Özet:

A three-layer artificial neural network (ANN) model was developed to predict the removal efficiency of Cu ions from simulated wastewater by fungal biomass based on 150 experimental sets obtained in a laboratory batch study. The effect of different experimental parameters such as contact time (0-240 min), initial pH of the solution (3-8), initial metal concentration (50-250 mg/L), adsorbent dosage (.1- 4 g/100 mL), agitation speed (0-250 rpm) and temperature (5- 60 ºC) were studied. The best values of these parameters that achieve the maximum removal efficiency of Copper were: 90 min, 6, 50 mg/L, 2 gm, 200 rpm and 25 ºC, respectively.The configuration of the back propagation neural network giving the smallest mean square error was three-layer NN with tangent sigmoid transfer function at hidden layer with 10 neurons, linear transfer function at output layer and Levenberg-Marquardt back propagation training algorithm.The obtained modeling results have shown that the used of neural network has better adjusted the equilibrium data of the system when compared with the conventional sorption Isotherm models.

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