Machine learning-based prediction of biocrude yields and higher heating values from hydrothermal liquefaction of wet biomass and wastes: A literature review-derived dataset study

Published: 26 June 2024| Version 1 | DOI: 10.17632/bccj6wrwrj.1
Tossapon katongtung


Global warming, primarily driven by greenhouse gas emissions, presents a critical challenge that necessitates innovative solutions. This study explores biomass hydrothermal liquefaction as a promising method to mitigate this issue by converting organic matter into renewable bio-oil. HTL processes biomass, including agricultural and forestry residues, under high temperature and pressure with water, resulting in a high-quality bio-oil compatible with existing fuel infrastructure. Machine learning emerges as a crucial tool in modeling and optimizing the HTL process due to its complex and nonlinear nature. ML algorithms can predict process behavior, optimize operational conditions, and enhance overall efficiency and sustainability. By handling extensive datasets, these algorithms aim to predict product properties and minimize environmental impacts. The dataset compiled in this study is pivotal for advancing ML applications in HTL, providing researchers with valuable information to refine models and reduce data collection time. This comprehensive dataset includes details on the biological properties of biomass, extending the scope of existing studies.



Chiang Mai University


Bioenergy, Machine Learning, Biomass, Biomass Conversion