Review and assessment of models for predicting biocrude yields from hydrothermal liquefaction of biomass

Abstract

Hydrothermal liquefaction (HTL) is a thermochemical process that converts biomass into a renewable, heavy oil that can be upgraded and refined to make liquid fuels. Quantitative models for correlating and then predicting yields of crude bio-oil from HTL of biomass date back to 2011. The literature provides 18 variations of component additivity models and another 24 different lumped kinetic models. Herein, we review the progress, development, and implementation of both types of models and assess their abilities to predict the biocrude yields from an extensive set of experimental data published for HTL of a range of different biomass feedstocks. We identify two component additivity models that provided the lowest mean absolute residuals and the kinetics model that best predicted the published biocrude yields. There is no single model that predicts well the biocrude yields for HTL of all the different types of biomass feedstocks. We offer guidance regarding which model to choose for any specific feedstock. This review and assessment also identifies opportunities for improving the quantitative modeling of HTL outcomes.