ABSTRACT
In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) were employed to develop an approach for the evaluation of heavy metal biosorption process. A batch sorption process was performed using Nigella sativa seeds (black cumin), a novel and natural biosorbent, to remove lead ions from aqueous solutions. The effects of process variables which are pH, biosorbent mass, and temperature, on the sorbed amount of lead were investigated through two-levels, three-factors central composite design (CCD). Same design was also utilized to obtain a training set for ANN. The results of two methodologies were compared for their predictive capabilities in terms of the coefficient of determination-R(2) and root mean square error-RMSE based on the validation data set. The results showed that the ANN model is much more accurate in prediction as compared to CCD.
