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Computational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl esters

Author

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  • Redel-Macías, M.D.
  • Hervás-Martínez, C.
  • Gutiérrez, P.A.
  • Pinzi, S.
  • Cubero-Atienza, A.J.
  • Dorado, M.P.

Abstract

The properties of biodiesel differ depending on feedstock fatty acid content. Moreover, biodiesel fatty acid composition influences the combustion process. For these reasons, noise emissions of a direct injection Perkins diesel engine fueled with olive pomace oil methyl ester (monounsaturated methyl esters) and palm oil methyl ester (saturated methyl esters) were studied under several steady-state engine operating conditions. In this work, different approaches for sound prediction of the engine based on Neural Network (NN) models, such as Product Unit NN (PUNN), Radial Basis Function NN (RBFNN) and response surface models have been proposed. Error was measured considering Mean Square Error (MSE) and Standard Error of Prediction (SEP). It can be concluded that the use of a hybrid model combining PU and RBF improves noise prediction accuracy, providing an acceptable value of both MSE and SEP when monounsaturated methyl ester/diesel fuel blends are used. However, best results for saturated methyl ester/diesel fuel blends were achieved by PUNN model. Whereas taking into account the simplicity of the model, PUNN model is the most appropriate for both monounsaturated and saturated methyl ester/diesel fuel blends. Response surface models have shown worse results based on the coefficient of correlation. Also, the effect of independent variables in the models has been studied and an inverse relationship between frequency and engine noise has been found.

Suggested Citation

  • Redel-Macías, M.D. & Hervás-Martínez, C. & Gutiérrez, P.A. & Pinzi, S. & Cubero-Atienza, A.J. & Dorado, M.P., 2018. "Computational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl esters," Energy, Elsevier, vol. 144(C), pages 110-119.
    • Handle: RePEc:eee:energy:v:144:y:2018:i:c:p:110-119
    DOI: 10.1016/j.energy.2017.11.143
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