Application status and prospects of machine learning in research on hydrogen production from catalytic cracking of biomass tar

• ML deciphers conversion mechanisms of complex tar for clean hydrogen production. • Tar-to-hydrogen conversion is vital for advancing sustainable clean energy solutions. • ML models accurately predict tar cracking products (R²>0.9) and optimizing process efficiency. • Temperature and catalysts are key for tar-to-hydrogen with ML quantifying their precise impacts. • Data-driven ML overcomes experimental limits and achieves faster high-value tar utilization. Biomass tar, a key byproduct of multi-source organic waste, is formed through radical condensation and cracking reactions during the thermochemical conversion of biomass into clean energy carriers (such as green hydrogen). However, the accumulation of tar presents significant challenges, including high energy consumption, environmental pollution, increased production costs, and reduced conversion efficiency, which hinder the large-scale application of the technology of biomass thermochemical to produce hydrogen. In the catalytic conversion of biomass, integrating machine learning (ML) with the classification of tar-derived fuel products holds promise for enhancing energy conversion efficiency and advancing the industrialization of biomass-based hydrogen production. This case study reviews the current applications of ML in domains such as medical diagnostics, image recognition, and natural language processing, while placing particular emphasis on its predictive performance regarding the energy content of tar catalytic conversion products and hydrogen-rich gas yield. Finally, the study explores the prospects of ML in supporting catalytic cracking technologies for tar-based hydrogen production through the transformation between classification and predictive modeling capabilities. This study distinguishes itself by focusing explicitly on the synergistic integration of ML with tar catalytic conversion processes. Specifically, we systematically analyze how ML bridges classification tasks (e.g., tar-derived fuel product categorization) and predictive modeling (e.g., hydrogen yield forecasting, catalyst performance prediction), and highlight its role in addressing domain-specific bottlenecks such as complex reaction kinetics and catalyst design optimization by synthesizing the gap between general ML methodologies and their tailored application in tar conversion.