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通过生物炭去除长链 perfluoroalkyl carboxylic 酸:实验研究和基于不确定性的数据驱动的预测模型

Sepideh Nasrollahpour¹, Amin Tanhadoust², Rama Pulicharla¹

¹Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada.

|November 6, 2024

概括

这项研究表明,堆肥生物炭有效地从水中去除了像PFNA和PFDA这样的持久性 perfluoroalkyl carboxylic 酸 (PFCA). 机器学习模型预测了高吸附率,突出了生物炭.

科学领域:

环境化学环境化学
材料科学材料科学材料科学

背景情况:

长链 perfluoroalkyl carboxylic 酸 (PFCAs) 是持久的,有毒的,并且在环境中的度正在增加.
迫切需要有效的PFCA清除策略,以减轻环境和健康风险.

研究的目的:

用木材和堆肥衍生的生物炭来研究 perfluorononanoic 酸 (PFNA) 和 perfluorodecanoic 酸 (PFDA) 的吸附性去除.
分析生物炭特性和初始污染物度对吸附效率的影响.
使用深度神经网络建模PFCA吸附行为.

主要方法:

使用木材和堆肥生物炭的吸附实验,使用不同度的PFCA.
生物炭大小,重量和PFCA度对去除效率的影响的分析.
深度神经网络的应用与噪声注入用于吸附建模.

主要成果:

与木材生物炭相比,由堆肥制成的生物炭表现出更高的PFCA吸附性,这是由于芳香度和功能群的较高.
获得了高吸附效率:PFDA的90.13%和PFNA的85.8%在500μg/L时.
PFDA的吸附度高于PFNA,这是由于其高化链的长度更长.
机器学习模型准确地预测了吸附行为,考虑到数据的不确定性.

结论:

生物炭,特别是来自堆肥的生物炭,是从污染水中去除持久PFCAs的有前途的吸附剂.
生物炭的特性和污染物的特性显著影响吸附效率.
先进的建模技术可以有效地预测和优化用于环境修复的吸附过程.

关键词:

应用科学应用科学化学工程是化学工程的组成部分.化学化学化学工程工程师工程师工程师

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