Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale

Das, Dhrubajyoti D.; John, Peter C.; McEnally, Charles S.; Kim, Seonah; Pfefferle, Lisa D.

doi:10.1016/j.combustflame.2017.12.005

Title: Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale

Journal Article · Wed Dec 06 00:00:00 EST 2017 · Combustion and Flame

DOI:https://doi.org/10.1016/j.combustflame.2017.12.005· OSTI ID:1416906

Das, Dhrubajyoti D. ^[1]; John, Peter C. ^[2]; McEnally, Charles S. ^[1]; Kim, Seonah ^[2]; Pfefferle, Lisa D. ^[1]

Yale Univ., New Haven, CT (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

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, which expands the chemical space of relevant compounds. 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. Then, 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. Our 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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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (United States); Yale Univ., New Haven, CT (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); National Science Foundation (NSF)

Grant/Contract Number:: AC36-08GO28308; EE0007983; CBET 1604983

OSTI ID:: 1416906

Alternate ID(s):: OSTI ID: 1415227; OSTI ID: 1548871

Report Number(s):: NREL/JA-2700-70702

Journal Information:: Combustion and Flame, Vol. 190, Issue C; ISSN 0010-2180

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 93 works

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