| dc.contributor.author | Yildiz, Zeynep | |
| dc.contributor.author | Uzun, Harun | |
| dc.date.accessioned | 2020-06-21T13:46:43Z | |
| dc.date.available | 2020-06-21T13:46:43Z | |
| dc.date.issued | 2015 | |
| dc.identifier.issn | 1387-1811 | |
| dc.identifier.issn | 1873-3093 | |
| dc.identifier.uri | https://doi.org/10.1016/j.micromeso.2015.01.037 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12712/14345 | |
| dc.description | WOS: 000352927000006 | en_US |
| dc.description.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. | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Elsevier | en_US |
| dc.relation.isversionof | 10.1016/j.micromeso.2015.01.037 | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Adsorption | en_US |
| dc.subject | Artificial neural network | en_US |
| dc.subject | Gas storage | en_US |
| dc.subject | MOFs | en_US |
| dc.title | Prediction of gas storage capacities in metal organic frameworks using artificial neural network | en_US |
| dc.type | article | en_US |
| dc.contributor.department | OMÜ | en_US |
| dc.identifier.volume | 208 | en_US |
| dc.identifier.startpage | 50 | en_US |
| dc.identifier.endpage | 54 | en_US |
| dc.relation.journal | Microporous and Mesoporous Materials | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
