Machine learning techniques to predict the flame state, temperature and species concentrations in counter-flow diffusion flames operated with CH4/CO/H2-air mixtures

René Josef Prieler*, Matthias Moser, Sven Eckart, Hartmut Krause, Christoph Hochenauer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The usage of artificial intelligence (AI) is increasing in many fields of research, since complex physical problems can be ‘learned’ and reproduced by AI methods. Thus, instead of numerically solving partial differential equations, describing the physical processes in detail, appropriate AI methods can be used to decrease the calculation time significantly. In the present study, artificial neural networks (ANNs) were used to predict temperature and species concentrations in a laminar counter-flow diffusion flame. To improve the accuracy of the ANNs, a support vector machine (SVM) was used to subdivide the wide range of operating conditions (air–fuel ratio, strain rate, fuel mixture) into ‘flame’ and ‘no flame’ cases. Due to classification with the SVM the prediction performance of the ANNs was optimized and an average error to the reference values (GRI3.0) below 10 K for all cases was detected, whereas the calculation time was decreased by a factor of about 4,800 (solving the transport equations with GRI3.0).
Original languageEnglish
Article number124915
JournalFuel
Volume326
Issue number15
DOIs
Publication statusPublished - 15 Oct 2022

Keywords

  • Artificial neural network
  • Combustion modelling
  • Counter-flow diffusion flame
  • Reaction kinetic
  • Support vector machine

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Chemical Engineering(all)
  • Fuel Technology
  • Organic Chemistry

Fields of Expertise

  • Sustainable Systems

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