Syngas production through forest waste gasification and prediction of its species using advanced novel metaheuristic driven hybrid machine learning algorithms

ÜREGEN GÜLER N., Bakır H., YUMURTACI Z., AĞBULUT Ü.

International Journal of Hydrogen Energy, cilt.196, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 196
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.ijhydene.2025.152529
  • Dergi Adı: International Journal of Hydrogen Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Artic & Antarctic Regions, Chemical Abstracts Core, Chimica, Compendex, Environment Index, INSPEC
  • Anahtar Kelimeler: Biomass gasification, Forest waste, Metaheuristic algorithm-driven ANN, Syngas production
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

The present study aims to produce syngas from forest waste through the gasification process. Through this process, significant operation parameters (gasification temperature from 650 to 1000 °C with an increment of 50 °C, gasifier pressure (1, 3, 5, and 7 bar), equivalence ratio (ER) (0.1, 0.2, 0.3, 0.4, and 0.7)) are varied to observe the changes on hydrogen (H2), carbon monoxide (CO), and carbon dioxide (CO2) concentrations. Furthermore, this work proposes single-layer hybrid ANN-THRO, ANN-BSLO, and ANN-QIO predictive models to forecast H2, CO, and CO2 concentrations within the syngas. The results showed that increasing the gasification temperature and reducing the gasifier pressure led to higher concentrations of H2 and CO in the syngas. However, the ER exhibited a bell-shaped trend for these gases. On the other hand, the CO2 formation generally exhibits an adverse trend against H2 and CO. In terms of the prediction results, the proposed three hybrid algorithms showed a very satisfactory prediction performance of significant syngas species with an R2 value of >0.96, rRMSE value of <6 %, and MAPE value of <5.3 %.