Enhancement of combustion characteristics of waste alcohol using n-heptane through RSM in an HCCI engine

Gürsoy H. O., Solmaz H., Kocakulak T., Alp Arslan T., Calam A.

Energy, cilt.313, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 313
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.energy.2024.133838
  • 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
  • Anahtar Kelimeler: Combustion, Fusel oil, HCCI engine, Optimization, Response surface methodology, Waste alcohol
  • Gazi Üniversitesi Adresli: Evet

Özet

The HCCI combustion mode, revealed as a result of internal combustion engine development studies, shortens the combustion duration and can operate at high compression ratios. Thanks to these advantages, it offers high thermal efficiency, high combustion efficiency, low NOx, and low soot emissions. In this study, an engine operating in HCCI combustion mode with fusel oil/n-heptane mixture fuel was optimized by RSM. The F30 fuel mixture used in the study consists of a volumetric combination of 30 % fusel oil-70 % n-heptane. The minimum and maximum values of the variable parameters are, an engine speed of 800–1400 rpm, a compression ratio of 1.9–2.7, and lambda 11–13, respectively. As a result of the optimization, optimum input parameters were calculated as 981 rpm engine speed, 1.9 lambda, and 11 compression ratio. The response parameters obtained depending on optimum input parameters are BSFC 261.628 g/kWh, IMEP 4.85 bar, ITE 31.69 %, HC 395.767 ppm, and CO 0.948 %. The importance and interactions of these input parameters were analyzed using the ANOVA method. As a result of the study, it has been seen that all multiple regression models developed with the RSM method can be used successfully to optimize engine performance and exhaust emissions.