Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel

Farzad Jaliliantabar, Barat Ghobadian, Gholamhassan Najafi and Talal Yusaf
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Farzad Jaliliantabar: Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran
Barat Ghobadian: Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran
Gholamhassan Najafi: Mechanics of Biosystems Engineering Department, Tarbiat Modares University, Tehran 14115-336, Iran
Talal Yusaf: Office of the Pro Vice-Chancellor, Federation University, Ballarat, VIC 3350, Australia

Energies, 2018, vol. 11, issue 9, 1-24

Abstract: In the present research work, a neural network model has been developed to predict the exhaust emissions and performance of a compression ignition engine. The significance and novelty of the work, with respect to existing literature, is the application of sensitivity analysis and an artificial neural network (ANN) simultaneously in order to predict the engine parameters. The inputs of the model were engine load (0, 25, 50, 75 and 100%), engine speed (1700, 2100, 2500 and 2900 rpm) and the percent of biodiesel fuel derived from waste cooking oil in diesel fuel (B0, B5, B10, B15 and B20). The relationship between the input parameters and engine cylinder performance and emissions can be determined by the network. The global sensitivity analysis results show that all the investigated factors are effective on the created model and cannot be ignored. In addition, it is found that the most emissions decreased while using biodiesel fuel in the compression ignition engine.

Keywords: ANN; emission; MLP; sensitivity analysis; waste cooking oil biodiesel; performance (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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