Tuning Pressure Drop in Isoporous Membranes: Design with Fabrication Variability,Advanced Theory and Simulations

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Tuning Pressure Drop in Isoporous Membranes: Design with Fabrication Variability
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2021-05-06 , DOI: 10.1002/adts.202100088
Shi Ke Ong ₁ , Erik Birgersson ₂ , Hong Yee Low ₁

Affiliation

Isoporous membranes consist of well-defined micro and nanoscale pore architecture comprising uniform pore sizes with straight pore channels. In contrast to traditional random porous membranes with tortuous flow paths, isoporous membranes offer the opportunity to achieve a high degree of membrane customization and low pressure drop. Here, a physics-based machine learning methodology that enables the predictive design of a single-layer isoporous membrane in terms of the pressure drop is reported. In short, the methodology consists of a hybrid approach that includes experimental data on the variability of the pore architecture and the resulting pressure drop, training of a neural network with data from validated physics-based simulations of laminar flow through the membrane, and Monte Carlo simulation (MCS) to stochastically account for the inherent variabilities of the pore architecture of fabricated isoporous membranes. Overall, the neural network and MCS predict the range of Δp for a given single-layer membrane well. Experimental values fall within 90% of the minimum and maximum predicted Δp values. In addition, a sensitivity analysis with MCS is carried out to quantify how design and operating parameters affect the overall pressure drop. The methodology can be extended to membranes comprising multiple layers and to account for filtration efficiency.

中文翻译：

调节等孔膜中的压降：具有制造可变性的设计

等孔膜由明确定义的微米和纳米级孔结构组成，包括均匀孔径和直孔通道。与具有曲折流动路径的传统随机多孔膜相比，等孔膜提供了实现高度膜定制和低压降的机会。在这里，报告了一种基于物理的机器学习方法，可以根据压降对单层等孔膜进行预测设计。简而言之，该方法由一种混合方法组成，其中包括有关孔隙结构变化和由此产生的压降的实验数据，使用来自经过验证的基于物理的膜层流模拟数据训练神经网络，和蒙特卡罗模拟 (MCS) 来随机解释制造的等孔膜的孔结构的固有可变性。总的来说，神经网络和MCS预测Δ的范围p对于给定的单层膜孔。实验值落在最小和最大预测 Δ p值的90% 以内。此外，还使用 MCS 进行了灵敏度分析，以量化设计和操作参数如何影响整体压降。该方法可以扩展到包含多层的膜并考虑过滤效率。

更新日期：2021-05-06

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