Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

Aug 6, 2026

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This review illuminates the pivotal synergy between machine learning and biobased polymers, highlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers from renewable sources are presented for their roles across energy and fuel applications. When integrated with machine learning, these polymers show improved functionality, supporting optimization of renewable energy systems, storage, and conversion. Case studies also connect the intersection to biochemical production advances, including drug delivery and medical devices, reinforcing future sustainability.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Biobased polymers as renewable alternatives

Machine learning methods and their role

Rationale for combining ML with biobased polymers

Scotland's Rural College Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future Bin Abu Sofian, Abu Danish Aiman; Sun, Xun; Gupta, Vijai Kumar; Berenjian, Aydin; Xia, Ao; Ma, Zengling; Show, Pau Loke Published in: Energy & Fuels DOI: 10.1021/acs.energyfuels.3c03842 Print publication: 01/02/2024 Document Version Publisher's PDF, also known as Version of record Link to publication Citation for pulished version (APA): Bin Abu Sofian, A. D. A. , Sun, X. , Gupta, V. K. , Berenjian, A. , Xia, A. , Ma, Z. , & Show, P. L. (2024) . Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals fora Sustainable Future. Energy & Fuels, 38(3), 1593-1617 . [https://doi.org/10.1021/acs.energyfuels.3c03842](https://doi.org/10.1021/acs.energyfuels.3c03842) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 20. Sep. 2025 This article is licensed under CC-BY 4.0 [pubs.acs.org/EF](pubs.acs.org/EF) Review Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future Abu Danish Aiman Bin Abu Sofian, Xun Sun, Vijai Kumar Gupta, Aydin Berenjian, Ao Xia, Zengling Ma, * and Pau Loke Show* Cite This: [https://doi.org/10.1021/acs.energyfuels.3c03842](https://doi.org/10.1021/acs.energyfuels.3c03842) Read Online Downloaded via SCOTLANDS RURAL COLL on January 3 1, 2024 at 10:17:56 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles. ACCESS Metrics & More Article Recommendations ABSTRACT: This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, and biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for their roles in sustainable energy and fuel sectors. These polymers, when integrated with ML techniques, exhibit enhanced functionalities, optimizing renewable energy systems, storage, and conversion. Detailed case studies reveal the potential of biobased polymers in energy applications and the fuel industry, further showcasing how ML bolsters fuel efficiency and innovation. The intersection of biobased polymers and ML also marks advancements in biochemical production, emphasizing innovations in drug delivery and medical device development. This review underscores the imperative of harnessing the convergence of ML and biobased polymers for future global sustainability endeavors in energy, fuels, and biochemicals. The collective evidence presented asserts the immense promise this union holds for steering a sustainable and innovative trajectory. 1. INTRODUCTION In the quest for a sustainable future, numerous challenges have been encountered.1,2 Global reliance on nonrenewable energy sources has exacted a significant toll on the environment.3−5 Traditional materials, primarily derived from finite resources, exacerbate the environmental burden, contributing to pollution and resource depletion.

FAQs

What is the focus of the review?

The review focuses on the synergy between machine learning and biobased polymers and how their combination can advance sustainable energy, fuels, and biochemicals.

How do biobased polymers contribute to sustainable energy and fuels?

Biobased polymers derived from renewable biomass are discussed for their functional roles in sustainable energy and fuel sectors, including energy applications and fuel industry relevance.

What impact does integrating machine learning have on biobased polymer applications?

Integrating machine learning is described as improving biopolymer functionality and enabling optimization of renewable energy systems, storage, and conversion, while also supporting innovation in biochemical production.

Aug 6, 2026

Upload by Patrick