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

基于吸附溶液理论的多溶质吸附平衡预测模型

• 批准号: 1804708
• 负责人: Huichun Zhang
• 金额: $ 35.85万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1804708&HistoricalAwards=false
• 关键词: Predictive; Modeling; Multi; Solute; Adsorption

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.

有机污染物（OC）在环境中的发生是国家面临的最大环境挑战之一。不同的补救技术用于从受污染的水中取出OC。但是，对于这些技术在广泛的溶液条件下的吸附属性和方法知之甚少。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，因为它耗时，并且难以在饮用水和废水中实验获得大量OC的数据。这项研究的目的是开发准确的预测模型，以预测广泛的OC混合物的吸附。从饮用水中清除OC将直接保护人类健康，从废水中清除OC将保护环境并使水再利用。研究工作将与旨在的教育和外展计划相结合：1）扩大代表性不足的研究的参与； 2）将最新研究结果与基本环境概念相结合，以使大学生更广泛地传播； 3）培训未来的工程师，并提高社区对OC的认识。拟议的研究旨在开发两种常见的吸附剂在不存在或存在自然有机物（NOM）的情况下，通过两个共同的吸附剂来开发多种吸附式OCS的多种吸附模型（NOM）。在存在或不存在六种代表性的NOM混合物的情况下，将获得两种代表性吸附剂的2-6个芳族溶质混合物的吸附等温线。等温数据将用于建模双色体混合物的吸附相活性系数，以建立在无限稀释时活性系数的聚参数线性自由能关系（PP-LFER）。接下来，将基于PP-LFERS和实际吸附溶液理论的组合而开发用于多增强吸附的预测模型。最后，NOM将被视为一种或两种等效的背景化合物，并将在存在六种NOM混合物的情况下进行多色吸附的预测模型。这种新的预测建模方法将使环境工程师更容易地研究多彩吸附，并克服研究单糖吸附或理想混合物的限制，而无需考虑溶质相互作用。开发多种吸附的预测模型有助于将吸附到OC去除的应用中有重大进步。此外，教育和外展计划将采用多种方法，包括涉及代表性不足的毕业生，本科生和高中生研究，将项目的发现与凯斯西部储备大学的环境课程相结合，并将各种发现与社区与多样化的发现与各种各样的发现与各种各样的发现融为一体背景。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估值得支持的。

项目成果

Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation

• DOI: 10.1016/j.cej.2020.127998
• 发表时间: 2021-03
• 期刊: Chemical Engineering Journal
• 影响因子: 15.1
• 作者: Shifa Zhong;Jiajie Hu;X. Yu;Huichun Zhang

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

• DOI: 10.1021/acs.est.1c01339
• 发表时间: 2021-08-17
• 期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
• 影响因子: 11.4
• 作者: Zhong, Shifa;Zhang, Kai;Zhang, Huichun
• 通讯作者: Zhang, Huichun