Supplementary material from: Application of machine learning techniques to predict biodiesel iodine value
Supplementary material from: Application of machine learning techniques to predict biodiesel iodine value
Supplementary material from: Diez Valbuena, G., García Tuero, A., Díez, J., Rodriguez, E., & Hernández Battez, A. (2024). Application of machine learning techniques to predict biodiesel iodine value. Energy, 292(130638), 11. https://doi.org/10.1016/j.energy.2024.130638 The dataset is divided into three sections within the worksheet. The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors. The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs. The third and final section describes the measured property Iodine Value .
- University of Oviedo Spain
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!