scholarly journals Estimating Activity Coefficients of Target Components in Poorly Specified Mixtures with NMR Spectroscopy and COSMO-RS

2021 ◽  
Author(s):  
Thomas Specht ◽  
Kerstin Münnemann ◽  
Fabian Jirasek ◽  
Hans Hasse

Poorly specified mixtures are common in process engineering, especially in bioprocess engineering. The properties of such mixtures of unknown composition cannot be described using conventional thermodynamic models. The NEAT method, which has recently been developed in our group, enables the calculation of activity coefficients of known target components in such poorly specified mixtures. In NEAT, the group composition of the mixture is determined by NMR spectroscopy and a thermodynamic group contribution method is used for calculating the activity coefficients. In all previous studies with NEAT, the UNIFAC group contribution method was used. In the present work, we demonstrate that NEAT can also be applied with another important method for predicting activity coefficients: COSMO-RS. COSMO-RS (OL) developed in Oldenburg together with its group contribution version GC-COSMO-RS (OL) is used here. The new version of NEAT was successfully tested. For a variety of aqueous mixtures excellent agreement of the NEAT predictions, for which only information on the target component was used, with results that were obtained using the full knowledge on the composition of the mixture was found. The results demonstrate the generic nature of the idea of NEAT and the broad applicability of the method.

2021 ◽  
Author(s):  
Fabian Jirasek ◽  
Jakob Burger ◽  
Hans Hasse

Mixtures of which the composition is not fully known are important in many fields of engineering and science, for example, in biotechnology. Owing to the lack of information on the composition, such mixtures cannot be described with common thermodynamic models. In the present work, a method is described with which this obstacle can be overcome for an important class of problems. The method enables the estimation of the activity coefficients of target components in poorly specified mixtures and is based on a combination of NMR spectroscopy with a thermodynamic group contribution method. It is therefore called the NEAT method (NMR spectroscopy for the Estimation of Activity coefficients of Target components in poorly specified mixtures). In NEAT, NMR spectroscopy is used to obtain information on the concentrations of chemical groups in the mixture. The elucidation of the speciation is not required, only the target component has to be known. Modified UNIFAC (Dortmund) is applied in the present work as group contribution method, but NEAT can be extended to any other group contribution method. NEAT was introduced recently by our group in a short communication, but only the basic ideas were presented. In the present work, NEAT is described in full detail. Different options of using NEAT are discussed, and examples for the application of the method are given. They include a variety of aqueous and nonaqueous mixtures. The results show very good agreement of the activity coefficients that are predicted by NEAT with the corresponding results for the fully specified mixtures.


2021 ◽  
Author(s):  
Fabian Jirasek ◽  
Jakob Burger ◽  
Hans Hasse

Mixtures that contain a known target component but are otherwise poorly specified 15 are important in many fields. Previously, the activity of the target component, which is needed e.g. to design separation processes, could not be predicted in such mixtures. A method was developed to solve this problem. It combines a thermodynamic group contribution method for the activity coefficient with NMR spectroscopy, which is used for estimating the nature and amount of the different chemical groups in the mixture. The knowledge of the component 20 speciation of the mixture is not required. Test cases that are inspired by bioprocess engineering applications show that the new method gives surprisingly good results.


AIChE Journal ◽  
1994 ◽  
Vol 40 (7) ◽  
pp. 1210-1222 ◽  
Author(s):  
Christian Achard ◽  
Claude-Gilles Dussap ◽  
Jean-Bernard Gros

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