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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

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 07 Apr 2018 Australia, Germany, Germany Publisher:MDPI AGJournal:Energies, volume 11, page 860 (eissn: 1996-1073,

Authors: Bahman Najafi; Sina Ardabili; Timon Rabczuk; Amir Mosavi; Amir Mosavi; Shahaboddin Shamshirband;

doi: 10.3390/en11040860

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

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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.

Countries

Australia, Germany, Germany

Related Organizations

University of Mohaghegh Ardabili
Iran (Islamic Republic of)
Óbuda University
Hungary
Ton Duc Thang University
Viet Nam
Ton Duc Thang University
Viet Nam
An Giang University
Viet Nam

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Keywords

exergy, ddc:621, Technology, ANN modeling, T, energy, exergy, ddc:333, mathematical modeling, 621, biodiesel, OA-Publikationsfonds2018, 333, bk:48.30, bk:52.56, Artificial Intelligence, Biodiesel, ANN modeling; biodiesel; diesel engines; energy, exergy; mathematical modeling, diesel engines, energy

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	75
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%