Physical and Chemical Fuel Property Prediction

Capability Title Physical and chemical fuel property prediction
Laboratories National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Lawrence Livermore, Sandia National Laboratories (SNL)
Capability experts Tim Bays (PNNL), Anthe George (SNL), Seonah Kim (NREL), Scott Wagnon (LLNL)
  • Theoretical prediction of combustion relevant property metrics such as RON, MON, Cetane for potential candidates not amenable to experimental evaluation due to their limited availability including high through-put computational techniques for screening
  • Classical structure-function relationship development of bio-blendstocks for identification of promising targets
  • Machine learning and artificial neural network-based approaches for correlating structure to fuel properties
  • Computational chemistry including quantum chemistry and thermodynamic methods for accurate calculation of physical fuel properties such as RVP, vapor pressure, viscosity both for pure compounds, complex mixtures and blends
  • Yield Sooting Index (YSI) prediction tool kit developed based on molecular structure-property relationship.
  • Quantitative prediction of phi-sensitivity for rapid screening of fuel candidates with kinetic mechanisms and 0-D simulations
  • Fuel property predictions for complex mixtures from 13C NMR carbon-type analyses. Capable of predicting a variety of fuel properties from a 200 µL sample, including an assessment of fuel classification based upon historical data.
  • Detailed kinetic modeling for prediction of ignition delay, RON, MON, flame speed and sooting tendency (YSI) of blendstocks comprised of well-characterized components.
Limitations All techniques developed and used within the Co-optima program have limitations; Co-optima researchers are experts in understanding which techniques will give the most appropriate and high impact results for a given application.
Unique aspects Computational tools and techniques have been specifically developed within Co-optima to enable the identification of the promising fuel targets for a given combustion approach, by understanding which molecules and molecular classes have the most advantageous properties. Once properties have been predicted and molecules identified using a variety of computational techniques, the retrosynthesis approach can give optimal production pathways to be tested and validated.
Availability Staff availability determines availability of individual capabilities. Contact Co-Optima for more information.

RetSynth: determining all optimal and sub-optimal synthetic pathways that facilitate synthesis of target compounds in chassis organisms, Whitmore et al., BMC Bioinformatics volume 20, Article number: 461 (2019).

 Development of a data-derived sooting index including oxygen-containing fuel components, Peter St. John, Seonah Kim, Robert L. McCormick, Energy & Fuels 2019 (accepted)

Measuring and Predicting Sooting Tendencies of Oxygenates, Alkanes, Alkenes, Cycloalkanes, and Aromatics on a Unified Scale, Dhruhajyoti D. Das, Peter St. John et al., Combustion and Flame, 190, 349-364 (2018).

A quantitative model for the prediction of sooting tendency from molecular structure, Peter C. St John et al., Energy & Fuels, 31 (9), 9983-9990 (2017).

Measuring and predicting the vapor pressure of gasoline containing oxygenates, Gaspar et al.,

Functional Group Analysis for Diesel-like Mixing-Controlled Compression Ignition Combustion Blendstocks, Gaspar et al.,

Mapping chemical performance on molecular structures using locally interpretable explanations, Whitmore et al., arXiv preprint arXiv:1611.07443.

RON prediction models for new fuel and vehicle systems, Hudson et al.,

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