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

环境中有机污染物（OC）的出现是国家面临的最大环境挑战之一。不同的补救技术被用来从受污染的水中去除具有成本效益的有机化合物。然而，鲜为人知的是，这些技术的吸附性能和方法在广泛的解决方案的条件。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OC的数据既耗时又困难。本研究的目的是开发准确的预测模型，可以预测广泛的OC混合物的吸附。去除饮用水中的有机化合物将直接保护人类健康，去除废水中的有机化合物将保护环境并使水能够重复利用。研究工作将与教育和推广计划相结合，旨在：1）扩大代表性不足的群体对研究的参与; 2）将最新的研究成果与基本的环境概念结合起来，向大学生更广泛地传播;和3）培养未来的工程师和提高社区内对有机碳的认识。拟议的研究旨在开发多溶质吸附平衡的预测模型，在不存在或存在天然有机物（NOM）的情况下，通过两种常见的吸附剂对一系列有机化合物进行吸附。在存在或不存在六种代表性NOM混合物的情况下，将获得两种代表性吸附剂的2-6种芳香族溶质的多溶质混合物的吸附等温线。等温线数据将用于模拟双溶质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-LFER）。接下来，多溶质吸附的预测模型将开发的pp-LFER和真实的吸附溶液理论的组合的基础上。最后，NOM将被视为一个或两个等效的背景化合物，并在六个NOM混合物的存在下的多溶质吸附的预测模型将被建立。这种新的预测建模方法将为环境工程师提供一种更容易研究多溶质吸附的工具，并克服在不考虑溶质相互作用的情况下研究单溶质吸附或理想混合物的限制。开发多溶质吸附的预测模型有助于吸附去除OC的应用中的一个重大进展。此外，在教育和推广计划中将采用多种方法，包括让代表性不足的研究生、本科生和高中生参与研究，将项目研究结果纳入凯斯西储大学的环境课程，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响评审标准。

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.