Delineating Biomass Gasification Models: The Convergence of First Principles and Machine Learning to Hybrid Modeling—A Review

The current climate scenario calls for sustainable solutions to develop greener fuels and chemical products. Biomass gasification converts low-cost feedstocks into high-value products for wide industrial use. However, its complexity presents a challenge to mechanistic modeling, which requires a deeper understanding of the process, and data-driven approaches, which demand large amounts of data. Hybrid modeling combines the assets of both parametric and non-parametric models, mitigating their disadvantages. This review aimed to examine the latest modeling techniques applied to biomass gasification, including mechanistic and data-driven approaches. It also describes hybrid models that combine these two approaches and identifies the gaps where this novel method can be applied. The Scopus, Web of Science, and ScienceDirect databases were consulted, yielding 18 works on mechanistic modeling from the last 10 years, 55 on data-driven modeling, and 14 on hybrid modeling. Although inconsistencies in performance metrics hindered comparisons across works, hybrid models performed similarly to data-driven models. They were superior to mechanistic models, suggesting potential for dynamic modeling, tar prediction, pyrolysis product estimation, and description of kinetic behavior.