State-of-the-art and future directions of machine learning for biomass characterization and for sustainable biorefinery

Machine learning (ML) has emerged as a significant tool in the field of biorefinery, offering the capability to analyze and predict complex processes with efficiency. This article reviews the current state of biore-finery and its classification, highlighting various commercially successful biorefineries. Further, we delve into different categories of ML models, including their algorithms and applications in various stages of biorefinery lifecycle, such as biomass characterization, pretreatment, lignin valorization, chemical, ther-mochemical and biochemical conversion processes, supply chain analysis, and life cycle assessment. The benefits and limitations of each of these algorithms are discussed in detail. Finally, the article concludes with a discussion of the limitations and future prospects of ML in the field of biorefineries.(c) 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. All rights reserved.

Automated summary

This article reviews the significance of machine learning in the field of biorefinery, including its classification and various applications in different stages of the biorefinery lifecycle. The benefits and limitations of different machine learning algorithms are discussed, and the future prospects of machine learning in the field of biorefineries are explored.