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 language | English |
|---|---|
| Title of host publication | Computer Aided Chemical Engineering |
| Publisher | Elsevier B.V. |
| Pages | 295-300 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - Jan 2022 |
| Externally published | Yes |
Publication series
| Name | Computer Aided Chemical Engineering |
|---|---|
| Volume | 49 |
| ISSN (Print) | 1570-7946 |
Bibliographical note
Publisher Copyright:© 2022 Elsevier B.V.
Keywords
- Adsorption process design
- Gas separation
- Hypothetical MOF
- MOF targeting
- Machine learning