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了解聚胺膜对微量有机污染物的排斥机制,通过数据知识编码驱动的机器学习

Hejia Wang¹, Jin Zeng², Ruobin Dai¹

¹State Key Laboratory of Pollution Control and Resource Reuse, Shanghai Institute of Pollution Control and Ecological Security, School of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.

Environmental science & technology

|March 18, 2024

概括

一个新的机器学习模型整合了数据和领域知识,以预测聚胺膜对微量有机污染物的排斥. 这种方法提高了对排斥机制的理解,并有助于针对性的污染物清除策略.

科学领域:

环境科学环境科学
化学工程是化学工程的重要组成部分.
材料科学材料科学材料科学

背景情况:

机器学习 (ML) 显示了预测聚胺 (PA) 膜对微量有机污染物 (TrOC) 排斥的潜力.
数据稀缺性和混变量限制了数据驱动的ML模型的直接应用.
了解TROC排斥机制对于有效的水处理至关重要.

研究的目的:

开发一个数据知识共同驱动的ML模型,用于PA膜对TROC的排斥.
通过使用领域知识来提高ML模型的预测性能和可解释性.
量化不同机制对TROC排斥的贡献.

主要方法:

通过嵌入领域知识,开发了一个数据知识共同驱动的ML模型.
尺寸排除,电荷效应和疏水相互作用的量化贡献.
对纳米过 (NF) 和反透 (RO) PA膜的TrOC排斥机制进行比较.

主要成果:

共同驱动的ML模型提高了预测准确性和可解释性.
确定了Log D和分子电荷是TROC排斥变异的关键因素.
NF膜更多地依赖于电荷效应和疏水相互作用,而RO膜优先考虑TROC排斥的尺寸排除.

结论:

数据知识共同驱动的ML方法有效地提高了对PA膜对TROC排斥的理解.
域知识集成对于环境工程中强大的ML应用至关重要.
该方法为制定有针对性的TROC清除策略提供了一个框架.

关键词:

数据稀缺性数据稀缺性嵌入域名知识嵌入.机器学习是机器学习.聚胺膜是多聚胺的膜.有机污染物的痕迹有机污染物.

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