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NRCC Cetane Index – 1: An Improved Cetane Number Predictor

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What this work says

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

inferredcites →https://doi.org/10.1016/0010-2180(86)90123-9openalex-snapshot

Ignition quality determination of diesel fuels from hydrogen type distribution of hydrocarbons

inferredcites →https://doi.org/10.1021/i300022a005openalex-snapshot

Prediction of cetane number of diesel fuels from carbon type structural composition determined by proton NMR spectroscopy

inferredcites →https://doi.org/10.4271/600112openalex-snapshot

COMBUSTION CHARACTERISTICS OF COMPRESSION IGNITION ENGINE FUEL COMPONENTS

inferredcites →https://doi.org/10.4271/841341openalex-snapshot

Cetane Number Estimation of Diesel Fuels from Carbon Type Structural Composition

inferredcites →https://doi.org/10.4271/841344openalex-snapshot

Development of the Canadian General Standards Board (CGBS) Cetane Index

inferredcites →https://doi.org/10.4271/852080openalex-snapshot

Ignition Quality Rating Methods for Diesel Fuels-A Critical Appraisal

inferredcites →https://doi.org/10.4271/860250openalex-snapshot

Improved Predictive Equations for Cetane Number

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4664979openalex-extracted

inferred← citeshttps://doi.org/10.1016/0016-2361(95)00040-copenalex-snapshot

Equations for predicting the cetane number of diesel fuels from their physical properties

inferred← citeshttps://doi.org/10.1016/j.fuel.2024.131619openalex-snapshot

Prediction of Hydrocarbons Ignition Performances Using Machine Learning Modeling

inferred← citeshttps://doi.org/10.1021/acs.energyfuels.0c03899openalex-snapshot

Prediction of the Derived Cetane Number and Carbon/Hydrogen Ratio from Infrared Spectroscopic Data

inferred← citeshttps://doi.org/10.1021/acs.energyfuels.6b01690openalex-snapshot

Predicting Fuel Ignition Quality Using 1H NMR Spectroscopy and Multiple Linear Regression

inferred← citeshttps://doi.org/10.25781/kaust-zbb21openalex-snapshot

A functional group approach for predicting fuel properties

inferred← citeshttps://doi.org/10.33140/pcii/02/01/04openalex-snapshot

Relationship between Fuel Properties and Cetane Response of Cetane Improver for Non-Aromatic and Aromatic Fuels Used In A Single Cylinder Heavy Duty Diesel Engine

inferred← citeshttps://doi.org/10.4271/04-14-02-0005openalex-snapshot

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

inferred← citeshttps://doi.org/10.4271/861520openalex-snapshot

Surface Tension as an Indicator of Cetane Number of Diesel Fuels

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