Prediction of gas storage capacities in metal organic frameworks using artificial neural network
Yazarlar (2)
Doç. Dr. Zeynep YILDIZ UZUN Ondokuz Mayis Üniversitesi, Türkiye
Harun Uzun Ondokuz Mayis Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Microporous and Mesoporous Materials (Q1) | ||
| Dergi ISSN | 1387-1811 Dergi Bilgileri (2015) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 05-2015 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-05-2015 |
| Cilt / Sayı / Sayfa | 208 / 1 / 50–54 | DOI | 10.1016/j.micromeso.2015.01.037 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S1387181115000554 | ||
| UAK Araştırma Alanları |
Adsorpsiyonlu Süreçler
Yakıtlar ve Yanma
Modelleme ve Optimizasyon
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| 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. |
| Anahtar Kelimeler |
| Adsorption | Artificial neural network | Gas storage | MOFs |
Science Direct
Prediction of gas storage capacities in metal organic frameworks using artificial neural network
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 54 |
| Scopus | 60 |