Central composite design optimization and artificial neural network modeling of copper removal by chemically modified orange peel

The ability to remove Cu2+ ions from aqueous solution using calcium oxide (Ca(OH)(2)) treated orange peel was investigated in the present study. Response Surface Methodology (RSM) was applied for the optimization of the process parameters responsible for the reduction of metal ion effect and to evaluate the effects and interactions of the process variables. The optimum reduction of copper was 93.4253% at pH 4.75, 55.5mg/l copper concentration and 33.91min of contact time. The deviation between experimental and RSM model equation was very less. Computational simulated artificial neural network (ANN) was formulated to get a good correlation between the input parameters responsible for copper removal and the output parameters (% removal) of the process. The correlation coefficient (R) of ANN is 0.967. The optimization process shows a close interaction between the observational and modeled values of copper removal.