Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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: 2Citation - Scopus: 2Assessing Edible Composite Film Polymer From Potato Industry Effluent Under High Hydrostatic Pressure and Its Antimicrobial Properties(Wiley, 2022) Akdemir Evrendilek, Gülsün; Bulut, Nurullah; Uzuner, SibelDevelopment of edible film from potato industry effluent having antimicrobial properties against Salmonella enteritidis and Escherichia coli O157:H7 by addition of Citrus sinensis volatile oil (VO), and changes of its textural properties under high hydrostatic pressure (HHP) are investigated. The optimum operational conditions are determined as 500 MPa pressure, 36.97 µL VO, and 15 min processing time with the minimum force value of 372.33 × g. Textural properties are also modeled through empirical modeling, best fit Box-Behnken design, and artificial neuron network. Inhibition zones for Salmonella enteritidis and E. coli O157:H7 at the optimum HHP conditions are 1.50 ± 0.11 and 2.18 ± 0.07 cm, respectively. Textural properties of force and elongation at break of the HHP-processed films range from 2.27 ± 0.52 to 5.23 ± 0.38 N, and from 8.57 ± 1.31 to 13.36 ± 1.36 mm, respectively. Thermal transition of the edible film is observed at 87.42 °C for 7.36 min. Addition of C. sinensis VO improves the antimicrobial properties, whereas HHP improves the textural properties of the film. It is suggested that the developed film has potential to be used as an edible food packaging material.Article 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: 72Citation - Scopus: 79Artificial Neural Networks for Estimating Daily Total Suspended Sediment in Natural Streams(IWA Publishing, 2006) Tayfur, Gökmen; Güldal, VeyselEstimates of sediment loads in natural streams are required for a wide spectrum of water resources engineering problems from optimal reservoir design to water quality in lakes. Suspended sediment constitutes 75-95% of the total load. The nonlinear problem of suspended sediment estimation requires a nonlinear model. An artificial neural network (ANN) model has been developed to predict daily total suspended sediment (TSS) in rivers. The model is constructed as a three-layer feedforward network using the back-propagation algorithm as a training tool. The model predicts TSS rates using precipitation (P) data as input. For network training and testing 240 sets of data sets were used. The model successfully predicted daily TSS loads using the present and past 4 days precipitation data in the input vector with R2 = 0.91 and MAE = 34.22 mg/L. The performance of the model was also tested against the most recently developed non-linear black box model based upon two-dimensional unit sediment graph theory (2D-USGT). The comparison of results revealed that the ANN has a significantly better performance than the 2D-USGT. Investigation results revealed that the ANN model requires a period of more than 75 d of measured P-TSS data for training the model for satisfactory TSS estimation. The statistical parameter range (xmin - xmax) plays a major role for optimal partitioning of data into training and testing sets. Both sets should have comparable values for the range parameter.Article Citation - WoS: 1Citation - Scopus: 1Estimation of Mechanical Properties of Limestone Using Regression Analyses and Ann(Foundation Cement, Lime, Concrete, 2012) Teomete, Egemen; Tayfur, Gökmen; Aktaş, EnginEstimation of mechanical properties of rocks is important for researchers and field engineers working in cement and concrete industry. Limestone is used in cement production. In this study, Schmidt hammer, ultrasonic pulse velocity, porosity, uniaxial compression and indirect tension tests were conducted on limestone obtained from a historical structure. Regression analyses were used to develop models relating mechanical properties of limestone. Artificial Neural Network (ANN) was performed to determine the mechanical properties. The performance of regression models and ANN were compared by existing models in the literature. The results showed that the regression models and ANN yield satisfactory performance with minimum error. The regression models between tensile strength and wave velocity, tensile strength and porosity, wave velocity and porosity have been developed for the first time in literature. The ANN is used for the first time to estimate the mechanical properties of limestone. The use of separate training and testing sets in the regression analyses of mechanical properties of limestone is conducted for the first time. The models developed in this study can be used by researchers and field engineers to relate the mechanical properties of limestone.Article Citation - WoS: 17Citation - Scopus: 21The Use of Neural Networks for the Prediction of Cone Penetration Resistance of Silty Sands(Springer Verlag, 2017) Erzin, Yusuf; Ecemiş, NurhanIn this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.Article Citation - WoS: 2Citation - Scopus: 2Soft Computing and Regression Modelling Approaches for Link-Capacity Functions(Czech Technical University in Prague, 2016) Koşun, Çağlar; Tayfur, Gökmen; Çelik, Hüseyin MuratLink-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.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.