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Tahereh Shojaeimehr,Farshad Rahimpour,Mohammad Ali Khadivi,Marzieh Sadeghi 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.3
In the present study, response surface methodology (RSM) and artificial neural network (ANN) were usedto develop an approach for the evaluation of heavy metal adsorption process. LECA was used as a greenand low cost adsorbent to remove Cu2+ from aqueous solution in batch system. The effect of theoperational parameters such as initial pH, temperature, initial Cu2+ concentration, and sorbent dosagewas studied using Central Composite Face (CCF) design. Same design was also utilized to obtain atraining set for ANN. A comparison between the model results and experimental data gave a highcorrelation coefficient (R2ANN ¼ 0:962, R2RSM ¼ 0:941) and showed that two models were able to predictCu2+ removal by LECA. The Langmuir and Freundlich isotherm models were applied to the equilibriumdata at different temperatures. The results revealed that the Freundlich isotherm fitted better than theLangmuir isotherm. The Cu2+ adsorption kinetic was well described by the pseudo-second order kineticmodel. The rate of Cu2+ removal was controlled by film diffusion and intra-particle diffusion. Thethermodynamic studies proved that Cu2+ removal was physical, spontaneous, feasible, endothermic, andrandom process.