Ana Comesana
As a Scientific Engineering Associate at Lawrence Berkeley National Laboratory, Ana conducts multidisciplinary research focused on the development of innovative solutions, including tools to accelerate jet fuel research or autonomously design semantic models and data infrastructure for buildings. Ana enjoys using machine learning and data science to discover complex patterns and ultimately advance scientific research.
Session
Synthetic aviation fuels (SAFs) offer a pathway to improving efficiency, but high cost and volume requirements hinder property testing and increase risk of developing low-performing fuels. To promote productive SAF research, we used Fourier Transform Infrared (FTIR) spectra to train accurate, interpretable fuel property models. In this presentation, we will discuss how we leveraged standard Python libraries – NumPy, pandas, and scikit-learn – and Non-negative Matrix Factorization to decompose FTIR spectra and develop predictive models. Specifically, we will review the pipeline developed for preprocessing FTIR data, the ensemble models used for property prediction, and how the features correlate with physicochemical properties.