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Characterizing complex reaction mechanisms using machine learning clustering techniques

Blurock, Edward LU (2004) In International Journal of Chemical Kinetics 36(2). p.107-118
Abstract
A machine learning conceptual clustering method applied to reaction mechanisms provides an automatic and, hence, unbiased means to differentiate between reactive phases within a total reactive process. Similar reactive phases were defined by means of local reaction sensitivity values. The method was applied to the Hochgreb and Dryer aldehyde combustion mechanism of 36 reactions. Three major time ranges were found and characterized: an initial phase of aldehyde reaction, an intermediate phase where only a small amount of aldehyde is left, and an end phase of reactions to final products. Further refinements of these phases into subtime intervals were found. All ranges found could be chemically justified. This method is meant as a supplement... (More)
A machine learning conceptual clustering method applied to reaction mechanisms provides an automatic and, hence, unbiased means to differentiate between reactive phases within a total reactive process. Similar reactive phases were defined by means of local reaction sensitivity values. The method was applied to the Hochgreb and Dryer aldehyde combustion mechanism of 36 reactions. Three major time ranges were found and characterized: an initial phase of aldehyde reaction, an intermediate phase where only a small amount of aldehyde is left, and an end phase of reactions to final products. Further refinements of these phases into subtime intervals were found. All ranges found could be chemically justified. This method is meant as a supplement to existing methods of mechanism analysis and its main purpose is the automatic characterization of existing mechanisms and can potentially be used for mechanism reduction. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
International Journal of Chemical Kinetics
volume
36
issue
2
pages
107 - 118
publisher
John Wiley & Sons
external identifiers
  • wos:000188088300005
  • scopus:0742307312
ISSN
0538-8066
DOI
10.1002/kin.10179
language
English
LU publication?
yes
id
5f1bd434-e5e8-4df8-a092-69133122c968 (old id 289735)
date added to LUP
2007-10-17 10:01:16
date last changed
2017-11-05 03:30:42
@article{5f1bd434-e5e8-4df8-a092-69133122c968,
  abstract     = {A machine learning conceptual clustering method applied to reaction mechanisms provides an automatic and, hence, unbiased means to differentiate between reactive phases within a total reactive process. Similar reactive phases were defined by means of local reaction sensitivity values. The method was applied to the Hochgreb and Dryer aldehyde combustion mechanism of 36 reactions. Three major time ranges were found and characterized: an initial phase of aldehyde reaction, an intermediate phase where only a small amount of aldehyde is left, and an end phase of reactions to final products. Further refinements of these phases into subtime intervals were found. All ranges found could be chemically justified. This method is meant as a supplement to existing methods of mechanism analysis and its main purpose is the automatic characterization of existing mechanisms and can potentially be used for mechanism reduction.},
  author       = {Blurock, Edward},
  issn         = {0538-8066},
  language     = {eng},
  number       = {2},
  pages        = {107--118},
  publisher    = {John Wiley & Sons},
  series       = {International Journal of Chemical Kinetics},
  title        = {Characterizing complex reaction mechanisms using machine learning clustering techniques},
  url          = {http://dx.doi.org/10.1002/kin.10179},
  volume       = {36},
  year         = {2004},
}