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Developing chemical kinetic models for thermochemical applications

Nature Protocols volume 21, pages 635–688 (2026)Cite this article

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Abstract

A general procedure for the development of chemical kinetic models relevant to thermochemical applications (pyrolysis, gasification and combustion) is described. Here we present techniques that aim at producing models that are modular in structure, thoroughly validated, and applicable to a wide variety of conditions (generality), while balancing accuracy and computational burden. Starting from a core mechanism describing the pyrolysis and oxidation of light species, heavier compounds are added to the model in a hierarchical fashion, starting from archetypal species of each class of compounds. Using analogy rules derived from the archetypal species, a list of reactions and reaction rate parameters are compiled for molecules belonging to the classes of interest, obtaining detailed or semidetailed reaction mechanisms. The model is then validated using data available from the literature and/or novel experiments performed ad hoc. Depending on the applications of interest and on the size of the model, a mechanism reduction can be performed using a combination of lumping techniques and flux or sensitivity analyses. These procedures, although partially automated, still require some level of expert knowledge. The development of reaction rate rules and the identification of reaction pathways require indeed critical analysis and are most effective when the operator has previous experience in the field. A rigorously built mechanism, obeying the general principles presented here, provides high predictivity and permits extrapolating fuel behavior with greater confidence outside the range of validation conditions compared with models assembled from nonconsistently sourced submechanism from the literature, or based on limited datasets and empirical information.

Key points

  • The combustion of even simple fuels involves a large number of interconnected chemical reactions. Furthermore, it is difficult to obtain enough reliable kinetic and thermodynamic experimental data when more complex feedstock compounds are considered.

  • The approach used in this protocol aims at building a robust kinetic model through a systematic procedure. Complexity is reduced by chemical lumping (representation of multiple species with a single reference component), flux or sensitivity analyses.

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Fig. 1: Schematic representation of the high temperature decomposition mechanism on n-pentane and 1-pentanol.
Fig. 2: β-scission reaction involving homologous radicals of pentane and 1-pentanol.
Fig. 3: Flowchart of the main phases of the mechanism development protocol.
Fig. 4: Venn representation of the complex interconnections among the modules comprised by a general high-temperature oxidation mechanism for hydrocarbon fuels.
Fig. 5: Conceptual representation of the relations between archetypal species and complex fuels.
Fig. 6: Schematic representation of n-heptane oxidation pathways.
Fig. 7: The evaluation process of a detailed kinetic mechanism.
Fig. 8: Different approaches to compute the similarity between the models and the experimental data and their pitfalls.
Fig. 9: Flowchart detailing the steps involved in the definition of the mechanisms of archetypal species.
Fig. 10: Flowchart detailing the steps necessary to the definition of reaction rate rules.
Fig. 11: The main steps involved in the study of unknown transition states (TS) and reaction rates via AI-TST-ME techniques.
Fig. 12: Development of a reduced kinetic mechanism for n-heptane oxidation.
Fig. 13: Sensitivity analysis.
Fig. 14: Experimental and calculated rate constants for the H-abstraction reaction by methyl from benzene.
Fig. 15: Experimental and simulated CO and \(\dot{\mathbf{H}}\) mole fraction profiles using two versions of the CRECK model.
Fig. 16: Effect of the introduction of the Korcek decomposition mechanism of ketohydroperoxide species154.
Fig. 17: Data coverage.

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Data availability

The data used in the development of the procedure and supporting its application are publicly available as part of our supporting primary research publications. The kinetic mechanisms developed by the authors using the procedures here discussed are available at https://www.creckmodeling.polimi.it/. The kinetic mechanisms developed as a worked example are available through the GitHub repository here linked: https://github.com/CRECKMODELING/nhept_mech/releases/tag/v0-published

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Acknowledgements

The recent work carried out by A. Nobili, T. Dinelli, E. Ramalli and many other PhD students before them in model development, validation and the implementation and testing of procedures here described is acknowledged.

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Authors and Affiliations

  1. CRECK Modeling Lab, Department of Chemistry, Materials, and Chemical Engineering ‘G. Natta’, Politecnico di Milano, Milan, Italy

    Marco Mehl, Matteo Pelucchi, Luna Pratali Maffei, Alessandro Stagni, Alberto Cuoci, Alessio Frassoldati, Eliseo Ranzi & Tiziano Faravelli

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  1. Marco Mehl
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  2. Matteo Pelucchi
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  3. Luna Pratali Maffei
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  4. Alessandro Stagni
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  5. Alberto Cuoci
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  6. Alessio Frassoldati
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  7. Eliseo Ranzi
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  8. Tiziano Faravelli
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Contributions

M.M.: conceptualization, writing and revision. M.P.: conceptualization, writing and revision. L.P.M.: conceptualization, writing and revision. A.S.: conceptualization, writing and revision. A.C.: conceptualization, writing and revision. A.F.: conceptualization, writing and revision. E.R.: conceptualization, writing and revision. T.F.: conceptualization, writing and revision.

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Correspondence to Tiziano Faravelli.

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Key references

Bernardi, M. S. et al. Combust. Flame 168, 186–203 (2016): https://doi.org/10.1016/j.combustflame.2016.03.019

Stagni, A. et al. Combust. Flame 163, 382–393 (2016): https://doi.org/10.1016/j.combustflame.2015.10.013

Ranzi, E. et al. Prog. Energy Combust. Sci. 27, 99–139 (2001): https://doi.org/10.1016/S0360-1285(00)00013-7

Pratali Maffei, L. et al. Proc. Combust. Inst. 39, 695–703 (2023): https://doi.org/10.1016/j.proci.2022.08.020

Extended data

Extended Data Fig. 1

Reaction paths controlling the oxidation of heavy alkanes (a) and alcohols (b) adopted from 92.

Extended Data Fig. 2 Snapshot of SciExpeM analytical representation of the performance index of model prediction versus experimental data for the core mechanism adopted in the CRECK framework.

Red squares identify discrepancies.

Extended Data Fig. 3 Detailed representation in the T, P, Φ of the performance index of model predictions vs experimental data for the core mechanism adopted in the CRECK framework.

Red points identify discrepancies. No systematic trends can be observed.

Extended Data Fig. 4

Heat map highlighting the performances of three different iso-pentanol mechanism from literature: CRECK, NUIG (National University of Galway, Ireland), and KAUST (King Abdullah University of Science and Technology).

Extended Data Fig. 5 Detailed representation of the performance index of model prediction vs experimental data for the iso-pentanol model developed at CRECK.

A higher score implies better agreement.

Supplementary information

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Mehl, M., Pelucchi, M., Maffei, L.P. et al. Developing chemical kinetic models for thermochemical applications. Nat Protoc 21, 635–688 (2026). https://doi.org/10.1038/s41596-025-01195-z

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