Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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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, YılmazBu 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: 40Citation - Scopus: 47Boron in Geothermal Energy: Sources, Environmental Impacts, and Management in Geothermal Fluid(Elsevier, 2022) Mott, A.; Baba, Alper; Hadi Mosleh, Mojgan; Ökten, Hatice Eser; Babaei, Masoud; Gören, Ayşegül Yağmur; Feng, C.; Recepoğlu, Yaşar Kemal; Uzelli, Taygun; Uytun, Hüseyin; Morata, Diego; Yüksel Özşen, AslıThe problem of hazardous chemicals in geothermal fluid is a critical environmental concern in geothermal energy developments. Boron is among the hazardous contaminants reported to be present at high concentrations in geothermal fluids in various countries. Poor management and inadequate treatment of geothermal fluids can release excessive boron to the environment that has toxic effects on plants, humans, and animals. Despite the importance of boron management in geothermal fluid, limited and fragmented resources exist that provide a comprehensive understanding of its sources, transport and fate, and the treatment strategies in geothermal energy context. This paper presents the first critical review from a systematic and comprehensive review on different aspects of boron in geothermal fluid including its generation, sources, toxicity, ranges and the management approaches and treatment technologies. Our research highlights the origin of boron in geothermal water to be mainly from historical water-rock interactions and magmatic intrusion. Excessive concentrations of boron in geothermal fluids have been reported (over 500 mg/L in some case studies). Our review indicated that possible boron contamination in geothermal sites are mostly due to flawed construction of production/re-injection wells and uncontrolled discharge of geothermal water to surface water. The dominancy of non-ionic H3BO3 species makes the selection of the suitable treatment method for geothermal waters limited. Combining boron selective resins and membrane technologies, hybrid systems have provided effluents suitable for irrigation. However, their high energy consumption and course structure of boron selective resins encourage further research to develop cost-effective and environmentally friendly alternatives.Article Citation - WoS: 11Citation - Scopus: 13Experimental 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.