Machine learning and circular bioeconomy

Tsui, To Hung; Loosdrecht, Mark C.M.; Dai, Yanjun; Tong, Yen Wah

doi:10.1016/j.biortech.2022.128445

Machine learning and circular bioeconomy

Building new resource efficiency from diverse waste streams

Review (2023)

Authors

To Hung Tsui National University of Singapore, Campus for Research Excellence and Technological Enterprise

Mark C.M. Loosdrecht BT/Environmental Biotechnology -

Yanjun Dai Shanghai Jiao Tong University

Yen Wah Tong Campus for Research Excellence and Technological Enterprise, National University of Singapore

Research Group

BT/Environmental Biotechnology () (TU Delft)

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

Sustainability Resource recovery Supply chain Biorefinery Multiscale modeling

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

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Published Date

2023

Language

English

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Faculty

Applied Sciences

Department

BT/Biotechnologie

Research Group

BT/Environmental Biotechnology

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.

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