An ANN based hybrid chemistry framework for complex fuels

Rishikesh Ranade, Sultan Alqahtani, Aamir Farooq, Tarek Echekki*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


The oxidation chemistry of complex hydrocarbons involves large mechanisms with hundreds or thousands of chemical species and reactions. For practical applications and computational ease, it is desirable to reduce their chemistry. To this end, high-temperature fuel oxidation for large carbon number fuels may be described as comprising two steps, fuel pyrolysis and small species oxidation. Such an approach has recently been adopted as ‘hybrid chemistry’ or HyChem to handle high-temperature chemistry of jet fuels by utilizing time-series measurements of pyrolysis products. In the approach proposed here, a shallow Artificial Neural Network (ANN) is used to fit temporal profiles of fuel fragments to directly extract chemical reaction rate information. This information is then correlated with the species concentrations to build an ANN-based model for the fragments’ chemistry during the pyrolysis stage. Finally, this model is combined with a C0-C4 chemical mechanism to model high-temperature fuel oxidation. This new hybrid chemistry approach is demonstrated using homogeneous chemistry calculations of n-dodecane (n-C12H26) oxidation. The experimental uncertainty is simulated by introducing realistic noise in the data. The comparison shows a good agreement between the proposed ANN hybrid chemistry approach and detailed chemistry results.

Original languageEnglish (US)
Pages (from-to)625-636
Number of pages12
StatePublished - Apr 1 2019

Bibliographical note

Publisher Copyright:
© 2018 Elsevier Ltd


  • Artificial neural networks
  • Chemistry reduction
  • Hydrocarbon oxidation
  • Pyrolysis

ASJC Scopus subject areas

  • General Chemical Engineering
  • Energy Engineering and Power Technology
  • Fuel Technology
  • Organic Chemistry


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