Metal-Organic Framework Targeting for Optimal Pressure Swing Adsorption Processes

Xiang Zhang, Teng Zhou, Kai Sundmacher

Research output: Chapter in Book/Conference Proceeding/ReportBook Chapterpeer-review

Abstract

Gas separation accounts for a major production cost in chemical industries. So far, pressure swing adsorption (PSA) has been widely used for gas separation applications such as H2 purification and CO2 capture. For PSA processes, the adsorption efficiency is greatly affected by the selected adsorbent and process operating conditions. Over the past decade, porous metal-organic frameworks (MOFs) have been recognized as innovative adsorbents featuring tunable properties. For achieving a high separation efficiency, a novel two-step integrated MOF and PSA process design approach has been recently proposed. In the first step, MOF is represented as a set of geometric and chemical descriptors. The MOF descriptors and process operating conditions are simultaneously optimized to maximize the process performance. In this work, the second step, namely MOF targeting, is presented. The objective is to use various computational tools to synthesize hypothetical MOFs and identify potential candidates based on the optimized MOF descriptors. The involved computational tools include Tobacco for computational MOF synthesis, Poreblazer for geometry characterization, and RASPA for rigorous adsorption isotherm simulation.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages295-300
Number of pages6
DOIs
Publication statusPublished - Jan 2022
Externally publishedYes

Publication series

NameComputer Aided Chemical Engineering
Volume49
ISSN (Print)1570-7946

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Adsorption process design
  • Gas separation
  • Hypothetical MOF
  • MOF targeting
  • Machine learning

Fingerprint

Dive into the research topics of 'Metal-Organic Framework Targeting for Optimal Pressure Swing Adsorption Processes'. Together they form a unique fingerprint.

Cite this