Machine learning and circular bioeconomy
Building new resource efficiency from diverse waste streams
To Hung Tsui (Campus for Research Excellence and Technological Enterprise, National University of Singapore)
Mark M.C. van Loosdrecht (TU Delft - BT/Environmental Biotechnology)
Yanjun Dai (Shanghai Jiao Tong University)
Yen Wah Tong (Campus for Research Excellence and Technological Enterprise, National University of Singapore)
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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.