Synergistic optimization of process parameters for hydrothermal liquefaction of food waste toward high-yield bio-oil and energy recovery

Hydrothermal liquefaction (HTL) represents a promising approach for the high-efficiency valorization of food waste. However, the effects of key process parameters on product distribution and bio-oil quality remain insufficiently understood. This study systematically investigated the influence of reaction temperature (250–370°C), residence time (15–75 min), and solid content (5–25%) on HTL performance through single-factor experiments and response surface methodology (RSM). Additionally, predictive models were developed to optimize process conditions and improve energy recovery efficiency. The results showed that reaction temperature, residence time, and solid content significantly affected bio-oil yield and properties, with temperature being the most influential factor. Under the optimal single-factor conditions of 310°C, 30 min, and 20% solid content, the maximum bio-oil yield reached 51.69%. Meanwhile, the higher heating value (HHV) of the bio-oil increased markedly from 21.48 MJ/kg for the raw feedstock to 34.67 MJ/kg. The proportion of light distillation fractions (<343°C) reached 66.50%, indicating improved fuel quality. RSM analysis revealed significant interaction effects among process parameters. The quadratic polynomial regression models for bio-oil yield and energy recovery efficiency demonstrated good predictive capability and enabled effective optimization of HTL conditions. By establishing these regression models for bio-oil yield and energy recovery efficiency, this study achieved quantitative prediction and optimization of HTL process parameters, providing critical theoretical insights and technical data support for the large-scale energy conversion of food waste.