Recent advances and computational approaches in biomass gasifier modeling: from thermodynamics to AI-driven techniques

Abstract

The transition towards sustainable energy systems has underscored the need for efficient biomass gasification technologies. This study presents an integrated experimental and computational review of gasifier modeling, with a focus on the production of hydrogen-enriched syngas from biomass. It compares major modeling approaches, including thermodynamic equilibrium, kinetic modeling, computational fluid dynamics (CFD), and data-driven techniques such as artificial neural networks (ANN). A detailed comparative analysis is provided regarding model assumptions, accuracy, computational demand, and validation techniques. The review paper explores recent developments in hybrid and AI-integrated models, including digital twins and machine learning-assisted simulations. This work aims to guide researchers in selecting appropriate modeling strategies while highlighting future directions in high-fidelity and scalable biomass gasification modeling.