Prediction of the cold flow properties of biodiesel using the fame distribution and machine learning techniques

dc.contributor.author	Díez Valbuena, Guillermo
dc.contributor.author	García Tuero, Alejandro
dc.contributor.author	Díez Peláez, Jorge
dc.contributor.author	Rodríguez Ordóñez, Eduardo
dc.contributor.author	Hernández Battez, Antolín Esteban
dc.date.accessioned	2024-10-22T06:08:34Z
dc.date.available	2024-10-22T06:08:34Z
dc.date.issued	2024
dc.identifier.citation	Journal of Molecular Liquids, 400, (2024); doi:10.1016/j.molliq.2024.124555
dc.identifier.issn	0167-7322
dc.identifier.uri	https://hdl.handle.net/10651/75326
dc.description.sponsorship	Ministry of Science, Innovation and Universities (Spain); State Investigation Agency [PID2022-136656NB-I00]; Foundation for the Promotion of Applied Scientific Research and Technology in Asturias (Spain); Guillermo Diez- Valbuena at the University of Oviedo (Spain) [SV-PA-21- AYUD/2021/50987]; Government of the Principality of Asturias under Severo Ochoa predoctoral program [BP22-153]
dc.language.iso	eng
dc.relation.ispartof	Journal of Molecular Liquids
dc.rights	©,
dc.source	Scopus
dc.source.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189066383&doi=10.1016%2fj.molliq.2024.124555&partnerID=40&md5=69690549b1742290237968fe36768841
dc.title	Prediction of the cold flow properties of biodiesel using the fame distribution and machine learning techniques
dc.type	journal article
dc.identifier.doi	10.1016/j.molliq.2024.124555
dc.relation.publisherversion	http://dx.doi.org/10.1016/j.molliq.2024.124555

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