Hydrogen-enriched hazelnut oil methyl-ester/diesel blends in dual-fuel modes: An experimental analysis and modelling of engine characteristics with developed hybrid algorithms
Process Safety and Environmental Protection, cilt.220, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 220
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.psep.2026.109599
- Dergi Adı: Process Safety and Environmental Protection
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Chemical Abstracts Core, Chimica, Compendex, INSPEC
- Anahtar Kelimeler: Combustion, Engine performance, Exhaust pollutants, Hazelnut methyl ester, Hybrid Predictive algorithms, Hydrogen enrichment
- İstanbul Ticaret Üniversitesi Adresli: Evet
Özet
The ecological balance is compromised by greenhouse gas emissions and fossil fuel reliance, which escalate the risks of global climate change and the exhaustion of energy supplies. Integrating clean and sustainable alternative fuels into the system is critical to solving these problems. While the use of biofuels alone, a strategic alternative, presents technical limitations, enriching biodiesel with hydrogen offers significant potential in optimizing combustion characteristics and improving emission performance. Motivated by this, test fuels were prepared by adding hydrogen to the B30 (30% biodiesel by volume) fuel, derived from hazelnut oil extracted from hazelnuts, of which our country is a world leader in production, through the intake manifold at flow rates of 15 and 30 Lpm. The results showed that while the brake-specific energy consumption (BSEC) of B30 fuel increased by an average of 3.06% compared to D100 fuel, the addition of 15 and 30 Lpm hydrogen decreased the BSEC by 8.60% and 10.67%, respectively. Brake thermal efficiency (BTE), which tended to decrease with B30 use, increased with hydrogen enrichment; this confirms the positive effect of hydrogen on combustion efficiency and performance. In addition, the lowest CO emissions were recorded with B30 + 30 Lpm H2 fuel, and an increase in emissions was observed in all mixtures in parallel with engine load. Furthermore, the developed hybrid predictive approaches (BCO-ANN, HSO-ANN, KLA-ANN) and original ANN are used to model the CI engine performance and emission characteristics. The results show that the proposed hybrid metaheuristic-driven ANN models provide more accurate predictions than the conventional ANN.