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

作者：Xueqin Li, Zhiwei Wang, Peng Liu, Tingzhou Lei, Gaofeng Chen, Na Guo, Xiao Wei, Bo Gao · 发表于：Applications in Energy and Combustion Science · 年份：2025 · DOI：10.1016/j.jaecs.2025.100369 · 被引用次数：4 · 研究领域：Coal and Coke Industries Research、Industrial Engineering and Technologies、Engineering Diagnostics and Reliability

• 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 conve...