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An In-Depth Process Model for Fuel Production via Hydrothermal Liquefaction and Catalytic Hydrotreating

One of the more promising technologies for future renewable fuel production from biomass is hydrothermal liquefaction (HTL). Although enormous progress in the context of continuous experiments on demonstration plants has been made in the last years, still many research questions concerning the under...

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Bibliographic Details
Published in:Processes 2021-07, Vol.9 (7), p.1172
Main Authors: Moser, Leonard, Penke, Christina, Batteiger, Valentin
Format: Article
Language:English
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Summary:One of the more promising technologies for future renewable fuel production from biomass is hydrothermal liquefaction (HTL). Although enormous progress in the context of continuous experiments on demonstration plants has been made in the last years, still many research questions concerning the understanding of the HTL reaction network remain unanswered. In this study, a unique process model of an HTL process chain has been developed in Aspen Plus® for three feedstock, microalgae, sewage sludge and wheat straw. A process chain consisting of HTL, hydrotreatment (HT) and catalytic hydrothermal gasification (cHTG) build the core process steps of the model, which uses 51 model compounds representing the hydrolysis products of the different biochemical groups lipids, proteins, carbohydrates, lignin, extractives and ash for modeling the biomass. Two extensive reaction networks of 272 and 290 reactions for the HTL and HT process step, respectively, lead to the intermediate biocrude (~200 model compounds) and the final upgraded biocrude product (~130 model compounds). The model can reproduce important characteristics, such as yields, elemental analyses, boiling point distribution, product fractions, density and higher heating values of experimental results from continuous experiments as well as literature values. The model can be applied as basis for techno-economic and environmental assessments of HTL fuel production, and may be further developed into a predictive yield modeling tool.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr9071172