Enhanced defluoridation by nano-crystalline alum-doped hydroxyapatite and artificial intelligence (AI) modeling approach

Abstract

The study aimed to investigate the defluoridation capacity of nano-hydroxyapatite (HAP) [Ca10(PO4)6(OH)2] and alum-doped hydroxyapatite (AHAP) [Ca8Al(PO4)6.(OH)2] as an environmental friendly adsorbent. The physicochemical characteristics of both the material (HAP and AHAP) were examined using XRD, FE-SEM-EDS, and BET techniques. The batch adsorption study revealed a fluoride removal efficiency of up to 83% (AHAP) and 74% (HAP) under acidic conditions (pH-2). The doping of alum alters the surface chemistry and enhances the affinity of AHAP for fluoride adsorption. The pseudo-second-order kinetic (R2–0.9941) and Langmuir isotherm (R2–0.9425) models best describe the adsorption mechanism and behavior. The thermodynamic analysis indicated the spontaneous and endothermic nature of the adsorption process. The study also tested the applicability of the artificial neural network (ANN) modeling approach using MATLAB R2013a to simulate the simulated absorptive efficiency of AHAP. This study suggests that AHAP proved an effective adsorbent for defluoridation.