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Öğe Artificial Neural Network Approach for Modeling of Cr (VI) Adsorption from Waste Water by Lewatit MP64 and Dowex 1x8(IEEE, 2019) Tumer, Abdullah Erdal; Edebali, SerpilIn this study, an artificial neural network model was developed to estimate the removal efficiency of Cr (VI) ion from waste water by Lewatit MP64 and Dowex 1x8 resins. For this purpose, 36 experimental data obtained in a laboratory batch study. In the developed model, contact time, adsorbent dosage, pH and concentration were used as the input parameters, and removal efficiency for Lewatit MP64 and Dowex 1x8 were also used as output parameters. The model performances were determined by the mean square error and the coefficient of determination. The model using the Levenberg-Marquardt backpropagation algorithm (TrainLM) was found the best prediction. This model also has a hidden layer and 15 neurons (4-15-1). The coefficient of determination between experimental and estimates was found to be 0.99 removal efficiency for Lewatit MP64 and 0.92 for Dowex 1x8. The results show that removal efficiency can be predicted successfully with artificial neural networks.Öğe An Artificial Neural Network Model for Wastewater Treatment Plant of Konya(2015) Tümer, Abdullah Erdal; Edebali, SerpilIn this study, modelling of Konya wastewater treatment plant was studied by using artificial neural network with different architectures in Matlab software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account of input values of pH, temperature, COD, TSS and BOD with output values TSS. Performance of the model was compared via the parameters of Mean Squared Error (MSE), and correlation coefficient (R). The suitable architecture of the neural network model is determined after several trial and error steps. According to the modelling study, the ANN can predict the plant performance with correlation coefficient (R) between the observed and predicted output variable reached up to 0.96.Öğe Modeling and Optimization of Hexavalent Chromium Sorption onto Amberjet 1200H by Using Multiple-Linear Regression(IEEE, 2018) Tumer, Abdullah Erdal; Edebali, SerpilIn this study, multiple-linear regression (MLR) model was used to predict the efficiency of two commercial resins, Amberjet 1200H and Diaion CR11, used for the removal of Cr (III) from aqueous solutions. The effects of descriptors used in the experiments (pH, amount of resin, temperature, contact time and concentration) on the removal were investigated with 36 different laboratory studies. The removal efficiency was calculated. Two regression models were developed with MLR analysis which is used to describe the effects of experiment parameters. The performances of both models developed to determine the removal efficiency of these sorption systems were found satisfactory. Statistical results indicated that Amberjet 1200H was more effective than Diaion CR11 for the removal of Cr(III).Öğe Modeling of Removal of Chromium (VI) from Aqueous Solutions Using Artificial Neural Network(Jihad Daneshgahi, 2020) Tumer, Erdal Abdullah; Edebali, Serpil; Gulcu, SabanThere is a need for knowledge, experience, laboratory, materials, and time to conduct chemical experiments. The results depend on the process and are also quite costly. For economic and rapid results, chemical processes can be modeled by utilizing data obtained in the past. In this paper, an artificial neural network model is proposed for predicting the removal efficiency of Cr (VI) from aqueous solutions with Amberlite IRA-96 resin, as being independent of chemical processes. Multiple linear regression, linear and quadratic particle swarm optimization are also used to compare prediction success. A total of 34 experimental data were used for training and validation of the model. pH, amount of resin, contact time, and concentration were used as input data. The removal efficiency is considered as output data for each model. The statistical methods of root-mean-square error, mean absolute percentage error, variance absolute relative error, and the coefficient of determination were used to evaluate the performance of the developed models. The system has been analyzed using a feature selection method to assess the influence of input parameters on the sorption efficiency. The most significant factor was found in pH. The obtained results show that the proposed ANN model is more reliable than the other models for estimating removal efficiency.Öğe Modeling of Trivalent Chromium Sorption onto Commercial Resins by Artificial Neural Network(Taylor & Francis Inc, 2019) Tumer, Abdullah Erdal; Edebali, SerpilIn this research, artificial neural network (ANN) model having three layers was developed for precise estimation of Cr(III) sorption rate varying from 17% to 99% by commercial resins as a result of obtaining 38 experimental data. ANN was trained by using the data of sorption process obtained at different pH (2-7) values with Amberjet 1200H and Diaion CR11 amount (0.01-0.1 g) dosage, initial metal concentration (4.6-31.7 ppm), contact time (5-240 min), and a temperature of 25 degrees C. A feed-forward back propagation network type with one hidden layer, different algorithm (transcg, trainlm, traingdm, traincgp, and trainrp), different transfer function (logsig, tansig, and purelin) for hidden layer and purelin transfer function for output layer were used, respectively. Each model trained for cross-validation was compared with the data that were not used. The trainlm algorithm and purelin transfer functions with five neurons were well fitted to training data and cross-validation. After the best suitable coefficient of determination and mean squared error values were found in the current network, optimal result was searched by changing the number of neurons range from 1 to 20 in the current network hidden layer.Öğe Prediction of Wastewater Treatment Plant Performance Using Multilinear Regression and Artificial Neural Networks(IEEE, 2015) Tumer, Abdullah Erdal; Edebali, SerpilIn this study, modeling of Konya wastewater treatment plant was studied by using multilinear regression and artificial neural network with different architectures in SPSS and MATLAB software. All data were obtained from wastewater treatment plant of Konya during daily records over four month. Treatment efficiency of the plant was determined by taking into account the input values of pH, temperature, COD, TSS and BOD with output values of COD. To compare the performance of the model, coefficient of determination (R-2) and Mean Squared Error (MSE) were used. In Multilinear regression method, to understand the effects of the tested parameters, regression function was developed. The highest prediction efficiencies was obtained two hidden layers in Artificial Neural Network models. According to the modeling study, Artificial Neural Network models responded more satisfactory results than Multilinear Regression model.