Prediction of performance and exhaust emissions of a diesel engine fueled with biodiesel produced from waste frying palm oil |
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| Authors: | Mustafa Canakci Ahmet Necati Ozsezen Erol Arcaklioglu Ahmet Erdil |
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| Affiliation: | 1. Department of Mechanical Education, Kocaeli University, 41380 Izmit, Turkey;2. Alternative Fuels R&D Center, Kocaeli University, 41040 Izmit, Turkey;3. The Scientific and Technological Research Council of Turkey, 06100 Ankara, Turkey;4. Department of Mechatronics Engineering, Kocaeli University, 41380 Izmit, Turkey;1. Cihanbeyli Vocational School, Department of Motor Vehicles and Transportation Technology, Selcuk University, Konya, Turkey;2. Energy Division, Department of Mechanical Engineering, Bayburt University, 69000 Bayburt, Turkey;3. Energy Division, Department of Mechanical Engineering, Erciyes University, 38039 Kayseri, Turkey;1. Centre for Energy Sciences, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Department of Engineering, School of Engineering, Computing and Built Environment, KDU Penang University College, 32, Jalan Anson, 10400 Georgetown, Penang, Malaysia;3. School of Mechanical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia;1. Istanbul Ayd?n University, Mechanical Engineering Department, ?stanbul, Turkey;2. Karabuk University, Mechanical Engineering Department, Karabuk, Turkey;1. Engine Research Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India;2. Combustion Engine and Energy Conversion Laboratory, School of Mechanical Engineering, College of Engineering, Hanyang University, Seoul 133-791, Republic of Korea |
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| Abstract: | Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility with the petroleum-based diesel fuel (PBDF). Therefore, in this study, the prediction of the engine performance and exhaust emissions is carried out for five different neural networks to define how the inputs affect the outputs using the biodiesel blends produced from waste frying palm oil. PBDF, B100, and biodiesel blends with PBDF, which are 50% (B50), 20% (B20) and 5% (B5), were used to measure the engine performance and exhaust emissions for different engine speeds at full load conditions. Using the artificial neural network (ANN) model, the performance and exhaust emissions of a diesel engine have been predicted for biodiesel blends. According to the results, the fifth network is sufficient for all the outputs. In the fifth network, fuel properties, engine speed, and environmental conditions are taken as the input parameters, while the values of flow rates, maximum injection pressure, emissions, engine load, maximum cylinder gas pressure, and thermal efficiency are used as the output parameters. For all the networks, the learning algorithm called back-propagation was applied for a single hidden layer. Scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) have been used for the variants of the algorithm, and the formulations for outputs obtained from the weights are given in this study. The fifth network has produced R2 values of 0.99, and the mean % errors are smaller than five except for some emissions. Higher mean errors are obtained for the emissions such as CO, NOx and UHC. The complexity of the burning process and the measurement errors in the experimental study can cause higher mean errors. |
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