A neural network approach to the prediction of diesel fuel lubricity

dc.contributor.author	Κορρές, Δημήτριος Μ.	el
dc.contributor.author	Αναστόπουλος, Γεώργιος	el
dc.contributor.author	Λόης, Ευριπίδης	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.date.accessioned	2015-06-04T15:27:33Z
dc.date.available	2015-06-04T15:27:33Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15078
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Diesel
dc.subject	Lubricity
dc.subject	Neural networks
dc.subject	λιπαντικότητα
dc.subject	νευρωνικά δίκτυα
dc.subject	Πετρέλαιο
dc.title	A neural network approach to the prediction of diesel fuel lubricity	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Chemical technology
heal.classification	Τεχνολογία
heal.classification	Χημική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C00565
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Χημική τεχνολογία
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.contributorName	Μπάφας, Γιώργος Β.	el
heal.identifier.secondary	DOI: 10.1016/S0016-2361(02)00020-0
heal.language	en
heal.access	campus
heal.publicationDate	2002-07
heal.bibliographicCitation	KORRES, D.M., ANASTOPOULOS, G., LOIS, E., ALEXANDRIDIS, A.P., SARIMVEIS, H.K., et al. (2002). A neural network approach to the prediction of diesel fuel lubricity. Fuel. [online] 81 (10). p. 1243-1250. Available from: http://www.elsevier.com/[Accessed 08/02/2002]	en
heal.abstract	The continuous sulfur reduction in diesel fuel has resulted in poor fuel lubricity and engine pump failure, a fact that led to the development of a number of methods that measure the actual fuel lubricity level. However, lubricity measurement is costly and time consuming, and a number of predictive models have been developed in the past, based mainly on various fuel properties. In the present paper, a black box modeling approach is proposed, where the lubricity is approximated by a radial basis function (RBF) neural network that uses other fuel properties as inputs. The HFRR apparatus was used for lubricity measurements. In the present model, the variables used included the diesel fuel conductivity, density, kinematic viscosity at 40 °C, sulfur content and 90% distillation point, which produced the smallest error in the validation data.	en
heal.publisher	Elsevier	en
heal.journalName	Fuel	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true