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Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner

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  • Adewole, Bamiji Z.
  • Abidakun, Olatunde A.
  • Asere, Abraham A.

Abstract

This study deals with ANN (artificial neural network) modeling of a swirl burner. The model was used to predict the flame temperature and pollutant emissions (CO (carbon monoxide) and NOx (nitrogen oxide)) from combustion of LPG (liquefied petroleum gas) in the swirl burner. The data for the training and testing of the proposed ANN was obtained by combusting LPG at various equivalent ratios (LPG/air ratios) and swirler's vane angles in a low swirl burner. Vane angles of 35–60° in steps of 5° and equivalent ratios of 0.94, 0.90, 0.85, 0.80, 0.75, 0.71, 0.66 and 0.61 were considered. An ANN model based on standard back-propagation algorithms for the swirl burner was developed using some of the experimental data for training and validation. The performance of the ANN was tested by comparing the predicted outputs with the experimental values that were not used in training the network. R values of 0.94 were obtained for CO and NOx and 0.99 for flame temperature. These results show that very strong correlation exists between the ANN predicted values and the experimental results. Therefore, this study demonstrates that the performance and emissions of swirl burner can be accurately predicted using ANN approach.

Suggested Citation

  • Adewole, Bamiji Z. & Abidakun, Olatunde A. & Asere, Abraham A., 2013. "Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner," Energy, Elsevier, vol. 61(C), pages 606-611.
    • Handle: RePEc:eee:energy:v:61:y:2013:i:c:p:606-611
    DOI: 10.1016/j.energy.2013.08.027
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    1. Almonacid, F. & Rus, C. & Pérez-Higueras, P. & Hontoria, L., 2011. "Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks," Energy, Elsevier, vol. 36(1), pages 375-384.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Mujeebu, M. Abdul & Abdullah, M.Z. & Bakar, M.Z. Abu & Mohamad, A.A. & Abdullah, M.K., 2009. "Applications of porous media combustion technology - A review," Applied Energy, Elsevier, vol. 86(9), pages 1365-1375, September.
    4. Tira, H.S. & Herreros, J.M. & Tsolakis, A. & Wyszynski, M.L., 2012. "Characteristics of LPG-diesel dual fuelled engine operated with rapeseed methyl ester and gas-to-liquid diesel fuels," Energy, Elsevier, vol. 47(1), pages 620-629.
    5. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    6. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    7. Pahlavan, Reza & Omid, Mahmoud & Akram, Asadollah, 2012. "Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production," Energy, Elsevier, vol. 37(1), pages 171-176.
    8. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    9. Bekat, Tugce & Erdogan, Muharrem & Inal, Fikret & Genc, Ayten, 2012. "Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks," Energy, Elsevier, vol. 45(1), pages 882-887.
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    Cited by:

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    3. Yurdusevimli Metin, Ece & Aygün, Hakan, 2019. "Energy and power aspects of an experimental target drone engine at non-linear controller loads," Energy, Elsevier, vol. 185(C), pages 981-993.

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