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

  • Beemkumar Nagappan , Ganesan Subbiah , Ravi Kumar Paliwal , Satish Choudhury , Kreeti Rai , Kulmani Mehar , Aseel Samrat and K. Kamakshi Priya EMAIL logo
Published/Copyright: October 6, 2025
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Abstract

Biomass conversion technologies are integral to the realization of sustainable, low-carbon energy systems; however, their scalability is significantly hampered by pronounced sensitivity to the composition of feedstock and the temperature of the processes employed. This review synthesizes insights on how temperature regimes and lignocellulosic composition interact to influence energy yields and product quality across various methodologies, including torrefaction, pyrolysis, gasification, and hydrothermal liquefaction. Furthermore, it elucidates how machine learning (ML) presents revolutionary prospects for mitigating variability, facilitating feedstock-agnostic forecasting of higher heating value, yields of bio-oil/char/biogas, syngas H2/CO ratios, and tar propensity; enabling adaptive closed-loop control of operational parameters; and promoting multi-objective optimization that incorporates techno-economic and life cycle considerations. A comprehensive, data-driven roadmap is proposed to expedite deployment, comprising: (i) process matching and operational set-points that are cognizant of composition; (ii) hybrid models informed by physics for enhanced interpretability; (iii) frameworks for federated and active learning to bolster generalization across diverse regions and feedstocks; and (iv) optimization integrated with techno-economic analysis (TEA) and life cycle assessment (LCA) to guarantee economic feasibility and environmental sustainability. This roadmap not only amalgamates disparate insights into a cohesive strategy but also furnishes practical guidance for stabilizing the quality of outputs, minimizing operational expenses, and promoting decentralized, intelligent bioenergy infrastructures. Subsequent research endeavors should focus on establishing standardized biomass datasets, integrating robust sensors, and developing explainable artificial intelligence frameworks to ensure the scalable, reliable, and sustainable deployment of these systems.


Corresponding author: K. Kamakshi Priya, Department of Physics, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India; and Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: The authors acknowledge the use of AI-assisted tools, notably Grammarly, to improve linguistic accuracy, correct grammatical errors, and increase the overall coherence of this article. The content, analytical discussion, and conclusions presented in this work are distinctly the original contributions of the author, with AI tools utilized solely to enhance the presentation and clarity of the text. The use of AI complies with the journal's requirements for transparency and ethical norms in authorship processes.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-06-14
Accepted: 2025-09-19
Published Online: 2025-10-06

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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