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

Area of Science:

• Biotechnology and biochemical engineering
• Sustainable chemical processing
• Computational biology and machine learning applications

Background:

• Lignocellulosic biorefineries convert biomass into valuable products through multiple stages: pretreatment, enzymatic hydrolysis, fermentation, and digestion.
• Traditional process optimization relies on empirical methods, which are often time-consuming and suboptimal.
• Existing research frequently focuses on individual biorefinery modules, lacking a holistic optimization approach.

Purpose of the Study:

• To provide a comprehensive review of machine learning (ML) applications across the entire lignocellulosic biorefinery pipeline.
• To highlight ML's potential for optimizing process parameters and strain development.
• To discuss advanced ML strategies like transfer learning and hybrid models for enhanced performance and interpretability.

Main Methods:

• Holistic review of ML integration in lignocellulosic biorefinery processes.
• Exploration of ML model construction, evaluation, and validation strategies.
• Discussion of emerging ML techniques, including transfer learning and hybrid models.

Main Results:

• Machine learning offers a powerful alternative to traditional methods for optimizing complex biorefinery operations.
• ML can enhance efficiency and yield across pretreatment, hydrolysis, fermentation, and digestion stages.
• Advanced ML models show promise in overcoming data limitations and improving model understanding.

Conclusions:

• Integrating ML into lignocellulosic biorefineries is crucial for achieving sustainable and economically viable bio-based production.
• A holistic ML-guided approach can significantly improve overall system performance compared to module-specific optimization.
• Further research into ML, particularly transfer learning and hybrid models, will accelerate the development of competitive biorefinery systems.