Yazar "Yüceer, Mehmet" seçeneğine göre listele

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    Öğe
    A parameter identifiability and estimation study in Yesilirmak River
    (2009) Berber, Rıdvan; Yüceer, Mehmet; Karadurmuş, Erdal
    Water quality models have relatively large number of parameters, which need to be estimated against observed data through a non-trivial task that is associated with substantial difficulties. This work involves a systematic model calibration and validation study for river water quality. The model considered was composed of dynamic mass balances for eleven pollution constituents, stemming from QUAL2E water quality model by considering a river segment as a series of continuous stirred-tank reactors (CSTRs). Parameter identifiability was analyzed from the perspective of sensitivity measure and collinearity index, which indicated that 8 parameters would fall within the identifiability range. The model parameters were then estimated by an integration based optimization algorithm coupled with sequential quadratic programming. Dynamic field data consisting of major pollutant concentrations were collected from sampling stations along Yesilirmak River around the city of Amasya in Turkey, and compared with model predictions. The calibrated model responses were in good agreement with the observed river water quality data, and this indicated that the suggested procedure provided an effective means for reliable estimation of model parameters and dynamic simulation for river streams.
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    An artificial neural network model for the effects of chicken manure on ground water
    (2012) Karadurmuş, Erdal; Çeşmeci, Mustafa; Yüceer, Mehmet; Berber, Rıdvan
    In the areas where broiler industry is located, poultry manure from chicken farms could be a major source of ground water pollution, and this may have extensive effects particularly when the farms use nearby ground water as their fresh water supply. Therefore the prediction the extent of this pollution, either from rigorous mathematical diffusion modeling or from the perspective of experimental data evaluation bears importance. In this work, we have investigated modeling of the effects of chicken manure on ground water by artificial neural networks. An ANN model was developed to predict the total coliform in the ground water well in poultry farms. The back-propagation algorithm was employed for training and testing the network, and the Levenberg-Marquardt algorithm was utilized for optimization. The MATLAB 7.0 environment with Neural Network Toolbox was used for coding. Given the associated input parameters such as the number of chickens, type of manure pool management and depth of well, the model estimates the possible amount of total coliform in the wells to a satisfactory degree. Therefore it is expected to be of help in future for estimating the ground water pollution resulting from chicken farms. © 2011 Elsevier B.V. All rights reserved.
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    An interactive gis-based software for dynamic monitoring of rivers
    (Scibulcom Ltd., 2014) Yetik, Mehmet Kazım; Yüceer, Mehmet; Karadurmuş, Erdal; Semizer, Eda; Çalımlı, Ayla; Berber, Rıdvan
    Water quality research and development attempts have been the most valuable resources in the sense of model calibration and verification techniques. Due to the fact that current degree of pollution in rivers and importance of the sustainable water resources management, the interactive river monitoring becomes inevitable. Within the scope of river water quality monitoring, Geographical Information Systems (GIS) are gaining widespread acceptance besides this fast and reliable water quality models and parameter estimation techniques are becoming available. However, integrating water quality models with GIS is limited in literature. This study presents an integrated platform on which ArcMap as a GIS and a water quality model in MATLAB are brought together in an interactive and user friendly manner. The software provides a considerable developments in future real time river monitoring and environmental pollution assessment.
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    Modeling of blending of mineral base oils via artificial neural networks
    (Czech Society of Chemical Engineering, 2014) Karadurmuş, Erdal; Akyazı, Habib; Yüceer, Mehmet
    [No abstract available]
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    Prediction of bromate removal in drinking water using artificial neural networks
    (Taylor and Francis Inc., 2018) Karadurmuş, Erdal; Taşkın, Nur; Göz, Eda; Yüceer, Mehmet
    In treatment of natural water resources, bromide transforms into carcinogenic bromate, especially during the ozonation process. Adsorption was used in the experimental part of this study to remove this harmful compound from drinking water. For this purpose, technically, HCl-, NaOH-, and NH3-modified activated carbons were used. Scanning Electron Microscopy (SEM) and Brunauer–Emmett–Teller (BET) analyses were carried out within the characterization study. Moreover, the effects of diameters and heights of adsorption columns, flowrate, and particle size of adsorbent were investigated on the removal amounts of bromate. Optimum conditions were obtained from the experiments, and regional/real samples were collected and analyzed. After the experiments, an artificial neural network (ANN) was used to predict bromate removal percentage by using the observed data. Within this context, a feed-forward back-propagation ANN was chosen in this study. Additionally, the transfer function was selected as tangent sigmoid and 3 neurons were used in the hidden layer. Particle size and amount of the activated carbon, height and diameter of the column, volumetric flowrate, and initial concentration were selected as the input variables. Bromate removal percentage was selected as the output. It was found that the model an R value of 0.988, RMSE value of 3.47 and mean absolute percentage error (MAPE) of 5.19% in the test phase. © 2018, © 2018 International Ozone Association.
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    Prediction of characteristic properties of crude oil blending with ANN
    (Taylor and Francis Inc., 2018) Karadurmuş, Erdal; Akyazı, Habib; Göz, Eda; Yüceer, Mehmet
    Mineral oil is one of the most important materials on earth and it is used widely for its several features. Mineral oils derived from petroleum products are commonly used to decrease the friction effects in machine parts and, thus, they both prevent wear/overheating and facilitate power transmission. In this study, various binary mixtures of various base oils (SN-80, SN-100, SN-150, SN-50, SN-500) were prepared at different volumetric ratios. Kinematic viscosity (at 40°C and 100°C), viscosity index, flash point, pour point, and density (at 20°C) measurements were performed for characterization of the prepared mixtures. These values were modeled by an artificial neural network (ANN) and the model was tested with root mean squared error (RMSE), mean absolute percentage error (MAPE, %), and regression coefficient (R) values. A higher value of correlation coefficient and smaller values of MAPE and RMSE indicate that the model performs better. For predicting kinematic viscosity at 40°C, correlation coefficients were calculated for training and testing the network as 0.9999 and 0.9995, respectively. Respective MAPE values were determined as 1.011% and 1.8771%. © 2017, © 2017 Taylor & Francis.
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    River water quality model verification through a GIS based software
    (2009) Yetik, Mehmet Kazım; Yüceer, Mehmet; Berber, Rıdvan; Karadurmuş, Erdal
    Research and development attempts on water quality models created valuable resources in the sense of model calibration and verification techniques. Recognizing the current degree of pollution in rivers and the importance of the sustainable water resources management, the interactive river monitoring appears to be at the center of recent focus. However the available information in this area is still far from expectations. On one side, the Geographical Information Systems (GIS) are gaining widespread acceptance and on the other side fast and reliable water quality models and parameter estimation techniques are becoming available. However, previous work on integrating water quality models and GIS is very limited. This work brings an integrated platform on which ArcMap as a GIS and a water quality model in Matlab™ are brought together in an interactive and user-friendly manner. The software developed allows the user to enter the data collected from the river, runs the dynamic model in the Matlab™ environment, predicts the values of pollution constituents along the river, extracts the results and displays the water quality on the map in different forms. The software thus provides a considerable ease in future real time application for on site river monitoring and environmental pollution assessment.