Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
16 results
Search Results
Article Citation - WoS: 12Citation - Scopus: 12Enhancing a Bio-Waste Driven Polygeneration System Through Artificial Neural Networks and Multi-Objective Genetic Algorithm: Assessment and Optimization(Elsevier Ltd, 2024) Hajimohammadi Tabriz,Z.; Taheri,M.H.; Khani,L.; Çağlar,B.; Mohammadpourfard,M.This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. © 2024 Hydrogen Energy Publications LLCArticle Citation - WoS: 28Citation - Scopus: 29Detection of Vinegar Adulteration With Spirit Vinegar and Acetic Acid Using Uv–visible and Fourier Transform Infrared Spectroscopy(Elsevier, 2022) Çavdaroğlu, Çağrı; Özen, BanuVinegar is one of the commonly adulterated food products, and variations in product and adulterant spectrum make the detection of adulteration a challenging task. This study aims to determine adulteration of grape vinegars with spirit vinegar and synthetic acetic acid using different spectroscopic methods. For this purpose, grape vinegars were mixed separately with spirit vinegar and diluted synthetic acetic acid (4%) at 1–50% (v/v) ratios. Spectra of vinegars and mixtures were obtained with UV–visible and Fourier-transform infrared (FTIR) spectrometers. Data were evaluated with various chemometric methods and artificial neural networks (ANN). Correct classification rates of at least 94.3% and higher values were obtained by the evaluation of both spectroscopic data along with their combination with chemometric methods and ANN for discrimination of non-adulterated and adulterated vinegars. UV–vis and FTIR spectroscopy can be rapid and accurate ways of detecting adulteration in vinegars regardless of adulterant type.Article Citation - WoS: 16Citation - Scopus: 23Energy Efficient Resource Allocation for Underlaying Multi-D2d Enabled Multiple-Antennas Communications(Institute of Electrical and Electronics Engineers Inc., 2020) Özbek, Berna; Pischella, M.; Le Ruyet, DidierEnergy efficiency has a significant importance to optimize the wireless communications systems by providing high data rates. In order to develop energy efficient systems, one of the promising methods is to use multiple device-to-device (D2D) underlaying multiple antenna cellular systems. The interference from cellular users to D2D pairs, the interference between D2D pairs and the interference at the base station (BS) caused by D2D pairs occur in these communications systems. In this article, we propose energy efficient resource allocation algorithms for underlaying multi-D2D enabled multiple-antennas communications by employing different multiple antenna processing techniques at the BS. A joint method based on Dinkelbach algorithm and Message Passing Algorithm (MPA) and an approach based on deep learning with multi-layer artificial neural network are proposed to maximize the global energy efficiency (GEE) while satisfying the data rate requirements of both cellular users and D2D pairs. In MPA, the factor graph of the D2D pairs is constructed by taking into account the interference among the D2D pairs and the interference level at the BS to avoid any interruption in the cellular transmission. By relying on the training based on the proposed joint algorithm, a deep neural network approach is presented for off-line implementation. The performance results of the proposed energy efficient resource allocation algorithms show the superiority of multi-D2D communications over conventional single-D2D communications. © 1967-2012 IEEE.Article Citation - WoS: 34Citation - Scopus: 38The Use of Neural Networks for Cpt-Based Liquefaction Screening(Springer Verlag, 2014) Erzin, Yusuf; Ecemiş, NurhanThis study deals with development of two different artificial neural network (ANN) models: one for predicting cone penetration resistance and the other for predicting liquefaction resistance. For this purpose, cone penetration numerical simulations and cyclic triaxial tests conducted on Ottawa sand–silt mixes at different fines content were used. Results obtained from ANN models were compared with simulation and experimental results and found close to them. In addition, the performance indices such as coefficient of determination, root mean square error, mean absolute error, and variance were used to check the prediction capacity of the ANN models developed. Both ANN models have shown a high prediction performance based on the performance indices. It has been demonstrated that the ANN models developed in this study can be employed for predicting cone penetration and liquefaction resistances of sand–silt mixes quite efficiently.Article Citation - WoS: 96Citation - Scopus: 105Comparative Study of a Building Energy Performance Software (kep-Iyte and Ann-Based Building Heat Load Estimation(Elsevier Ltd., 2014) Turhan, Cihan; Kazanasmaz, Zehra Tuğçe; Erlalelitepe Uygun, İlknur; Ekmen, Kenan Evren; Gökçen Akkurt, GüldenThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.Article Citation - WoS: 37Citation - Scopus: 38Prediction of the Bottom Ash Formed in a Coal-Fired Power Plant Using Artificial Neural Networks(Elsevier Ltd., 2012) Bekat, Tuğçe; Erdoğan, Muharrem; İnal, Fikret; Genç, Aytenhe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.Article Citation - WoS: 29Citation - Scopus: 31Evaluating the Knowledge Management Practices of Construction Firms by Using Importance-Comparative Performance Analysis Maps(American Society of Civil Engineers (ASCE), 2011) Kale, Serdar; Karaman, Erkan A.The emergence of the effective management of knowledge resources as a key factor in gaining and sustaining competitive advantage presents new challenges to construction firms. Evaluating knowledge management practices is considered one of the most important challenges facing firms in today's business environment. This paper proposes a model for evaluating the knowledge management practices of construction firms. The proposed model incorporates knowledge management concepts and multilayer perceptron (MLP) neural networks to construct an importance-comparative performance analysis (ICPA) map, a simple visual tool that can provide powerful diagnostic information to executives of construction firms. The model evaluates a firm's knowledge management practices, identifies its competitive advantages and disadvantages in each knowledge management practice, and sets priorities for managerial actions to improve knowledge management practices. A real-world case study was conducted by administering a survey to 105 construction firms operating in Turkey and is presented to illustrate the implementation and utility of the proposed model. The case study findings provided preliminary support for the validity of the proposed model.Article Citation - WoS: 4Citation - Scopus: 5Biophysical and Microbiological Study of High Hydrostatic Pressure Inactivation of Bovine Viral Diarrheavirus Type 1 on Serum(Elsevier Ltd., 2012) Ceylan, Çağatay; Severcan, Feride; Özkul, Aykut; Severcan, Mete; Bozoğlu, Faruk; Taheri, NusretThe effect of high hydrostatic pressure application on fetal bovine serum components and the model microorganism (Bovine Viral Diarrheavirus type 1 NADL strain) was studied at 132 and 220MPa pressure for 5min at 25°C. Protein secondary structures were found to be unaffected by an artificial neural network application on the amide I region for both untreated and HHP treated samples. FTIR spectroscopy study of both the HHP-treated and control samples revealed changes in the intensity of some bands in the finger-print region (1500-900cm -1) originating mainly from lipids which are thought to result from changes in the lipoprotein structure. The virus strain lost its infectivity completely after 220MPa HHP treatments. These results indicate that HHP can be successfully used for inactivation of pestiviruses while leaving structural and functional properties of serum and serum products unaffected. © 2011 Elsevier B.V.Article Citation - WoS: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, SelcenA prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.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%.