Filtreler

2016 - 20193
Filtreleri Sıfırla

Ayarlar

Yazar: Ertuğrul, Ömer Faruk × Bölüm: Batman Üniversitesi Mühendislik - Mimarlık Fakültesi Makine Mühendisliği Bölümü ×

Arama Sonuçları

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
  • Küçük Resim
    Öğe
    Developing correlations by extreme learning machine for calculating higher heating values of waste frying oils from their physical properties
    (Springer Nature, 2017-11-01) Altun, Şehmus; Ertuğrul, Ömer Faruk
    In this study, a novel approach was proposed based on extreme learning machine (ELM) for developing correlations in order to calculate higher heating values (HHVs, kj/kg) of waste frying oils from their physical properties such as density (ρ, kg/m 3 ) and kinematic viscosity (v, mm 2 /s) values. These values can easily be determined by using laboratory equipment. For developing the correlations, an experimental dataset from the literature covering 35 samples was collected to be employed in the training and validation steps. The obtained optimum parameters of artificial neural network in the training stage by ELM were employed to develop new correlations. The HHVs calculated by using density-based correlation (HHV = 50823.183 − 12.34095ρ) showed the mean absolute and relative errors of 145.8048 kJ/kg and 0.3695 %, respectively. In the case of the viscosity-based correlation (HHV = 40172.85 − 17.93615v), they were found as 129.04 kJ/kg and 0.327 %, respectively. Additionally, new correlations were performed better than those available in the literature and those obtained by other machine learning methods; therefore, it is highly suggested that the proposed approach can be used for developing new correlations.
  • Küçük Resim
    Öğe
    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 Faruk
    Artificial 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.