posted on 2021-12-28, 08:43authored byDylan Cronin, Andrew J. Schmidt, Justin Billing, Todd R. Hart, Samuel P. Fox, Xavier Fonoll, John Norton, Michael R. Thorson
Hydrothermal
liquefaction (HTL) liquifies wet feedstocks to produce
a biocrude under moderate temperatures (300–450 °C) and
high pressures (>2500 psi). The biocrude can be upgraded to transportation
fuels (predominantly diesel) using typical refinery unit operations
(e.g., hydrotreater and distillation). HTL of wet-wastes is a promising
route to produce environmentally friendly and cost-competitive fuels;
however, the feedstock significantly impacts the product quality and
the process yield. Consequently, it is important to rigorously compare
different feedstocks to determine the critical material attributes,
which impact the biocrude yield and quality. A few published comprehensive
studies evaluate the performance of numerous different wet-waste HTL
feed types, processed using the same reactor configuration and analytical
approach. This is particularly true for continuous flow HTL. HTL studies
generally investigate one or a few surrogate feedstocks or model compound
materials or attempt comparative reviews by collecting the results
of numerous different research groups. Such an approach involves numerous
assumptions that can significantly compromise the legitimacy of the
data compared and the conclusions drawn. This work investigates HTL
of 13 different real-world wet-waste feedstocks, belonging to multiple
different classes of municipal wet-waste, including food waste, biosolids,
sludge, fermentation residues, manure, and blends thereof. The biocrude
carbon yields obtained throughout the study ranged from 39.7 to 74.3%.
The biocrude yield varied through a range of different process changes,
such as increasing reactor flow rate and feed solid loading, and addition
of Fe-based additives. By analyzing a broad range of materials, and
through the comprehensive characterization profiles prepared for both
the feedstock and products, this study has produced a significant
volume of data, which was then analyzed using robust statistical methods.