Abstract

Hydrothermal liquefaction (HTL) of algae is a viable pathway to produce drop-in fuels. However, determining the optimal processing conditions of the algae-derived biofuel via HTL is challenging due to the tradeoff between biomass productivity, lipid content, and biocrude energy yield. To address this issue, we integrate a biomass productivity model that correlates the lipid content and biomass productivity, a biomass production cost conclumodel that correlates the biomass productivity and minimum selling price of biomass, and a machine learning model that predicts biocrude energy yield based on biochemical compositions and processing conditions. Results are fed into a techno-economic model to determine the minimum fuel selling price (MFSP) of algae-derived biofuel. Our results demonstrate the integration of the models gives quick evaluations of the MFSP of algae-derived biofuel and provides insights on optimal pathways to achieve better economic performance. In particular, algae with lipid content around 20–25% at 300 °C give the most favorable economic performance ($6/gasoline-gallon-equivalent (GGE)).

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