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Öğe The use of waste transformer oil as alternative fuel in a diesel power generator(International Journal of Automotive Engineering and Technologies, 2019-10-31) Altun, Şehmus; Yıldız, AbdulkerimIn this study, waste transformer oil (WTO) was investigated as a fuel candidate for a diesel power generator set in terms of its fuel and combustion characteristics. Kinematic viscosity, which is the most restrictive property of WTO (9.6 mm2 /s at 40 oC), was measured on different blends with a conventional diesel fuel (in 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100% by volume) in order to detect the suitable blending ratio to be used in engine without any modification, and to propose some blending strategies to optimize engine performance and emissions. The blended fuels containing WTO up to 50% with diesel by volume were found to have a viscosity which is within standard value specified for conventional diesel fuels, i.e., 2.0-4.5 mm2 /s in EN590, and in case 60% WTO, it agrees with EN14214 (3.5-5.0 mm2 /s) alternative diesel fuel standards. It is also found that a fuel blend containing 20% WTO and 80% diesel have a kinematic viscosity and density which are very close to conventional diesel’ values. Therefore, WTO was blended with diesel at the rate of 20% by volume and then tested in a 4-stroke and 4-cylinder diesel engine powered generator set under constant speed-variable load conditions. Measured and calculated results were compared with the results of conventional diesel tested under the same conditions. Experimental results showed that specific fuel consumption, NOx and unburned HC emissions reduced when using blended fuel instead of conventional diesel. Cylinder gas pressure was higher for blended fuels than that of conventional diesel while the start of combustion was later in the case of blended fuelsÖğe Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator(International Journal of Automotive Engineering and Technologies, 2019-04-03) Altun, Şehmus; Ertuğrul, Ömer FarukArtificial neural network (ANN) methods were employed and suggested in modeling the emissions and performance of a diesel generator fueled with waste cooking oil derived biodiesel during steady-state operation. These papers are generally built on determining optimal network structure, but the modelling accuracy of an ANN is also highly dependent on employed training method. In modeling, operating conditions and fuel blend ratio were used as the inputs while the performance and emission parameters were the outputs. The modeling results obtained by conventional ANNs that were trained by back propagation (BP) learning algorithm, radial basis function (RBF), and extreme learning machine (ELM) were compared with experimental results and each other. The accuracy of the estimations by ELM was above 95% for all the output parameters except for specific fuel consumption and thermal efficiency. Moreover, ELM performed better than BP and RBF with lower mean relative error (MRE) in case where the emissions were estimated. The ELM provided correlation coefficients of 0.987, 0.950 and 0.996 for unburned hydrocarbons (HCs), nitrogen oxides (NOx) and smoke opacity (SO), respectively, while for BP, they were 0.973, 0.818, 0.993, and for RBF, 0.975, 0.640 and 0.981. The most suitable training function for each emission and performance parameters of diesel generator was determined based on obtained accuracies.
