Integration of artificial intelligence in lignocellulosic biomass valorization in biorefineries: Enabling energy efficiency through analysis of feedstocks and conversion pathways

This critical review examines recent applications of artificial intelligence (AI) and machine learning (ML) across lignocellulosic biorefineries, with emphasis on practical deployment, quantitative performance gains, and energy reduction outcomes. AI-driven integration of non-destructive spectroscopic techniques has enabled rapid feedstock characterization and compositional prediction, achieving reported accuracies exceeding R 2 = 0.90 for properties such as cellulose, lignin, and moisture content. ML models including Random Forest, Gradient Boosting, and Support Vector Regressor are frequently employed due to their robustness with limited datasets, while Artificial Neural Networks, which require larger datasets, representing approximately 25% of reported applications in compositional analysis, pretreatment optimization, and conversion modeling. Specialized applications such as wood species identification can achieve accuracies as high as 98%, while transfer learning reduce training data requirements for complex pretreatments by ∼70%. Graph neural networks applied to deep eutectic solvents design have yielded solubility predictions with low mean absolute error. ML-optimized pretreatments strategies, including mechanical refining for enzymatic conversion, have achieved energy savings of up to 15% by reducing operating temperatures while maintaining cellulose accessibility. At the process scale, AI-enabled digital twins and predictive control frameworks are emerging as tools for real-time monitoring, anomaly detection, and process optimization. Integration of AI models with techno-economic analysis and life-cycle assessment enables data-driven evaluation of sustainability trade-offs and supports informed pathway selection. Limitations, including data scarcity, model transferability, and overfitting, are critically assessed, along with advances in explainable AI that improve interpretability for industrial adoption. Overall, this review demonstrates that AI can accelerate the development of scalable biorefineries.