Kinetics and biochemical composition coupling: A unified framework for optimizing energy recovery in continuous hydrothermal liquefaction systems

Hydrothermal liquefaction (HTL) can advance the renewable energy transition by converting diverse wet biomass into energy-dense products without drying. This study introduces a process simulation framework that integrates a kinetic plug-flow reactor within Honeywell UniSim-Design to evaluate energy recovery (ER) across multiple feedstocks. Five schemes for estimating feedstock higher-heating value (HHV) were compared: one based on reported values and four based on biochemical composition using new or published correlations. Simulation validation was performed against experimental ER data for biocrude (ER-BC) and solids (ER-Sol) from microalgae HTL, and ER-BC from HTL of microalgae, macroalgae, food waste, model protein, model lipid, and model carbohydrate. Among the tested schemes, a calibrated biochemical correlation (Scheme C) achieved the highest accuracy, particularly for microalgae and food waste. Using this scheme, we explored ER behavior under varying operating conditions and constructed heatmaps to identify optimal design regions. For microalgae and food waste, 47 and 28 process configurations achieved ER-BC ≥ 60%, demonstrating the potential for systematic optimization of HTL systems. Our results highlight the critical influence of feedstock composition, especially the protein and lipid fractions, on ER performance and reveal substantial overestimation for macroalgae, model protein, and model carbohydrate, and underestimation for model lipid. This work is the first to couple detailed reaction kinetics with biochemical composition in a process simulation for HTL energy balance analysis, providing a robust platform for reactor design and multi-feedstock optimization.