Group publishes work aiding process monitoring in biomass upgrading

Congrats to group members Mohammed and Yeonjoon for their work on the prediction of bio-oil output composition being published in ACS Sustainable Chemistry and Engineering!

Short Overview:
In collaboration with NREL, we developed a preliminary simple ML model (random forest) capable of translating a mass spectra (MS) to a set of chemically-interpretable compositional descriptors, the Paraffins, Isoparaffins, Olefins, Naphthenes, and Aromatics (PIONA) fractions. The key spectral features were extracted from the MS, and these features are used as the input of the random forest model which outputs the predicted PIONA values. These studies are expected to provide generally applicable strategies to predict bio-oil output compositions.


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