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Current state of predictive activity models when applied to cyclic bio-compounds and biodiesel related mixtures.

Authors :
Froehlich, Josiel Dimas
Bettio Staudt, Paula
de Pelegrini Soares, Rafael
Source :
Chemical Engineering Science. Apr2024, Vol. 288, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

The rise in greenhouse gas emissions makes the research and development of renewable energy sources a crucial area, with a particular emphasis on biofuels derived from biomass. In this context, design, simulation, an optimization of such bio-based process will also pose many challenges for thermodynamic models. Classical equations of state usually fail to describe these systems and there is not enough experimental data to correlate binary parameters even for group contribution models like UNIFAC. In this case, COSMO-based models become an interesting alternative, since they do not depend directly on experimental data. Even for the well-know biodiesel production there are intermediaries and byproducts showing phase equilibria that are difficult to predict. The UNIFAC (Do) and COSMO-SAC-HB2 variant were used to predict phase equilibria for mixtures related to bio-compounds. For COSMO-SAC, the first step was the revision and extension of the sigma-LVPP public database for the related molecules. For the modified UNIFAC, literature parameters were considered. Vapor-liquid, infinite dilution activity coefficient, liquid-liquid and excess enthalpy data were collected, with a total of 135 systems were investigated. The COSMO-SAC-HB2 yielded satisfactory deviation results in equilibrium of mixtures involving biocompounds, water, and alcohols. UNIFAC (Do) demonstrated satisfactory results in mixtures involving biocompounds and esters, hydrocarbons and alcohols. • UNIFAC (Do) and COSMO-SAC-HB2 variant were used to predict phase equilibria for mixtures related to bio-compounds. • VLE, LLE, IDAC and excess enthalpy experimental data were used in this study. • COSMO-SAC-HB2 and UNIFAC (Do) alternate better results. • COSMO based models have the great advantage of being able to perform without any previous experimental information. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00092509
Volume :
288
Database :
Academic Search Index
Journal :
Chemical Engineering Science
Publication Type :
Academic Journal
Accession number :
175499498
Full Text :
https://doi.org/10.1016/j.ces.2024.119760