Can Machine Learning Help Optimize Biomass Production for Sustainability?

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Can Machine Learning Help Optimize Biomass Production for Sustainability? Yale (Credit: Canva Pro)

Biomass has long been touted as a renewable alternative to fossil fuels, with experts suggesting that it could play a crucial role in combating climate change. This renewable option can store carbon and be converted into bio-based products and energy, making it useful for improving soil quality, treating wastewater, and producing renewable feedstock. However, large-scale production of biomass has been limited due to economic constraints and challenges in optimizing and controlling biomass conversion. This is where machine learning comes into play.

To address these issues, researchers at Yale School of the Environment conducted a study on the use of machine learning in the development of biomass products. The study, led by Assistant Professor of Industrial Ecology and Sustainable Systems, Yuan Yao, and doctoral student Hannah Szu-Han Wang, analyzed current machine learning applications for biomass and biomass-derived materials (BDM) to determine whether machine learning is advancing the research and development of biomass products.

The study authors found that while some studies have applied machine learning to address data challenges for life cycle assessment, most studies only applied machine learning to predict and optimize the technical performance of biomass conversion and applications. No study has reviewed machine learning applications across the entire lifecycle, starting from biomass cultivation to the production of BDM and its applications.

According to Yao, "If we want to try each combination using the traditional trial-and-error experimental approach, this will take a lot of time, labor, effort, and energy. We already generate a lot of data from these past experiments, so we are asking, can we apply machine learning to help us to figure out how we can better design BDM?"

The researchers believe that machine learning has the potential to support sustainability-informed design for biomass-derived materials. By considering the entire lifecycle of the materials, from how they are generated to their potential environmental impact, machine learning can help to identify the most sustainable ways to create BDM.

Further, the study has led to additional research on data gaps in machine learning on biomass-derived materials. "There needs to be a full pathway prediction to enhance our understanding of how various factors regarding BDM interact and contribute to sustainability," says Wang.

Overall, the use of machine learning in the development of biomass products shows promise for improving sustainability and reducing the environmental impact of biomass production. By considering the entire lifecycle of biomass-derived materials, researchers can identify the most sustainable ways to create BDM, helping to combat climate change and promote a more sustainable future.

Environment + Energy Leader