abstract
For the accurate design, optimization and simulation of chemical processes limited by mass transfer kinetics it is important the knowledge of transport properties, namely, diffusion coefficients, D12. In this work, the D12 values of six unsaturated linear ketones (i.e., propanone, butanone, propan-2-one, propan3-one, hexan-2-one and hexan-3-one) and three unsaturated linear aldehydes (i.e., butanal, pentanal and hexanal) in (compressed) liquid ethanol were measured at temperatures from 303.15 K to 333.15 K and pressures up to 150 bar. The D12 values of ketones are in the range of 1.28 x 10-5 - 2.89 x 10-5 cm2 s-1 and of the aldehydes are between 1.39 x 10-5 and 2.68 x 10-5 cm2 s-1. The general trends of D12 regarding temperature, pressure, Stokes-Einstein coordinate, and free volume are presented and discussed. The diffusivities of the various ketones position isomers and aldehyde/ketone isomers were statistically compared, being possible to conclude that the former ones exhibit indistinguishable diffusivities while different values appear for aldehydes/ketones isomers. Finally, five models and a machine learning algorithm from the literature were tested to predict/correlate the new data. It is suggested that the TLSM model should be the preferred approach for D12 prediction of linear unsaturated aldehydes and ketones in liquid compressed ethanol.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
keywords
BINARY DIFFUSION-COEFFICIENTS; SUPERCRITICAL CARBON-DIOXIDE; TRANSPORT-COEFFICIENTS; ORGANIC-COMPOUNDS; PRESSURE RANGE; LENNARD-JONES; DENSE FLUIDS; WIDE RANGES; HARD-SPHERE; CO2
subject category
Chemistry; Physics
authors
Zezere, B; Buchgeister, S; Faria, S; Portugal, I; Gomes, JRB; Silva, CM
our authors
Bruno Miguel Martins Zêzere
PhD Student
Carlos Manuel Silva
Associate Professor
Inês Portugal
Assistant Professor
José R. B. Gomes
Principal ResearcherGroups
G4 - Renewable Materials and Circular Economy
G6 - Virtual Materials and Artificial Intelligence
Projects
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
CICECO - Aveiro Institute of Materials (UIDP/50011/2020)
Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)
acknowledgements
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 & LA/P/0006/2020, financed by national funds through the FCT/MEC (PIDDAC). Bruno Zezere thanks FCT for the PhD grant SFRH/BD/137751/2018.