Effects of hydration parameters on chemical properties of biocrudes based on machine learning and experiments

Xinxing Zhou*, Jun Zhao, Meizhu Chen, Shaopeng Wu, Guangyuan Zhao, Song Xu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

9 Citations (Scopus)


To investigate the effects of temperature and biomass concentration of Hydrothermal liquefaction (HTL) on chemical properties of biocrudes, machine learning (ML) was used to predict the weight of hydration parameters on the properties of biocrudes. The elemental compositions, molecular weights, functional groups, thermal degradation, molecular structure of biocrudes were studied. The optimum yield of biocrudes was 65% and the highest heat value reached up to 34.28 kJ/g, showing comparable fuel properties. It was found that the hydration temperature significantly affects the elemental components, functional groups and molecular weight and structures of biocrudes. In addition, biomass concentration also affect the functional groups and structures of biocrudes. ML results indicated that Support Vector Machine Linear Kernel method is suitable for heat value prediction.

Original languageEnglish
Article number126923
Number of pages7
JournalBioresource Technology
Early online date28 Feb 2022
Publication statusPublished - Apr 2022

Scopus Subject Areas

  • Bioengineering
  • Environmental Engineering
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal

User-Defined Keywords

  • Algorithm optimization
  • Chemical property
  • Hydrothermal liquefaction
  • Machine learning
  • Woody biocrude


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