Preprint has been published in a journal as an article
DOI of the published article https://doi.org/10.1016/j.combustflame.2017.12.005
Preprint / Version 1

Measuring and Predicting Sooting Tendencies of Oxygenates, Alkanes, Alkenes, Cycloalkanes, and Aromatics on a Unified Scale

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DOI:

https://doi.org/10.31224/osf.io/9wqfn

Keywords:

alternative fuels, ic engines, particulate emissions

Abstract

Soot from internal combustion engines negatively affects health and climate. Soot emissions might be reduced through the expanded usage of appropriate biomass-derived fuels. Databases of sooting indices, based on measuring some aspect of sooting behavior in a standardized combustion environment, are useful in providing information on the comparative sooting tendencies of different fuels or pure compounds. However, newer biofuels have varied chemical structures including both aromatic and oxygenated functional groups, making an accurate measurement or prediction of their sooting tendency difficult. In this work, we propose a unified sooting tendency database for pure compounds, including both regular and oxygenated hydrocarbons, which is based on combining two disparate databases of yield-based sooting tendency measurements in the literature. Unification of the different databases was made possible by leveraging the greater dynamic range of the color ratio pyrometry soot diagnostic. This unified database contains a substantial number of pure compounds (≥ 400 total) from multiple categories of hydrocarbons important in modern fuels and establishes the sooting tendencies of aromatic and oxygenated hydrocarbons on the same numeric scale for the first time. Using this unified sooting tendency database, we have developed a predictive model for sooting behavior applicable to a broad range of hydrocarbons and oxygenated hydrocarbons. The model decomposes each compound into single-carbon fragments and assigns a sooting tendency contribution to each fragment based on regression against the unified database. The model’s predictive accuracy (as demonstrated by leave-one-out cross-validation) is comparable to a previously developed, more detailed predictive model. The fitted model provides insight into the effects of chemical structure on soot formation, and cases where its predictions fail reveal the presence of more complicated kinetic sooting mechanisms. This work will therefore enable the rational design of low-sooting fuel blends from a wide range of feedstocks and chemical functionalities.

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Posted

2017-09-14