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

环境中有机污染物(OCs)的出现是我国面临的最大环境挑战之一。不同的补救技术被用来经济有效地从受污染的水中清除OCS。然而，人们对这些技术在广泛的溶液条件下的吸附特性和方法知之甚少。这些知识差距限制了我们设计吸附系统以去除这些污染物的能力，因为通过实验获得饮用水和废水中大量OCS的数据既耗时又困难。这项研究的目的是开发准确的预测模型，可以预测各种有机碳混合物的吸附。去除饮用水中的OCS将直接保护人类健康，去除废水中的OCS将保护环境，实现水的再利用。研究工作将与教育和推广计划相结合，旨在：1)扩大未被充分代表的群体在研究中的参与；2)将最新的研究成果与基本的环境概念相结合，以便在大学生中更广泛地传播；以及3)培训未来的工程师，并提高社区对有机化合物的认识。拟议的研究旨在开发预测模型，以预测在没有或存在天然有机物(NOM)的情况下，两种共同的吸附剂对一套OCS的多溶质吸附平衡。在存在或不存在6种有代表性的NOM混合物的情况下，得到了2-6种芳香族溶质的多溶质混合物在两种代表性的吸附剂上的吸附等温线。等温线数据将用于模拟双水溶液混合物的吸附相活度系数，以建立无限稀释时活度系数的多参数线性自由能关系(pp-LFERs)。下一步，将基于PP-LFERs和真实吸附溶液理论相结合的方法建立多溶质吸附的预测模型。最后，将NOM视为一种或两种等价的背景化合物，并建立了六种NOM混合物存在下的多溶质吸附预测模型。这种新的预测模型方法将为环境工程师提供一个更容易研究多溶质吸附的工具，克服了研究单一溶质吸附或不考虑溶质相互作用的理想混合物的局限性。多溶质吸附预测模型的建立有助于将吸附应用于有机碳去除。此外，教育和推广计划将采用多种方法，包括让代表性不足的研究生、本科生和高中生参与研究，将项目成果整合到凯斯西储大学的环境课程中，并向具有不同背景的社区广泛传播研究成果。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.