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SUMMARY:Using Python to accelerate sustainable aviation fuel research and 
 development - Ana Comesana
DTSTART;TZID=America/Chicago:20230713T142000
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DTSTAMP:20261007T040019Z
UID:pretalx-2023-XSQKSA@cfp.scipy.org
DESCRIPTION:Aviation comprises 2-3% of global CO2 emissions. Transitioning
  to cleaner\, more sustainable aviation fuels can reduce its environmental
  impacts. To help accelerate sustainable aviation fuel development\, we tr
 ained machine learning models to predict fundamental properties of biofuel
  blends using Fourier transform infrared (FTIR) spectra. We leveraged TPOT
  and standard libraries like NumPy\, pandas\, and scikit-learn to develop 
 the models. This presentation will discuss how we overcame challenges with
  decomposing FTIR spectra data and using machine learning on small dataset
 s (<100 samples). We will also discuss integration of the models into our 
 open-source webtool to support biofuel research.
LOCATION:Zlotnik Ballroom
URL:https://cfp.scipy.org/2023/talk/XSQKSA/
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