Currently, drinking water treatment of per- and polyfluoroalkyl substances (PFAS) is primarily accomplished by granular activated carbon (GAC) sorption of PFAS. However, rapid PFAS breakthrough in GAC beds and a growing concern around short-chain PFAS compounds not well removed by GAC have contributed to the emergence of ion exchange as a promising alternative treatment technology. Some anion exchange resins (AERs) are associated with higher PFAS selectivity (e.g., KPFOS/Cl values up to 108) and capacity relative to GAC, but their longer operational lifetimes prohibit rapid assessment of resin performance in bench-scale experiments. Therefore, a predictive model is needed to translate results from short-term batch experiments to practical treatment operations, estimate relevant design parameters (e.g., resin regeneration/replacement frequency), and inform decision-making for resin selection, optimal resin combinations, and combined treatment technologies. While many current models rely on coupling experimental breakthrough data with theoretical equations to determine site-specific operational parameters, here we describe a model that has been developed to predict compound breakthrough in ion exchange columns based upon PFAS/Cl- selectivity, maximum PFAS adsorption capacity, and kinetics data obtained in batch experiments rather than manipulated best-fit parameters. Sorption behavior of PFAS compounds is described by an analytical solution derived to ensure continual conservation of analyte mass and exchange sites in all spatial segments while conforming to competitive multi-solute selectivity constraints. Batch selectivity coefficients, sorption kinetic rate constants, resin PFAS capacities, and compound diffusivities are utilized in these models, which are assessed for predictive accuracy based on preliminary column data. We will also describe plans to calibrate and validate the model with pilot- and full-scale treatment data for a range of PFASs measured in groundwater impacted by historical aqueous film-forming foam (AFFF) usage.

Development of a predictive model for ion exchange treatment of mixtures of per- and polyfluoroalkyl substances (PFAS)

ACS Fall 2020 Virtual Meeting

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