The pyrolysis process verification of hydrogen rich gas H rG production by artificial neural network ANN
Yazarlar (4)
Doç. Dr. 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
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Hydrogen Energy (Q1) | ||
| Dergi ISSN | 0360-3199 Dergi Bilgileri (2016) | ||
| Dergi Tarandığı Indeksler | SCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2016 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-03-2016 |
| Cilt / Sayı / Sayfa | 41 / 8 / 4570–4578 | DOI | 10.1016/j.ijhydene.2016.01.094 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0360319915315755 | ||
| UAK Araştırma Alanları |
Okul Öncesi Eğitimi
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| 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; ZnCl |
| Anahtar Kelimeler |
| Artificial neural network | Biomass waste | Hydrogen rich gas | Prediction | Pyrolysis |
Science Direct
The pyrolysis process verification of hydrogen rich gas H rG production by artificial neural network ANN
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 63 |