Yayınlanmış 1 Ocak 2011 | Sürüm v1
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PREDICTION OF ADSORPTION EFFICIENCY FOR THE REMOVAL OF NICKEL (II) IONS BY ZEOLITE USING ARTIFICIAL NEURAL NETWORK (ANN) APPROACH

  • 1. Sakarya Univ, Dept Environm Engn, TR-54187 Sakarya, Turkey

Açıklama

This paper presents the development of an artificial neural network (ANN) model for the prediction of the adsorption efficiency (AE %) of nickel (II) ions from aqueous solution by zeolite based on 120 experimental data sets obtained in a bench scale experiments. The ANN models developed in this study used three input variables including initial concentration of Ni (II) ions, adsorbent dosage, and contact time for predicting corresponding AE %. The performance of the ANN models were assessed through root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R-2), and T statistics. The ANN model was able to predict AE % of Ni (II) ions with a tangent sigmoid transfer function (tansig) in hidden layer with 12 neurons and a linear transfer function (purelin) in output layer and the BFGS quasi-Newton algorithm (trainbfg) was found as the best training algorithm with a minimum RMSE of 0.0222. The modeling results indicated that there was an excellent agreement between the experimental data and predicted values.

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