Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges.

Journal: Bioresource technology
PMID: 40139471

Abstract

The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and strain modification rather than experimental or experience-based ones. This review takes a holistic perspective on the entire pipeline, discussing how ML could aid lignocellulosic, while other published work has focused on individual modules within the pipeline. This review also explores the model construction and evaluation strategies and highlights the emerging potential of transfer learning and hybrid ML models to address data insufficiency and improve model interpretability. Furthermore, challenges and future prospects of ML in lignocellulosic biorefinery will be elaborated in this review. Integrating ML into lignocellulosic biorefinery offers a promising pathway towards sustainable and competitive biorefinery systems.

Authors

  • Xiao-Yan Huang
    State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.
  • Xue Zhang
    School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
  • Lei Xing
    Department of Radiation Oncology, Stanford University, CA, USA.
  • Shu-Xia Huang
    State Key Laboratory of Biological Fermentation Engineering of Beer, Tsingtao Brewery Co., Ltd., Qingdao 266000, China.
  • Cui Zhang
    School of Kinesiology, Shanghai University of Sport, Shanghai, China.
  • Xiao-Cong Hu
    State Key Laboratory of Biological Fermentation Engineering of Beer, Tsingtao Brewery Co., Ltd., Qingdao 266000, China.
  • Chen-Guang Liu
    State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: cg.liu@sjtu.edu.cn.

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