On the public citation graph

Machine learning-driven prediction and interpretation of biodiesel properties for rational fuel design

https://doi.org/10.2139/ssrn.6668940
0 signed · 38 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
inferredcites →https://doi.org/10.1007/bf03326109openalex-snapshot
Correlation between the chemical structure of biodiesel and its physical properties
inferredcites →https://doi.org/10.1007/s11746-999-0238-5openalex-snapshot
Predicting the surface tension of biodiesel fuels from their fatty acid composition
inferredcites →https://doi.org/10.1016/j.biombioe.2025.108594openalex-snapshot
Machine learning-driven prediction of biochar yield and fuel properties from biomass characteristics and pyrolysis conditions
inferredcites →https://doi.org/10.1016/j.biortech.2008.06.039openalex-snapshot
Influence of fatty acid composition of raw materials on biodiesel properties
inferredcites →https://doi.org/10.1016/j.commatsci.2019.109203openalex-snapshot
Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation
inferredcites →https://doi.org/10.1016/j.enconman.2012.07.023openalex-snapshot
Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression
inferredcites →https://doi.org/10.1016/j.enconman.2017.06.050openalex-snapshot
The individual effects of cetane number, oxygen content or fuel properties on the ignition delay, combustion characteristics, and cyclic variation of a turbocharged CRDI diesel engine – Part 1
inferredcites →https://doi.org/10.1016/j.energy.2010.08.030openalex-snapshot
Fatty acids composition as a means to estimate the high heating value (HHV) of vegetable oils and biodiesel fuels
inferredcites →https://doi.org/10.1016/j.fuel.2008.11.011openalex-snapshot
Predicting the temperature dependent viscosity of biodiesel fuels
inferredcites →https://doi.org/10.1016/j.fuel.2011.06.070openalex-snapshot
Predicting cetane number, kinematic viscosity, density and higher heating value of biodiesel from its fatty acid methyl ester composition
inferredcites →https://doi.org/10.1016/j.fuel.2012.09.083openalex-snapshot
Novel data and a group contribution method for the prediction of the speed of sound and isentropic compressibility of pure fatty acids methyl and ethyl esters
inferredcites →https://doi.org/10.1016/j.fuel.2013.11.020openalex-snapshot
A comprehensive evaluation of the cetane numbers of fatty acid methyl esters
inferredcites →https://doi.org/10.1016/j.fuel.2013.12.029openalex-snapshot
Neural network prediction of biodiesel kinematic viscosity at 313K
inferredcites →https://doi.org/10.1016/j.fuel.2014.01.104openalex-snapshot
High pressure physicochemical properties of biodiesel components derived from coconut oil or babassu oil
inferredcites →https://doi.org/10.1016/j.fuel.2015.01.024openalex-snapshot
Artificial neural network models to predict density, dynamic viscosity, and cetane number of biodiesel
inferredcites →https://doi.org/10.1016/j.fuel.2015.10.109openalex-snapshot
Surface tension and rheological behavior of sal oil methyl ester biodiesel and its blend with petrodiesel fuel
inferredcites →https://doi.org/10.1016/j.fuel.2020.119056openalex-snapshot
A composition based approach to predict density, viscosity and surface tension of biodiesel fuels
inferredcites →https://doi.org/10.1016/j.fuel.2021.121085openalex-snapshot
A comprehensive review of biodiesel property prediction models for combustion modeling studies
inferredcites →https://doi.org/10.1016/j.fuproc.2004.11.002openalex-snapshot
Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters
inferredcites →https://doi.org/10.1016/j.neucom.2003.11.012openalex-snapshot
A heuristic training for support vector regression
inferredcites →https://doi.org/10.1016/j.renene.2011.04.007openalex-snapshot
Biodiesel improves lubricity of new low sulphur diesel fuels
inferredcites →https://doi.org/10.1016/j.renene.2015.07.103openalex-snapshot
Analysis of blended fuel properties and engine performance with palm biodiesel–diesel blended fuel
inferredcites →https://doi.org/10.1016/j.rser.2011.07.143openalex-snapshot
Review of biodiesel composition, properties, and specifications
inferredcites →https://doi.org/10.1016/j.rser.2012.11.022openalex-snapshot
Density and viscosity of biodiesel as a function of temperature: Empirical models
inferredcites →https://doi.org/10.1016/j.rser.2017.03.045openalex-snapshot
Performance and emission characteristics of biodiesel–diesel blend and environmental and economic impacts of biodiesel production: A review
inferredcites →https://doi.org/10.1016/j.rser.2023.113235openalex-snapshot
Advancement of biodiesel fuel quality and NOx emission control techniques
inferredcites →https://doi.org/10.1016/j.rser.2025.115769openalex-snapshot
Status, developments, and sustainability of biowaste feedstock: A review of current progress
inferredcites →https://doi.org/10.1021/acs.energyfuels.6b01343openalex-snapshot
Predicting the Cetane Number of Biodiesel Fuels from Their Fatty Acid Methyl Ester Composition
inferredcites →https://doi.org/10.1021/acs.energyfuels.7b00561openalex-snapshot
Experimental Investigation and Modeling of Thermophysical Properties of Pure Methyl and Ethyl Esters at High Pressures
inferredcites →https://doi.org/10.1021/ef101096xopenalex-snapshot
Effect of the Degree of Unsaturation of Biodiesel Fuels on Engine Performance, Combustion Characteristics, and Emissions
inferredcites →https://doi.org/10.1021/ef201217qopenalex-snapshot
Measurement and Prediction of Biodiesel Surface Tensions
inferredcites →https://doi.org/10.1021/je000232dopenalex-snapshot
Use of the DIPPR Database for Development of QSPR Correlations: Surface Tension
inferredcites →https://doi.org/10.1021/je100042copenalex-snapshot
Densities and Viscosities of Fatty Acid Methyl and Ethyl Esters
inferredcites →https://doi.org/10.1021/je1012235openalex-snapshot
Densities and Viscosities of Minority Fatty Acid Methyl and Ethyl Esters Present in Biodiesel
inferredcites →https://doi.org/10.1023/b:stco.0000035301.49549.88openalex-snapshot
A tutorial on support vector regression
inferredcites →https://doi.org/10.1063/1.3253106openalex-snapshot
The Surface Tension of Pure Liquid Compounds
inferredcites →https://doi.org/10.1080/00319104.2017.1286340openalex-snapshot
Densities of FAMEs or FAEEs with ethanol at temperatures from 283.15 to 318.15 K
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.