Enhanced thermodynamic modelling for hydrothermal liquefaction

•Gibbs free energy minimization performed through SLSPQ algorithm.•Gas-liquid equilibrium implemented by using UNIFAC model and SRK equation of state.•Thermodynamic approach is feasible to predict bio-crude oil yield and composition.•Predicted biocrude yield is 17.60 wt% vs. experimental yield of 14...

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Vydáno v:Fuel (Guildford) Ročník 298; s. 120796
Hlavní autoři: Cascioli, Alessandro, Baratieri, Marco
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 15.08.2021
Elsevier BV
Témata:
Activity coefficients
Algorithms
Biofuels
Chemical equilibrium
Design optimization
Equations of state
Equilibrium
Free energy
Gibbs free energy
High-Pressure Thermodynamics
HTL
Liquefaction
Programming languages
Thermodynamic equilibrium
Thermodynamic Modelling
Thermodynamic models
HTL
Chemical equilibrium
Thermodynamic Modelling
High-Pressure Thermodynamics
ISSN:0016-2361, 1873-7153
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Shrnutí:•Gibbs free energy minimization performed through SLSPQ algorithm.•Gas-liquid equilibrium implemented by using UNIFAC model and SRK equation of state.•Thermodynamic approach is feasible to predict bio-crude oil yield and composition.•Predicted biocrude yield is 17.60 wt% vs. experimental yield of 14.23 wt%.•The highest oil yield, obtained at 450 °C and 250 bar, is 21.34%. Hydrothermal liquefaction is a promising technology to produce drop-in biofuels from wet biomasses. In this paper a thermodynamic analysis, including chemical equilibrium and gas–liquid equilibrium in high-pressure conditions was conducted. The chemical equilibrium model is based on the minimization of the Gibbs free energy, while the high-pressure gas–liquid equilibrium model is implemented by using the modified universal functional activity coefficient (UNIFAC) model and the Soave-Redlich-Kwong (SRK) equation of state. The Gibbs free energy minimization is performed through the minimization algorithm based on Sequential Least SQuares Programming (SLSQP) available in the Python environment. The research aims at providing an engineering predictive tool to support the design of biomass hydrothermal liquefaction and optimize its operation.
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ISSN:0016-2361
1873-7153
DOI:10.1016/j.fuel.2021.120796