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Modeling nitrogen chemistry in combustion

Open access
Glarborg, Peter, Miller, James A., Ruscic, Branko, Klippenstein, Stephen J.·2018·Progress in Energy and Combustion Science

Abstract

Abstract Understanding of the chemical processes that govern formation and destruction of nitrogen oxides (NOx) in combustion processes continues to be a challenge. Even though this area has been the subject of extensive research over the last four decades, there are still unresolved issues that may limit the accuracy of engineering calculations and thereby the potential of primary measures for NOx control. In this review our current understanding of the mechanisms that are responsible for combustion-generated nitrogen-containing air pollutants is discussed. The thermochemistry of the relevant nitrogen compounds is updated, using the Active Thermochemical Tables (ATcT) approach. Rate parameters for the key gas-phase reactions of the nitrogen species are surveyed, based on available information from experiments and high-level theory. The mechanisms for thermal and prompt-NO, for fuel-NO, and NO formation via NNH or N2O are discussed, along with the chemistry of NO removal processes such as reburning and Selective Non-Catalytic Reduction of NO. Each subset of the mechanism is evaluated against experimental data and the accuracy of modeling predictions is discussed.

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Metadata

Authors
Glarborg, P., Miller, J. A., Ruscic, B., & Klippenstein, S. J.
Venue
Progress in Energy and Combustion Science
Publisher
Elsevier BV
Published
2018-07
Volume
67
Pages
31-68
ISSN
0360-1285 (ISSN-L)
OpenAlex
W2790320648
Cited by
1,998
References
0
FWCI
60.62
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Modeling nitrogen chemistry in combustion. https://app.cochat.ai/papers/10.1016/j.pecs.2018.01.002
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Glarborg, P., Miller, J. A., Ruscic, B., & Klippenstein, S. J. (2018). Modeling nitrogen chemistry in combustion. Progress in Energy and Combustion Science. https://doi.org/10.1016/j.pecs.2018.01.002

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.