Deep Neural Network for Prediction of Adsorbent Selectivity on Hydrogen Purification

Chenglong Li*, Chengsi Xie, Yi Zong, Richard Chahine, Tianqi Yang, Feng Ye, Jinsheng Xiao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

37 Downloads (Pure)

Abstract

With emergence of new materials, more and more materials are available for adsorption and separation processes. The adsorption selectivity of adsorbent to adsorbate is one of the important indicators in choosing materials. Because the adsorption experiment of the mixture is time-consuming and difficult, the selectivity of the adsorbent is generally calculated by the ideal adsorbed solution theory (IAST). Taking the CO2/H2 gas mixture as an example, this paper proposes a new adsorption selectivity calculation method based on a deep neural network (DNN) with 5 hidden layers, which takes the molar fraction of CO2, adsorption pressure and Langmuir adsorption isotherm parameters as the inputs of DNN. Combining the DNN and the NIST/ARPA-E database to quickly and accurately calculate the adsorption selectivity, the hydrogen purification and carbon dioxide storage materials can be quickly screened.
Original languageEnglish
Title of host publicationProceedings of the 10th Hydrogen Technology Convention
EditorsHexu Sun, Wei Pei, Yan Dong, Hongmei Yu, Shi You
Number of pages8
Volume1
PublisherSpringer
Publication date2024
Pages214-221
ISBN (Print)978-981-99-8630-9, 978-981-99-8633-0
ISBN (Electronic)978-981-99-8631-6
DOIs
Publication statusPublished - 2024
Event10th Hydrogen Technology Convention - Foshan, China
Duration: 22 May 202326 May 2023
Conference number: 10

Conference

Conference10th Hydrogen Technology Convention
Number10
Country/TerritoryChina
CityFoshan
Period22/05/202326/05/2023
SeriesSpringer Proceedings in Physics
Volume393
ISSN0930-8989

Keywords

  • Hydrogen purification
  • Ideal absorbed solution theory
  • Langmuir isotherm
  • Selectivity
  • Deep neural network

Fingerprint

Dive into the research topics of 'Deep Neural Network for Prediction of Adsorbent Selectivity on Hydrogen Purification'. Together they form a unique fingerprint.

Cite this