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

Basit Öğe Kaydı
dc.authoridYESILYURT, Murat Kadir/0000-0003-0870-7564
dc.authoridUslu, Samet/0000-0001-9118-5108
dc.contributor.authorUslu, Samet
dc.contributor.authorYesilyurt, Murat Kadir
dc.contributor.authorYaman, Hayri
dc.date.accessioned2025-01-21T16:41:41Z
dc.date.available2025-01-21T16:41:41Z
dc.date.issued2022
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn 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 degrees 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.
dc.description.sponsorshipScientific Research Projects Coordination Unit of Kirikkale University [2018/067]
dc.description.sponsorshipThis work was supported by Scientific Research Projects Coordination Unit of Kirikkale University. Project number: 2018/067.
dc.identifier.doi10.2516/stet/2022010
dc.identifier.issn2804-7699
dc.identifier.scopus2-s2.0-85131313747
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.2516/stet/2022010
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24922
dc.identifier.volume77
dc.identifier.wosWOS:000798258000001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEdp Sciences S A
dc.relation.ispartofScience and Technology For Energy Transition
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectArtificial neural network; Response surface methodology; Acetone; Optimization; Spark ignition engine
dc.titleImpact prediction model of acetone at various ignition advance by artificial neural network and response surface methodology techniques for spark ignition engine
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu