Mechanism‐guided AI Modeling

This chapter reviews the application of mechanism-guided and hybrid artificial intelligence (AI) modeling approaches for biomass conversion processes. It covers classical mechanism-driven modeling, including pyrolysis, gasification, and other conversion pathways, highlighting equilibrium, kinetic, and computational fluid dynamics models. Hybrid strategies are also discussed, such as direct hybrid modeling, reduced-order models, physics-informed neural networks, TG profile-assisted modeling, and agent-based hybrid approaches. This chapter emphasizes the integration of experimental, theoretical, and AI-driven methods to improve predictive accuracy and interpretability, and discusses future opportunities for AI in linking feedstock properties, conversion pathways, and product characteristics.