Abstract
The adsorptive performances for anionic dye, Eriochrome Black T (EBT), uptake from water using a hybridized aluminum-cobalt double layered hydroxide (LDH) and its clay intercalated nanocomposites were evaluated using response surface methodology (RSM). The predictive performances of the RSM models were compared with that artificial neural network (ANN) models developed using Bayesian Regulation algorithm considering root mean square error and coefficient of determination (R-2). The maximum removal efficiencies and adsorption capacities data obtained for the adsorbents well fitted cubic RSM models with insignificant lack of fit (R-2 = 0.991-0.997). The adsorption capacity increased with decrease in pH and increase in initial concentration of the EBT, while the temperature increase tends to decrease it. Respectively, the parent LDH maximum EBT adsorption capacity of 328.1 mg/g was significantly increased to 530 mg/g for the bentonite-clay intercalated LDH, both obtained at temperature 25 degrees C, pH 2 and 100 mg/L initial EBT concentration. The bentonite-clay intercalated nanocomposite exhibited higher adsorption capacity for EBT compared with other adsorbents under similar operational condition. Even though, both the RSM and ANN models were reliable to interpret EBT adsorption by CO-Al-LDH and B-CoAl-LDH yet, higher values of R-2 of the ANN model (All R-2 = 0.999) and their corresponding lower values of the RSME indicate ANN better performances. The excellent reusability of the spent CO-Al-LDH and B-CoAl-LDH for three consecutive regeneration cycles indicates the high potentials bentonite hybridized LDH as an adsorbent for the uptake of dyes from industrial contaminated water and wastewater.