Adaptive Neuro Fuzzy Inference System and Artificial Neural Network modeling for the adsorption of methylene blue by novel adsorbent in a fixed - bed column method

Document Type: Research Article

Authors

1 Department of Chemistry, Research scholar Government College of Engineering Salem

2 Department of Chemistry, Government College of Engineering, Salem-11

3 Department of Chemistry Government college of Engineering Salem-11

Abstract

A column study for the removal of methylene blue (MB) by the activated carbon from the leaves of Calotropis Gigantea (CGLAC) was studied. The CGLAC was characterized by SEM, FT-IR, Raman, TGA/DTA EDAX, BET and XPS. TGA/DTA studies of CGLAC showed a high fixed carbon content, which indicated that the activated carbon was highly efficient for adsorption. XPS studies confirmed the presence of aromatic conjugate pi system, C = O and C = C as the main functional group in CGLAC. The pHpzc studies showed that CGLAC has a negative surface charge density and hence adsorption of the cationic dye would be highly efficient. In the column studies, the effect of the parameters like initial concentration (100 – 500 mg L-1) of dye, bed height (1, 1.5 and 2 cm), pH (2, 6.5 and 10), flow rate (3.5, 5 and 6.5 ml) and temperature (303, 318 and 333 K) for the removal of MB were tested. Dynamic column models were tested for BTC and it was found that BDST model fitted the experimental data most. R2 for BDST model was found to be greater than 0.99 for most of the parameters studied. Other models were found to fit BTC model at certain conditions. Higher flow rate showed a better fit towards Thomas model with R2 value of 0.95. The highest % of removal of MB by CGLAC was found to be 86.4 % for 3.5 ml min-1flow rate, 100 mg L-1 concentration and 2 cm bed height. ANN and ANFIS predictive model were applied to the study and both the models were found to give good results. ANFIS model showed a highest R2 value for validation data compared to ANN and hence ANFIS could be applied to prediction of adsorption compared to ANN for the column studies.

Keywords