Application of NEAT for the simulation of liquid–liquid extraction processes with poorly specified feeds

Abstract

The conceptual design of fluid separation processes is particularly challenging if the considered mixtures are poorly specified, since classical thermodynamic models cannot be applied when the composition is unknown. We have recently developed a method (NEAT) to predict activity coefficients in such mixtures. It combines the thermodynamic group contribution concept with the ability of NMR spectroscopy to quantify chemical groups. In the present work, we describe how NEAT can be applied to equilibrium stage simulations of liquid-liquid extraction processes with poorly specified feeds. Only a single 13 C NMR spectrum of the feed is needed for predicting the distribution of a target component for different process parameters, such as temperature or extracting agent. The predictions from several test cases are compared to results that are obtained using the full knowledge on the composition of the feed and surprisingly good agreement is found.

Hybridizing physical and data-driven prediction methods for physicochemical properties

“…[1][2][3][4][5][6] GCMs can also be used for predicting properties of mixtures of which the composition is (partially) unknown. [7][8][9] The most successful GCMs for describing the properties of mixtures are the different versions of UNIFAC 10-12 that model the excess Gibbs energy based on binary group-interaction parameters. The groupcontribution concept dramatically reduces the number of model parameters and the amount of data needed for fitting GCMs.…”

Estimating activity coefficients of target components in poorly specified mixtures with NMR spectroscopy and COSMO-RS

“…However, the activity coefficient γ T of the target component is directly related to the affinity of the target component to a second phase. In a recent study, we have demonstrated how NEAT can thus be applied for the conceptual design of liquid-liquid extraction processes [20].…”

“…However, in all previous works on NEAT [17][18][19][20], the same thermodynamic group contribution method was used: UNIFAC. In this work, we demonstrate that NEAT is a generic framework that is not restricted to the use of UNIFAC, but can in principle be combined with any group contribution method.…”

“…For all systems, the predictions obtained with the present version of NEAT (NMR + GC-COSMO-RS (OL)) were compared to results that were obtained using the full knowledge of the speciation of the mixture. Additionally, in one case the predictions of the newly introduced version of NEAT are compared to predictions of the recently described version of NEAT (NMR + modified UNIFAC (Dortmund) [2,[17][18][19][20] as well as with results that were obtained with the respective G E models using the full knowledge of the speciation of the mixture.…”

Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties

“…[1][2][3][4] GCMs can also be used for predicting properties of mixtures of which the composition is unknown. [5][6][7] The most successful GCMs for mixtures are the different versions of UNIFAC 8-10 that model the excess Gibbs energy based on binary group-interaction parameters. The group-contribution concept greatly reduces the number of model parameters and the amount of data needed for fitting GCMs.…”