Sürdürülebilir Yeşil Kampüs Koleksiyonu / Sustainable Green Campus Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7755
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Article Effects of Reactor Pressure and Inlet Temperature on N-butane/Dimethyl Ether Oxidation and the Formation Pathways of the Aromatic Species(John Wiley and Sons Inc., 2016) Bekat, Tuğçe; İnal, Fikret; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyOxidation of n-butane/dimethyl ether (DME)/O2/Ar system was studied by chemical kinetic modeling in a tubular reactor operated adiabatically and at constant pressure. Effects of the reactor pressure on the formation of various major, minor, and trace oxidation products were investigated for two different pressures (1 and 5 atm) and at six different inlet temperature values (700, 800, 900, 1100, 1300, and 1500 K). The analysis was carried out for two different concentrations of dimethyl ether in the inlet fuel mixture (20 and 50 mol %). Higher pressure (5 atm) resulted in higher mole fractions of methane, vinylacetylene, and cyclopentadiene; and lower mole fractions of formaldehyde, acetylene, acetaldehyde, ethane, propargyl, and propane. The mole fractions of CO and CO2 were not affected considerably by the pressure change. The main formation routes of benzene were developed at two different inlet temperature values (1100 and 1300 K), and the main precursors participating in these routes were found to be propargyl, propene, and diacetylene. A skeletal mechanism was developed for the oxidation of n-butane/DME mixture from the detailed mechanism by reduction of the elementary reactions by 79%, and it was tested for accuracy by comparison with the data from the literature.Article Citation - WoS: 24Citation - Scopus: 25Atmospheric Concentrations and Phase Partitioning of Polycyclic Aromatic Hydrocarbons in Izmir, Turkey(John Wiley and Sons Inc., 2011) Demircioğlu, Eylem; Sofuoğlu, Aysun; Odabaşı, Mustafa; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyAmbient air polycyclic aromatic hydrocarbon (PAH) samples were collected at a suburban (n=63) and at an urban site (n=14) in Izmir, Turkey. Average gas-phase total PAH (∑ 14PAH) concentrations were 23.5ngm -3 for suburban and 109.7ngm -3 for urban sites while average particle-phase total PAH concentrations were 12.3 and 34.5ngm -3 for suburban and urban sites, respectively. Higher ambient PAH concentrations were measured in the gas-phase and ∑ 14PAH concentrations were dominated by lower molecular weight PAHs. Multiple linear regression analysis indicated that the meteorological parameters were effective on ambient PAH concentrations. Emission sources of particle-phase PAHs were investigated using a diagnostic plot of fluorene (FLN)/(fluorine+pyrene; PY) versus indeno[1,2,3-cd]PY/(indeno[1,2,3-cd]PY+benzo[g,h,i]perylene) and several diagnostic ratios. These approaches have indicated that traffic emissions (petroleum combustion) were the dominant PAH sources at both sites for summer and winter seasons. Experimental gas-particle partition coefficients (K P) were compared to the predictions of octanol-air (K OA) and soot-air (K SA) partition coefficient models. The correlations between experimental and modeled K P values were significant (r 2=0.79 and 0.94 for suburban and urban sites, respectively, p<0.01). Octanol-based absorptive partitioning model predicted lower partition coefficients especially for relatively volatile PAHs. However, overall there was a relatively good agreement between the measured K P and soot-based model predictions. Ambient air polycyclic aromatic hydrocarbon (PAH) samples were collected at a suburban and at an urban site in Izmir, Turkey. The multiple linear regression analysis indicated that the meteorological parameters were effective on the measured ambient PAH concentrations. The results indicated that traffic emissions were the dominant PAH sources at both sites for summer and winter seasons.Article Citation - WoS: 28Citation - Scopus: 18Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey(John Wiley and Sons Inc., 2010) İnal, Fikret; İnal, Fikret; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.