Impact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine

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Uslu S., YEŞİLYURT M. K., Yaman H.

Science and Technology for Energy Transition (STET), cilt.77, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 77
  • Basım Tarihi: 2022
  • Doi Numarası: 10.2516/stet/2022010
  • Dergi Adı: Science and Technology for Energy Transition (STET)
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Artificial neural network, Response surface methodology, Acetone, Optimization, Spark ignition engine, EXHAUST EMISSIONS, DIESEL-ENGINE, GASOLINE BLENDS, FUEL, PERFORMANCE, OPTIMIZATION, COMBUSTION, ALCOHOL, RSM
  • Yozgat Bozok Üniversitesi Adresli: Evet

Özet

© 2022 AuthorsIn this study, it was aimed to predict and optimize the effects of acetone/gasoline mixtures on spark ignition engine responses at different engine speeds and ignition advance values with artificial neural network and response surface methodology. The regression results obtained from response surface methodology show that absolute variance ratio values for all answers are greater than 0.96. Correlation coefficient values obtained from artificial neural network were obtained higher than 0.91. Mean absolute percentage error values were between 0.8859% and 9.01427% for artificial neural network, while it was between 1.146% and 8.957% for response surface methodology. Optimization study with response surface methodology revealed that the optimum results are 1700 rpm engine speed, 2% acetone ratio and 11 before top dead center ignition advance with a combined desirability factor of 0.76523%. Additionally, in accordance with the confirmation analysis among the optimal outcomes and the estimation outcomes, it was stated that there is a great harmony with a maximum error percentage of 7.662%. As a result, it is concluded that the applied response surface methodology and artificial neural network models can perfectly provide the impact of acetone percentage on spark ignition engine responses at different engine speeds and ignition advance values.