Skeletal mechanisms for prediction of NOx emission in solid fuel combustion

Tian Li*, Øyvind Skreiberg, Terese Løvås, Peter Glarborg

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Emission of nitrogen oxides (NOx) is a major challenge for combustion of solid fuels. Strategies for emission control can be developed from computational fluid dynamics (CFD) simulation. This, furthermore, requires a computational efficient kinetic model that is able to capture both formation and destruction of NOx in a wide range of conditions. In this work, three skeletal mechanisms with varying degrees of reduction were developed based on a detailed kinetics model proposed recently (148 species and 2764 reactions). By preserving all major reaction pathways of NO formation, the most comprehensive skeletal mechanism Li45 (45 species and 788 reactions) behaved very similar compared to the base mechanism with regard to the prediction of NO. The more compact skeletal mechanism Li37 (37 species and 303 reactions) was generated specifically for the conditions relevant to large scale industrial combustion of solid fuels. The Li37 mechanism is capable of predicting NO formation as well as simulating common measures of NOx reduction such as the staged combustion and selective non-catalytic reduction (SNCR). Without the consideration of SNCR, the smallest skeletal mechanism Li32 (32 species and 255 reactions) still maintained a good predictability over broad temperature and excess air ratio ranges. Compared to the base mechanism, the skeletal mechanisms achieved over 70% reduction in species. Furthermore, the computational cost was lowered to a large extent, particularly with Li37 and Li32. This makes the developed skeletal mechanisms very suitable to be implemented in CFD simulations.
Original languageEnglish
Article number115569
Number of pages10
Publication statusPublished - 2019


  • Chemical kinetics
  • Combustion
  • Mechanism reduction
  • NOx
  • SNCR
  • Solid fuel


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