skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [2];  [2];  [1]; ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]; ORCiD logo [3]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
  2. Yale Univ., New Haven, CT (United States)
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
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)
OSTI Identifier:
1394743
Report Number(s):
NREL/JA-2700-69123
Journal ID: ISSN 0887-0624
Grant/Contract Number:  
AC36-08GO28308; ACI-1053575
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy and Fuels
Additional Journal Information:
Journal Volume: 31; Journal Issue: 9; Journal ID: ISSN 0887-0624
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; 54 ENVIRONMENTAL SCIENCES; sooting tendency; emissions

Citation Formats

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., and Kim, Seonah. A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure. United States: N. p., 2017. Web. doi:10.1021/acs.energyfuels.7b00616.
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. A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure. United States. https://doi.org/10.1021/acs.energyfuels.7b00616
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., and Kim, Seonah. 2017. "A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure". United States. https://doi.org/10.1021/acs.energyfuels.7b00616. https://www.osti.gov/servlets/purl/1394743.
@article{osti_1394743,
title = {A Quantitative Model for the Prediction of Sooting Tendency from Molecular Structure},
author = {St. John, Peter C. and Kairys, Paul and Das, Dhrubajyoti D. and McEnally, Charles S. and Pfefferle, Lisa D. and Robichaud, David J. and Nimlos, Mark R. and Zigler, Bradley T. and McCormick, Robert L. and Foust, Thomas D. and Bomble, Yannick J. and Kim, Seonah},
abstractNote = {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.},
doi = {10.1021/acs.energyfuels.7b00616},
url = {https://www.osti.gov/biblio/1394743}, journal = {Energy and Fuels},
issn = {0887-0624},
number = 9,
volume = 31,
place = {United States},
year = {Mon Jul 24 00:00:00 EDT 2017},
month = {Mon Jul 24 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 38 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Airborne Particulate Matter and Human Health: Toxicological Assessment and Importance of Size and Composition of Particles for Oxidative Damage and Carcinogenic Mechanisms
journal, November 2008


Ash Effects on Diesel Particulate Filter Pressure Drop Sensitivity to Soot and Implications for Regeneration Frequency and DPF Control
journal, April 2010


Strategies Towards Meeting Future Particulate Matter Emission Requirements in Homogeneous Gasoline Direct Injection Engines
journal, April 2011


Assessing the Climate Trade-Offs of Gasoline Direct Injection Engines
journal, July 2016


Particulate matter indices using fuel smoke point for vehicle emissions with gasoline, ethanol blends, and butanol blends
journal, May 2016


Effect of molecular structure on incipient soot formation
journal, January 1983


Group additivity in soot formation for the example of C-5 oxygenated hydrocarbon fuels
journal, August 2013


Novel Micropyrolyis Index (MPI) to Estimate the Sooting Tendency of Fuels
journal, July 2008


Sooting tendencies of nonvolatile aromatic hydrocarbons
journal, January 2009


Sooting Tendencies of Oxygenated Hydrocarbons in Laboratory-Scale Flames
journal, March 2011


Sooting tendencies of unsaturated esters in nonpremixed flames
journal, April 2015


Computational Methods in Developing Quantitative Structure-Activity Relationships (QSAR): A Review
journal, March 2006


Principles of QSAR models validation: internal and external
journal, May 2007


QSPR Models for Octane Number Prediction
journal, August 2014


Flash Point and Cetane Number Predictions for Fuel Compounds Using Quantitative Structure Property Relationship (QSPR) Methods
journal, September 2011


Relation of Smoke Point to Molecular Structure
journal, March 1953


Additivity Rules for the Estimation of Molecular Properties. Thermodynamic Properties
journal, September 1958


Structural group analysis for soot reduction tendency of oxygenated fuels
journal, July 2008


Prediction of Sooting Tendency for Hydrocarbon Liquids in Diffusion Flames
journal, November 2005


Two-dimensional soot volume fraction measurements in flames doped with large hydrocarbons
journal, January 2017


SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
journal, February 1988


Computational and experimental study of soot formation in a coflow, laminar ethylene diffusion flame
journal, January 1998


Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling?
journal, September 2012


Relationships between Molecular Complexity, Biological Activity, and Structural Diversity
journal, March 2006


QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review
journal, October 2005


Isolation Forest
conference, December 2008


Reaction mechanism of soot formation in flames
journal, February 2002


Formation of Naphthalene, Indene, and Benzene from Cyclopentadiene Pyrolysis:  A DFT Study
journal, April 2006


Analysis of Some Reaction Pathways Active during Cyclopentadiene Pyrolysis
journal, March 2012


Mechanism of the Pyrolysis of Bicyclo [2.2.1]heptadiene. Kinetics of the Bicyclo [2.2.1]heptadiene to Toluene Isomerization
journal, May 1964


The thermal decomposition of the benzyl radical in a heated micro-reactor. II. Pyrolysis of the tropyl radical
journal, July 2016


Radical Chemistry in the Thermal Decomposition of Anisole and Deuterated Anisoles: An Investigation of Aromatic Growth
journal, September 2010


The Effect of Compression Ratio, Fuel Octane Rating, and Ethanol Content on Spark-Ignition Engine Efficiency
journal, July 2015


The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013
journal, November 2012


Estimation of Soot Thresholds for Fuel Mixtures
journal, September 1984


Development of a Predictive Model for Gasoline Vehicle Particulate Matter Emissions
journal, August 2010


Knock Resistance and Fine Particle Emissions for Several Biomass-Derived Oxygenates in a Direct-Injection Spark-Ignition Engine
journal, April 2016


Works referencing / citing this record:

Message-passing neural networks for high-throughput polymer screening
journal, June 2019


Performance-advantaged ether diesel bioblendstock production by a priori design
journal, December 2019