Civil Engineering / İnşaat Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/13

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End date: 2009Department: search.filters.department.İzmir Institute of Technology. Chemical Engineering

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  • Research Project
    Polimerlerin transport özelliklerinin gravimetrik yöntemle ölçülmesi
    (TÜBİTAK - Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, 2003) Alsoy Altınkaya, Sacide; Tıhmınlıoğlu, Funda; Yürekli, Yılmaz
    Bu çalışmanın amacı ülkemizde oldukça yaygın bulunan sektörlerden biri olan boya sektöründe kullanılan metilmetakrilat bütilakrilat kopolimerinin kopolimerinin içinde metilmetakrilat monomerinin difüzyon katsayıları ve çözünürlüğünün ölçülmesi ve bu verilerden pratik bir korelasyon elde edilmesidir. Çalışmanın bir diğer amacı da bu korelasyonun türetilecek bir matematik model içinde kullanılarak boyada kalan monomerin havaya geçiş hızının ve monomerin havadaki konsantrasyonunun hesaplanmasıdır.
  • 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; Melton, Tyler R.; Senkan, Selim M.
    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.