2023
DOI: 10.1021/acs.iecr.3c01096
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Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design

Abstract: In many chemical engineering processes, mixtures occur whose composition is not well-known. Simulations of processes with such poorly specified mixtures are basically infeasible, as thermodynamic models require knowledge of the speciation. As a workaround, pseudo-components can be introduced which are generally defined using ad-hoc assumptions. In a previous series of works, we have developed and tested a method for quantifying the group composition of poorly specified mixtures based on simple NMR experiments … Show more

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Cited by 3 publications
(2 citation statements)
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“…However, analyzing the mixture using spectroscopic techniques and involving group contribution methods to estimate the mixture excess properties is still possible. In this regard, many works have been published [19][20][21][22][23][24]. Furthermore, some frameworks have been developed to estimate the propagation of the uncertainty from the physical parameters (e.g., activity coefficients and excess properties) to the flowsheet simulation and optimization [25,26].…”
Section: Introductionmentioning
confidence: 99%
“…However, analyzing the mixture using spectroscopic techniques and involving group contribution methods to estimate the mixture excess properties is still possible. In this regard, many works have been published [19][20][21][22][23][24]. Furthermore, some frameworks have been developed to estimate the propagation of the uncertainty from the physical parameters (e.g., activity coefficients and excess properties) to the flowsheet simulation and optimization [25,26].…”
Section: Introductionmentioning
confidence: 99%
“…Thermodynamic modeling was discussed by Specht et al . for the case of poorly specified mixtures, while Yousefi Seyf and Asgari focused on parametrizing the PC-SAFT equation of state for the solvents used in the pharmaceutical industry.…”
mentioning
confidence: 99%