Optimization of Cu (II) biosorption onto sea urchin test using response surface methodology and artificial neural networks

Copper biosorption potential of the biomass prepared from shells of sea urchin from aqueous solutions at optimum process conditions was studied. Response surface methodology and artificial neural network combined with central composite design were used for modeling and optimization of biosorption and to study interaction effects of process variables. A two-level three-factor face-centered central composite design was used for the experimental design. The influence of pH, initial copper concentration and biosorbent dosage on biosorption of copper was investigated. Prediction capacities of both models were compared and found that response surface methodology showed better prediction performance than artificial neural networks. Kinetic data were well fitted to second-order rate equation showing maximum biosorption capacity of 15.625mg/g for 100mg/l metal solution concentration. It was further confirmed by fitting the data to Elovich model. Biosorption mechanism was investigated using intra-particle diffusion and Boyd models. The optimum copper removal efficiency of the biosorbent was found as 89.09%.