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基于频率的配对质量距离方法揭示了矿化处理过程中有机化合物的转化途径选择

Xiaofeng Zhang¹, Congyu Hou¹, Wenkai An¹

¹Center for Water and Ecology, State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing 100084, China.

|December 25, 2025

概括

一种新的基于频率的配对质量距离 (F-PMD) 方法精确地识别了废水中的有机化合物转化. 这种方法提高了总有机碳的去除,并引导处理升级,以获得更好的矿化性能.

科学领域:

环境化学环境化学
分析化学分析化学
水处理技术水处理技术

背景情况:

了解有机化合物转化对于优化废水处理至关重要.
在复杂的废水矩阵中识别关键反应仍然是一个重大挑战.
里埃转换离子循环子子共振质谱法 (FT-ICR MS) 是分析复杂有机混合物的强大工具.

研究的目的:

开发和验证一种新的方法,基于频率的配对质量距离 (F-PMD),用于识别废水中的有机转化.
将F-PMD方法应用于pH自我调节的紫外线- (UV/Cl2) 系统,以评估其有效性.
利用可解释机器学习 (IML) 来阐明分子性质对有机化合物转化和矿化的影响.

主要方法:

开发基于频率的配对质量距离 (F-PMD) 方法来分析FT-ICR MS数据.
将F-PMD应用于通过pH自我调节的UV/Cl2系统处理的真实废水盐水中.
使用可解释机器学习 (IML) 来将分子特性与反应途径相关联.

主要成果:

值自我调节的UV/Cl2系统显著改善了总有机碳 (TOC) 的去除,从56.47%提高到82.13%.
F-PMD成功地确定了占主导地位的有机反应,覆盖了超过70%的前体和产品,超过了传统方法.
矿化性能是由碳损失和碳添加反应之间的平衡决定的,碳损失途径更有效.

结论:

F-PMD方法提供了一种全面的方法,用于识别废水处理中的关键有机转化.
对于碳损失途径的高选择性,受诸如OH度和特定分子性质 (例如,C数,NOSC,N/C,Cl/C,AI_mod) 等因素的影响,增强了矿化.
该研究提供了机械洞察力和先进的分析工具,以提高废水处理效率.

关键词:

机器学习机器学习矿物化的矿化.配对的质量距离是配对质量距离.转换路径的转换路径用紫外线和进行治疗.

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