Optimization of As(V) removal by dried bacterial biomass: nonlinear and linear regression analysis for isotherm and kinetic modelling

Abstract

Arsenic occurrence and toxicity records in various industrial effluents have prompted researchers to find cost-effective, quick, and efficient methods for removing arsenic from the environment. Adsorption of As(V) onto dried bacterial biomass is proposed in the current work, which continues a line of previous research. Dried bacterial biomass of WS3 (DBB) has been examined for its potential to remove As(V) ions from aqueous solutions under various conditions. Under optimal conditions, an initial concentration of 7.5 ppm, pH 7, adsorbent dose of 0.5 mg, and contact period of 8 h at 37 °C results in maximum removal of 94%. Similarly, amine, amide, and hydroxyl groups were shown to contribute to As(V) removal by Fourier transform infrared spectroscopy (FTIR), and the adsorption of As(V) in the cell wall of DBB was verified by FESEM-EDX. In addition, equilibrium adsorption findings were analyzed using nonlinear and linear isotherms and kinetics models. The predicted best-fit model was selected by calculating the coefficient of determination (R2). Adsorption parameters representative of the adsorption of As(V) ions onto DBB at R2 values were found to be more easily attained using the nonlinear Langmuir isotherm model (0.95). Moreover, it was discovered that the nonlinear pseudo-second-order rate model using a nonlinear regression technique better predicted experimental data with R2 than the linear model (0.98). The current study verified the nonlinear approach as a suitable way to forecast the optimal adsorption isotherm and kinetic data.