An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis

Bahman Najafi, Sina Faizollahzadeh Ardabili, Amir Mosavi, Shahaboddin Shamshirband and Timon Rabczuk
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Bahman Najafi: Biosystem Engineering Department, University of Mohaghegh Ardabili, 56199-11367 Ardabil, Iran
Sina Faizollahzadeh Ardabili: Biosystem Engineering Department, University of Mohaghegh Ardabili, 56199-11367 Ardabil, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Timon Rabczuk: Institute of Structural Mechanics, Bauhaus University Weimar, 99423 Weimar, Germany
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2018, vol. 11, issue 4, 1-18

Abstract: Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25–30% of full load, which is the same as the calculated range obtained from mathematical modeling.

Keywords: ANN modeling; biodiesel; diesel engines; energy, exergy; mathematical modeling (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 (12)

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