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Response Surface Methodology (RSM) is the most popular approach for optimization study in various biochemical processes nowadays. Artificial Neural Network (ANN) has emerged as one of the most efficient methods in empirical modeling and optimization, particularly for non-linear systems. In this study, the estimation capability of RSM and ANN models was compared in copper removal from aqueous solution. The experiments were carried out based on a 3-level and 4-variable Central Composite Design (CCD). The RSM results revealed that the relationship between the response and independent variable could be represented by the quadratic polynomial model. In the development of ANN model, the optimal configuration of the model was found to be 4-10-1. Estimated responses from both models were compared with the experimentally determined responses to determine predictive capabilities of both techniques. Comparison of two methodologies showed that the ANN model was more accurate and exhibited better generalization capability than RSM, thus indicated a clear superiority than the latter in capturing the non-linear behaviour of the adsorption process using chicken eggshell as biosorbent

Chicken eggshell as biosorbent: Artificial intelligence as promising approach in optimizing study