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A neural network approach to the prediction of diesel fuel lubricity

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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


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