Prediction of gas storage capacities in metal organic frameworks using artificial neural network

Yildiz Z., Uzun H.

MICROPOROUS AND MESOPOROUS MATERIALS, vol.208, pp.50-54, 2015 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 208
  • Publication Date: 2015
  • Doi Number: 10.1016/j.micromeso.2015.01.037
  • Journal Name: MICROPOROUS AND MESOPOROUS MATERIALS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.50-54
  • Keywords: Adsorption, Artificial neural network, Gas storage, MOFs
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In this study, artificial neural network was developed to forecast adsorption capacity of hydrogen gas in metal organic frameworks. Surface area, adsorption enthalpy, temperature and pressure were selected as input parameters. Hydrogen storage capacities of MOFs were computed using these four parameters. An artificial neural network was used to model the adsorption process. The prediction results were remarkably agreed with the experimental data. (C) 2015 Elsevier Inc. All rights reserved.