Rational method for defining and quantifying pseudo-components based on NMR spectroscopy

Specht, Thomas; Münnemann, Kerstin; Hasse, Hans; Jirasek, Fabian

doi:10.1039/d3cp00509g

Cited by 4 publications

(28 citation statements)

References 62 publications

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“…The NMR fingerprinting was based on the approach of our previous work, in which the groups identified in the fingerprinting were already mapped to groups of UNIFAC. , Additionally, in this work, also a mapping of the groups determined in the fingerprinting to the groups in the method of refs and for the prediction of vapor pressures was carried out. For technical details, we refer to the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…Table gives an overview of the three mixtures that were considered as feed mixtures in the application examples studied in the present work. The information on the feed mixtures was adopted from ref ; no NMR experiments were carried out in the present work. All mixtures are aqueous solutions that contain a cocktail of diluted substances (the mole fractions of all other components were below 0.038 mol/mol), a situation that is often encountered in biotechnological processes.…”

Section: Overview Of Studied Mixturesmentioning

confidence: 99%

“…We have recently introduced a new generic way to define and quantify pseudo-components that is free from ad-hoc assumptions . It is based on a series of previous papers in which we have introduced a method for quantifying the group composition of poorly specified mixtures based on simple NMR experiments. − …”

Section: Introductionmentioning

confidence: 99%

“…In our first works, − the quantification of the group composition was carried out using standard 1 H or 13 C NMR experiments. Using additional 13 C distortionless enhancement by polarization transfer (DEPT) NMR experiments, as done in subsequent work, is beneficial, as this enables the reliable discrimination of the degree of substitution of the carbon atoms, which is valuable information for group-contribution methods. The NMR method that yields the group composition of a poorly specified mixture is designated as NMR fingerprinting in the following text.…”

Section: Introductionmentioning

confidence: 99%

“…These pseudo-components may be identical to actual components, but groups of different components with similar diffusion coefficients may also be clustered into the same pseudo-component. A K -medians algorithm was used for the clustering, and the optimal number of clusters (pseudo-components) K was found from maximizing the silhouette score. , …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design

Specht

Hasse

Jirasek

2023

Ind. Eng. Chem. Res.

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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 without having to solve the much more complicated task of quantifying all species. This method has been extended recently to a rational method for defining pseudo-components in poorly specified mixtures. In the present work, this method is combined with several thermodynamic group-contribution methods. The resulting thermodynamic models were applied to various poorly specified mixtures and used for solving two typical tasks from conceptual fluid separation process design: a) solvent screening for liquid−liquid extraction processes and b) the simulation of open evaporation processes. The results show excellent agreement with those obtained using the complete information on the speciation, which was known for the studied test mixtures but disregarded in applying the new approach.

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Section: Methodsmentioning

confidence: 99%

Section: Overview Of Studied Mixturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design

Specht

Hasse

Jirasek

2023

Ind. Eng. Chem. Res.

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Automated nuclear magnetic resonance fingerprinting of mixtures

Specht

Arweiler

Stüber

et al. 2023

Magnetic Reson in Chemistry

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Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for qualitative and quantitative analysis. However, for complex mixtures, determining the speciation from NMR spectra can be tedious and sometimes even unfeasible. On the other hand, identifying and quantifying structural groups in a mixture from NMR spectra is much easier than doing the same for components. We call this group‐based approach “NMR fingerprinting.” In this work, we show that NMR fingerprinting can even be performed in an automated way, without expert knowledge, based only on standard NMR spectra, namely, 13C, 1H, and 13C DEPT NMR spectra. Our approach is based on the machine‐learning method of support vector classification (SVC), which was trained here on thousands of labeled pure‐component NMR spectra from open‐source data banks. We demonstrate the applicability of the automated NMR fingerprinting using test mixtures, of which spectra were taken using a simple benchtop NMR spectrometer. The results from the NMR fingerprinting agree remarkably well with the ground truth, which was known from the gravimetric preparation of the samples. To facilitate the application of the method, we provide an interactive website (https://nmr-fingerprinting.de), where spectral information can be uploaded and which returns the NMR fingerprint. The NMR fingerprinting can be used in many ways, for example, for process monitoring or thermodynamic modeling using group‐contribution methods—or simply as a first step in species analysis.

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Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Nicolle,

Deng,

Ihme

et al. 2024

J. Chem. Inf. Model.

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Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the ’recomposition’ of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure–Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

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Rational method for defining and quantifying pseudo-components based on NMR spectroscopy

Abstract: Poorly specified mixtures, whose composition is unknown, are ubiquitous in chemical and biochemical engineering. In the present work, we propose a rational method for defining and quantifying pseudo-components in such...

Cited by 4 publications

References 62 publications

Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design

Predictive Thermodynamic Modeling of Poorly Specified Mixtures and Applications in Conceptual Fluid Separation Process Design

Automated nuclear magnetic resonance fingerprinting of mixtures

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

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