Engineering Sciences

Yazarlar: Beytullah EREN

Konular:-

DOI:10.12739/nwsaes.v6i1.5000067042

Anahtar Kelimeler:Adsorption,Artificial Neural Networks,Modeling,Nickel (II) Ions,Zeolite

Özet: This paper presents the development of an artificial neural network (ANN) model for the prediction of the removal efficiency (Re %) of Nickel (II) ions from leachate based on 90 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 Re %. The performance of the ANN models were assessed through mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R2). The ANN model was able to predict Re % of Ni (II) ions with a tangent sigmoid transfer function (tansig) in hidden layer with 10 neurons and a linear transfer function (purelin) in output layer.The Levenberg–Marquardt algorithm (trainlm) was found as the best training algorithm with a minimum MSE of 0,00049. The modeling results indicated that there was an excellent agreement between the experimental data and predicted values.

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