A neural network approach to the prediction of diesel fuel lubricity

Korres, DM; Anastopoulos, G; Lois, E; Alexandridis, A; Sarimveis, H; Bafas, G

dc.contributor.author	Korres, DM	en
dc.contributor.author	Anastopoulos, G	en
dc.contributor.author	Lois, E	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Bafas, G	en
dc.date.accessioned	2014-03-01T01:17:22Z
dc.date.available	2014-03-01T01:17:22Z
dc.date.issued	2002	en
dc.identifier.issn	0016-2361	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14492
dc.subject	Diesel	en
dc.subject	Lubricity	en
dc.subject	Neural networks	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Density of liquids	en
dc.subject.other	Distillation	en
dc.subject.other	Kinematics	en
dc.subject.other	Lubrication	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Viscosity	en
dc.subject.other	Diesel fuel lubricity	en
dc.subject.other	Diesel fuels	en
dc.title	A neural network approach to the prediction of diesel fuel lubricity	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0016-2361(02)00020-0	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0016-2361(02)00020-0	en
heal.language	English	en
heal.publicationDate	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 degreesC, sulfur content and 90% distillation point, which produced the smallest error in the validation data. (C) 2002 Elsevier Science Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Fuel	en
dc.identifier.doi	10.1016/S0016-2361(02)00020-0	en
dc.identifier.isi	ISI:000176802000001	en
dc.identifier.volume	81	en
dc.identifier.issue	10	en
dc.identifier.spage	1243	en
dc.identifier.epage	1250	en