Data-driven future for nanofiltration: Escaping linearity

被引:10
|
作者
Ignacz, Gergo [1 ]
Beke, Aron K. [1 ]
Szekely, Gyorgy [1 ]
机构
[1] King Abdullah Univ Sci & Technol KAUST, Adv Membranes & Porous Mat Ctr, Phys Sci & Engn Div PSE, Thuwal 239556900, Saudi Arabia
来源
JOURNAL OF MEMBRANE SCIENCE LETTERS | 2023年 / 3卷 / 01期
关键词
Machine learning; Big data; Inverse design; Data science; Process analytical technologies; ORGANIC-SOLVENT NANOFILTRATION; HIGH-THROUGHPUT; ARTIFICIAL-INTELLIGENCE; MEMBRANES; DESIGN; OPTIMIZATION; PERFORMANCE; VALIDATION; ALGORITHM; TRANSPORT;
D O I
10.1016/j.memlet.2023.100040
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
Compared with traditional membrane separation methods such as distillation and chromatography, nanofiltra-tion (NF) affords decreased waste generation and energy consumption. Despite the multiple advantages of NF and materials available for NF membranes, the industrial applicability of this process requires improvement. To address these challenges, we propose four important pillars for the future of membrane materials and process development. These four pillars are digitalization, structure-property analysis, miniaturization, and automation. We fill gaps in the development of NF membranes and processes by fostering the most promising contemporary technologies, e.g., the integration of process analytical technologies and the development of a parallel artificial nanofiltration permeability assay (PANPA) or large online databases. Moreover, we propose the extensive use of density functional theory-aided structure-property relationship methods to understand solute transport process at a molecular level. Realizing an inverse design would allow researchers and industrial scientists to develop custom membranes for specific applications using optimized properties.
引用
收藏
页数:6
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