A Fragment-Based Mechanistic Kinetic Modeling Framework for Complex Systems

There is great interest
in developing chemical kinetic models with
predictive power. A predictive kinetic model maintains high fidelity
to the true chemistry usually via a full-detail molecule representation
which distinguishes all the molecules and isomers. Unfortunately such
full-detail representation quickly becomes intractable for large molecules
because the number of isomers grows very rapidly with size. Several
less detailed representations have been developed, including the popular
Structure-Oriented Lumping (SOL) method, which allow one to model
larger molecules. In this paper we first examine scalability for two
existing representations (full-detail and SOL) and propose a new modeling
methodology tailored for heavy systems: fragment-based modeling, which
promises the best scalability. We show via a case study of heavy molecule
pyrolysis that the new approach creates a much smaller reaction network
but with similar prediction accuracy on feedstock conversion and products’
molecular weight distribution compared to the corresponding model
using full-detail representation. An open source modeling package AutoFragmentModeling using this methodology has been
developed. Some suggestions are presented for future work based on
this new modeling approach.