Machine learning and circular bioeconomy

Title

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

 Author

Tsui, To Hung (National University of Singapore; Campus for Research Excellence and Technological Enterprise)
van Loosdrecht, Mark C.M. (TU Delft BT/Environmental Biotechnology) ORCID 0000-0003-0658-4775
Dai, Yanjun (Shanghai Jiao Tong University)
Tong, Yen Wah (National University of Singapore; Campus for Research Excellence and Technological Enterprise)

Date

2023

 Abstract

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.


 Subject

Biorefinery
Multiscale modeling
Resource recovery
Supply chain
Sustainability

 To reference this document use:

http://resolver.tudelft.nl/uuid:f65a75bc-c174-47b0-9f12-762a9f289cb1

 DOI

https://doi.org/10.1016/j.biortech.2022.128445

 Embargo date

2023-06-05

 ISSN

0960-8524

 Source

Bioresource Technology, 369

 Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

 Part of collection

Institutional Repository

 Document type

review

 Rights

© 2023 To Hung Tsui, Mark C.M. van Loosdrecht, Yanjun Dai, Yen Wah Tong
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