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dc.contributor.author
Ferreira, Abel G.M.
dc.contributor.author
Talavera Prieto, Nieves María del Carmen
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dc.contributor.author
Portugal, António A.
dc.contributor.author
Moreira, Rui J.
dc.date.available
2023-01-04T18:26:11Z
dc.date.issued
2021-03
dc.identifier.citation
Ferreira, Abel G.M.; Talavera Prieto, Nieves María del Carmen; Portugal, António A.; Moreira, Rui J.; REVIEW: Models for predicting viscosities of biodiesel fuels over extended ranges of temperature and pressure; Elsevier; Fuel; 287; 119544; 3-2021; 1-28
dc.identifier.issn
0016-2361
dc.identifier.uri
http://hdl.handle.net/11336/183396
dc.description.abstract
Fuel viscosity is an important property that has a significant effect in fuel injection, spray development and combustion in Compression Ignition (CI) engines. Current and future injector designs of diesel engines (such as rail injection systems) work at high pressures (>100 MPa), meaning that fuel viscosity increases substantially over the atmospheric values. The estimation of biodiesel (BD) viscosity based on the knowledge of its composition would be of great potential in the optimization of biodiesel production processes, particularly in the blending of raw materials and refined products. In this work, comprehensive data sets were chosen from literature regarding several BD classes, in order to establish new correlations and new predictive methods of viscosity. The proposed methodologies were validated using available viscosity data of BDs having different chemical compositions in wide ranges of temperature and pressure. The new methods developed at atmospheric pressure for predicting BD viscosity were found to have better predictive ability than those commonly used in literature. In particular, the models developed with the Lewis and Squires equation fitted to biodiesel feedstock (LSDB model) and the same equation using the predicted degree of unsaturation (DU) (LSDU1 model) presented a very good performance with average relative deviation (ARD) < 2.5%. The results of those models were comparable to the uncertainty of the experimental measurements reported in the literature. For high pressure, the methods developed in the literature for oils and (in particular) vegetable oils were adapted here for biodiesel. A new method presented in this work using Avramov equation (AVR model) was able to predict the viscosity of different BD classes with ARD < 3%, which is better than that (ARD < 5%) predicted by the only method reported so far in literature.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
BIODIESEL
dc.subject
PRESSURE
dc.subject
TEMPERATURE
dc.subject
VISCOSITY
dc.subject
VISCOSITY PREDICTION MODEL
dc.subject.classification
Física de los Materiales Condensados
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dc.subject.classification
Ciencias Físicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
REVIEW: Models for predicting viscosities of biodiesel fuels over extended ranges of temperature and pressure
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-08-19T18:16:56Z
dc.identifier.eissn
1873-7153
dc.journal.volume
287
dc.journal.number
119544
dc.journal.pagination
1-28
dc.journal.pais
Países Bajos
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dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Ferreira, Abel G.M.. Universidad de Coimbra; Portugal
dc.description.fil
Fil: Talavera Prieto, Nieves María del Carmen. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; Argentina
dc.description.fil
Fil: Portugal, António A.. Universidad de Coimbra; Portugal
dc.description.fil
Fil: Moreira, Rui J.. Universidad de Coimbra; Portugal
dc.journal.title
Fuel
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.fuel.2020.119544
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0016236120325400
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