Predictive Modeling of Multi-Solute Adsorption Equilibrium based on Adsorbed Solution Theories

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The occurrence of organic contaminants (OCs) in the environment is one of the greatest environmental challenges facing the Nation. Different remedial techniques are used to cost-effectively remove OCs from contaminated water. However, little is known about the adsorption properties and methods of these techniques over a broad range of solution conditions. These knowledge gaps limit our ability to design adsorption systems to remove these pollutants, as it is time-consuming and difficult to experimentally obtain data for the vast number of OCs in drinking water and wastewater. The objective of this research is to develop accurate predictive models that can predict adsorption of a wide range of OC mixtures. Removal of OCs from drinking water will directly protect human health, and removal of OCs from wastewater will protect the environment and enable water reuse. The research efforts will be coupled with an educational and outreach plan designed to: 1) broaden participation from underrepresented groups in research; 2) integrate the latest research findings with fundamental environmental concepts for broader dissemination to college students; and 3) train future engineers and increase awareness within communities about OCs.The proposed research aims to develop predictive models for multisolute adsorption equilibria of a suite of OCs by two common adsorbents in either the absence or the presence of natural organic matter (NOM). The adsorption isotherms of multisolute mixtures of 2-6 aromatic solutes will be obtained for two representative adsorbents in the presence or absence of six representative NOM mixtures. The isotherm data will be used to model adsorbed phase activity coefficients of bisolute mixtures to establish poly-parameter linear free energy relationships (pp-LFERs) for the activity coefficients at infinite dilution. Next, predictive models for multisolute adsorption will be developed based on a combination of pp-LFERs and Real Adsorbed Solution Theory. Finally, NOM will be treated as one or two equivalent background compounds, and predictive models for multisolute adsorption in the presence of the six NOMs mixtures will be established. This new predictive modeling approach will give environmental engineers a tool to study multisolute adsorption more easily and overcome the limits of studying single-solute adsorption or ideal mixtures without consideration of solute interactions. Developing predictive models for multisolute adsorption contributes to a major advance in the application of adsorption to OC removal. In addition, multiple approaches will be employed in the educational and outreach plan, including involving underrepresented graduate, undergraduate, and high school students in research, integrating project findings into the environmental curriculum at Case Western Reserve University, and broadly disseminating findings to communities with diverse backgrounds.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.