Process simulation and techno-economic analysis of fast pyrolysis bio-oil hydrodeoxygenation using a slurry catalyst reactor

• Simulation and techno-economic analysis of two-stage hydrodeoxygenation of bio-oil. • Slurry catalyst reactor configuration applied for the first reactor stage. • Surrogate model components in simulation express measurable mixture bulk properties. • Utilizing by-product streams for energy generation beneficial for process economics. • Profitability most sensitive to feedstock costs and product prices. Hydrodeoxygenation is a promising pathway for upgrading pyrolysis bio-oils for use as fuel blendstocks. However, the properties of raw bio-oils cause challenges in the operation of conventional catalytic fixed bed reactors. In this study, a two-stage catalytic hydrodeoxygenation process for upgrading fast pyrolysis bio-oil with green hydrogen to bionaphtha, biojet and biogasoil fuel blendstock fractions was simulated using Aspen Plus. The first reactor stage was operated in a slurry catalyst configuration, followed by a more conventional fixed bed reactor. Surrogate model components were applied in the simulation for the most important functional groups present in the material streams to express measurable complex mixture bulk properties. The techno-economics of processing 400 kt/operating year (330 d) of wet raw high oxygen content (37.3 wt.% O,dry ) bio-oil into 150 kt/operating year of low cumulative oxygen content (1.0 wt.% O,dry ) fuel blendstocks were evaluated, with a base case capital expenditure of 153.5 million euros. Base case production costs for the stand-alone hydrodeoxygenation process were evaluated as 1754 EUR/tonne or 5.01 EUR/gasoline gallon equivalent (GGE) of total blendstock products, respectively. A scenario of hydrogen recovery and utilizing the gaseous by-product streams for generation of heat and power was investigated, with potential to lower the production costs to 1537 EUR/tonne or 4.39 EUR/GGE, respectively. Sensitivity analyses for various operation cost, capital cost and investment analysis variables highlighted the fact that feedstock costs and product prices would have to move in a more positive direction for process profitability to increase.