PhD Student (RA)
Colorado School of Mines
Anderson Ellis is a PhD student at Colorado School of Mines whose research centers around removal of per- and poly-fluoroalkyl substances (PFAS) from drinking water via sorptive mechanisms. Anderson received his bachelor's degree from Yale University before attending Mines to study Hydrology and Environmental Engineering. His overarching research goal is to inform treatment train decision-making using a combination of bench-scale experiments, predictive modeling, and life cycle analysis tools.
Development of a predictive model for continuous ion exchange treatment of mixtures of per- and poly-fluoroalkyl substances (PFAS)
Currently, treatment of per- and poly-fluoroalkyl substances (PFAS) in drinking water primarily consists of fixed beds of carbonaceous sorbents like granular activated carbon (GAC). However, rapid PFAS breakthrough in GAC adsorber beds and a growing concern around short-chain compounds not well removed by GAC have contributed to the emergence of ion exchange resins as a promising treatment alternative. These resins are associated with higher PFAS selectivity and capacity, but their longer operational bed lives 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, enabling estimation of relevant design parameters (e.g. resin regeneration/replacement frequency) and informing decision-making for resin selection, optimal resin combinations, and combined treatment technologies. While many current models rely on coupling experimental data with theoretical equations using fitting parameters, this model has been developed to predict ion exchange column breakthrough from selectivity and kinetic data obtained in batch experiments rather than manipulated best-fit parameters. Batch selectivity coefficients, sorption kinetic rate constants, and PFAS compound diffusivities are utilized in these models, which are assessed for predictive accuracy based on preliminary column data. Model performance is evaluated for various compounds, breakthrough thresholds, matrix constituents, and a number of mass transfer-limited kinetic equations.