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
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
Science Direct
BM Sürdürülebilir Kalkınma Amaçları
Atıf Sayıları
Web of Science 50
Scopus 62
Google Scholar 76
The pyrolysis process verification of hydrogen rich gas H rG production by artificial neural network ANN

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