Predicting Molecule Size Distribution in Hydrocarbon Pyrolysis using Random Graph Theory

TL;DR

This paper introduces a computationally efficient method combining a ten-reaction model with random graph theory to accurately predict molecule size distributions in hydrocarbon pyrolysis across various conditions, aiding planetary science research.

Contribution

The paper presents a novel approach integrating random graph theory with chemical reaction modeling to predict hydrocarbon molecule sizes during pyrolysis.

Findings

Accurately predicts small molecule size distributions.

Provides reliable estimates of the largest molecule size under certain conditions.

Works across a range of temperatures and initial compositions.

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.