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

Yildiz Z., Uzun H.

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 208
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.micromeso.2015.01.037
  • Dergi Adı: MICROPOROUS AND MESOPOROUS MATERIALS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.50-54
  • Anahtar Kelimeler: Adsorption, Artificial neural network, Gas storage, MOFs
  • Ondokuz Mayıs Üniversitesi Adresli: Evet

Özet

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.