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Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network

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  • Roy, Sumit
  • Banerjee, Rahul
  • Bose, Probir Kumar

Abstract

The present study explores the potential of artificial neural network to predict the performance and exhaust emissions of an existing single cylinder four-stroke CRDI engine under varying EGR strategies. Based on the experimental data an ANN model is developed to predict BSFC, BTE, CO2, NOx and PM with load, fuel injection pressure, EGR and fuel injected per cycle as input parameters for the network. The study was carried out with 70% of total experimental data selected for training the neural network, 15% for the network’s cross-validation and remaining 15% data has been used for testing the performance of the trained network. The developed ANN model was capable of predicting the performance and emissions of the experimental engine with excellent agreement as observed from correlation coefficients within the range of 0.987–0.999, mean absolute percentage error in the range of 1.1–4.57% with noticeably low root mean square errors. In addition to common correlation coefficients, the present study incorporated special statistical error and performance metrics such as mean square relative error, forecasting uncertainty Theil U2, Nash–Sutcliffe Coefficient of Efficiency and Kling–Gupta Efficiency. Low values of MSRE and Theil U2 combined with commendable indices of NSE and KGE proved beyond doubt the robustness and applicability of the model so developed. Furthermore, the developed ANN model was capable of mapping the PM–NOx–BSFC trade-off potential of the CRDI operation under EGR for all cases of actual observations with significant accuracy.

Suggested Citation

  • Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:330-340
    DOI: 10.1016/j.apenergy.2014.01.044
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    1. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    2. Maiboom, Alain & Tauzia, Xavier & Hétet, Jean-François, 2008. "Experimental study of various effects of exhaust gas recirculation (EGR) on combustion and emissions of an automotive direct injection diesel engine," Energy, Elsevier, vol. 33(1), pages 22-34.
    3. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    4. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    5. Suh, Hyun Kyu, 2011. "Investigations of multiple injection strategies for the improvement of combustion and exhaust emissions characteristics in a low compression ratio (CR) engine," Applied Energy, Elsevier, vol. 88(12), pages 5013-5019.
    6. Canakci, Mustafa & Erdil, Ahmet & Arcaklioglu, Erol, 2006. "Performance and exhaust emissions of a biodiesel engine," Applied Energy, Elsevier, vol. 83(6), pages 594-605, June.
    7. Kannan, G.R. & Anand, R., 2011. "Experimental investigation on diesel engine with diestrol–water micro emulsions," Energy, Elsevier, vol. 36(3), pages 1680-1687.
    8. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    9. Çelik, Veli & Arcaklioglu, Erol, 2005. "Performance maps of a diesel engine," Applied Energy, Elsevier, vol. 81(3), pages 247-259, July.
    10. Mani, M. & Nagarajan, G., 2009. "Influence of injection timing on performance, emission and combustion characteristics of a DI diesel engine running on waste plastic oil," Energy, Elsevier, vol. 34(10), pages 1617-1623.
    11. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    12. Rakopoulos, Constantine D. & Dimaratos, Athanasios M. & Giakoumis, Evangelos G. & Rakopoulos, Dimitrios C., 2010. "Investigating the emissions during acceleration of a turbocharged diesel engine operating with bio-diesel or n-butanol diesel fuel blends," Energy, Elsevier, vol. 35(12), pages 5173-5184.
    13. Yusaf, Talal F. & Buttsworth, D.R. & Saleh, Khalid H. & Yousif, B.F., 2010. "CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network," Applied Energy, Elsevier, vol. 87(5), pages 1661-1669, May.
    14. Hountalas, D.T. & Mavropoulos, G.C. & Binder, K.B., 2008. "Effect of exhaust gas recirculation (EGR) temperature for various EGR rates on heavy duty DI diesel engine performance and emissions," Energy, Elsevier, vol. 33(2), pages 272-283.
    15. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    16. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    17. Pradeep, V. & Sharma, R.P., 2007. "Use of HOT EGR for NOx control in a compression ignition engine fuelled with bio-diesel from Jatropha oil," Renewable Energy, Elsevier, vol. 32(7), pages 1136-1154.
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