=ADD=
=reftype=
14
=number=
98-11
=url=
ftp://ftp.risc.uni-linz.ac.at/pub/techreports/1998/98-11.ps.gz
=year=
1998
=month=
07
=author=
Blurock; Edward S.
=title=
Characterising Complex Reaction Mechanisms using Machine Learning Clustering Techniques
=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 characterised, 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 sub-time 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 characterisation of existing mechanisms and
can only indirectly (i.e., when supplemented with other methods) be used for
mechanism reduction.
=sponsor=