Title
A sooting tendency database for accelerating the introduction of biomass-derived fuels
Website
http://pfefferlehallerlabs.yale.edu/
Description
One of the many potential benefits of biomass-derived fuels is lower emissions of particulate matter. In order to enable the selection of biofuels that maximize this benefit, we have been building a database of a property — yield sooting index — that characterizes tendency to produce particulates, by measuring it for hundreds of pure hydrocarbons. We have encountered many data management issues while trying to maximize the usefulness of this database. Our research field does not have a general data repository, so we are currently posting the data to the Harvard Dataverse (https://dx.doi.org/10.7910/DVN/7HGFT8). We have direct collaborators at the National Renewable Energy Laboratory who are using machine learning techniques to develop empirical models that fit the data and can make predictions for new compounds. We have additional collaborators at Penn State University who are modeling the data from first principles.
A sooting tendency database for accelerating the introduction of biomass-derived fuels
One of the many potential benefits of biomass-derived fuels is lower emissions of particulate matter. In order to enable the selection of biofuels that maximize this benefit, we have been building a database of a property — yield sooting index — that characterizes tendency to produce particulates, by measuring it for hundreds of pure hydrocarbons. We have encountered many data management issues while trying to maximize the usefulness of this database. Our research field does not have a general data repository, so we are currently posting the data to the Harvard Dataverse (https://dx.doi.org/10.7910/DVN/7HGFT8). We have direct collaborators at the National Renewable Energy Laboratory who are using machine learning techniques to develop empirical models that fit the data and can make predictions for new compounds. We have additional collaborators at Penn State University who are modeling the data from first principles.
https://elischolar.library.yale.edu/dayofdata/2017/posters/8