Pathway Generation and Byproduct Estimation for Chemical Oxidation Processes in Water Treatment

水处理中化学氧化过程的路径生成和副产物估算

• 批准号: 0607332
• 负责人: Paul Westerhoff
• 金额: --
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0607332&HistoricalAwards=false
• 关键词: Pathway; Generation; Byproduct; Estimation; Chemical

0607332CrittendenThis project will develop a heuristic model to predict the degradation products and the toxicities of these products resulting from the oxidation of organic molecules by advanced oxidation processes (AOPs). These processes have promise to destroy many emerging organic contaminants in water and are being considered in potable water treatment, wastewater treatment, site remediation, and industrial applications. AOPs are mechanistically complex. This fact, combined with the diversity of structures of contaminants, makes it difficult to study the degradation pathways of potential contaminants and the fate of intermediates and byproducts. The modeling approach to be used will be based on an algorithm to solve reactions as systems of ordinary differential equations using graph theory. Quantum theory, linear free energy relationships, and QSARs will be used to estimate reaction rate constants. The model will help researchers and regulators better understand the potential efficacy, toxicity, and risks associated with a given AOP when specific compounds are present in a water supply. The project is a "high risk, high reward" effort because of the breath of the task and complexity of the reactions to be modeled and because it has a potentially large payoff that will promote pollution prevention and sustainability. The end-user community for the model could be large.

0607332 Crittendenthis项目将开发一个启发式模型，以预测由高级氧化过程（AOPS）氧化有机分子引起的这些产物的降解产物和毒性。这些过程有望破坏水中的许多新兴有机污染物，并在饮用水处理，废水处理，现场修复和工业应用中被考虑。 AOP在机械上是复杂的。这一事实与污染物的结构多样性相结合，使研究潜在污染物的降解途径以及中间体和副产品的命运变得困难。要使用的建模方法将基于一种算法，用于使用图理论求解作为普通微分方程的系统。量子理论，线性自由能关系和QSAR将用于估计反应速率常数。该模型将帮助研究人员和监管机构更好地了解当供水中存在特定化合物时，与给定AOP相关的潜在功效，毒性和风险。该项目是“高风险，高奖励”努力，因为要建模的反应的任务和复杂性，并且由于它具有巨大的回报，这将促进预防污染和可持续性。该模型的最终用户社区可能很大。