Application of Response Surface Methodology and Artificial Neural Network in Removal of Methylene Blue with Olive Pits Activated Carbon

Abstract

Activated carbon produced from olive pits (OPAC) is a low-cost adsorbent that removes methylene blue (MB) from aqueous solutions. OPAC was characterized using FTIR and SEM analysis. The response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, varying levels of independent variables were employed, including initial dye concentration ranging from 25 to 125 mg/L, adsorbent dosage ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R2 value (R2 = 0.9804) indicates that the regression can effectively forecast the response within the examined range of the adsorption process. This research showcases the capability of optimizing and predicting the colour removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption on OPAC as a viable primary treatment method for the removal of colour from wastewater containing dyes.