Configuration-Dependent Multi-Parameter Optimization in Biomass Gasification: From Operating Windows to AI-Assisted Control

Rajiv Kamaraj; Shih-Yen Hu; Yi-Jen Huang; Jui-Ching Hsieh

doi:10.53941/gefr.2026.100004

Abstract

This mini-review provides a focused overview of configuration-dependent and multi-parameter optimization in biomass gasification systems. Rather than broadly summarizing biomass gasification, it examines how key operating variables, including temperature, equivalence ratio, and steam-to-biomass ratio, interact across different gasifier configurations and influence syngas composition, tar formation, and process efficiency. Major reactor types, including fixed-bed, fluidized-bed, dual fluidized-bed, entrained-flow, and supercritical water gasifiers, show different sensitivities to these parameters because of differences in hydrodynamics, heat transfer, and reaction environments. Based on a comparative assessment of recent studies, the review discusses configuration-specific trends, parameter trade-offs, and the limited transferability of operating conditions between systems. Emerging data-driven approaches, including computational fluid dynamics, machine learning, and hybrid modeling, are also considered in the context of multi-parameter optimization. Although these approaches have shown potential for improving hydrogen yield, carbon conversion, and cold-gas efficiency, their current limitations related to data availability, model generalizability, and industrial implementation are also discussed. Overall, the review indicates a gradual shift from fixed operating windows toward more adaptive and condition-responsive gasification strategies. Future research needs include improved data consistency, integrated multi-parameter optimization, real-time process monitoring, and further evaluation of AI-assisted approaches for scalable biomass gasification systems.

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