abstract
The conversion of renewable resources into biobased chemicals and energy is a critical challenge in biorefineries, where the efficient separation of compounds plays a key role. Aqueous-organic biphasic systems (AOBS) comprising at least one organic solvent totally or partially immiscible in water are widely employed for this purpose, but given the huge number of potential AOBS, their selection depends on accurate predictions of partition coefficients to avoid the costly and time-consuming experimental tests. This study systematically evaluates COSMO-RS method for predicting partition coefficients in AOBS, using a database of 1,766 points comprising binary, ternary, and quaternary systems (including Arizona-type systems). The results showed that the combination of TZVPD_FINE parametrization including experimental equilibrium data yielded the most accurate predictions, with root mean square deviations (RMSD) below 0.8. This conclusion highlights the importance of using experimental equilibrium data in systems with high polarity or specific molecular interactions. In fully predictive scenarios, the accuracy decreases, particularly for systems with strong polarity differences, such as chloroform-water, where RMSD reached 1.09. Overall, this study confirms the robustness of the COSMO-RS method as a predictive tool for the partition of solutes in biphasic systems. The predictions were enhanced when experimental LLE was introduced. However, it can also be considered a fully predictive tool to avoid long and costly experimental screening to optimize separation processes in biorefinery applications.
keywords
SCREENING MODEL; LIQUID-SYSTEMS; SOLVENT; WATER; SEPARATION; SELECTION; FRACTIONATION; SOLUBILITY; MIXTURES; SOLUTES
subject category
Engineering
authors
Santiago, R; Sosa, FHB; Diaz, I; González-Miquel, M; Coutinho, JAP
our authors
Filipe Hobi Bordon Sosa
Junior Researcher
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)
Collaboratory for Emerging Technologies, CoLab (EMERGING TECHNOLOGIES)
acknowledgements
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020 (DOI: 10.54499/UIDB/50011/2020) , UIDP/50011/2020 (DOI: 10.54499/UIDP/50011/2020) and LA/P/0006/2020 (DOI: 10.54499/LA/P/0006/2020) , financed by national funds through the FCT/MCTES (PIDDAC) . Filipe H. B. Sosa acknowledges FCT - Fundacao para a Ciencia e a Tecnologia, I.P. for the researcher contract CEECIND/07209/2022 (DOI: 10.54499/2022.07209.CEECIND/CP1720/CT0019) , under the Scientific Employment Stimulus-Individual Call. Ruben Santiago thanks UNED-Santander for financial support through the project: "Ayudas a Investigadores Tempranos" UNED-SANTANDER 2024. Ruben Santiago, Ismael Diaz and Maria Gonzalez-Miquel also thank Comunidad de Madrid (project TEC-2024/BIO-27) for financial support.