Accurate and predictive mixture models applied to mixtures with CO2

Jäger, Andreas ORCID; Mickoleit, Erik; Breitkopf, Cornelia GND

Supercritical CO2 as a working fluid offers distinct advantages for power cycles, such as a comparably low critical temperature and pressure. However, the favorable properties of supercritical CO2 could potentially be enhanced by blending CO2 with suitable additives. In order to find promising additives for CO2, a theoretical screening seems to be most feasible, as extensive experimental studies would be very time-consuming. For this purpose, a mixture model is needed that on the one hand yields good predictive results and on the other hand should be as accurate as possible. Therefore, in this work the performance of the multi-fluid mixture model combined with UNIFAC and with different versions of COSMO-SAC is evaluated for mixtures containing CO2. The results are compared to results calculated with accurate multi-fluid mixture models. It is demonstrated that the predictive results for phase equilibria as well as for homogeneous densities of the multi-fluid mixture model combined with UNIFAC and COSMO-SAC are superior to the results of the multi-fluid mixture model with standard mixing rules. However, it can also be seen that the parameters of UNIFAC and COSMO-SAC should be readjusted in order to further improve the results.

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Jäger, A., Mickoleit, E., Breitkopf, C., 2019. Accurate and predictive mixture models applied to mixtures with CO2. 3rd European Conference on Supercritical CO2 (sCO2) Power Systems 2019. https://doi.org/10.17185/duepublico/48891
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