| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Hydrogen Energy | ||
| Dergi ISSN | 0360-3199 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2016 |
| Cilt / Sayı / Sayfa | 41 / 8 / 4570–4578 | DOI | 10.1016/j.ijhydene.2016.01.094 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0360319915315755 | ||
| Özet |
| The main aim of this study is subject of thermochemical conversion process data into computational modelling. Especially, prediction of hydrogen gas from the pyrolysis of waste materials regarded as environmentally pollutants were accomplished by Artificial Neural Network (ANN) in context of sustainability. The data obtained from pyrolysis of biomass wastes; cotton cocoon shell (cotton–S), tea waste (tea–W) and olive husk (olive–H) were categorized and hydrogen rich gas (H–rG) portion was introduced to the NFTOOL of MATLAB program for ANN. The variables in the pyrolysis process were catalyst type, amount, temperature and biomass diversity. The H–rG production was rendered by catalysts; ZnCl2, NaCO3 and K2CO3. The combination of following condition; ZnCl2–10%, Olive–H and 973 K yield the best ANN models. This helped us save comprehensive amount of labour and time during experimentations … |
| Anahtar Kelimeler |
| Artificial neural network | Biomass waste | Hydrogen rich gas | Prediction | Pyrolysis |
| Atıf Sayıları | |
| Web of Science | 50 |
| Scopus | 62 |
| Google Scholar | 76 |
| Dergi Adı | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY |
| Yayıncı | Elsevier Ltd |
| Açık Erişim | Hayır |
| ISSN | 0360-3199 |
| E-ISSN | 1879-3487 |
| CiteScore | 13,3 |
| SJR | 1,685 |
| SNIP | 1,777 |