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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 und K. Kamakshi Priya EMAIL logo
Veröffentlicht/Copyright: 6. Oktober 2025
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International Journal of Chemical Reactor Engineering
Aus der Zeitschrift International Journal of Chemical Reactor Engineering Band 23 Heft 11

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.

Keywords: biomass conversion; composition–process mapping; temperature–yield sensitivity; physics-informed machine learning; techno-economic and life cycle optimization
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

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. Data-driven optimization of biomass conversion pathways: integrating thermochemical processes
  4. Lignocellulose-based nanomaterials: synthesis, characterization, and sustainable applications in wastewater treatment
  5. Articles
  6. Effect of magnetic field strength and flow rate on separation distance and recovery rate in a cell-separating microfluidic device
  7. Hydrogen rich syngas and high-value biofuel production by pyrolysis of different biomass generations and waste plastics: theoretical investigation and feasibility assessment
  8. Tubular microreactor-based Fe(II)-driven advanced oxidation: comparative assessment of percarbonate and persulfate systems for toxic dye removal
  9. Large eddy simulation study of a Venturi cavitation nozzle with two auxiliary flow channels
  10. Designing heat exchanger using EDTR method and python as a computational tool
  11. Sustainable recovery of valuable metals from high-grade nickel matte residue via sulfur dioxide pressure leaching and freeze crystallization
  12. Innovative adsorption of lead and copper using recycled concrete in aqueous solutions
https://doi.org/10.1515/ijcre-2025-0107
Schlagwörter für diesen Artikel
biomass conversion; composition–process mapping; temperature–yield sensitivity; physics-informed machine learning; techno-economic and life cycle optimization

Artikel in diesem Heft

  1. Frontmatter
  2. Reviews
  3. Data-driven optimization of biomass conversion pathways: integrating thermochemical processes
  4. Lignocellulose-based nanomaterials: synthesis, characterization, and sustainable applications in wastewater treatment
  5. Articles
  6. Effect of magnetic field strength and flow rate on separation distance and recovery rate in a cell-separating microfluidic device
  7. Hydrogen rich syngas and high-value biofuel production by pyrolysis of different biomass generations and waste plastics: theoretical investigation and feasibility assessment
  8. Tubular microreactor-based Fe(II)-driven advanced oxidation: comparative assessment of percarbonate and persulfate systems for toxic dye removal
  9. Large eddy simulation study of a Venturi cavitation nozzle with two auxiliary flow channels
  10. Designing heat exchanger using EDTR method and python as a computational tool
  11. Sustainable recovery of valuable metals from high-grade nickel matte residue via sulfur dioxide pressure leaching and freeze crystallization
  12. Innovative adsorption of lead and copper using recycled concrete in aqueous solutions
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