Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Tayfur, GökmenDepartment: search.filters.department.İzmir Institute of Technology. Chemical Engineering

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  • Article
    Citation - WoS: 11
    Citation - Scopus: 13
    Experimental and Artificial Neural Network Modeling Study on Soot Formation in Premixed Hydrocarbon Flames
    (Elsevier Ltd., 2003) İnal, Fikret; Tayfur, Gökmen; Tayfur, Gökmen; İnal, Fikret; 03.03. Department of Civil Engineering; 03.02. Department of Chemical Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 22
    Forecasting Ambient Air So2 Concentrations Using Artificial Neural Networks
    (Taylor and Francis Ltd., 2006) Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; Tayfur, Gökmen; Tayfur, Gökmen; Sofuoğlu, Sait Cemil; Sofuoğlu, Aysun; 03.02. Department of Chemical Engineering; 03.07. Department of Environmental Engineering; 03.03. Department of Civil Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    An Artificial Neural Networks (ANNs) model is constructed to forecast SO 2 concentrations in Izmir air. The model uses meteorological variables (wind speed and temperature) and measured particulate matter concentrations as input variables. The correlation coefficient between observed and forecasted concentrations is 0.94 for the network that uses all three variables as input parameters. The root mean square error value of the model is 3.60 g/mt 3 . Considering the limited number of available input variables, model performances show that ANNs are a promising method of modeling to forecast ambient air SO 2 concentrations in Izmir.