Cetane number prediction from molecular structure using artificial neural networks

T. Sennott, C. Gotianun, R. Serres, J. H. Mack, R. Dibble

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


The production of next-generation biofuels is being explored through a variety of chemical and biological approaches, all aiming at lowering costs and increasing yields while producing viable alternatives to gasoline or diesel fuel. Chemical synthesis can lead to a huge variety of different fuels and the guidelines for which molecules would yield desirable properties as a fuel are largely based on intuition. One such property of interest is the cetane number, a measure of the ignition quality of diesel fuel. The present work improves on existing models and extends them to more oxygenates (primarily ethers) as an interim step in extending the model for further compounds of interest such as furans and tetrahydrofurans. Various members of these classes are being considered as fuels of interest by chemists and engineers in biofuel production. The present model uses artificial neural networks (ANN's) as a tool for quantitative structure property relationship (QSPR) analysis. Predicting the cetane number of a fuel is especially important because a large volume of pure sample (100mL or more) is typically required for lab testing, the production of which can be difficult and time-consuming at the lab scale. To this end, a predictive model will allow chemists to eliminate unlikely targets and focus their attention on promising candidates.

Original languageEnglish (US)
Title of host publication8th US National Combustion Meeting 2013
PublisherWestern States Section/Combustion Institute
Number of pages12
ISBN (Electronic)9781627488426
StatePublished - 2013
Externally publishedYes
Event8th US National Combustion Meeting 2013 - Park City, United States
Duration: May 19 2013May 22 2013

Publication series

Name8th US National Combustion Meeting 2013


Other8th US National Combustion Meeting 2013
Country/TerritoryUnited States
CityPark City

ASJC Scopus subject areas

  • General Chemical Engineering
  • Mechanical Engineering
  • Physical and Theoretical Chemistry


Dive into the research topics of 'Cetane number prediction from molecular structure using artificial neural networks'. Together they form a unique fingerprint.

Cite this