Evaluation of Heterocyclic Aromatic Compound Dye (Methylene Blue) on Chitosan Adsorbent Sourced from African Snail Shell: Modelling and Optimization Studies

Abstract

In this article, the modelling and optimization of five operational process parameters involving initial concentration, adsorbent dosage, contact time, temperature and pH of the solution as it affects the treatment of aqueous solution contaminated with methylene blue, a heterocyclic aromatic compound, on chitosan sourced from African Snail Shell were studied using response surface methodology (RSM) and artificial neural network (ANN) techniques coupled with genetic algorithm. The single and interactive effects of the variables were examined by way of analysis of variance (ANOVA). A comparison of the model techniques was done and an evaluation was carried out with some selected error functions. Both modelling and optimization tools performed creditably well. However, the hybrid ANN-GA proved to be a superior modelling and optimization technique with excellent generalization ability which gave an average absolute deviation between the experimental and predicted data of both response variables considered. The insightful relative importance of the process variables based on the renowned Garson and Olden’s algorithm methods coupled with step by step approach initiated in the Matlab environment were equally investigated. The findings from this study revealed in clear terms that pH and initial concentrations were the most influential parameters and the maximum value of 99.28% of methylene blue removed at optimum conditions affirmed that the chitosan adsorbent is viable for the treatment of effluents from the textile industry.

Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix

“…The data points are segmented into 2 parts, training and testing sets, and normalized using the expression [85]:…”

“…All the single terms have synergistic effect on the response variables while the square terms and interactive terms have antagonistic effect on the response variables. To ascertain the effect in terms of percentage, the Pareto analysis was employed, based on Equation (30) [85,95]: (30) where i P is the Pareto percentage importance of variables and 2 i B the regression coefficient of each process variables.…”