Process modeling, characterization, optimization, and mechanisms of fluoride adsorption using magnetic agro-based adsorbent

In this study, fluoride removal from polluted potable water using magnetic carbon-based adsorbents derived from agricultural biomass was thoroughly investigated. An experimental matrix is designed considering the interactive effects of independent process variables (pH, adsorbent dose, contact time, and initial fluoride concentration) on the removal efficiency. Isotherms and kinetics studies, as well as anions interactions, were also investigated to understand the adsorption mechanisms further. The model parameters of isotherms and kinetics are estimated using nonlinear differential evolution optimization (DEO). Approaches like adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) are implemented to predict the fluoride removal and identify the optimal process values. The optimum removal efficiency of GAC-Fe3O4 (89.34%) was found to be higher than that of PAC-Fe3O4 (85.14%). Kinetics experiments indicated that they follow the intraparticle diffusion model, and adsorption isotherms indicated that they follow Langmuir and Freundlich models. Both PAC-Fe3O4 and GAC-Fe3O4 adsorbents have shown an adsorption capacity of 1.20 and 2.74 mg/g, respectively. The model predictions from ANFIS have a strong correlation with experimental results and superior to RSM predictions. The shape of the contours depicts the nonlinearity of the interactive effects and the mechanisms in the adsorption process. •Adsorptive removal of fluoride from water by magnetic carbon adsorbents.•Differential evolution optimization-based isotherm and kinetic model parameters.•Response surface methodology-based design of experiments.•Adaptive neuro-fuzzy inference system for predicting optimal process variables.