Saka C.,Siirt State University
Journal of Analytical and Applied Pyrolysis | Year: 2012
Activated carbons were produced from acorn shell by chemical activation with zinc chloride (ZnCl 2) at 600°C in N 2 atmosphere and their characteristics were investigated. The effects of activation temperature, duration time, impregnation concentration of agent and impregnation time were examined. Adsorption capacity was demonstrated with BET and iodine number. The obtained activated carbons were characterized by measuring their porosities and pore size distributions. BET surface area of the best produced activated carbon was 1289 m 2/g. The surface chemical characteristics of activated carbons were determined by FT-IR spectroscopic method. The microstructure of the produced activated carbons was examined by scanning electron microscopy (SEM). Thermal gravimetry (TG) and derivative thermal gravimetry (DTG) analysis of produced activated carbon was carried out. © 2012 Elsevier B.V. All rights reserved.
Akcan N.,Siirt State University
African Journal of Biotechnology | Year: 2012
The various nutrients belonging to carbon, nitrogen and amino acid sources, were investigated in terms of their effect on the production of extracellular protease by Bacillus licheniformis ATCC 12759. Comparison with the control in media containing all the simple sugars resulted in a decrease in proteolytic activity, while there was significant increase in enzyme yield in the case of the supplementation complex carbon source such as wheat flour and rice flour. Urea and sodium nitrate were the best organic and inorganic nitrogen sources, respectively. Among the amino acid sources tested, L-phenylalanine, L-cysteine, glycine and L-valine favored the production, respectively. FeSO 4, ZnSO 4 and CuSO 4 completely repressed protease production. Maximum protease production (10738.2±44.2 U/mg) was obtained in a medium containing 0.1% MgSO 4 in 24 h 37°C. © 2012 Academic Journals.
Sahin O.,Siirt State University |
Saka C.,Siirt State University
Bioresource Technology | Year: 2013
Activated carbons have been prepared by physical activation with H2O-CO2 in two-step pre-treatment including ZnCl2-HCl from acorn shell at 850°C. The active carbons were characterized by N2 adsorption at 77K. Adsorption capacity was demonstrated by the iodine numbers. The surface chemical characteristics of activated carbons were determined by FTIR spectroscopic method. The microstructure of the activated carbons prepared was examined by scanning electron microscopy. The maximum BET surface area of the obtained activated carbon was found to be around 1779m2/g. © 2013 Elsevier Ltd.
Balbay A.,Siirt State University |
Sahin O.,Siirt State University
Drying Technology | Year: 2012
Liquorice root (LR) (Glycyrrize glabra) is known as a sweetener and medicine plant. Drying kinetics of LR with initial moisture content of 49.5% (wet basis (w.b)) were experimentally investigated in a microwave drying system. The drying experiments were carried out at different drying temperatures (40, 45, 50, and 55°C) and microwave power levels (250, 500 and 750 W). Several models from literature were selected to fit the experimental data. The fit quality of models was evaluated using the coefficient of determination (R 2), sum square error (SSE), and root mean square error (RMSE). A new model has been proposed for LR drying in the microwave drying. This new model best describes the experimental data for LRs. The activation energy was calculated to be 46.807 kJ/mol and effective diffusivity ranged from 2.9 × 10 -9 to 5.41 × 10 -9 m 2/s, depending on drying temperatures at constant microwave power level. © 2012 Copyright Taylor and Francis Group, LLC.
Kaya Y.,Siirt State University |
Uyar M.,Siirt State University
Applied Soft Computing Journal | Year: 2013
Hepatitis is a disease which is seen at all levels of age. Hepatitis disease solely does not have a lethal effect, but the early diagnosis and treatment of hepatitis is crucial as it triggers other diseases. In this study, a new hybrid medical decision support system based on rough set (RS) and extreme learning machine (ELM) has been proposed for the diagnosis of hepatitis disease. RS-ELM consists of two stages. In the first one, redundant features have been removed from the data set through RS approach. In the second one, classification process has been implemented through ELM by using remaining features. Hepatitis data set, taken from UCI machine learning repository has been used to test the proposed hybrid model. A major part of the data set (48.3%) includes missing values. As removal of missing values from the data set leads to data loss, feature selection has been done in the first stage without deleting missing values. In the second stage, the classification process has been performed through ELM after the removal of missing values from sub-featured data sets that were reduced in different dimensions. The results showed that the highest 100.00% classification accuracy has been achieved through RS-ELM and it has been observed that RS-ELM model has been considerably successful compared to the other methods in the literature. Furthermore in this study, the most significant features have been determined for the diagnosis of the hepatitis. It is considered that proposed method is to be useful in similar medical applications. © 2013 Elsevier B.V. All rights reserved.
Balbay A.,Siirt State University
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2012
In this study, the drying characteristics of bittims (Pistacia terebinthus) grown in Siirt, Turkey were investigated by using a temperature controlled microwave dryer system. Natural outer shells unpeeled and peeled bittims were used. The initial moisture content (MC) of samples was determined by oven drying at a temperature of 130 °C about 6 hours. The drying experiments were conducted at three different temperatures (35, 40 and 45 °C), air flow rates (FRs) (0.4, 0.55 and 0.7 m 3/h) and microwave power (250 W, 500 W and 750 W). The fit quality of models was evaluated using the determination coefficient, chi-square and root mean square error. Among the selected models, the Modified Henderson and Pabis et al model was found to be the best model for describing the drying characteristics of bittims. © Sila Science.
Sahin M.,Siirt State University
Advances in Space Research | Year: 2012
The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg-Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey-Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991-1.254 K, respectively. © 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Ozdemir S.,Siirt State University |
Kilinc E.,Dicle University
Microchimica Acta | Year: 2012
Geobacillus thermoleovorans subsp stromboliensis, was immobilized on an Amberlite XAD-4 ion exchanger and used as a solid phase extractant for the preconcentration of U(VI) ions prior to their determination by UV-VIS spectrophotometry. Parameters affecting the preconcentration (such as the pH value of the sample solution, the concentration of U(VI), the volume and type of eluent, the flow rate and the effect of potentially interfering ions) were studied. The optimum pH for the sorption of U(VI) was found to be pH 5. 0. 5. 0 mL of 1 M hydrochloric acid were used to eluate the U(VI) from the column. The loading capacity is 11 mg g -1. The limits of detection and quantification are 2. 7 and 9. 0 μg L -1, respectively, and relative standard deviations are <10 %. The method was applied to the determination of U(VI) in a certified reference sample (NCS ZC-73014; tea leaves) and in natural water samples. © 2012 Springer-Verlag.
Sahin M.,Siirt State University
International Journal of Remote Sensing | Year: 2013
In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R2), mean bias error (MBE), root mean square error (RMSE), and Willmott's index (WI) values were calculated as 0.943, -0.148 MJ m-2, 1.604 MJ m-2, and 0.996, respectively. While R2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJ m-2, 0.672 MJ m-2, and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation. © 2013 Copyright Taylor & Francis.
Balbay A.,Siirt State University |
Esen M.,Firat University
Acta Scientiarum - Technology | Year: 2013
Temperature distribution which occurs in pavement and bridge slabs heated for de-icing and snow melting during cold periods is determined by using vertical ground-source heat pump (GSHP) systems with U-tube ground heat exchanger (GHE). The bridge and pavement models (slabs) for de-icing and snow melting were constructed. A three-dimensional finite element model (FEM) was developed to simulate temperature distribution of bridge slab (BS) and pavement slab (PS). The temperature distribution simulations of PS and BS were conducted numerically by computational fluid dynamics (CFD) program named 'Fluent'. Congruence between the simulations and experimental data was determined.