Optimization of Methylene Blue Adsorption on Olive Seed Activated Carbon Using Response Surface Methodology (RSM) Modeling-Artificial Neural Network

In this study, olive seed activated carbon (OSAC), which is primarily agricultural waste, was used as an adsorbent. Response surface methodology (RSM) approach and Artificial Neural Network (ANN) was used to optimize/model the cationic dyestuff removal by adsorption. RSM was first applied to evaluate the process using four controllable operating parameters, namely the amount of OSAC, initial pH (pH initial), mixing time and dyestuff concentration, and optimal conditions for decolorization were determined. In the optimization method, initial dye concentration (50–150 mg/L), adsorbent dosage (0.1–0.5 g), pH (3–9) and contact time (10–90 min) were used as independent variables, percent removal efficiency was chosen as the dependent variable. Also, the value of R² (R² = 0.9714) shows that regression can predict the response for the adsorption process in the studied range. It shows that it is possible to optimize/estimate the dyestuff removal process using the RSM approach and shows that adsorption to OSAC can be used to remove colour from dye effluent.