Biomass is widely considered a renewable alternative to fossil fuels, and many experts say it can play a critical role in combating climate change. Biomass stores carbon and can be turned into bio-based products and energy that can be used to improve soil, treat wastewater, and produce renewable feedstock.
Yet large-scale production of it has been limited due to economic constraints and challenges to optimizing and controlling biomass conversion.
A new study led by Yale School of the Environment’s Yuan Yao, assistant professor of industrial ecology and sustainable systems, and doctoral student Hannah Szu-Han Wang, analyzed current machine learning applications for biomass and biomass-derived materials (BDM) to determine if machine learning is advancing the research and development of biomass products. The study authors found that machine learning has not been applied across the entire life cycle of BDM, limiting its ability for development.
Read more at Yale University
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