TY  - JOUR
A1  - Mosavi, Amir
A1  - Najafi, Bahman
A1  - Faizollahzadeh Ardabili, Sina
A1  - Shamshirband, Shahaboddin
A1  - Rabczuk, Timon
T1  - 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
JF  - Energies
N2  - 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.
KW  - Biodiesel
KW  - ANN modeling
KW  - biodiesel
KW  - Artificial Intelligence
KW  - diesel engines
KW  - energy, exergy
KW  - mathematical modeling
KW  - OA-Publikationsfonds2018
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20180507-37467
UR  - http://www.mdpi.com/1996-1073/11/4/860
VL  - 2018
IS  - 11, 4
PB  - MDPI
CY  - Basel
ER  -