Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory

Vincent Dufour-Décieux, Maria Kourkina Cameron, Evan J. Reed, Christopher Moakler

Publication date: 26 May 2022

Abstract: Hydrocarbon pyrolysis is a complex process involving large numbers of chemical species and types of chemical reactions. Its quantitative description is important for planetary sciences, in particular, for understanding the processes occurring in the interior of icy planets, such as Uranus and Neptune, where small hydrocarbons are subjected to high temperature and pressure. We propose a computationally cheap methodology based on an originally developed ten-reaction model, and the configurational model from random graph theory. This methodology yields to accurate predictions for molecule size distributions for a variety of initial chemical compositions and temperatures ranging from 3200K to 5000K. Specifically, we show that the size distribution of small molecules is particularly well predicted, and the size of the largest molecule can be accurately predicted provided that it is not too large.

Has companion code repository: https://github.com/mar1akc/randomgraphtheory4hydrocarbonpyrolysis

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