Yazar "Yuceer, Mehmet" seçeneğine göre listele

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    BROMATE REMOVAL PREDICTION IN DRINKING WATER BY USING THE LEAST SQUARES SUPPORT VECTOR MACHINE (LS-SVM)
    (Yildiz Technical Univ, 2020) Karadurnius, Erdal; Goz, Eda; Taskin, Nur; Yuceer, Mehmet
    The main objective of this study was to develop Least Squares Support Vector Machine (LS-SVM) algorithm for prediction of bromate removal in drinking water. Adsorption method known as environmental-friendly and economical was used in the experimental part of this study to remove this harmful compound from drinking water. Technically (pure), HCl-, NaOH- and NH3-modified activated carbons were prepared as adsorbent. Experimental studies were carried out with synthetic samples in three different concentrations. To forecast bromate removal percentage 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 Radial basis kernel function was selected as activation function in algorithm. Algorithm parameters that gamma and sigma(2) values set as 415 and 3.956 respectively. To evaluate model performance some performance indices were calculated. Correlation coefficient (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE) value for the training and testing phase R:0.996, MAPE%: 2.59 RMSE: 2.14 and R:0.994, MAPE%: 3.21 RMSE: 2.51 respectively. These results obtained from this study were compared with the ANN model previously developed with the same input data. As a result, LS-SVM has better performance than ANN.
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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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    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.