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机器学习辅助的 PFAS 从受污染的水中通过基于膜的处理方法去除的建模和分析

Lei Yao¹, Tianyi Shao¹, Minmin Zhang²

¹School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.

Journal of hazardous materials

|January 7, 2026

概括

这项研究优化了机器学习模型,以预测使用纳米过和反透膜去除 perfluoroalkyl 和 polyfluoroalkyl 物质 (PFAS). 这些发现提供了一个数据驱动的框架,用于提高水处理中的PFAS分离效率.

科学领域:

环境科学与工程环境科学与工程
水处理技术水处理技术
材料科学材料科学材料科学

背景情况:

perfluoroalkyl 和 polyfluoroalkyl 物质 (PFAS) 是持久性环境污染物,对水生生态系统产生不利影响.
膜分离,包括纳米过 (NF) 和反透 (RO),对PFAS去除有希望,但需要系统地调查影响因素.
现有的研究缺乏全面了解膜特性,操作条件和PFAS清除效率之间的复杂相互作用.

研究的目的:

开发和优化一个预测模型来评估各种NF和RO膜在从污染水中去除各种PFAS的性能.
确定通过膜工艺去除PFAS的关键特征变量及其合效应.
建立一个数据驱动的理论框架,以优化膜的选择和设计,以实现高效的PFAS分离.

主要方法:

开发了一个使用Sparrow Search Algorithm (SSA) 和AdaBoost优化的内核极端学习机器 (KELM) 模型.
对24种类型的RO和NF膜进行分析,以去除17种不同的PFAS,使用12个特征变量作为模型输入.
应用夏普利添加式扩展 (SHAP) 和部分依赖图来确定特征重要性和变量相互作用.

主要成果:

与基线KELM (R2=0.56,RMSE=10.31) 和其他机器学习模型相比,优化的SSA-AdaBoost-KELM模型实现了显著提高的预测准确性 (R2=0.85,RMSE=5.04).
确定了PFAS分子量,溶液pH和膜孔大小是影响PFAS排斥的主要因素.
最佳的PFAS去除与特定的膜特征相关:孔径大小<0.4nm,泽塔电位≈-28mV,表面粗度>82nm,接触角度>28°.

结论:

这项研究成功地证明了SSA-AdaBoost-KELM模型在预测NF/RO膜的PFAS去除方面的有效性.
对控制PFAS分离的关键因素的定量理解为设计先进的水处理系统提供了宝贵的见解.
开发的理论框架使得膜工艺的数据驱动优化成为可能,以便更好地去除持久的PFAS污染物.

关键词:

机器学习是机器学习.膜膜是一种膜.纳米过的纳米过方法在PFAS中,我们可以使用PFAS.逆透是指反透的过程.

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