Multi objective optimization of emission and performance characteristics in a spark ignition engine with a novel hydrogen generator

Yuce B. E., Oral F.

ENERGY, cilt.289, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 289
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.energy.2023.129957
  • Dergi Adı: ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

This study applies a novel approach using Taguchi, ANOVA, and Grey relational analysis to investigate perfor-mance in terms of engine torque, engine power, and emission rates of an SI engine supported by a novel hydrogen generator. The analysis focused on fuel type, engine speed (rpm), engine torque (Nm), engine power (kW), specific fuel consumption (gr/kWh), carbon monoxide (%), hydrocarbon emissions (ppm), carbon dioxide emissions (%), and nitrogen oxides emissions (ppm). There are four distinct fuels used in the experiment: pure gasoline, a blend of 95 % gasoline and 5 % ethanol by volume, pure gasoline combined with a mixture of hydrogen and oxygen, and a blend of 95 % gasoline and 5 % ethanol combined with a mix of hydrogen and oxygen. Additionally, the experiment involves testing these fuels at four different engine speeds: 200, 2500, 3000, and 3500 rpm. The results obtained from the experiments were interpreted in two distinct ways. The Taguchi method was first employed to identify each objective's optimal fuel type and engine speed. Among these parameters, the most influential one was determined. Using ANOVA analysis, the impacts of these factors were calculated as the contribution ratio, thus confirming the findings of the Taguchi analysis.