Artificial Neural Network Approach for Modeling of Cr (VI) Adsorption from Waste Water by Lewatit MP64 and Dowex 1×8

In 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 1×8 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 1×8 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 1×8. The results show that removal efficiency can be predicted successfully with artificial neural networks.