Rational design of ion separation membranes

Deniz Rall, Daniel Menne, Artur M. Schweidtmann, Johannes Kamp, Lars von Kolzenberg, Alexander Mitsos, Matthias Wessling*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

32 Citations (Scopus)

Abstract

Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an extensive set of new ion separation membranes synthesized with a layer-by-layer methodology, we demonstrate for the first time that an artificial neural network (ANN) can predict ion retention and water flux values based on membrane fabrication conditions. The predictive ANN is used in a local single-objective optimization approach to identify manufacturing conditions that improve permeability of existing membranes. A deterministic global multi-objective optimization is performed in order to identify the upper bound (Pareto front) of the delicate trade-off between ion retention characteristics and permeability. Ultimately, a coupling of the ANN into a hybrid model enables physical insight into the influence of fabrication conditions on apparent membrane properties.

Original languageEnglish
Pages (from-to)209-219
JournalJournal of Membrane Science
Volume569
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Artificial neural network
  • Global optimization
  • Layer-by-layer
  • Nanofiltration
  • Upper bound

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