A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

St. John, Peter C.; Kairys, Paul; Das, Dhrubajyoti D.; McEnally, Charles S.; Pfefferle, Lisa D.; Robichaud, David J.; Nimlos, Mark R.; Zigler, Bradley T.; McCormick, Robert L.; Foust, Thomas D.; Bomble, Yannick J.; Kim, Seonah

doi:10.1021/acs.energyfuels.7b00616

Title: A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure

Journal Article · Mon Jul 24 00:00:00 EDT 2017 · Energy and Fuels

DOI:https://doi.org/10.1021/acs.energyfuels.7b00616· OSTI ID:1394743

^[1];

^[2]; McEnally, Charles S. ^[2]; Pfefferle, Lisa D. ^[2]; Robichaud, David J. ^[1];

^[1]; Zigler, Bradley T. ^[1];

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^[1]; Bomble, Yannick J. ^[1];

^[3]

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

Particulate matter emissions negatively affect public health and global climate, yet newer fuel-efficient gasoline direct injection engines tend to produce more soot than their port-fuel injection counterparts. Fortunately, the search for sustainable biomass-based fuel blendstocks provides an opportunity to develop fuels that suppress soot formation in more efficient engine designs. However, as emissions tests are experimentally cumbersome and the search space for potential bioblendstocks is vast, new techniques are needed to estimate the sooting tendency of a diverse range of compounds. In this study, we develop a quantitative structure-activity relationship (QSAR) model of sooting tendency based on the experimental yield sooting index (YSI), which ranks molecules on a scale from n-hexane, 0, to benzene, 100. The model includes a rigorously defined applicability domain, and the predictive performance is checked using both internal and external validation. Model predictions for compounds in the external test set had a median absolute error of ~3 YSI units. An investigation of compounds that are poorly predicted by the model lends new insight into the complex mechanisms governing soot formation. Predictive models of soot formation can therefore be expected to play an increasingly important role in the screening and development of next-generation biofuels.

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Research Organization:: National Renewable Energy Lab. (NREL), Golden, CO (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; ACI-1053575

OSTI ID:: 1394743

Report Number(s):: NREL/JA-2700-69123

Journal Information:: Energy and Fuels, Vol. 31, Issue 9; ISSN 0887-0624

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 38 works

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Performance-advantaged ether diesel bioblendstock production by a priori design Huq, Nabila A.; Huo, Xiangchen; Hafenstine, Glenn R. Proceedings of the National Academy of Sciences, Vol. 116, Issue 52 https://doi.org/10.1073/pnas.1911107116	journal	December 2019

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