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.

环境中有机污染物（OCs）的出现是国家面临的最大环境挑战之一。不同的补救技术用于经济有效地从受污染的水中去除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。本研究的目的是建立准确的预测模型，以预测各种OC混合物的吸附。从饮用水中去除OCs将直接保护人类健康，从废水中去除OCs将保护环境并实现水的再利用。研究工作将与一项教育和推广计划相结合，旨在：1)扩大代表性不足群体对研究的参与；2)将最新的研究成果与基本的环境理念相结合，在大学生中广泛传播；3)培训未来的工程师，提高社区对OCs的认识。本研究旨在建立两种常见吸附剂在天然有机物（NOM）存在或不存在的情况下对一组有机碳的多溶质吸附平衡的预测模型。将得到两种代表性吸附剂在存在或不存在六种代表性NOM混合物的情况下，2-6芳香溶质多溶质混合物的吸附等温线。等温线数据将用于模拟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-LFERs）。下一步，将基于pp- lfer和Real吸附溶液理论的结合，开发多溶质吸附的预测模型。最后，将NOM视为一种或两种等效的背景化合物，并建立六种NOM混合物存在时多溶质吸附的预测模型。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，并克服研究单溶质吸附或理想混合物而不考虑溶质相互作用的限制。多溶质吸附预测模型的建立是多溶质吸附在除OC中的应用取得重大进展。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，将项目结果整合到凯斯西储大学的环境课程中，并向不同背景的社区广泛传播研究结果。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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