Reducing carbon footprint in hydrothermal liquefaction through AI-optimized thermal recovery

• Heat integration reduces energy use via thermal reallocation for slurry preheating. • Evaporation-based reduction of biomass concentration is energy-intensive. • GA-driven optimization boosts energy efficiency (54%→56%) and cuts CO2e by 20%. • Biocrude yield and feedstock concentration majorly impact efficiency and CO2e. • Optimized setup lowers energy input to 0.06 MJ/MJ oil and CO2e to 0.43 kg/kg oil. Hydrothermal liquefaction (HTL) of microalgae is a promising pathway to biocrude oil, but its high energy demand remains a key challenge. This study addresses this issue by developing and optimizing an enhanced heat integration strategy using Aspen Plus® and a genetic algorithm. The strategy integrates an evaporation step and additional heat exchangers to concentrate the algae slurry and recover thermal energy from the reactor effluent. A genetic algorithm was employed to optimize key process variables, including pump pressures and heat exchanger temperatures, with the goal of maximizing energy efficiency and minimizing CO 2 e emissions. The optimized process achieved notable improvements compared with the baseline, increasing energy efficiency from 54% to 56%, reducing the net energy ratio by 40% (from 0.1 to 0.06 MJ input per MJ oil), and lowering CO 2 e emissions by 20% (from 0.54 to 0.43 kg CO 2 per kg oil). Sensitivity analyses revealed that biocrude yield and feedstock concentration were the most influential parameters affecting overall performance. This work demonstrates a viable, low-carbon pathway for microalgae-based biofuel production and provides a roadmap for future research and industrial-scale implementation.