超越活性炭特性和疏水性:基于数据的有机微污染物可处理性评估和机械洞察力
Zichu Wang1, Qi Wang2, Greg Kyritsakas3
1National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, Research Center for Eco-Environmental Sciences, Beijing 100085, China; State Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, 100085, Beijing, China; University of Chinese Academy of Sciences, 100049, Beijing, China.
Water research
|July 4, 2025
概括
机器学习模型准确地预测有机微污染物 (OMP) 的活性碳 (AC) 吸附性. 这种方法建立了一个标准化的基准,改善了OMP去除和吸附过程的设计.
科学领域:
- 环境化学环境化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 活性炭 (AC) 对于去除有机微污染物 (OMP) 至关重要.
- 评估AC吸附性是复杂的,因为不同的OMP和异质吸附剂.
- 传统的指标,如疏水性 (logD) 有局限性.
研究的目的:
- 开发一种基于机器学习 (ML) 的策略,用于评估OMP在AC上的吸附性.
- 建立一个标准化,数据驱动的AC吸附性参考.
- 阐明分子性质和吸附机制之间的关系.
主要方法:
- 使用XGBoost,随机森林 (RF) 和额外树木 (ET) ML模型.
- 各种AC的吸附性与56个OMP的假设"标准AC"对齐.
- 使用可解释的ML分析来识别吸附机制驱动器.
主要成果:
- 在测试数据上,ML模型实现了高预测准确性 (R2 = 0.880.98).
- 确定了一个logD值 (≈2),表明吸附机制的转变.
- 吸附性与分子量和灵活性相关,影响毛孔填充效率.
结论:
- 基于机器学习的战略为OMP治疗性评估提供了一个强大的框架.
- 这种方法为设计交流吸附过程提供了实际的基准.
- 了解分子特性可以提高通过AC消除OMP的预测.
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