WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 11Citation - Scopus: 18Ann Model for Prediction of Powder Packing(Elsevier Ltd., 2007) Sütçü, Mücahit; Akkurt, SedatA multilayer feed forward backpropagation (MFFB) learning algorithm was used as an artificial neural network (ANN) tool to predict packing of fused alumina powder mixtures of three different sizes in green state. The data used in model construction were collected by mixing and pressing powders with average particle sizes of 350, 30 and 3 μm and with narrow particle size distributions. The data sets that were composed of green densities of cylindrical pellets were first randomly partitioned into two for training and testing of the ANN models. Based on the training data an ANN model of the packing efficiencies was created with low average error levels (3.36%). Testing of the model was also performed with successfully good average error levels of 3.39%.Article Doe and Ann Models for Powder Mixture Packing(American Ceramic Society, 2007) Akkurt, Sedat; Romagnoli, Marcello; Sütçü, MücahitDesign of experiments (DOE) and artificial neural network (ANN) techniques were used to study packing of fused alumina powders composed of three different sizes of particles. The first is the mixture design technique that produces a polynomial model of the powder-packing system. While, the ANN technique is extensively used to model complex systems in many fields. The methodological approach used is mixture design, which can be used to study the influences of two or more additives. It is a structured and organized method for determining the relationship between the components and the output of that process. The mixture design approach permits optimization of size distribution to obtain a target value of porosity. Sensitivity analysis involves the use of the developed ANN model to predict outputs (porosity) at varying levels of the input factor effects.Article Citation - WoS: 121Citation - Scopus: 136Quantification of Caco3-Caso3·0.5h 2o-Caso4·2h2o Mixtures by Ftir Analysis and Its Ann Model(Elsevier Ltd., 2004) Böke, Hasan; Akkurt, Sedat; Özdemir, Serhan; Göktürk, E. Hale; Caner Saltık, Emine N.A new quantitative analysis method for mixtures of calcium carbonate (CaCO3), calcium sulphite hemihydrate (CaSO 3·1/2H2O) and gypsum (CaSO 4·2H2O) by FTIR spectroscopy is developed. The method involves the FTIR analysis of powder mixtures of several compositions on KBr disc specimens. Intensities of the resulting absorbance peaks for CaCO 3, CaSO3·1/2H2O and CaSO 4·2H2O at 1453, 980, 1146 cm-1 were used as input data for an artificial neural network (ANN) model, the output being the weight percent compositions of the mixtures. The training and testing data were randomly separated from the complete original data set. Testing of the model was done with successfully low-average error levels. The utility of the model is in the potential ability to use FTIR spectrum to predict the proportions of the three substances in unknown mixtures.Article Citation - WoS: 175Citation - Scopus: 203Fuzzy Logic Model for the Prediction of Cement Compressive Strength(Elsevier Ltd., 2004) Akkurt, Sedat; Tayfur, Gökmen; Can, SeverA fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO3, and C3S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.Conference Object Citation - WoS: 4Citation - Scopus: 4Prediction of the Slag Corrosion of Mgo-C Ladle Refractories by the Use of Artificial Neural Networks(Trans Tech Publications, 2004) Akkurt, SedatA multilayer feed-forward back-propagation learning algorithm was employed as an artificial neural network (ANN) tool to create a model to predict the corrosion of MgO-C ladle refractory bricks based on laboratory slag corrosion test data. The corrosion process occurred by immersion of the rectangular refractory specimens in molten slag-steel bath. An ANN model to predict the amount of corrosion was created by using the training data. The model was also tested with experimentally measured data and relatively low error levels were achieved. This model was then used to predict the response of the slag-corrosion system to different values of the factors affecting the corrosion of bricks at high temperatures. Exposure time, exposure temperature of slag-brick contact and CaO/SiO2 ratio of the slag were the factors used for modelling. Model results provided the potential for selection of the best conditions for avoiding the factor combinations that may accelerate corrosion.Article Citation - WoS: 135Citation - Scopus: 157The Use of Ga-Anns in the Modelling of Compressive Strength of Cement Mortar(Elsevier Ltd., 2003) Akkurt, Sedat; Özdemir, Serhan; Tayfur, Gökmen; Akyol, BurakIn this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C3S, SO3 and surface area led to increased strength within the limits of the model. C2S decreased the strength whereas C3A decreased or increased the strength depending on the SO3 level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.