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, Pau Loke Show*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

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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.
Original languageEnglish
JournalEnergy & Fuels
Early online date16 Jan 2024
DOIs
Publication statusFirst published - 16 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Published by American Chemical Society.

Keywords

  • Energy Engineering and Power Technology
  • Fuel Technology
  • General Chemical Engineering

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