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 biorefinery 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, thermochemical 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.