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Rate-Based Construction of Kinetic Models for Complex Systems
Abstract
A general-purpose rate-based algorithm for the construction of chemical kinetic models for systems with hundreds or thousands of reacting species is presented. The algorithm comprehensively works out the details of the chemistry implied by given reaction rate estimation rules, identifies the species and reactions that are numerically significant, and solves the resulting system of differential equations to compute the concentrations of the significant species as a function of time. A key innovation is a definition and numerical test for the “completeness” of the kinetic scheme. This approach obviates the need to arbitrarily neglect certain species and reactions in order to keep reaction schemes small enough to be manageable and allows chemical kinetic modelers to focus on the chemistry rather than on the computational details. Examples of hydrocarbon pyrolysis and combustion applications are presented, where the computer evaluates the importance of nearly 100 000 reactions in the process of identifying the few hundred species that are kinetically significant. The new algorithm, given reliable rate estimation rules, provides a framework for systematically constructing kinetic schemes including all of the numerically significant species, even for systems involving so many reactions that they could not be handled manually.
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Citations
Cited by
Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms
Gao, C. W., Allen, J. W., Green, W. H., & West, R. H. (2016) · Computer Physics Communications · doi:10.1016/j.cpc.2016.02.013 · Cited by 803
Self-citationReaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation
Liu, M., Grinberg Dana, A., Johnson, M. S., Goldman, M. J., Jocher, A., Payne, A. M., Grambow, C. A., Han, K., Yee, N. W., Mazeau, E. J., et al. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01480 · Cited by 319
Self-citationBridging the complexity gap in computational heterogeneous catalysis with machine learning
Mou, T., Pillai, H. S., Wang, S., Wan, M., Han, X., Schweitzer, N. M., Che, F., & Xin, H. (2023) · Nature Catalysis · doi:10.1038/s41929-023-00911-w · Cited by 280
Comprehensive reaction mechanism for n-butanol pyrolysis and combustion
Harper, M. R., Van Geem, K. M., Pyl, S. P., Marin, G. B., & Green, W. H. (2011) · Combustion and Flame · doi:10.1016/j.combustflame.2010.06.002 · Cited by 273
Self-citationMatera, S., Schneider, W. F., Heyden, A., & Savara, A. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b01234 · Cited by 222
Metadata
- Authors
- Susnow, R. G., Dean, A. M., Green, W. H., Peczak, P., & Broadbelt, L. J.
- Venue
- The Journal of Physical Chemistry A
- Publisher
- American Chemical Society (ACS)
- Published
- 1997-05-01
- Volume
- 101
- Issue
- 20
- Pages
- 3731-3740
- ISSN
- 1089-5639 (ISSN-L)
- 1520-5215
- OpenAlex
- W2131014869
- Cited by
- 245
- References
- 0
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- Susnow, R. G., Dean, A. M., Green, W. H., Peczak, P., & Broadbelt, L. J. (1997). Rate-Based Construction of Kinetic Models for Complex Systems. The Journal of Physical Chemistry A. https://doi.org/10.1021/jp9637690
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