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Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods

https://doi.org/10.1021/ef3001339
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A multiscale graph neural network for predicting the properties of high-density cycloalkane-based diesel and jet range biofuels
inferred← citeshttps://doi.org/10.1039/d5me00035aopenalex-snapshot
Evaluating the density of organic compounds at variable temperatures by a norm descriptor-based QSPR model
inferred← citeshttps://doi.org/10.1051/itmconf/20257701023openalex-snapshot
Application of information technologies, including quantum chemistry methods, in creating high-efficiency fuel compositions
inferred← citeshttps://doi.org/10.1063/1674-0068/cjcp2104079openalex-snapshot
High-performance chemical information database towards accelerating discovery of metal-organic frameworks for gas adsorption with machine learning
inferred← citeshttps://doi.org/10.1063/5.0038267openalex-snapshot
Shear viscosity prediction of alcohols, hydrocarbons, halogenated, carbonyl, nitrogen-containing, and sulfur compounds using the variable force fields
inferred← citeshttps://doi.org/10.1063/5.0100575openalex-snapshot
Rheological identification of jetted fluid using machine learning
inferred← citeshttps://doi.org/10.1080/1062936x.2013.766634openalex-snapshot
On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods
inferred← citeshttps://doi.org/10.1080/1062936x.2023.2244410openalex-snapshot
Machine learning-based models for accessing thermal conductivity of liquids at different temperature conditions
inferred← citeshttps://doi.org/10.1080/17597269.2021.1894002openalex-snapshot
Bioethanol fuel quality assessment using dielectric spectroscopy
inferred← citeshttps://doi.org/10.1109/icerect56837.2022.10060764openalex-snapshot
Prediction of Experimental Yield during Transesterification of Mahua Oil Biodiesel Using Artificial Neural Network
inferred← citeshttps://doi.org/10.1109/pgsret.2018.8685928openalex-snapshot
Data-based Sensing of Composition and Quality of Product in Biodiesel Production
inferred← citeshttps://doi.org/10.1186/s13321-024-00820-5openalex-snapshot
Advancing material property prediction: using physics-informed machine learning models for viscosity
inferred← citeshttps://doi.org/10.14355/ijcsa.2014.0303.01openalex-snapshot
Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality
inferred← citeshttps://doi.org/10.15255/kui.2019.053openalex-snapshot
Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis
inferred← citeshttps://doi.org/10.2118/164091-msopenalex-snapshot
Application of Quantitative Structure-Property Relationship (QSPR) Method for Chemical EOR Surfactant Selection
inferred← citeshttps://doi.org/10.24275/uami.5m60qs05sopenalex-snapshot
Predicción de densidad y viscosidad del biodiésel a partir de redes neuronales artificiales
inferred← citeshttps://doi.org/10.2516/ogst/2012034openalex-snapshot
Rational Formulation of Alternative Fuels using QSPR Methods: Application to Jet Fuels
inferred← citeshttps://doi.org/10.26434/chemrxiv-2023-5dgm9openalex-snapshot
Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents. 1: Prediction of Density and Refractive Index Using Machine Learning Algorithms with correlation-group parallel feature analysis
inferred← citeshttps://doi.org/10.31219/osf.io/cj3w7openalex-snapshot
Digitalization in Thermodynamics
inferred← citeshttps://doi.org/10.3389/fenrg.2022.1074699openalex-snapshot
Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures
inferred← citeshttps://doi.org/10.3390/fluids7030116openalex-snapshot
Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review
inferred← citeshttps://doi.org/10.3390/polym15092166openalex-snapshot
Rheological Properties of Small-Molecular Liquids at High Shear Strain Rates
inferred← citeshttps://doi.org/10.3390/proceedings2019029120openalex-snapshot
Effect of Temperature and Composition on Viscosity of Different Formulation of Ethyl Levulinate/Diesel Fuel/Biodiesel
inferred← citeshttps://doi.org/10.5204/thesis.eprints.211354openalex-snapshot
A data-driven smoothed particle hydrodynamics method for fluids
inferred← citeshttps://doi.org/10.5753/wcama.2017.3440openalex-snapshot
Aplicação de RNAs para Predição de Indice de Viscosidade: uma avaliação da qualidade de biodiesel
inferred← citeshttps://doi.org/10.5772/intechopen.70691openalex-snapshot
Applications of Artificial Neural Networks in Biofuels
inferred← citeshttps://doi.org/10.6084/m9.figshare.26693446openalex-snapshot
Additional file 1 of Advancing material property prediction: using physics-informed machine learning models for viscosity
inferred← citeshttps://doi.org/10.6084/m9.figshare.26693449openalex-snapshot
Additional file 2 of Advancing material property prediction: using physics-informed machine learning models for viscosity
inferred← citeshttps://doi.org/10.6084/m9.figshare.26693452openalex-snapshot
Additional file 3 of Advancing material property prediction: using physics-informed machine learning models for viscosity
inferred← citeshttps://doi.org/10.70675/f96832e6z6c9fz47c7za688z42522c6314b8openalex-snapshot
Thermal conductivity data acquisition using microfluidics and cheminformatics
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