Chemical kinetics of hydrothermal conversion

Hydrothermal liquefaction (HTL) is an emerging thermochemical technology for converting biomass into biofuels and valuable chemicals while contributing to greenhouse gas reduction. Operating under subcritical water conditions (200–374 °C, 10–25 MPa), HTL transforms biomass into four main fractions: a bio-oil rich in energy, an aqueous phase containing dissolved organic compounds, a solid residue (bio-char), and a gaseous phase mainly composed of CO₂. Its ability to process wet biomass without prior drying, combined with high conversion efficiency and feedstock flexibility, makes HTL a promising pathway for sustainable energy production.

This study aims to develop a kinetic model for biomass HTL that integrates operational parameters (temperature and residence time) and biomass biochemical composition. Three types of biomass with different compositions—walnut (lipid-rich), walnut cake (protein-rich), and sunflower cake (fiber-rich)—were investigated. Experiments were conducted at 300 °C and 350 °C with reaction times ranging from 5 to 15 minutes. Product distribution and composition were analyzed using gas chromatography–mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), and ion chromatography (IC).

The results demonstrate a strong influence of biochemical composition on bio-oil yield and optimal reaction conditions. The highest bio-oil yield (92%) was obtained from lipid-rich biomass at 300 °C for 5 minutes. Protein-rich walnut cake produced a maximum yield of 54% at 300 °C after 15 minutes, while fiber-rich sunflower cake required a higher temperature (340–350 °C) to reach its best yield (53%). These findings confirm that lipids favor bio-oil production under milder conditions, whereas fiber-rich biomass requires more severe processing.

Identified compounds were grouped into chemical families to construct a general reaction scheme. Overall, this work provides insight into biomass-dependent HTL behavior and supports the development of predictive kinetic models for process optimization.