Chemical Engineering / Kimya Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/14
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Article Citation - WoS: 3Citation - Scopus: 4Detailed Chemical Kinetic Modeling of Fuel-Rich N-Heptane Flame(Elsevier, 2020) Değirmenci, Emre; Alazreg, Abdalwahab; İnal, FikretThe main purpose of this study is to model one-dimensional, premixed, laminar, burner-stabilized, fuel-rich n-heptane flame to understand its combustion characteristics. Detailed chemical kinetic modeling technique was used to obtain more information about the formation nature of emissions in n-heptane flame. A detailed chemical kinetic mechanism was generated by combining several mechanisms from the literature that related with possible products of fuel-rich n-heptane combustion. The mechanism consists of 4185 reactions and 893 species. Validations of the mechanism were done by species mole fractions of premixed laminar flames and jet stirred reactors, and ignition delay times in shock tubes. A detailed investigation of the n-heptane flame was carried out using rate of production and reaction pathway analyses. Propargyl radical (C3H3), vinylacetylene (C4H4) and acetylene (C2H2) were found as the main precursors of benzene formation. The mechanism was able to predict most of the major, minor, and trace species up to four-fused aromatic rings formed in the flame. A skeletal mechanism was also generated using Directed Relation Graph with Error Propagation (DRGEP) method. It consists of 1879 reactions and 359 species. The skeletal mechanism was in a good agreement with the detailed mechanism on the species mole fraction predictions.Article Citation - WoS: 19Citation - Scopus: 23Artificial Neural Network Predictions of Polycyclic Aromatic Hydrocarbon Formation in Premixed N-Heptane Flames(Elsevier Ltd., 2006) İnal, FikretPolycyclic aromatic hydrocarbon formation in combustion systems has received considerable attention because of its health effects. The feed-forward, multi-layer perceptron type artificial neural networks with back-propagation learning were used to predict the total PAH amount in atmospheric pressure, premixed n-heptane and n-heptane/oxygenate flames. MTBE and ethanol were used as fuel oxygenates. The total fifty-four data sets were divided into three groups: training, cross-validation, and testing. The different network architectures were tested and the best predictions were obtained for a network of one hidden layer with five neurons. The transfer function was sigmoid function. The mean square and mean absolute errors were 10.52 and 2.60 ppm for the testing set, respectively. The correlation coefficient (R2) was 0.98. The results also showed that the total PAH amount was significantly influenced by the changes in equivalence ratio, presence of fuel oxygenates, and mole fractions of C4 species.