the in-cylinder and exhaust manifold diesel engine surfaces," Applied Energy, Elsevier, vol. 88(3), pages 867-881, March.
    18. Yang, L. & Entchev, E., 2014. "Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique," Applied Energy, Elsevier, vol. 134(C), pages 197-203.

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