Yazar "Karadurmus, Erdal" seçeneğine göre listele

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    An artificial neural network model for the effects of chicken manure on ground water
    (Elsevier, 2012) Karadurmus, Erdal; Cesmeci, Mustafa; Yuceer, Mehmet; Berber, Ridvan
    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. (C) 2011 Elsevier B. V. All rights reserved.
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    COMPARISON OF CONTROL STRATEGIES FOR DISSOLVED OXYGEN CONCENTRATION IN ACTIVATED SLUDGE PROCESS
    (Parlar Scientific Publications (P S P), 2016) Akyurek, Evrim; Karadurmus, Erdal; Yuceer, Mehmet; Goz, Eda; Atasoy, Ilknur; Berber, Ridvan
    Different control algorithms were compared and tested for activated sludge wastewater treatment process. Proportional-integral-derivative control (PID), Model Predictive Control (MPC) with linear model, MPC with non-linear model, Nonlinear Autoregressive-Moving Average (NARMA-L2) control, Neural Network Model Predictive Control (NN-MPC) and optimal control with Sequential Quadratic Programming (SQP) algorithm were evaluated via simulation of activated sludge model. Controlled and manipulated variables were selected as dissolved oxygen level and aeration rate, respectively. Rise time, overshoot, Integral Absolute Error (IAE) and Integral Square Error (ISE) were calculated for each controller. It was concluded that NARMA-L2 controller and optimal control with SQP would outperform the other control strategies.
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    Prediction of Bromate Removal in Drinking Water Using Artificial Neural Networks
    (Taylor & Francis Inc, 2019) Karadurmus, Erdal; Taskin, Nur; Goz, Eda; Yuceer, 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.
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    Prediction of characteristic properties of crude oil blending with ANN
    (Taylor & Francis Inc, 2018) Karadurmus, Erdal; Akyazi, Habib; Goz, Eda; Yuceer, 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 degrees C and 100 degrees C), viscosity index, flash point, pour point, and density (at 20 degrees 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 degrees 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%. [GRAPHICS] .
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    Total Organic Carbon Prediction with Artificial Intelligence Techniques
    (Elsevier Science Bv, 2019) Goz, Eda; Yuceer, Mehmet; Karadurmus, Erdal
    This study used the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM) and Artificial Neural Network (ANN) models with a feed-forward neural network structure and partial least squares (PLSR) methods to estimate total organic carbon. In order to develop models, on-line data measured at five-minute time intervals were collected through one year (2007-2008) from the online-monitoring stations which were built near the River Yesil1rmak in Amasya in North-Eastern Turkey. These stations were the first practice in Turkey. Twelve parameters as luminescent dissolved oxygen (LDO), pH, conductivity, nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total organic carbon (TOC), chloride, orthophosphate, temperature, turbidity, suspended solid and flow rate were measured at the on-line monitoring stations. To predict the total organic carbon, four input variables, pH, conductivity, dissolved oxygen and temperature were selected. Moreover, the data were also collected at the central office in Ankara via a General Packet Radio Service (GPRS) channel. The validity of models was tested by using statistical methods in MATLAB including correlation coefficients (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE). The best result was obtained in the presence of KELM with a radial basis function (RBF) kernel. R-test=0.984, MAPE(test)=3.01, RMSEtest=0.9676. Additionally, R-train=0.995, MAPE(train)=1.58 and RMSEtrain=0.532. Among the other two algorithms ANN provided better results than ELM and PLSR.