Advanced models for the prediction of product yield in hydrothermal liquefaction via a mixture design of biomass model components coupled with process variables. (1st January 2019)

• Record Type: Journal Article
• Authors: Yang, Jie; He, Quan (Sophia); Corscadden, Kenneth; Niu, Haibo; Lin, Jianan; Astatkie, Tess
• Is Part Of: Applied energy. Volume 233/234(2019)
• Page Start: 906
• Page End: 915
• Publication Date: 2019-01-01
• Subjects: Hydrothermal liquefaction -- Quantitative prediction model -- Bio-oil -- Biomass model compounds -- Mixture design; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2018.10.035
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Yield prediction models as a function of model biomolecules and process variables. Optimization of feedstock biochemical composition and HTL process variables. Interactions among biomass model components under different processing conditions. Abstract: Hydrothermal liquefaction (HTL) has recently attracted great interest as a thermochemical conversion technique for biofuels production, however, suffers a lack of broadly applicable models for the prediction of product yield. This study developed a unique model for the prediction of HTL products yield via a mixture design of biomass model components coupled with process variables. The model compounds used in this study were soya protein for a protein representative, a mixture of cellulose and xylan for a saccharide representative, alkaline lignin for a lignin representative and soybean oil for a lipid representative. Reaction temperature (270–320 °C), time (5–20 min) and mass ratio of water/feedstocks (6:1–12:1) were chosen as the process variables of interest. The developed predictive models for biocrude yield and solid residue yield showed accuracy of (R 2 adj 94.6% and 93.2%, respectively), and were further validated using modelled feedstock and actual feedstock. These models can be used either to optimize HTL conditions when feedstock is known, or to optimize the composition of feedstock when reaction conditions are given. It was also observed that within the experimental design range, relatively mild HTL conditions eliminated alkaline lignin-lipid interaction and protein-lipid interaction, and thus enhanced biocrude formation; while more severe HTL conditions were preferred to reduce solid residue formation through promoting protein-saccharide interaction and saccharide-alkaline lignin interaction.