Application of response surface and artificial neural network optimization approaches for exploring methylene blue adsorption using luffa fiber treated with sodium chlorite

Abstract

Wastewater often laden with colored impurities, presents a substantial environmental contamination risk, necessitating the exploration of effective pollutant removal methods. Clay particles have emerged as promising adsorbents for this purpose. This study delves into the intricacies of methylene blue removal in a dilute environment, utilizing pre-treated luffa fiber with sodium chlorite to eliminate the reactive pigment from aqueous solutions. The adsorption optimization is achieved by applying both response surface methodology (RSM) and an artificial neural network (ANN) architecture. The investigation focuses on four key parameters—sodium chlorite concentration, activation time, temperature, and their combined impact on methylene blue adsorption. Descriptive statistics discern optimal operational conditions for efficiently extracting methylene blue from sewage water solutions. Notably, the ANN models exhibit exceptional optimization accuracy, yielding a remarkable adsorption capacity of 133.58 mg/g. This surpasses the experimental (124.36 mg/g) and RSM (126.97 mg/g) values, indicating a 6.90 % improvement over experimental results and a 4.94 % increase over RSM outcomes. These findings underscore the high efficiency of the adsorption process, positioning it as a promising method for effectively removing ionic anthocyanin contaminants in sewage water treatment.