1. Yield Sooting Index (YSI)
A Sooting Tendency Database for Accelerating
the Implementation of Biomass-Derived Fuels
Contact Information
ORCID: X
Charles S. McEnally, Lance K. Tan, Dhrubajyoti D. Das, Lisa D. Pfefferle
Department of Chemical Engineering, Yale University, New Haven, CT
Sooting Tendencies
Yield Sooting Index (YSI)
The Co-optima Program
Soot is the second largest source of global warming
Ambient particles cause 3M deaths/year worldwide
The fuel strongly affects soot formation in flames
A sooting tendency quantifies these effects
A high quality database of sooting tendencies will enable selection of fuels that minimize soot emissions
Dope small amount of test fuel into reference methane/air flame
Measure soot concentration Fv
Sooting Tendency = maximum Fv
Rescale to Yield Sooting Index (YSI)
YSI = A × Fv,max + B
A and B chosen such that:
YSI = n-hexane
YSI = benzene
Our Objective: Build the knowledge base necessary to choose biomass compounds that produce the lowest possible emissions of soot particulates while satisfying requirements for transportation fuels
Overall Project Objective: accelerate the introduction of biomass-derived transportation fuels that have lower emissions, promote the domestic economy, and enable more efficient engines.
Fuel
Methane
Nitrogen
Test Fuel, 1000 ppm
Air
Fuel =
Ethanol
Fuel =
Benzene
Sponsor: Department of Energy’s Co-optimization of Fuels and Engines program (award DE-EE )
Online Database
Data Collaborations
Data Issues
We have posted our database to the Harvard Dataverse
Professionally-maintained website that is easy to use, has version control and appropriate backup, etc.
Disseminates data without our active involvement
Quantitative Structure-Property Relationships (QSPR) for predicting YSI of untested hydrocarbons Collaboration with Peter St. John and Seonah Kim of the National Renewable Energy Laboratory
Finding an appropriate archive for data
Yale and our research field do not have archives
Providing data in usable form with necessary metadata Different levels of data: raw camera images, 2D maps of
soot concentration, YSI values for each fuel
Open access and preprint servers
American Chem Society is hostile to preprints
Tools like Github, iPython, Latex
They have their benefits but most of our collaborators
do not use them
Bureaucratic constraints
DOE requirements
Material Transfer Agreement with PNNL for samples
Computational simulations of our flames using detailed mechanisms Collaboration with Yuan Xuan of Penn State and William Pitz of Lawrence Livermore National Laboratory
QR code
points to
database
Model Expt