Hydrothermal liquefaction of Chlorella vulgaris and catalytic upgrading of product: Effect of process parameter on bio-oil yield and thermodynamics modeling. (15th June 2022) - Moazezi, Mohammad Reza; Bayat, Hengameh; Tavakoli, Omid; Hallajisani, Ahmad
Highlights: Optimization of bio-oil performance from Chlorella Vulgaris was done. Ni-Mo/Al2 O3 reduced nitrogenous compounds and removed sulfur. The free Gibbs Energy of products minimized whit UNIFAC thermodynamic equation. Abstract: Replacing fossil fuel sources with biofuels is one way to degrade greenhouse gas issuance. Recently, the interest, effort, and investment of researchers around the world in biofuel technology, has been focused on microalgae. The aim of the present work was focused on optimizing the biocrude yield of hydrothermal liquefaction (HTL) of Chlorella vulgaris . The effect of temperature, reaction time and the weight ratio of feedstock in different conditions of hydrothermal liquefaction process has been investigated. Optimization of bio-oil performance was done applying design expert software and response surface method (central composite design). The consequences show that the highest yield of bio-oil was obtained from Chlorella vulgaris (56.21% by weight) at 287 °C, reaction time of 40 min, and microalgae to water ratio of 0.07 Analysis of variance (ANOVA), suggests that the liquefaction temperature and reaction time plays the influential role in the bio-oil yield. Ni-Mo/Al2 O3 was applied as catalyst for upgrading Bio-oil. The amount of sulfur in bio-oil have completely removed and HHV increased 7% (41.11 MJ/Kg) by using catalyst. Moreover, GC/MS analysis illustrates the number of nitrogenous compounds in bio-oil using Ni-Mo/Al2 O3 was reduced, so that the amount of pyrazine was changed from 4.25% to 2.10% and pyrrole from 4.75% to 3.11%. To thermodynamics modeling and predict the phase behavior of the liquefaction process, manipulation of the UNIFAC coefficients equation was model to minimize the Gibbs free energy. The theoretical model had a 9.8% prediction error. This generation of bio-oils is expected to be good support for biofuels.