Data-driven optimization of biomass conversion pathways: integrating thermochemical processes

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This review demonstrates how machine learning can enhance energy yields in biomass conversion processes, indicating pathways for sustainable energy.

Biomass conversion technologies enhance low-carbon energy systems but struggle with feedstock composition variability.
Temperature regimes and lignocellulosic composition significantly affect energy yields and product quality in various processes.
Data-driven roadmaps can optimize operational parameters and integrate techno-economic and life cycle considerations.
Future research should focus on creating standardized biomass datasets and integrating sensors for reliable deployment.

Beemkumar et al. (2025) studied this question.

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View Full Paper

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Authors

This review demonstrates how machine learning can enhance energy yields in biomass conversion processes, indicating pathways for sustainable energy.

Biomass conversion technologies enhance low-carbon energy systems but struggle with feedstock composition variability.
Temperature regimes and lignocellulosic composition significantly affect energy yields and product quality in various processes.
Data-driven roadmaps can optimize operational parameters and integrate techno-economic and life cycle considerations.
Future research should focus on creating standardized biomass datasets and integrating sensors for reliable deployment.

Beemkumar et al. (2025) studied this question.

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