Characterizing complex reaction mechanisms using machine learning clustering techniques

Abstract

AbstractA 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. © 2003 Wiley Periodicals, Inc. Int J Chem Kinet 36: 107–118, 2004