Removal of perfluorooctanoic acid from water using peroxydisulfate/layered double hydroxide system: Optimization using response surface methodology and artificial neural network

As perfluorooctanoic acid (PFOA) cannot be effectively removed using existing water treatment methods, research on PFOA removal is attracting increasing attention. In this study, PFOA removal was examined using layered double hydroxide (LDH) as an adsorbent as well as a heterogeneous catalyst for peroxydisulfate (PDS) activation. Based on the central composite design (CCD) experiment results, the optimal conditions for PFOA removal were a PDS concentration of 5 mM, LDH dose of 1 g/L, and initial pH of 2.5. The predictability of PFOA removal using response surface methodology (RSM) and an artificial neural network (ANN) showed significant differences between RSM and ANN in non-CCD conditions, with higher predictability (R-value = 0.7574) in RSM. A scavenger test was performed to analyze the effect of radicals generated during PDS activation, and the PFOA removal rate increased from 64 % to 83 % by controlling the hydroxyl radical using a chemical scavenger, which was verified through electron spin resonance analysis. Additionally, the prepared LDH showed high stability based on the reuse experiments and characterization results. These results suggest that the PDS/LDH system can be an attractive solution for the removal of PFOA by adsorption and degradation in wastewater and can optimize operational processes through multi-parameter modeling.