Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 1Citation - Scopus: 1Generalized Regression Neural Network and Empirical Models To Predict the Strength of Gypsum Pastes Containing Fly Ash and Blast Furnace Slag(Springer Verlag, 2020) Erdem, Tahir Kemal; Cengiz, Okan; Tayfur, GökmenGypsum is widely used in constructions owing to its easy application, zero shrinkage, and excellent fire resistance. Several parameters can affect the properties of gypsum pastes. To study the strength of the gypsum pastes experimentally by trying all these parameters is time-consuming and costly. Therefore, artificial intelligence methods can be very useful to predict the paste strength, which, in turn, can reduce the number of trial batches. Based on experimental data, the generalized regression neural network (GRNN) and empirical models were developed to predict strength of gypsum pastes containing fly ash (FA) and blast furnace slag (BFS). Gypsum content, pozzolan content, curing temperature, curing duration, and testing age constituted the input variables of the models while the paste strength was the target output. The trained and tested GRNN model was found to be successful in predicting strength. Sensitivity analysis by the GRNN model revealed that the curing duration and temperature were important sensitive parameters. In addition to the GRNN model, empirical models were proposed for the strength prediction. The same input variables formed the input vectors of the empirical models. The same dataset used for the calibration of the GRNN model was employed to establish the empirical models by employing genetic algorithm (GA) method. The empirical models were successfully validated. The GRNN and GA_based empirical models were also tested against the multi-linear regression (MLR) and multi-nonlinear regression (MNLR) models. The results showed the outperformance of the GRNN and the GA_based empirical models over the others.Article Citation - WoS: 61Citation - Scopus: 69Leaching Characteristics of Fly Ash From Fluidized Bed Combustion Thermal Power Plant: Case Study: Çan (çanakkale-Turkey)(Elsevier Ltd., 2010) Baba, Alper; Gürdal, Gülbin; Şengünalp, FatmaIt is known that the concentration of elements of fly ash varies due to the used-coal and the used-lime qualities varying in different periods. In the Çan Thermal Power Plant (CTPP) located at northwestern Turkey, Çan (Çanakkale) basin coals, which are classified as lignite to sub-bituminous C coal with high total sulphur (0.4-12.22%) and a broad range of ash contents (3.2-44.6%) are mainly used. Performed studies reveal that some toxic elements exit in the coal, including As, U and V. Also, while the As, Cu, Co and Hg contents in coal increases, the sulphur contents in coal also increase. Additionally, trace elements that have inorganic compounds in coal are mobilized into air during the combustion process. This poses a big risk for human health and keeping the environment when Çan Basins low quality lignite is burned, it's the fly ash that contains several toxic elements which can leach out and contaminate the water resources. In this study, toxicity tests were conducted on the fly ash samples that were obtained from the fluidized bed combustion of Çan Thermal Power Plant. The results showed that water temperature, pH and the quality of the limestone used were the most important factors affecting the leaching properties. Concentration of some toxic elements found in the fly ash, such as; As, Cd, Cr, Pb, Se and Zn were analyzed. Concentration richness of some heavy metals were attributed to the increase of water temperature, especially when pH is lower than 5. At pH=5 value, there is no clear explanation of each heavy metal presence in the fly ash from fluidized bed combustion thermal power plant. © 2010 Elsevier B.V. All rights reserved.