Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis
Yazarlar (4)
Harun Uzun Ondokuz Mayis Üniversitesi, Türkiye
Doç. Dr. Zeynep YILDIZ UZUN Ondokuz Mayis Üniversitesi, Türkiye
Jillian L. Goldfarb
Boston University College of Engineering, Amerika Birleşik Devletleri
Boston University College of Engineering, Amerika Birleşik Devletleri
Selim Ceylan Ondokuz Mayis Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Bioresource Technology (Q1) | ||
| Dergi ISSN | 0960-8524 Dergi Bilgileri (2017) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2017 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-06-2017 |
| Cilt / Sayı / Sayfa | 234 / 1 / 122–130 | DOI | 10.1016/j.biortech.2017.03.015 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S0960852417302857 | ||
| UAK Araştırma Alanları |
Adsorpsiyonlu Süreçler
Yakıtlar ve Yanma
Modelleme ve Optimizasyon
|
||
| Özet |
| As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses’ higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. |
| Anahtar Kelimeler |
| Artificial neural network | Biomass | Higher heating value | Proximate analysis |
Science Direct
Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 125 |
| Scopus | 156 |