| Yazarlar |
Abdulkadir Karacı
Kastamonu Üniversitesi, Türkiye |
Prof. Dr. Atila ÇAĞLAR
Kastamonu Üniversitesi, Türkiye |
Prof. Dr. Bahattin AYDINLI
Kastamonu Üniversitesi, Türkiye |
Doç. Dr. Sefa PEKOL
Kastamonu Üniversitesi, Türkiye |
| 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, which also result in sharpness data in energy and environmental issues and were very ambiguous. The results were discussed by using new concepts related with energy resources, hydrogen gas, modelling and sustainability. The presented perspective here can be a useful tool for researchers and users as well as planners. |
| Anahtar Kelimeler |
| Artificial neural network | Biomass waste | Hydrogen rich gas | Prediction | Pyrolysis |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | International Journal of Hydrogen Energy |
| Dergi ISSN | 0360-3199 |
| Dergi Tarandığı Indeksler | SCI |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2016 |
| Cilt No | 41 |
| Sayı | 8 |
| Sayfalar | 4570 / 4578 |
| Doi Numarası | 10.1016/j.ijhydene.2016.01.094 |
| Atıf Sayıları | |
| SCOPUS | 47 |
