Modeling and Optimization of Methylene Blue Adsorption from Aqueous Solution by Pumice Based on RSM-CCD and ANN-GA Methods

The discharge of a large volume of colored wastewater into receiving water sources has caused widespread concern around the world. The aim of this study was to investigate the efficiency of pumice as an adsorbent in removing methylene blue (MB) from aqueous solution. The adsorption process was also modeled and optimized by the methods as follows: central composite design-central composite design (RSM-CCD) and artificial neural network-genetic algorithm (ANN-GA).

Methods and Materials:

The independent variables in the study were included pH (3-11), contact time (10-50 minutes), adsorbent dose (0.1-2 g/L), and MB concentration (20-100 mg/l). The effect of these parameters on the efficiency of pumice in MB uptake was modeled and optimized by RSM-CCD and ANN-GA methods. A spectrophotometer at 620 nm was used to measure the residual MB concentration in solution.

The results showed that the RSM-CCD method has the ability to develop a quadratic polynomial model with high validity (R2 = 0.9997) for the adsorption process. Similarly, the ANN-GA method fitted well with experimental data to develop a model with high validity (R2 = 0.9978). The results of optimization process by RSM-CCD and ANN-GA methods obtained the highest adsorption efficiency at pH = 11, contact time = 50 minutes, adsorbent dose = 1 g/L, and concentration MB = 20 mg/L. Adsorption efficiency shows a direct relationship with pH, contact time and adsorbent dose and inversely with contaminant concentration. The linear effect of pollutant concentration and adsorbent dose variables had the greatest effect on adsorption efficiency.

In this study, it was observed that pumice as a cheap and available adsorbent can be considered as a suitable choice for absorbing dye pollutants from aqueous media. RSM-CCD and ANN-GA methods can also be used to model and optimize adsorption processes.