Abstract: In this study a neural network (NN) was proposed to
predict the sorption of binary mixture of copper-cobalt ions into
clinoptilolite as ion-exchanger. The configuration of the
backpropagation 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 backpropagation training algorithm.
Experiments have been carried out in the batch reactor to obtain
equilibrium data of the individual sorption and the mixture of coppercobalt
ions. The obtained modeling results have shown that the used
of neural network has better adjusted the equilibrium data of the
binary system when compared with the conventional sorption
isotherm models.
Abstract: Composite of Celatom-ZeoliteY (Cel-ZY) was used to
remove cobalt ion from an aqueous solution using batch mode.
ZeoliteY has successfully superimposed on Celatom FW-14 surface
using hydrothermal treatment .The product was synthesized as a
novel of hierarchical porous material. It was observed from the
results that Cel-ZY has higher ability to remove cobalt ions than the
pure ZeoliteY powder (PZY) synthesized under the same conditions.
Several parameters were studied in this project to investigate the
effect of removal cobalt ion such as pH and initial cobalt
concentration. It was clearly observed that the uptake of cobalt ions
was affected with increase these parameters. The results proved that
the product can be used effectively to remove Co2+ ions from
wastewater as an environmentally friendly alternative.