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. van Loosdrecht BT/Environmental Biotechnology - AS
Yanjun Dai Shanghai Jiao Tong University
Yen Wah Tong National University of Singapore, Campus for Research Excellence and Technological Enterprise
Research Group
BT/Environmental Biotechnology (AS) (TU Delft)
Copyright: © 2023 To Hung Tsui, Mark C.M. van Loosdrecht, Yanjun Dai, Yen Wah Tong
DOI: https://doi.org/10.1016/j.biortech.2022.128445
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Published Date

2023

Language

English

Reuse Rights

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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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- Embargo expired in 05-06-2023