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机器学习引导优化紫外线/工艺,以实现可持续的微污染物减排

Zhongyan Zhang¹, Bohan Li¹, Jing Zhao²

¹State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China; Institute for the Environment and Health, Nanjing University Suzhou Campus, Suzhou 215163, China.

Journal of hazardous materials

|March 10, 2026

概括

机器学习预测紫外线/水处理中的微污染物去除率,优化流程并减少对环境的影响. 这一框架帮助工程师有效控制微污染物,以获得更安全的饮用水.

科学领域:

环境化学环境化学
水处理技术水处理技术
计算化学的计算化学

背景情况:

基于紫外线的先进氧化工艺 (AOP) 对于清除饮用水中的微污染物和水的再利用至关重要.
挑战包括多种微污染物和当前AOPs的高碳足迹.

研究的目的:

开发一种机器学习 (ML) 框架,以预测UV/过程中的微污染物降解速率常数 (k).
提高水处理中微污染物清除的效率和可持续性.

主要方法:

训练了一种ML模型,在各种条件下使用254nm的紫外线/来预测k值.
用特定微污染物的外部数据集和实验数据验证了模型.
分析特征的重要性,以确定影响降解动力学的因素.

主要成果:

ML模型在预测k值方面表现出高准确度 (外部平均误差<50%,实验中<38%).
确定了一些关键的分子特征 (芳香碳,基,C-I键),增强了降解,而其他特征 (,异环) 则抑制了降解.
在真实水矩阵中成功预测了新出现的微污染物的降解,并提供了最佳的剂量建议.

结论:

ML提供了一个强大的工具来优化基于UV的AOP,以有效和可持续地控制微污染物.
开发的框架和网页界面可以帮助水工程师在实际应用中.
这项研究为预测副产品的形成和改善水处理策略铺平了道路.

关键词:

通过先进的氧化过程.机器学习机器学习微污染物微污染物紫外线/的使用紫外线是紫外线中的一种.

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