在聚合物膜中复杂有机混合物透的数据驱动预测
Young Joo Lee1, Lihua Chen2, Janhavi Nistane2
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Nature communications
|August 15, 2023
概括
本研究介绍了聚合物膜在分离复杂有机混合物 (如原油) 的性能方面的预测模型. 基于物理的机器学习模型准确地预测了分离效率,减少了对广泛物理实验的需求.
科学领域:
- 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
- 计算化学计算化学
背景情况:
- 基于膜的有机溶剂分离为净化提供了节能替代品.
- 聚合物膜有效地分离复杂有机混合物,包括原油.
- 目前用于预测复杂混合物的膜性能的方法是经验性的,缺乏预测能力.
研究的目的:
- 开发一个综合预测模型,用于复杂混合物中任意线性聚合物膜的分离性能.
- 基于混合物组成和聚合物化学,实现准确的预测,克服当前的临时方法.
主要方法:
- 结合了基于物理的机器学习 (ML) 算法与大规模运输模拟.
- 开发了一种能够预测多达400个成分的混合物分离的模型.
- 集成的ML预测器用于分子扩散和吸附特性与运输模拟器.
主要成果:
- 综合模型准确地预测了两个原油样本的分离,在实验测量的6-7%以内.
- 证明了该模型在预测复杂液体混合物的分离性能方面的有效性.
结论:
- 开发的基于物理的ML模型为预测聚合物膜分离性能提供了强大的工具.
- 能够快速选复杂混合物分离的聚合物膜,显著减少实验力度和成本.
- 通过提供数据驱动的预测方法,推进了基于膜的分离领域.
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