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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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